Pandas.ipynb

{
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Pandas\n",
    "Pandas is an open-source data analysis and manipulation library for Python. It is built on top of NumPy and provides high-level data structures and tools designed to make data analysis fast, easy, and expressive. Pandas is primarily used for working with structured data, such as data tables, and offers powerful, flexible, and efficient tools for data manipulation."
   ]
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  {
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   "id": "eeca84eb-ea0b-4fdb-9b6a-59a698749e00",
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   "outputs": [],
   "source": []
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   "source": [
    "## Where and Why Pandas is Used\n",
    "\n",
    "1. **Data Analysis**: Pandas is widely used for data analysis due to its powerful data manipulation capabilities. Analysts and data scientists use it to clean, transform, and analyze data efficiently.\n",
    "\n",
    "2. **Data Cleaning**: Handling missing data, removing duplicates, and transforming data into a usable format are common tasks made easier with Pandas.\n",
    "\n",
    "3. **Exploratory Data Analysis (EDA)**: Pandas is essential for EDA, allowing quick inspection and summary of data, generation of descriptive statistics, and visualization.\n",
    "\n",
    "4. **Data Wrangling**: Pandas is used to prepare data for further analysis or machine learning, including filtering, merging, and reshaping datasets.\n",
    "\n",
    "5. **Web Scraping and Data Importing**: Pandas can import data from various file formats such as CSV, Excel, SQL databases, and JSON, making it ideal for web scraping and data integration tasks.\n",
    "\n",
    "6. **Machine Learning**: In the machine learning pipeline, Pandas is used for data preprocessing, feature engineering, and exploratory analysis before feeding data into machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49b813c-58fb-4944-ad38-7896042ef789",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "188b1eec-1429-4c73-b2db-593060d5da2a",
   "metadata": {},
   "source": [
    "### Installing Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb81e04d-de40-443e-ae4d-59910746a5f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\haide\\anaconda3\\lib\\site-packages (2.1.4)\n",
      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\haide\\anaconda3\\lib\\site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\haide\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\haide\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\haide\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\haide\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3935038-57e2-4964-897c-530e5db632bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "61daca0b-6ad9-4319-b5d5-e8534838ca5b",
   "metadata": {},
   "source": [
    "### Importing Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b9644113-4f03-483b-8a1f-6fdb485e6fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "116232f9-b635-4dab-969d-83dad38ec617",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "773d94cb-5599-4774-938c-4aca7c4e2162",
   "metadata": {},
   "source": [
    "## What does Pandas deal with?\n",
    "There are two major categories of data that you can come across while doing data analysis.\n",
    "\n",
    "- One dimensional data (Series)\n",
    "- Two-dimensional data (DataFrame)\n",
    "\n",
    "Series in Pandas is one-dimensional data, and data frames are 2-dimensional data. A series can hold only a single data type, whereas a data frame is meant to contain more than one data type.\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cc4cb40-a5c8-4e40-a046-e30e62e4c8f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "94dc2c96-5754-4b90-b169-5a52689fb7d6",
   "metadata": {},
   "source": [
    "### Creating a Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a4d0e8ff-97af-48e6-a8db-8892d6e9eb1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Lahore\n",
      "1    Islamabad\n",
      "2      Karachi\n",
      "3     Peshawar\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "list = ['Lahore','Islamabad','Karachi','Peshawar']\n",
    "l = pd.Series(list)\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "149c0337-afb7-4776-8ee0-d957201aa7ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "64fae182-cabc-4e32-af01-e7478d23ad51",
   "metadata": {},
   "source": [
    "You can also pass indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79d667ca-76ad-40b5-8c26-ea742af940b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       Lahore\n",
       "2    Islamabad\n",
       "3      Karachi\n",
       "4     Peshawar\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = [1,2,3,4]\n",
    "l = pd.Series(list,index=indices)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83083464-152c-4f25-b91b-6d4e55cfecbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c7f541d9-7a1c-4552-a4ad-c4dc7d2aa396",
   "metadata": {},
   "source": [
    "You can assign a name to series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a2c0dfb-d1d9-4ce0-8a40-6c39e48aa54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Lahore\n",
       "1    Islamabad\n",
       "2      Karachi\n",
       "3     Peshawar\n",
       "Name: Cities, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = pd.Series(list,name='Cities')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "790704e1-b6b8-4c7d-9d08-287fc0007ca7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1b943e92-8914-4a0d-a98b-4d1ca1416145",
   "metadata": {},
   "source": [
    "You can assign a data type to series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45e85c94-71a5-4a02-b738-37c5b9e9d9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list = [1.0, 2.0, 3.0, 4.0]\n",
    "l = pd.Series(list,dtype=int)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f322a34b-479b-4cbc-a9c4-5c75535960b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "092ba4e8-578a-4e51-a4d7-df5898d7de67",
   "metadata": {},
   "source": [
    "**Creating Series with dictionary.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cd652b14-23c5-4fa3-a21f-e37fe0d8ab34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Haider\n",
       "RollNo        57\n",
       "Course      BSAI\n",
       "Name: Student_data, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary = {\"Name\":\"Haider\",\n",
    "             \"RollNo\":\"57\",\n",
    "             \"Course\":\"BSAI\"}\n",
    "l = pd.Series(dictionary,name=\"Student_data\")\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d72c767-73f3-4529-b5ae-a83120e5ebd5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "17b848f0-2574-40c5-a8f6-5d4de645eb45",
   "metadata": {},
   "source": [
    "When you create a series from dictionary, it will automatically take the keys as index and the values as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e62e9e2d-0c35-4d93-8319-24624aacca7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "129fc8e4-4ff3-4c93-96f2-8d706644a8d3",
   "metadata": {},
   "source": [
    "## Attributes of Pandas Series\n",
    "- `s.name` - it returns name of Series.\n",
    "- `s.index` - it returns indices of Series.\n",
    "- `s.values` - it returns values of Series.\n",
    "- `s.dtype` - it returns data type of underlying data.\n",
    "- `s.shape` - it returns a tuple of shape of underlying data.\n",
    "- `s.nbytes` - it returns number bytes of underlying data\n",
    "- `s.size` - it returns the number of elements in the underlying data.\n",
    "- `s.ndim` - it returns the number of dimensions.\n",
    "- `s.hasnans` - it returns True if there ara NaN values otherwise False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ecf53bb9-ddf4-4c49-abca-ad85f5fbcce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Student_data'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ec6e91b9-6303-4e92-9bde-09d4a0d7102a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'RollNo', 'Course'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d50e44aa-eebc-49bc-b99b-7a9f1a0082b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Haider', '57', 'BSAI'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3cbbe455-b0e1-4c89-9cce-d2b6d7194b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69c83017-c169-4618-8d2a-9e632d0fd886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "757c62bc-247d-4afd-8b75-4d2972d3ba41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "40d7626b-b793-450e-99cf-69288c9b1192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "afd36558-42e3-4946-968a-4578872ec347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7817175b-aaee-41d7-bfa3-1b174080a66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.hasnans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26624a53-84a8-4193-a5d9-4353d1b29ba9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ea69f3b4-0736-4fd1-bfd7-19dd14876cc6",
   "metadata": {},
   "source": [
    "## Accesssing Series Elements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac2656d-3a60-4116-9edb-688ebe3247d4",
   "metadata": {},
   "source": [
    "There are three ways to access elements of a series:\n",
    "\n",
    "- Using `s[]` operator and specifying the index (integer/label)\n",
    "- Using `s.loc[]` method and specifying the index (integer/label)\n",
    "- Using `s.iloc[]` method and specify the position (an integer value from 0 to length-1). It also support negative indexing, the last element can be accessed by an index of -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7ea5cafc-cef2-4468-a206-56360e42b8f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "list = ['Lahore','Islamabad','Karachi','Peshawar']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb60269-6c58-429a-abe7-415d4ee9f2c8",
   "metadata": {},
   "source": [
    "The Pandas series object supports both integer-based (default) and label/string-based indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4bc124cf-38a8-4f9f-8aef-ea4ca29c7239",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = ['c1','c2','c3','c4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cb565bb1-11d1-4584-90d6-329acfd2c0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c1       Lahore\n",
       "c2    Islamabad\n",
       "c3      Karachi\n",
       "c4     Peshawar\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = pd.Series(list,index=indices)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568d0d62-08a2-4674-ae9d-09309cbb9e67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "02bb907f-de75-44f3-b4c7-0b91f7f58316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lahore'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l['c1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7d475b76-03ef-449f-8032-4e38d8414b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Karachi'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l['c3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "92ffcafa-3e41-4888-9db1-6c641bbe8dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lahore'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.loc['c1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6f75269e-50a3-4e43-94d4-beff06c7bea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lahore'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "184d4d9c-de43-4f10-9128-2981c3cfa241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Karachi'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "616e1a31-f30d-487b-a8af-07bd2a1e0000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c1       Lahore\n",
       "c2    Islamabad\n",
       "dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.iloc[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "05ec5f34-acd4-4b78-b6c4-e88c9df95d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c1       Lahore\n",
       "c2    Islamabad\n",
       "c3      Karachi\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.loc['c1':'c3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a10bfb4f-e71d-4f0a-9065-4a98b82a9b65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "08ef8d13-044d-485f-aedf-04d72a2481f5",
   "metadata": {},
   "source": [
    "## Arithmetic Operations on Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "930fe40c-3ac6-4585-bb3b-dc3925913cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "list1 = [1,2,3,4,5]\n",
    "list2 = [1,2,3,4,5]\n",
    "list1 = pd.Series(list1)\n",
    "list2 = pd.Series(list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "288706d3-c523-4d91-9149-5092d096f4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     4\n",
       "2     6\n",
       "3     8\n",
       "4    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list3 = list1 + list2\n",
    "list3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d235da29-d3e1-4c11-9b08-2c14e2cb66fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list3 = list1-list2\n",
    "list3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b8f5c930-4d46-4989-93bc-47b639acdc4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     4\n",
       "2     9\n",
       "3    16\n",
       "4    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list3 = list1 * list2\n",
    "list3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "37fe3dfb-43e7-4a11-b257-ee7407e7bd6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "4    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list3 = list1 / list2\n",
    "list3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33e5e246-febd-426c-88be-b04df01f8150",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b28f3a1c-3e2a-466a-a7f4-28058e364837",
   "metadata": {},
   "source": [
    "If indices are different: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e3fa84a2-54f3-4248-957d-e0da084ea382",
   "metadata": {},
   "outputs": [],
   "source": [
    "list1 = [1,2,3,4,5]\n",
    "list2 = [1,2,3,4,5]\n",
    "indices1 = [6,8,9,0,1]\n",
    "indices2 = [4,5,6,7,8]\n",
    "list1 = pd.Series(list1,index=indices1)\n",
    "list2 = pd.Series(list2,index=indices2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c748b332-c48b-4191-893a-0603a016db5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "6    4.0\n",
       "7    NaN\n",
       "8    7.0\n",
       "9    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list3 = list1+list2\n",
    "list3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3315f417-9569-4a36-8f5b-ff08add86da2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dfbb017b-f903-4e7a-aaf9-73e8681b620f",
   "metadata": {},
   "source": [
    "We can fill these NaN values with these functions `s.add()`, `s.sub()`, `s.mul()` and `s.div()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "814e00a5-5e39-4829-a5de-863ff758a2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "1    5.0\n",
       "4    1.0\n",
       "5    2.0\n",
       "6    4.0\n",
       "7    4.0\n",
       "8    7.0\n",
       "9    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list3 = list1.add(list2,fill_value=0)\n",
    "list3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "374e73f9-c6c5-4280-bbb7-e287477a9f8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3e3e564e-4034-47bb-9a4e-3e2865712a72",
   "metadata": {},
   "source": [
    "## More Functions on Series\n",
    "- `reset_index()`\n",
    "- `s.pop(index)`\n",
    "- `s.drop(indexes)`\n",
    "- `s1.update(s2)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8924c366-94b1-4be0-94ed-01aadd8dcda5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a1    1\n",
       "a2    2\n",
       "a3    3\n",
       "a4    4\n",
       "a5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = [1,2,3,4,5]\n",
    "indices = ['a1','a2','a3','a4','a5']\n",
    "l = pd.Series(list1,index=indices)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66ab58b3-a751-4ec8-9d7e-fe80a3110a07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "595cc9a2-b489-4823-b99b-c3941ff7964d",
   "metadata": {},
   "source": [
    "`reset_index(drop=True)` resets indices to 0-array based indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "56f5a047-2fde-453a-aad1-93dc03d0de90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = l.reset_index(drop=True)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efaa9dde-46c0-46e9-b5c8-20eb7efb16cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e9cb33d5-6fc5-40cb-9466-6045751db8ff",
   "metadata": {},
   "source": [
    "`s.pop(index)` is passed an index and it returns the data item at the index and removes it from series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "05d4761e-582c-42e2-aeb8-a4283aa0d368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "dtype: int64\n",
      "Removed Value:  5\n"
     ]
    }
   ],
   "source": [
    "pop_value = l.pop(4)\n",
    "print(l)\n",
    "print(\"Removed Value: \",pop_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c57b8fed-d6b3-49ef-ba37-f79847273570",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ca59c5d3-60e2-480a-b269-75e56c11cc94",
   "metadata": {},
   "source": [
    "`s.drop(indexes)` is passed one or a list of indices and returns a series of the data items. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4d932d82-abe3-4cf6-8286-8975b19cff14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.drop([0,1],inplace=True)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2995c6-65f6-4310-b7ce-7292f6d00ddb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "befbaadd-88a2-4dbf-8e22-c160647110fa",
   "metadata": {},
   "source": [
    "`s1.update(s2)` is used to miduft the series s1 inplace using the values from passed series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "57b569cf-18b9-4cd2-b415-eac46b880140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    6\n",
       "2    7\n",
       "3    8\n",
       "4    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = pd.Series([1,2,3,4,5])\n",
    "list2 = pd.Series([5,6,7,8,9])\n",
    "list1.update(list2)\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9de8859-1d73-4157-970b-a01edac57f90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d1e7665e-25d9-4ab8-b064-5058aab115ca",
   "metadata": {},
   "source": [
    "## Creating a DataFrame\n",
    "- `pd.DataFrame(data=None, index=None, columns=None, dtype=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "fab93ff4-6db2-4727-ab9d-b197cac12bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9395e46-b04d-4cc8-9260-eb6500c8b885",
   "metadata": {},
   "source": [
    "Using Numpy Arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "f2a9e598-0244-40e7-9a37-e98305fb8d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.random.randint(10,50,size=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "f9188232-b680-4a78-8ed5-ea6c90435e1b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>39</td>\n",
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       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
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       "    0   1   2   3   4\n",
       "0  28  19  48  28  39\n",
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       "2  25  13  10  12  17\n",
       "3  48  47  22  14  29\n",
       "4  49  16  39  33  29"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(array)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d151c844-cfec-444e-8ae3-4232d4eeea59",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "markdown",
   "id": "e275f103-1f13-4a84-bfd8-9ce913beb644",
   "metadata": {},
   "source": [
    "You can pass row and column labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "00011e70-e25c-4d2c-a41a-7cad8c2cf3f7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>c1</th>\n",
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       "    <tr>\n",
       "      <th>r5</th>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
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       "    c1  c2  c3  c4  c5\n",
       "r1  28  19  48  28  39\n",
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       "r4  48  47  22  14  29\n",
       "r5  49  16  39  33  29"
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     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "row_indices = ['r1','r2','r3','r4','r5']\n",
    "col_indices = ['c1','c2','c3','c4','c5']\n",
    "df = pd.DataFrame(array,index=row_indices,columns=col_indices)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f4e32fd-af25-458e-87c9-e46a1f507bf8",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "markdown",
   "id": "7becd937-5d08-4ddc-8e22-4e06acd33b2c",
   "metadata": {},
   "source": [
    "Using Dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "c857dc62-f214-4140-b9de-c8532d4eb3fe",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Haider</td>\n",
       "      <td>19</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hasin</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arslan</td>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saqlain</td>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Marks\n",
       "0   Haider   19    100\n",
       "1    Hasin   20      0\n",
       "2   Arslan   20    100\n",
       "3  Saqlain   20    100"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary = {\"Name\":['Haider','Hasin','Arslan','Saqlain'],\n",
    "             \"Age\":[19,20,20,20],\n",
    "             \"Marks\":[100,0,100,100]}\n",
    "df = pd.DataFrame(dictionary)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f81903-2490-4d19-bc8f-23a5da8d1aa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba1b5939-fbf3-4d77-b3bc-68aa25709284",
   "metadata": {},
   "source": [
    "### Attributes of Pandas DataFrame\n",
    "- `df.head()`: Returns the first 5 rows of the DataFrame.\r\n",
    "- `df.tail()`: Returns the last 5 rows of the DataFrame.\r\n",
    "- `df.shape`: Returns a tuple representing the dimensionality of the DataFrame.\r\n",
    "- `df.ndim`: Returns the number of axes / array dimensions.\r\n",
    "- `df.size`: Returns the number of elements in the DataFrame.\r\n",
    "- `df.index`: Returns the index (row labels) of the DataFrame.\r\n",
    "- `df.columns`: Returns the column labels of the DataFrame.\r\n",
    "- `df.axes`: Returns a list of the row axis labels and column axis labels.\r\n",
    "- `df.values`: Returns a Numpy representation of the DataFrame.\r\n",
    "- `df.empty`: Returns True if the DataFrame is empty.\r\n",
    "- `df.dtypes`: Returns the data types of each column.\r\n",
    "- `df.isnull()`: Detects missing values.\r\n",
    "- `df.isnull().sum()`: Returns the count of missing values in each column.\r\n",
    "- `df.count()`: Returns the number of non-NA/null observations across the DataFrame.\r\n",
    "- `df['col'].unique()`: Returns unique values in the specified column.\r\n",
    "- `df['col'].nunique()`: Returns the number of unique values in the specified column.\r\n",
    "- `df.describe()`: Generates descriptive statistics.\r\n",
    "- `df.info()`: Prints a concise summary of the DataFrame.\r\n",
    "- `df.rename()`: Renames labels (columns or index).\r\n",
    "- `df.set_index()`: Sets the DataFrame index using existing columns.\r\n",
    "- `df.loc[]`: Accesses a group of rows and columns by labels or a boolean array.\r\n",
    "- `df.iloc[]`: Accesses a group of rows and columns by integer positions.\r\n",
    "- `Selection`: Used for selecting data based on conditions.\r\n",
    "- `isin`: Filters data based on whether values are contained in a list.\r\n",
    "- `select_dtypes`: Selects columns based on their data type.\r\n",
    "- `map()`: Maps values of a Series according to an input correspondence.\r\n",
    "- `df.replace()`: Replaces values in the DataFrame.\r\n",
    "- `df.apply()`: Applies a function along an axis of the DataFrame.\r\n",
    "- `del df['col']`: Deletes the specified column.\r\n",
    "- `astype`: Casts a Series or DataFrame to the specified data type.\r\n",
    "- `sort_values()`: Sorts the DataFrame by the specified column values.\r\n",
    "- `sort_index()`: Sorts the DataFrame by its index.\r\n",
    "- `df.isna()`: Detects missing values.\r\n",
    "- `df.notna()`: Detects non-missing values.\r\n",
    "- `df.value_counts()`: Returns the counts of unique values.\r\n",
    "- `agg`: Aggregates using one or more operations over the specified axis.\r\n",
    "- `groupby`: Groups data by columns and applies a function to each group.\r\n",
    "- `pd.merge`: Merges DataFrame objects by performing a database-style join.\r\n",
    "- `merge`: Merges DataFrame objects by performing a database-style join.\r\n",
    "- `concat`: Concatenates pandas objects along a particular axis.\r\n",
    "- `pivot()`: Reshapes data based on column values.\r\n",
    "- `pivot_table()`: Creates a spreadsheet-style pivot table as a DataFrame.\r\n",
    "- `melt()`: Unpivots a DataFrame from wide to long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "bf566239-eeaa-4e62-80bd-207165a8521d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Haider</td>\n",
       "      <td>19</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hasin</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arslan</td>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saqlain</td>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      Name  Age  Marks\n",
       "0   Haider   19    100\n",
       "1    Hasin   20      0\n",
       "2   Arslan   20    100\n",
       "3  Saqlain   20    100"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 207,
   "id": "a8c90155-e296-4d6c-868a-e70c516fd45f",
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    {
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       "      <td>Hasin</td>\n",
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       "      <td>Arslan</td>\n",
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       "      <td>Saqlain</td>\n",
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      "text/plain": [
       "      Name  Age  Marks\n",
       "0   Haider   19    100\n",
       "1    Hasin   20      0\n",
       "2   Arslan   20    100\n",
       "3  Saqlain   20    100"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "1df965c8-a22f-4f22-8d6e-e30d05541bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "22d7dd28-2b61-45bf-a70b-e2fd1684b7ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "2"
      ]
     },
     "execution_count": 201,
     "metadata": {},
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   "source": [
    "df.ndim"
   ]
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  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "38cb776c-beaa-4764-9397-7bf6ea36ec40",
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    {
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       "12"
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     "execution_count": 203,
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   "cell_type": "code",
   "execution_count": 205,
   "id": "891754b3-a25e-452b-8a06-1d859d2492a0",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df.index"
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   "execution_count": 207,
   "id": "7b3436f2-4b36-4344-a25c-6dc2e0fb2ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Marks'], dtype='object')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
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  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "3c6b64da-5b82-4d38-a5d3-af515b2f3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=4, step=1),\n",
       " Index(['Name', 'Age', 'Marks'], dtype='object')]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes"
   ]
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  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "89654b9d-037f-49bc-9b0e-26c19eeb4a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Haider', 19, 100],\n",
       "       ['Hasin', 20, 0],\n",
       "       ['Arslan', 20, 100],\n",
       "       ['Saqlain', 20, 100]], dtype=object)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
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  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "1171052e-6b3c-466f-ba70-fbce85e56b3c",
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   "outputs": [
    {
     "data": {
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       "False"
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     "execution_count": 216,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "86d4f225-59ce-4122-a239-e127beb415e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name     object\n",
       "Age       int64\n",
       "Marks     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
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  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "85b7cc63-de41-403b-b368-63002866d84e",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    Name    Age  Marks\n",
       "0  False  False  False\n",
       "1  False  False  False\n",
       "2  False  False  False\n",
       "3  False  False  False"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "80480e01-fd09-4e43-9df9-6efc2dbddddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name     0\n",
       "Age      0\n",
       "Marks    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
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    "df.isnull().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "501f5426-7339-4f5e-8d85-e1b148e20a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name     4\n",
       "Age      4\n",
       "Marks    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.count()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "25ab626f-4a20-481c-b77b-d71e0805bdda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Haider', 'Hasin', 'Arslan', 'Saqlain'], dtype=object)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "ba3e5279-a32d-469b-b62c-e4df455f6053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "ffb2219b-1045-444e-80f8-c81163365e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         Age  Marks\n",
       "count   4.00    4.0\n",
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       "std     0.50   50.0\n",
       "min    19.00    0.0\n",
       "25%    19.75   75.0\n",
       "50%    20.00  100.0\n",
       "75%    20.00  100.0\n",
       "max    20.00  100.0"
      ]
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     "execution_count": 241,
     "metadata": {},
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   "execution_count": 243,
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Name    4 non-null      object\n",
      " 1   Age     4 non-null      int64 \n",
      " 2   Marks   4 non-null      int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 228.0+ bytes\n"
     ]
    }
   ],
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   "execution_count": 103,
   "id": "0e5a31de-03e1-45da-afc4-22b15d0dc3e7",
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   "outputs": [
    {
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       "      name  Age  Marks\n",
       "0   Haider   19    100\n",
       "1    Hasin   20      0\n",
       "2   Arslan   20    100\n",
       "3  Saqlain   20    100"
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    "df.rename(columns={'Name':'name'}).head()"
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   "execution_count": 110,
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    {
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       "          Name  Age  Marks\n",
       "index                     \n",
       "s1      Haider   19    100\n",
       "s2       Hasin   20      0\n",
       "s3      Arslan   20    100\n",
       "s4     Saqlain   20    100"
      ]
     },
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    "df['index'] = ['s1','s2','s3','s4']\n",
    "df.set_index(keys='index').head()"
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   "execution_count": 209,
   "id": "f114bb0d-f9d1-4f09-9b63-ebfdac504d1f",
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   "outputs": [
    {
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       "      <td>4000.0</td>\n",
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       "      <td>72.8</td>\n",
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      "text/plain": [
       "   roll no      name  age    address    session    group  gender  subj1  \\\n",
       "0        1  Ehtisham   23     Lahore    MORNING  group C    Male   78.3   \n",
       "1        2    Ayesha   16  Islamabad        AFT  group A    Male   70.5   \n",
       "2        3       Ali   20    Karachi  AFTERNOON  group B  Female   64.9   \n",
       "3        4     Aisha   20     Lahore        MOR  group A    Male   82.0   \n",
       "4        5     Ahmed   21   Peshawer        AFT  group D  Female   65.9   \n",
       "\n",
       "   subj2  scholarship  \n",
       "0   84.4       5000.0  \n",
       "1   60.5       6000.0  \n",
       "2   75.1       8500.0  \n",
       "3   84.3       4000.0  \n",
       "4   72.8       3500.0  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/groupdata.csv')\n",
    "df.head()"
   ]
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   "execution_count": 116,
   "id": "c48b4145-1cef-448f-be86-6a161c7f6a4d",
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   "outputs": [
    {
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       "   roll no      name  age    address    session    group  gender  subj1  \\\n",
       "0        1  Ehtisham   23     Lahore    MORNING  group C    Male   78.3   \n",
       "1        2    Ayesha   16  Islamabad        AFT  group A    Male   70.5   \n",
       "2        3       Ali   20    Karachi  AFTERNOON  group B  Female   64.9   \n",
       "\n",
       "   subj2  scholarship  \n",
       "0   84.4       5000.0  \n",
       "1   60.5       6000.0  \n",
       "2   75.1       8500.0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[0:3]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cbd7a832-1669-47cf-a92a-9c6aade1070d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>10</th>\n",
       "      <td>11</td>\n",
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       "      <td>MOR</td>\n",
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       "      <td>81.3</td>\n",
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       "      <th>15</th>\n",
       "      <td>16</td>\n",
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       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
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      "text/plain": [
       "    roll no      name  age    address    session    group  gender  subj1  \\\n",
       "0         1  Ehtisham   23     Lahore    MORNING  group C    Male   78.3   \n",
       "5         6   Khadija   16     Lahore    MORNING  group C  Female   69.3   \n",
       "10       11   Hussein   12  Islamabad        MOR  group B    Male   90.5   \n",
       "15       16       Dua   11     Multan  AFTERNOON  group A    Male   90.5   \n",
       "\n",
       "    subj2  scholarship  \n",
       "0    84.4       5000.0  \n",
       "5    78.6          NaN  \n",
       "10   81.3       6000.0  \n",
       "15   81.3       3800.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[::5]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b9dc49c7-3efe-4cbd-b64b-35fb3e46ccbc",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   roll no      name  age    address\n",
       "0        1  Ehtisham   23     Lahore\n",
       "1        2    Ayesha   16  Islamabad\n",
       "2        3       Ali   20    Karachi\n",
       "3        4     Aisha   20     Lahore"
      ]
     },
     "execution_count": 126,
     "metadata": {},
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    }
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   "source": [
    "df.iloc[0:4,0:4]"
   ]
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   "cell_type": "code",
   "execution_count": 130,
   "id": "481b80ff-7a98-44f6-a277-e8e9bc9c18a6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Ayesha</td>\n",
       "      <td>16</td>\n",
       "      <td>Islamabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ali</td>\n",
       "      <td>20</td>\n",
       "      <td>Karachi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aisha</td>\n",
       "      <td>20</td>\n",
       "      <td>Lahore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll no      name  age    address\n",
       "0        1  Ehtisham   23     Lahore\n",
       "1        2    Ayesha   16  Islamabad\n",
       "2        3       Ali   20    Karachi\n",
       "3        4     Aisha   20     Lahore"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:3,['roll no','name','age','address']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "8e8ceaff-9a5c-40c8-9ef7-8ceee4502c6e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Ehtisham</td>\n",
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       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amina</td>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "        name  age  gender\n",
       "0   Ehtisham   23    Male\n",
       "13     Amina   28  Female"
      ]
     },
     "execution_count": 138,
     "metadata": {},
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   "source": [
    "df.loc[df['age'] > 22,['name','age','gender']]"
   ]
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   "execution_count": 140,
   "id": "d54ec371-7344-465d-8fda-05e3bef90d25",
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       "  <tbody>\n",
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       "      <td>Multan</td>\n",
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      "text/plain": [
       "    roll no   name  age address    session    group gender  subj1  subj2  \\\n",
       "7         8  Adeen    4  Multan    MORNING  group D   Male   84.1   76.0   \n",
       "15       16    Dua   11  Multan  AFTERNOON  group A   Male   90.5   81.3   \n",
       "\n",
       "    scholarship  \n",
       "7        8000.0  \n",
       "15       3800.0  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
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   ],
   "source": [
    "df[(df.age < 20) & (df.address == 'Multan')]"
   ]
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   "execution_count": 142,
   "id": "746ba103-89b6-4ce2-85e5-c02debf096dc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>NaN</td>\n",
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       "    roll no     name  age  address    session    group  gender  subj1  subj2  \\\n",
       "2         3      Ali   20  Karachi  AFTERNOON  group B  Female   64.9   75.1   \n",
       "6         7     Omar   12  Sialkot        AFT  group B  Female   90.2    NaN   \n",
       "8         9   Zainab   18  Karachi        AFT  group C    Male   90.5   81.3   \n",
       "11       12   Muskan   20  Karachi  AFTERNOON  group C    Male   90.5   81.3   \n",
       "14       15  Khubaib   13  Sialkot        AFT  group C  Female   90.5   81.3   \n",
       "\n",
       "    scholarship  \n",
       "2        8500.0  \n",
       "6        4000.0  \n",
       "8        3500.0  \n",
       "11          NaN  \n",
       "14       3500.0  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.address == 'Sialkot') | (df.address == 'Karachi')]"
   ]
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   "cell_type": "code",
   "execution_count": 144,
   "id": "ee7c7718-899f-4f95-acf6-7db32301e18c",
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   "outputs": [
    {
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       "      <th>14</th>\n",
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      ],
      "text/plain": [
       "    roll no     name  age   address    session    group  gender  subj1  subj2  \\\n",
       "2         3      Ali   20   Karachi  AFTERNOON  group B  Female   64.9   75.1   \n",
       "4         5    Ahmed   21  Peshawer        AFT  group D  Female   65.9   72.8   \n",
       "6         7     Omar   12   Sialkot        AFT  group B  Female   90.2    NaN   \n",
       "8         9   Zainab   18   Karachi        AFT  group C    Male   90.5   81.3   \n",
       "11       12   Muskan   20   Karachi  AFTERNOON  group C    Male   90.5   81.3   \n",
       "14       15  Khubaib   13   Sialkot        AFT  group C  Female   90.5   81.3   \n",
       "\n",
       "    scholarship  \n",
       "2        8500.0  \n",
       "4        3500.0  \n",
       "6        4000.0  \n",
       "8        3500.0  \n",
       "11          NaN  \n",
       "14       3500.0  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
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   ],
   "source": [
    "df[df['address'].isin(['Sialkot', 'Karachi','Peshawer'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7c96fb54-ba4e-4b5c-8203-624596a7c138",
   "metadata": {},
   "outputs": [
    {
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       "      <th>9</th>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hussein</td>\n",
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       "      <td>MOR</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Muskan</td>\n",
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       "      <td>group C</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Yusuf</td>\n",
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       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amina</td>\n",
       "      <td>Multan</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Khubaib</td>\n",
       "      <td>Sialkot</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group C</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dua</td>\n",
       "      <td>Multan</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name    address    session    group  gender\n",
       "0   Ehtisham     Lahore    MORNING  group C    Male\n",
       "1     Ayesha  Islamabad        AFT  group A    Male\n",
       "2        Ali    Karachi  AFTERNOON  group B  Female\n",
       "3      Aisha     Lahore        MOR  group A    Male\n",
       "4      Ahmed   Peshawer        AFT  group D  Female\n",
       "5    Khadija     Lahore    MORNING  group C  Female\n",
       "6       Omar    Sialkot        AFT  group B  Female\n",
       "7      Adeen     Multan    MORNING  group D    Male\n",
       "8     Zainab    Karachi        AFT  group C    Male\n",
       "9     Hassan     Lahore  AFTERNOON  group D    Male\n",
       "10   Hussein  Islamabad        MOR  group B    Male\n",
       "11    Muskan    Karachi  AFTERNOON  group C    Male\n",
       "12     Yusuf     Lahore    MORNING  group D    Male\n",
       "13     Amina     Multan  AFTERNOON  group A  Female\n",
       "14   Khubaib    Sialkot        AFT  group C  Female\n",
       "15       Dua     Multan  AFTERNOON  group A    Male"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "f38395d8-e36f-4c80-b1e5-f13e4948ce0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll no</th>\n",
       "      <th>age</th>\n",
       "      <th>subj1</th>\n",
       "      <th>subj2</th>\n",
       "      <th>scholarship</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>78.3</td>\n",
       "      <td>84.4</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>70.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>64.9</td>\n",
       "      <td>75.1</td>\n",
       "      <td>8500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.3</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>65.9</td>\n",
       "      <td>72.8</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>69.3</td>\n",
       "      <td>78.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>90.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>84.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.5</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>84.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    roll no  age  subj1  subj2  scholarship\n",
       "0         1   23   78.3   84.4       5000.0\n",
       "1         2   16   70.5   60.5       6000.0\n",
       "2         3   20   64.9   75.1       8500.0\n",
       "3         4   20   82.0   84.3       4000.0\n",
       "4         5   21   65.9   72.8       3500.0\n",
       "5         6   16   69.3   78.6          NaN\n",
       "6         7   12   90.2    NaN       4000.0\n",
       "7         8    4   84.1   76.0       8000.0\n",
       "8         9   18   90.5   81.3       3500.0\n",
       "9        10   15   90.5   81.3       3800.0\n",
       "10       11   12   90.5   81.3       6000.0\n",
       "11       12   20   90.5   81.3          NaN\n",
       "12       13   18    NaN   76.5       7000.0\n",
       "13       14   28   84.1   76.0       8000.0\n",
       "14       15   13   90.5   81.3       3500.0\n",
       "15       16   11   90.5   81.3       3800.0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(exclude=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249e3253-6aa0-4e1e-9d7a-a2736e00aee2",
   "metadata": {},
   "source": [
    "The `map(aFunction, *iterables)` function simply returns a map object after applying aFunction() to all the elements of iterable(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "52307312-ba2a-4c58-b3aa-65bd9ec2f98a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll no</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>session</th>\n",
       "      <th>group</th>\n",
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       "      <th>subj1</th>\n",
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       "      <th>scholarship</th>\n",
       "      <th>name_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ehtisham</td>\n",
       "      <td>23</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>78.3</td>\n",
       "      <td>84.4</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ayesha</td>\n",
       "      <td>16</td>\n",
       "      <td>Islamabad</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ali</td>\n",
       "      <td>20</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group B</td>\n",
       "      <td>Female</td>\n",
       "      <td>64.9</td>\n",
       "      <td>75.1</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aisha</td>\n",
       "      <td>20</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MOR</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.3</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ahmed</td>\n",
       "      <td>21</td>\n",
       "      <td>Peshawer</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group D</td>\n",
       "      <td>Female</td>\n",
       "      <td>65.9</td>\n",
       "      <td>72.8</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Khadija</td>\n",
       "      <td>16</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group C</td>\n",
       "      <td>Female</td>\n",
       "      <td>69.3</td>\n",
       "      <td>78.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Omar</td>\n",
       "      <td>12</td>\n",
       "      <td>Sialkot</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group B</td>\n",
       "      <td>Female</td>\n",
       "      <td>90.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Adeen</td>\n",
       "      <td>4</td>\n",
       "      <td>Multan</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "      <td>84.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Zainab</td>\n",
       "      <td>18</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Hassan</td>\n",
       "      <td>15</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Hussein</td>\n",
       "      <td>12</td>\n",
       "      <td>Islamabad</td>\n",
       "      <td>MOR</td>\n",
       "      <td>group B</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Muskan</td>\n",
       "      <td>20</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Yusuf</td>\n",
       "      <td>18</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.5</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Amina</td>\n",
       "      <td>28</td>\n",
       "      <td>Multan</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Female</td>\n",
       "      <td>84.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Khubaib</td>\n",
       "      <td>13</td>\n",
       "      <td>Sialkot</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group C</td>\n",
       "      <td>Female</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Dua</td>\n",
       "      <td>11</td>\n",
       "      <td>Multan</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    roll no      name  age    address    session    group  gender  subj1  \\\n",
       "0         1  Ehtisham   23     Lahore    MORNING  group C    Male   78.3   \n",
       "1         2    Ayesha   16  Islamabad        AFT  group A    Male   70.5   \n",
       "2         3       Ali   20    Karachi  AFTERNOON  group B  Female   64.9   \n",
       "3         4     Aisha   20     Lahore        MOR  group A    Male   82.0   \n",
       "4         5     Ahmed   21   Peshawer        AFT  group D  Female   65.9   \n",
       "5         6   Khadija   16     Lahore    MORNING  group C  Female   69.3   \n",
       "6         7      Omar   12    Sialkot        AFT  group B  Female   90.2   \n",
       "7         8     Adeen    4     Multan    MORNING  group D    Male   84.1   \n",
       "8         9    Zainab   18    Karachi        AFT  group C    Male   90.5   \n",
       "9        10    Hassan   15     Lahore  AFTERNOON  group D    Male   90.5   \n",
       "10       11   Hussein   12  Islamabad        MOR  group B    Male   90.5   \n",
       "11       12    Muskan   20    Karachi  AFTERNOON  group C    Male   90.5   \n",
       "12       13     Yusuf   18     Lahore    MORNING  group D    Male    NaN   \n",
       "13       14     Amina   28     Multan  AFTERNOON  group A  Female   84.1   \n",
       "14       15   Khubaib   13    Sialkot        AFT  group C  Female   90.5   \n",
       "15       16       Dua   11     Multan  AFTERNOON  group A    Male   90.5   \n",
       "\n",
       "    subj2  scholarship  name_len  \n",
       "0    84.4       5000.0         8  \n",
       "1    60.5       6000.0         6  \n",
       "2    75.1       8500.0         3  \n",
       "3    84.3       4000.0         5  \n",
       "4    72.8       3500.0         5  \n",
       "5    78.6          NaN         7  \n",
       "6     NaN       4000.0         4  \n",
       "7    76.0       8000.0         5  \n",
       "8    81.3       3500.0         6  \n",
       "9    81.3       3800.0         6  \n",
       "10   81.3       6000.0         7  \n",
       "11   81.3          NaN         6  \n",
       "12   76.5       7000.0         5  \n",
       "13   76.0       8000.0         5  \n",
       "14   81.3       3500.0         7  \n",
       "15   81.3       3800.0         3  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name_len'] = df['name'].map(len)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "82a6e265-27f9-401f-a484-4aaf8130d696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27, 16, 125, 36, 343, 6084, 5776, 3136]\n"
     ]
    }
   ],
   "source": [
    "list1 = [3,4,5,6,7,78,76,56]\n",
    "\n",
    "def return_square_cube(x):\n",
    "    if x%2==0:\n",
    "        return x**2\n",
    "    else:\n",
    "        return x**3\n",
    "    \n",
    "obj1 = map(return_square_cube, list1)\n",
    "print(list(obj1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "366f5308-a0b6-4954-a53e-4a339258ce67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Adult\n",
       "1     Child\n",
       "2     Adult\n",
       "3     Adult\n",
       "4     Adult\n",
       "5     Child\n",
       "6     Child\n",
       "7     Child\n",
       "8     Child\n",
       "9     Child\n",
       "10    Child\n",
       "11    Adult\n",
       "12    Child\n",
       "13    Adult\n",
       "14    Child\n",
       "15    Child\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].map(lambda x: \"Adult\" if x > 18 else \"Child\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6491dc68-3a22-4c39-b8ee-ebf29864cb6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6547c81d-723b-4ab3-9048-c786e0c62872",
   "metadata": {},
   "source": [
    "The `df.replace()` method is used to replace values given in to_replace with value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "05922a0e-a759-49c4-9bcb-e687415d5035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     M\n",
       "1     A\n",
       "2     A\n",
       "3     M\n",
       "4     A\n",
       "5     M\n",
       "6     A\n",
       "7     M\n",
       "8     A\n",
       "9     A\n",
       "10    M\n",
       "11    A\n",
       "12    M\n",
       "13    A\n",
       "14    A\n",
       "15    A\n",
       "Name: session, dtype: object"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['session'].replace({'MORNING':'M', 'AFTERNOON':'A','AFT':'A','MOR':'M'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3374edb3-9f30-446c-b3d4-609ba8625062",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3e3f1b1c-6c20-43d3-9e45-55552abd238a",
   "metadata": {},
   "source": [
    "`apply()` method runs a function on all the elements of a series of a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "767fe480-1a7c-4a17-924a-900780c3c49a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     8\n",
       "1     6\n",
       "2     3\n",
       "3     5\n",
       "4     5\n",
       "5     7\n",
       "6     4\n",
       "7     5\n",
       "8     6\n",
       "9     6\n",
       "10    7\n",
       "11    6\n",
       "12    5\n",
       "13    5\n",
       "14    7\n",
       "15    3\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6263b813-7286-4342-ab9c-ff0be39868e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "76e59944-af2f-4bfe-a5d0-611ab7d5f473",
   "metadata": {},
   "source": [
    "The `del df['colname']` will remove the column without returning it. It is inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "89f1037e-40a6-4cbf-80ac-227678927083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll no</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>session</th>\n",
       "      <th>group</th>\n",
       "      <th>gender</th>\n",
       "      <th>subj1</th>\n",
       "      <th>subj2</th>\n",
       "      <th>scholarship</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ehtisham</td>\n",
       "      <td>23</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>78.3</td>\n",
       "      <td>84.4</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ayesha</td>\n",
       "      <td>16</td>\n",
       "      <td>Islamabad</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ali</td>\n",
       "      <td>20</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group B</td>\n",
       "      <td>Female</td>\n",
       "      <td>64.9</td>\n",
       "      <td>75.1</td>\n",
       "      <td>8500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aisha</td>\n",
       "      <td>20</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MOR</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.3</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ahmed</td>\n",
       "      <td>21</td>\n",
       "      <td>Peshawer</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group D</td>\n",
       "      <td>Female</td>\n",
       "      <td>65.9</td>\n",
       "      <td>72.8</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll no      name  age    address    session    group  gender  subj1  \\\n",
       "0        1  Ehtisham   23     Lahore    MORNING  group C    Male   78.3   \n",
       "1        2    Ayesha   16  Islamabad        AFT  group A    Male   70.5   \n",
       "2        3       Ali   20    Karachi  AFTERNOON  group B  Female   64.9   \n",
       "3        4     Aisha   20     Lahore        MOR  group A    Male   82.0   \n",
       "4        5     Ahmed   21   Peshawer        AFT  group D  Female   65.9   \n",
       "\n",
       "   subj2  scholarship  \n",
       "0   84.4       5000.0  \n",
       "1   60.5       6000.0  \n",
       "2   75.1       8500.0  \n",
       "3   84.3       4000.0  \n",
       "4   72.8       3500.0  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['name_len']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "1cdd14cb-6075-4660-86b5-b6898e68213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll no          int64\n",
       "name            object\n",
       "age            float64\n",
       "address         object\n",
       "session         object\n",
       "group           object\n",
       "gender          object\n",
       "subj1          float64\n",
       "subj2          float64\n",
       "scholarship    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'] = df['age'].astype(float)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "daff301e-f680-45c8-b714-43213a2b8dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll no</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>session</th>\n",
       "      <th>group</th>\n",
       "      <th>gender</th>\n",
       "      <th>subj1</th>\n",
       "      <th>subj2</th>\n",
       "      <th>scholarship</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Adeen</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Multan</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "      <td>84.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Dua</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Multan</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Omar</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sialkot</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group B</td>\n",
       "      <td>Female</td>\n",
       "      <td>90.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Hussein</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Islamabad</td>\n",
       "      <td>MOR</td>\n",
       "      <td>group B</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Khubaib</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Sialkot</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group C</td>\n",
       "      <td>Female</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Hassan</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ayesha</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Islamabad</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Khadija</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group C</td>\n",
       "      <td>Female</td>\n",
       "      <td>69.3</td>\n",
       "      <td>78.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Zainab</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Yusuf</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group D</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.5</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ali</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group B</td>\n",
       "      <td>Female</td>\n",
       "      <td>64.9</td>\n",
       "      <td>75.1</td>\n",
       "      <td>8500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aisha</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MOR</td>\n",
       "      <td>group A</td>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.3</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Muskan</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>90.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ahmed</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Peshawer</td>\n",
       "      <td>AFT</td>\n",
       "      <td>group D</td>\n",
       "      <td>Female</td>\n",
       "      <td>65.9</td>\n",
       "      <td>72.8</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ehtisham</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>group C</td>\n",
       "      <td>Male</td>\n",
       "      <td>78.3</td>\n",
       "      <td>84.4</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Amina</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Multan</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>group A</td>\n",
       "      <td>Female</td>\n",
       "      <td>84.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    roll no      name   age    address    session    group  gender  subj1  \\\n",
       "7         8     Adeen   4.0     Multan    MORNING  group D    Male   84.1   \n",
       "15       16       Dua  11.0     Multan  AFTERNOON  group A    Male   90.5   \n",
       "6         7      Omar  12.0    Sialkot        AFT  group B  Female   90.2   \n",
       "10       11   Hussein  12.0  Islamabad        MOR  group B    Male   90.5   \n",
       "14       15   Khubaib  13.0    Sialkot        AFT  group C  Female   90.5   \n",
       "9        10    Hassan  15.0     Lahore  AFTERNOON  group D    Male   90.5   \n",
       "1         2    Ayesha  16.0  Islamabad        AFT  group A    Male   70.5   \n",
       "5         6   Khadija  16.0     Lahore    MORNING  group C  Female   69.3   \n",
       "8         9    Zainab  18.0    Karachi        AFT  group C    Male   90.5   \n",
       "12       13     Yusuf  18.0     Lahore    MORNING  group D    Male    NaN   \n",
       "2         3       Ali  20.0    Karachi  AFTERNOON  group B  Female   64.9   \n",
       "3         4     Aisha  20.0     Lahore        MOR  group A    Male   82.0   \n",
       "11       12    Muskan  20.0    Karachi  AFTERNOON  group C    Male   90.5   \n",
       "4         5     Ahmed  21.0   Peshawer        AFT  group D  Female   65.9   \n",
       "0         1  Ehtisham  23.0     Lahore    MORNING  group C    Male   78.3   \n",
       "13       14     Amina  28.0     Multan  AFTERNOON  group A  Female   84.1   \n",
       "\n",
       "    subj2  scholarship  \n",
       "7    76.0       8000.0  \n",
       "15   81.3       3800.0  \n",
       "6     NaN       4000.0  \n",
       "10   81.3       6000.0  \n",
       "14   81.3       3500.0  \n",
       "9    81.3       3800.0  \n",
       "1    60.5       6000.0  \n",
       "5    78.6          NaN  \n",
       "8    81.3       3500.0  \n",
       "12   76.5       7000.0  \n",
       "2    75.1       8500.0  \n",
       "3    84.3       4000.0  \n",
       "11   81.3          NaN  \n",
       "4    72.8       3500.0  \n",
       "0    84.4       5000.0  \n",
       "13   76.0       8000.0  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.sort_values(by='age')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "3f678b76-78ed-4275-9236-ce885c283190",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>address</th>\n",
       "      <th>age</th>\n",
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       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>roll no</th>\n",
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       "      <th>session</th>\n",
       "      <th>subj1</th>\n",
       "      <th>subj2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>group C</td>\n",
       "      <td>Ehtisham</td>\n",
       "      <td>1</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>MORNING</td>\n",
       "      <td>78.3</td>\n",
       "      <td>84.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>group A</td>\n",
       "      <td>Ayesha</td>\n",
       "      <td>2</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>AFT</td>\n",
       "      <td>70.5</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>group B</td>\n",
       "      <td>Ali</td>\n",
       "      <td>3</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>AFTERNOON</td>\n",
       "      <td>64.9</td>\n",
       "      <td>75.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>group A</td>\n",
       "      <td>Aisha</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>MOR</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>group D</td>\n",
       "      <td>Ahmed</td>\n",
       "      <td>5</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>AFT</td>\n",
       "      <td>65.9</td>\n",
       "      <td>72.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     address   age  gender    group      name  roll no  scholarship  \\\n",
       "0     Lahore  23.0    Male  group C  Ehtisham        1       5000.0   \n",
       "1  Islamabad  16.0    Male  group A    Ayesha        2       6000.0   \n",
       "2    Karachi  20.0  Female  group B       Ali        3       8500.0   \n",
       "3     Lahore  20.0    Male  group A     Aisha        4       4000.0   \n",
       "4   Peshawer  21.0  Female  group D     Ahmed        5       3500.0   \n",
       "\n",
       "     session  subj1  subj2  \n",
       "0    MORNING   78.3   84.4  \n",
       "1        AFT   70.5   60.5  \n",
       "2  AFTERNOON   64.9   75.1  \n",
       "3        MOR   82.0   84.3  \n",
       "4        AFT   65.9   72.8  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df1 = df.sort_index(axis=1)\n",
    "df1.head()"
   ]
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   "cell_type": "code",
   "execution_count": 211,
   "id": "62532d14-56ba-4a50-9d3c-8659c5bf7b61",
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   "outputs": [
    {
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "   roll no   name    age  address  session  group  gender  subj1  subj2  \\\n",
       "0    False  False  False    False    False  False   False  False  False   \n",
       "1    False  False  False    False    False  False   False  False  False   \n",
       "2    False  False  False    False    False  False   False  False  False   \n",
       "3    False  False  False    False    False  False   False  False  False   \n",
       "4    False  False  False    False    False  False   False  False  False   \n",
       "\n",
       "   scholarship  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  \n",
       "3        False  \n",
       "4        False  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 213,
   "id": "97f652f5-f672-4403-90b3-05e7c0e94156",
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   roll no  name   age  address  session  group  gender  subj1  subj2  \\\n",
       "0     True  True  True     True     True   True    True   True   True   \n",
       "1     True  True  True     True     True   True    True   True   True   \n",
       "2     True  True  True     True     True   True    True   True   True   \n",
       "3     True  True  True     True     True   True    True   True   True   \n",
       "4     True  True  True     True     True   True    True   True   True   \n",
       "\n",
       "   scholarship  \n",
       "0         True  \n",
       "1         True  \n",
       "2         True  \n",
       "3         True  \n",
       "4         True  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "bb58d8bf-761d-41d5-bc05-c88842153dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Male      10\n",
       "Female     6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "4c33ebda-a223-4a3a-8000-42be1b351696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "5ae26dc3-d461-4980-9780-6d2f10e27d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5000.0\n",
       "1     6000.0\n",
       "2     8500.0\n",
       "3     4000.0\n",
       "4     3500.0\n",
       "5        0.0\n",
       "6     4000.0\n",
       "7     8000.0\n",
       "8     3500.0\n",
       "9     3800.0\n",
       "10    6000.0\n",
       "11       0.0\n",
       "12    7000.0\n",
       "13    8000.0\n",
       "14    3500.0\n",
       "15    3800.0\n",
       "Name: scholarship, dtype: float64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "6425f23e-c99c-4bcb-8c7d-8a1e8f7921f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\haide\\AppData\\Local\\Temp\\ipykernel_3340\\1812189869.py:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df.scholarship.fillna(method = 'ffill')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     5000.0\n",
       "1     6000.0\n",
       "2     8500.0\n",
       "3     4000.0\n",
       "4     3500.0\n",
       "5     3500.0\n",
       "6     4000.0\n",
       "7     8000.0\n",
       "8     3500.0\n",
       "9     3800.0\n",
       "10    6000.0\n",
       "11    6000.0\n",
       "12    7000.0\n",
       "13    8000.0\n",
       "14    3500.0\n",
       "15    3800.0\n",
       "Name: scholarship, dtype: float64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.fillna(method = 'ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "3e2ba378-d9d8-40ca-8aea-16f8b11ab36b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\haide\\AppData\\Local\\Temp\\ipykernel_3340\\3816033606.py:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df.scholarship.fillna(method = 'bfill')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     5000.0\n",
       "1     6000.0\n",
       "2     8500.0\n",
       "3     4000.0\n",
       "4     3500.0\n",
       "5     4000.0\n",
       "6     4000.0\n",
       "7     8000.0\n",
       "8     3500.0\n",
       "9     3800.0\n",
       "10    6000.0\n",
       "11    7000.0\n",
       "12    7000.0\n",
       "13    8000.0\n",
       "14    3500.0\n",
       "15    3800.0\n",
       "Name: scholarship, dtype: float64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.fillna(method = 'bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "5d9225a0-2d27-4ba9-9c70-8017601cad25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "group C    5\n",
       "group A    4\n",
       "group D    4\n",
       "group B    3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.group.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "8b60505b-b5f5-4767-8707-da24bac5be99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 10)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "f4ccba3f-18a4-4c09-a71c-d238321f0043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 10)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.dropna(axis=0, how='any')\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "ac184d39-cda7-4903-9391-34b6cc2effdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 10)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.dropna(axis=0, how='all')\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fcdbe94-ca32-41fc-ab51-4f1a3432db2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "309e6954-b344-4717-b3ed-97c92e385158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 10)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.dropna(axis=0, how='any', subset=['subj1'])\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b802b9c0-80fb-4529-ac92-468652224328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01/01/2022</td>\n",
       "      <td>karachi</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>02/01/2022</td>\n",
       "      <td>karachi</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>03/01/2022</td>\n",
       "      <td>karachi</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>karachi</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>05/01/2022</td>\n",
       "      <td>karachi</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01/01/2022</td>\n",
       "      <td>murree</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>02/01/2022</td>\n",
       "      <td>murree</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>03/01/2022</td>\n",
       "      <td>murree</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>murree</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>05/01/2022</td>\n",
       "      <td>murree</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date     city  temperature\n",
       "0   01/01/2022   lahore            8\n",
       "1   02/01/2022   lahore           10\n",
       "2   03/01/2022   lahore            5\n",
       "3   04/01/2022   lahore            6\n",
       "4   05/01/2022   lahore           12\n",
       "5   01/01/2022  karachi           18\n",
       "6   02/01/2022  karachi           10\n",
       "7   03/01/2022  karachi           12\n",
       "8   04/01/2022  karachi           15\n",
       "9   05/01/2022  karachi           16\n",
       "10  01/01/2022   murree           -5\n",
       "11  02/01/2022   murree           -3\n",
       "12  03/01/2022   murree           -4\n",
       "13  04/01/2022   murree           -1\n",
       "14  05/01/2022   murree           -7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/groupbydata1.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e2ded04-553c-4e8e-ab05-b22a256d0606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min    -7\n",
       "max    18\n",
       "Name: temperature, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.temperature.agg(['min','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d854925f-b815-4f68-b6be-b15590d7c9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "karachi    10\n",
       "lahore      5\n",
       "murree     -7\n",
       "Name: temperature, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')['temperature'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a6901be7-1215-4776-be44-189476560cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/01/2022</td>\n",
       "      <td>lahore</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    city  temperature\n",
       "0  01/01/2022  lahore            8\n",
       "1  02/01/2022  lahore           10\n",
       "2  03/01/2022  lahore            5\n",
       "3  04/01/2022  lahore            6\n",
       "4  05/01/2022  lahore           12"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city').get_group('lahore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4ec4cdcc-a10c-4297-aefc-289290a8eddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>karachi</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lahore</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>murree</th>\n",
       "      <td>-7</td>\n",
       "      <td>-1</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         min  max  sum\n",
       "city                  \n",
       "karachi   10   18   71\n",
       "lahore     5   12   41\n",
       "murree    -7   -1  -20"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')['temperature'].agg(['min','max','sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cb18ec74-fd21-4b27-b87f-29e4b600d4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = pd.DataFrame({\n",
    "    'city': ['Lahore', 'Muree', 'Peshawer', 'Sialkot'],\n",
    "    'temperature' : [39, 14, 29, 32],\n",
    "})\n",
    "df_hum = pd.DataFrame({\n",
    "    'city': [ 'Karachi', 'Lahore', 'Peshawer', 'Lahore', 'Muree'],\n",
    "    'humidity' : [76, 95, 72, 70, 75],\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1d2cece7-e27f-45be-9e68-32ea24d985be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39</td>\n",
       "      <td>70</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Muree</td>\n",
       "      <td>14</td>\n",
       "      <td>75</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>29</td>\n",
       "      <td>72</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity _merge\n",
       "0    Lahore           39        95   both\n",
       "1    Lahore           39        70   both\n",
       "2     Muree           14        75   both\n",
       "3  Peshawer           29        72   both"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = pd.merge(df_temp, df_hum,on='city', how='inner',indicator=True)\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "114aed65-cc89-4d6e-91b8-74f06352e398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Muree</td>\n",
       "      <td>14.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>29.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sialkot</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity      _merge\n",
       "0    Lahore         39.0      95.0        both\n",
       "1    Lahore         39.0      70.0        both\n",
       "2     Muree         14.0      75.0        both\n",
       "3  Peshawer         29.0      72.0        both\n",
       "4   Sialkot         32.0       NaN   left_only\n",
       "5   Karachi          NaN      76.0  right_only"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = pd.merge(df_temp, df_hum, on='city', how='outer', indicator=True)\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4c5efd4e-71a1-4478-bc6e-d486ed79ad8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39</td>\n",
       "      <td>95.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39</td>\n",
       "      <td>70.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Muree</td>\n",
       "      <td>14</td>\n",
       "      <td>75.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>29</td>\n",
       "      <td>72.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sialkot</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity     _merge\n",
       "0    Lahore           39      95.0       both\n",
       "1    Lahore           39      70.0       both\n",
       "2     Muree           14      75.0       both\n",
       "3  Peshawer           29      72.0       both\n",
       "4   Sialkot           32       NaN  left_only"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d3 = pd.merge(df_temp, df_hum, on='city', how='left', indicator=True)\n",
    "d3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3f83019b-2007-4f53-8c2d-7d58aa80bac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39.0</td>\n",
       "      <td>95</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>29.0</td>\n",
       "      <td>72</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>39.0</td>\n",
       "      <td>70</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Muree</td>\n",
       "      <td>14.0</td>\n",
       "      <td>75</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity      _merge\n",
       "0   Karachi          NaN        76  right_only\n",
       "1    Lahore         39.0        95        both\n",
       "2  Peshawer         29.0        72        both\n",
       "3    Lahore         39.0        70        both\n",
       "4     Muree         14.0        75        both"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df_temp, df_hum, on='city', how='right', indicator=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dcb1d312-f1fc-434c-9e6e-15c4874e1e36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({\n",
    "    'city': [ 'Lahore', 'Karachi', 'Peshawer'],\n",
    "    'temperature' : [35, 39, 33],\n",
    "    'humidity' : [76, 95, 72]\n",
    "})\n",
    "df2 = pd.DataFrame({\n",
    "    'city': [ 'Karachi', 'Peshawer', 'Islamabad'],\n",
    "    'temperature' : [41, 44, 47],\n",
    "    'humidity' : [88, 99, 79]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c2a561e6-49df-4351-8e89-8643294ec14a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature_x</th>\n",
       "      <th>humidity_x</th>\n",
       "      <th>temperature_y</th>\n",
       "      <th>humidity_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>44</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature_x  humidity_x  temperature_y  humidity_y\n",
       "0   Karachi             39          95             41          88\n",
       "1  Peshawer             33          72             44          99"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1, df2, on='city', how='inner')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "13cea7d1-5f4c-419d-bba0-c5107daebd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature_left</th>\n",
       "      <th>humidity_left</th>\n",
       "      <th>temperature_right</th>\n",
       "      <th>humidity_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>44</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature_left  humidity_left  temperature_right  \\\n",
       "0   Karachi                39             95                 41   \n",
       "1  Peshawer                33             72                 44   \n",
       "\n",
       "   humidity_right  \n",
       "0              88  \n",
       "1              99  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d3 = pd.merge(df1, df2, on='city', how='inner', suffixes=('_left','_right'))\n",
    "d3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ed57f203-aa25-497d-9386-6099f7811d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({\n",
    "    'city': [ 'Lahore', 'Karachi', 'Muree'],\n",
    "    'temperature' : [35, 39, 15],\n",
    "})\n",
    "df2 = pd.DataFrame({\n",
    "    'city': [ 'Lahore', 'Karachi', 'Islamabad', 'Lahore'],\n",
    "    'humidity' : [76, 95, 72, 76],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0116d1e8-d06e-4c7f-b08e-faab06a4c337",
   "metadata": {},
   "source": [
    "`one_to_one` or 1:1: checks if merge keys are unique in both left and right dataframes, if not then throw exception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9b263ff4-a475-44f2-ba83-7b16dde20918",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.merge(df1, df2, on='city', how='outer', validate='one_to_one')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60105620-d086-4f1c-bb39-973dfc719894",
   "metadata": {},
   "source": [
    "`one_to_many` or 1:m: checks if merge keys are unique in left dataframe, if not then throw exception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f35ed6c8-2f4c-4c07-9677-5f5cb89bd5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Muree</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0     Lahore         35.0      76.0\n",
       "1     Lahore         35.0      76.0\n",
       "2    Karachi         39.0      95.0\n",
       "3      Muree         15.0       NaN\n",
       "4  Islamabad          NaN      72.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='city', how='outer', validate='one_to_many')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfa9262-c47d-4024-b4b4-df6c61aa48e0",
   "metadata": {},
   "source": [
    "`many_to_one` or m:1: checks if merge keys are unique in right dataframe, if not then throw exception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1986113e-03c5-405a-9e51-5ce8a32ead6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.merge(df1, df2, on='city', how='outer', validate='many_to_one')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9c72c0-98ef-4276-9afd-3c1e4fcebf8f",
   "metadata": {},
   "source": [
    "`many_to_many` or m:m: No checks are performed on keys uniqueness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dda28388-a297-4206-ae99-4e741c7a2bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35.0</td>\n",
       "      <td>76.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>both</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Muree</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity      _merge\n",
       "0     Lahore         35.0      76.0        both\n",
       "1     Lahore         35.0      76.0        both\n",
       "2    Karachi         39.0      95.0        both\n",
       "3      Muree         15.0       NaN   left_only\n",
       "4  Islamabad          NaN      72.0  right_only"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='city', how='outer', validate='many_to_many', indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bfdf7abc-e3e8-4a43-b3f7-daf20bc7d546",
   "metadata": {},
   "outputs": [],
   "source": [
    "Pak_Weather = pd.DataFrame({\n",
    "    'city': [ 'Lahore', 'Karachi', 'Peshawer', 'Islamabad', 'Muree'],\n",
    "    'temperature' : [35, 39, 33, 29, 15],\n",
    "    'humidity' : [76, 95, 72, 81, 70],\n",
    "})\n",
    "UAE_Weather = pd.DataFrame({\n",
    "    'city': [ 'Dubai', 'Sharja', 'Ajman', 'Abu Dhabi'],\n",
    "    'temperature' : [41, 44, 47, 45],\n",
    "    'humidity' : [88, 99, 79, 86],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "98bef88e-1324-4927-bd28-ab343e09aa31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>city</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Pak</th>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>29</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Muree</td>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Uae</th>\n",
       "      <th>0</th>\n",
       "      <td>Dubai</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sharja</td>\n",
       "      <td>44</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ajman</td>\n",
       "      <td>47</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>45</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  temperature  humidity\n",
       "Pak 0     Lahore           35        76\n",
       "    1    Karachi           39        95\n",
       "    2   Peshawer           33        72\n",
       "    3  Islamabad           29        81\n",
       "    4      Muree           15        70\n",
       "Uae 0      Dubai           41        88\n",
       "    1     Sharja           44        99\n",
       "    2      Ajman           47        79\n",
       "    3  Abu Dhabi           45        86"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.concat([Pak_Weather, UAE_Weather], axis=0,keys=['Pak','Uae'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "45474810-ef5a-45c0-b758-ee4562deb6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>29</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Muree</td>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dubai</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sharja</td>\n",
       "      <td>44</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ajman</td>\n",
       "      <td>47</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>45</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0     Lahore           35        76\n",
       "1    Karachi           39        95\n",
       "2   Peshawer           33        72\n",
       "3  Islamabad           29        81\n",
       "4      Muree           15        70\n",
       "5      Dubai           41        88\n",
       "6     Sharja           44        99\n",
       "7      Ajman           47        79\n",
       "8  Abu Dhabi           45        86"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.concat([Pak_Weather, UAE_Weather], axis=0,ignore_index=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "54f11c42-88b3-4562-b6d8-4b5c3c20b16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PAK</th>\n",
       "      <th colspan=\"3\" halign=\"left\">UAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore</td>\n",
       "      <td>35</td>\n",
       "      <td>76</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>41.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Karachi</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "      <td>Sharja</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peshawer</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>Ajman</td>\n",
       "      <td>47.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>29</td>\n",
       "      <td>81</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>45.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Muree</td>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PAK                             UAE                     \n",
       "        city temperature humidity       city temperature humidity\n",
       "0     Lahore          35       76      Dubai        41.0     88.0\n",
       "1    Karachi          39       95     Sharja        44.0     99.0\n",
       "2   Peshawer          33       72      Ajman        47.0     79.0\n",
       "3  Islamabad          29       81  Abu Dhabi        45.0     86.0\n",
       "4      Muree          15       70        NaN         NaN      NaN"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.concat([Pak_Weather, UAE_Weather], axis=1,keys=['PAK','UAE'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b9a175ae-32b4-486f-b7ee-17f84f8310a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">temperature</th>\n",
       "      <th colspan=\"2\" halign=\"left\">humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>20/06/2021</th>\n",
       "      <th>21/06/2021</th>\n",
       "      <th>20/06/2021</th>\n",
       "      <th>21/06/2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lahore</th>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>90</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muree</th>\n",
       "      <td>19</td>\n",
       "      <td>34</td>\n",
       "      <td>84</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature              humidity           \n",
       "date    20/06/2021 21/06/2021 20/06/2021 21/06/2021\n",
       "city                                               \n",
       "Lahore          34         27         90         78\n",
       "Muree           19         34         84         74"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">temperature</th>\n",
       "      <th colspan=\"2\" halign=\"left\">humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>Lahore</th>\n",
       "      <th>Muree</th>\n",
       "      <th>Lahore</th>\n",
       "      <th>Muree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20/06/2021</th>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21/06/2021</th>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           temperature       humidity      \n",
       "city            Lahore Muree   Lahore Muree\n",
       "date                                       \n",
       "20/06/2021          34    19       90    84\n",
       "21/06/2021          27    34       78    74"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "city = ['Lahore', 'Muree', ]\n",
    "city = sorted(city*2)\n",
    "date = ['20/06/2021','21/06/2021']\n",
    "date = date*2\n",
    "humidity = [np.random.randint(71,94) for i in range(4)]\n",
    "temperature = [np.random.randint(14,42) for i in range(4)]\n",
    "dict1 = {'date':date,\n",
    "        'city':city,\n",
    "        'temperature':temperature,\n",
    "         'humidity':humidity}\n",
    "df = pd.DataFrame(dict1)\n",
    "df1 = df.pivot(index='city',columns='date')\n",
    "df2 = df.pivot(index='date',columns='city')\n",
    "display(df1)\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ab681f80-3f51-4103-b046-e39a45043a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">BattingTeam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ballnumber</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>62</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>58</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>103</td>\n",
       "      <td>74</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "      <td>112</td>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>77</td>\n",
       "      <td>102</td>\n",
       "      <td>63</td>\n",
       "      <td>86</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "      <td>73</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>86</td>\n",
       "      <td>83</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "      <td>73</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>62</td>\n",
       "      <td>86</td>\n",
       "      <td>61</td>\n",
       "      <td>74</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>82</td>\n",
       "      <td>92</td>\n",
       "      <td>83</td>\n",
       "      <td>69</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>91</td>\n",
       "      <td>72</td>\n",
       "      <td>87</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>87</td>\n",
       "      <td>109</td>\n",
       "      <td>79</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>90</td>\n",
       "      <td>124</td>\n",
       "      <td>103</td>\n",
       "      <td>100</td>\n",
       "      <td>86</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>102</td>\n",
       "      <td>120</td>\n",
       "      <td>129</td>\n",
       "      <td>121</td>\n",
       "      <td>113</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>107</td>\n",
       "      <td>115</td>\n",
       "      <td>111</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>126</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>151</td>\n",
       "      <td>117</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>118</td>\n",
       "      <td>114</td>\n",
       "      <td>151</td>\n",
       "      <td>132</td>\n",
       "      <td>138</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>136</td>\n",
       "      <td>120</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>116</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BattingTeam                         \n",
       "ballnumber           1    2    3    4    5    6\n",
       "overs                                          \n",
       "1                    9   17   31   39   33   27\n",
       "2                   31   40   49   56   58   54\n",
       "3                   75   62   70   72   58   76\n",
       "4                   60   74   74  103   74   71\n",
       "5                   71   76  112   80   81   72\n",
       "6                   77  102   63   86   78   80\n",
       "7                   34   56   49   59   64   38\n",
       "8                   59   62   73   70   69   56\n",
       "9                   86   83   79   81   73   52\n",
       "10                  54   62   86   61   74   67\n",
       "11                  82   92   83   69   72   70\n",
       "12                  91   72   87   79   87   70\n",
       "13                  87  109   79  100  100   84\n",
       "14                 101  101   99   97   90   88\n",
       "15                  90  124  103  100   86  106\n",
       "16                 102  120  129  121  113   96\n",
       "17                 107  115  111  100  120  101\n",
       "18                 126  142  137  151  117  129\n",
       "19                 118  114  151  132  138  128\n",
       "20                 136  120  151  151  116  148"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets/IPL_Ball_by_Ball_2008_2022.csv')\n",
    "df = df.query('batsman_run == 6').pivot_table(index='overs',columns='ballnumber',aggfunc='count').iloc[:,0:6].astype(int)\n",
    "df.index = df.index+1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "0ba96558-71e6-44ad-ac1f-35a6bf772708",
   "metadata": {},
   "outputs": [],
   "source": [
    "day = ['Monday','Tuesday','Wednesday','Thursday','Friday','Sunday']\n",
    "lahore = [np.random.randint(30,80) for i in range(6)]\n",
    "karachi = [np.random.randint(30,80) for i in range(6)]\n",
    "islamabad = [np.random.randint(30,80) for i in range(6)]\n",
    "dictionary = {'Day':day,\n",
    "             'Lahore':lahore,\n",
    "             'Karachi':karachi,\n",
    "             'Islamabad':islamabad}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7ae66135-3cd6-473b-8844-3774e50b60d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>City</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day    City  Temperature\n",
       "0     Monday  Lahore           76\n",
       "1    Tuesday  Lahore           49\n",
       "2  Wednesday  Lahore           72\n",
       "3   Thursday  Lahore           47\n",
       "4     Friday  Lahore           54\n",
       "5     Sunday  Lahore           49"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dictionary)\n",
    "df = pd.melt(df,id_vars=['Day'],value_vars=['Lahore'],var_name='City',value_name='Temperature')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "350f0b3f-b7ab-41d2-9a0c-5593d02260af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73270ca9-a251-4f81-a3fa-0b73557f20e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "305dfd80-a226-49b2-9b20-824e4bc72d99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da50a7be-4224-4fcb-9947-6e70bc03c49d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6872b198-4ebd-447a-9032-5c76105fce9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "365da500-a49c-4537-8b0c-988b888ebb82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aae30b0-98c1-488b-abf2-3c79b5abcba7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd5626a8-d9f2-442b-8c4d-7a681195a68c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "150d2fc8-7302-4dec-9a67-98417242b9b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a5f84f03-dec8-479b-98e5-54e6370a76a2",
   "metadata": {},
   "source": [
    "## Importing Files with Pandas\n",
    "- Import from csv\n",
    "- Import from excel\n",
    "- Import from text\n",
    "- Import from json\n",
    "- Import from API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d046b848-0034-43d2-9aea-8f6d00a6fcac",
   "metadata": {},
   "source": [
    "#### Opening a Local CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02aa8fb0-801f-4418-9671-0fd2fd5459a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batter</th>\n",
       "      <th>runs</th>\n",
       "      <th>avg</th>\n",
       "      <th>strike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>6634</td>\n",
       "      <td>36.251366</td>\n",
       "      <td>125.977972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>6244</td>\n",
       "      <td>34.882682</td>\n",
       "      <td>122.840842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>5883</td>\n",
       "      <td>41.429577</td>\n",
       "      <td>136.401577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>5881</td>\n",
       "      <td>30.314433</td>\n",
       "      <td>126.964594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>5536</td>\n",
       "      <td>32.374269</td>\n",
       "      <td>132.535312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batter  runs        avg  strike_rate\n",
       "0    V Kohli  6634  36.251366   125.977972\n",
       "1   S Dhawan  6244  34.882682   122.840842\n",
       "2  DA Warner  5883  41.429577   136.401577\n",
       "3  RG Sharma  5881  30.314433   126.964594\n",
       "4   SK Raina  5536  32.374269   132.535312"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/batter.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f06ec2b-e5d7-42eb-bdef-b552fbbbcaee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "afb6d9b5-ba71-4810-abbb-be03ce938792",
   "metadata": {},
   "source": [
    "#### Opening a csv file from an URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a11b1e3b-8d8b-4935-907a-a86b454ac0bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Benin</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burkina</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country  Region\n",
       "0   Algeria  AFRICA\n",
       "1    Angola  AFRICA\n",
       "2     Benin  AFRICA\n",
       "3  Botswana  AFRICA\n",
       "4   Burkina  AFRICA"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/cs109/2014_data/master/countries.csv\"\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f247bfb7-6286-412c-ab44-9f94029a1806",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ad6174d4-1ce8-4e15-bffe-185afd7bdb70",
   "metadata": {},
   "source": [
    "#### Opening a tsv file from sep parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44782530-4efa-4b30-bec6-c8dc7539cf3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No</th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m0</td>\n",
       "      <td>10 things i hate about you</td>\n",
       "      <td>1999</td>\n",
       "      <td>6.9</td>\n",
       "      <td>62847.0</td>\n",
       "      <td>['comedy' 'romance']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m1</td>\n",
       "      <td>1492: conquest of paradise</td>\n",
       "      <td>1992</td>\n",
       "      <td>6.2</td>\n",
       "      <td>10421.0</td>\n",
       "      <td>['adventure' 'biography' 'drama' 'history']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m2</td>\n",
       "      <td>15 minutes</td>\n",
       "      <td>2001</td>\n",
       "      <td>6.1</td>\n",
       "      <td>25854.0</td>\n",
       "      <td>['action' 'crime' 'drama' 'thriller']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m3</td>\n",
       "      <td>2001: a space odyssey</td>\n",
       "      <td>1968</td>\n",
       "      <td>8.4</td>\n",
       "      <td>163227.0</td>\n",
       "      <td>['adventure' 'mystery' 'sci-fi']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m4</td>\n",
       "      <td>48 hrs.</td>\n",
       "      <td>1982</td>\n",
       "      <td>6.9</td>\n",
       "      <td>22289.0</td>\n",
       "      <td>['action' 'comedy' 'crime' 'drama' 'thriller']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Serial No                  Movie Name Release Year  Rating     Votes  \\\n",
       "0        m0  10 things i hate about you         1999     6.9   62847.0   \n",
       "1        m1  1492: conquest of paradise         1992     6.2   10421.0   \n",
       "2        m2                  15 minutes         2001     6.1   25854.0   \n",
       "3        m3       2001: a space odyssey         1968     8.4  163227.0   \n",
       "4        m4                     48 hrs.         1982     6.9   22289.0   \n",
       "\n",
       "                                           Genres  \n",
       "0                            ['comedy' 'romance']  \n",
       "1     ['adventure' 'biography' 'drama' 'history']  \n",
       "2           ['action' 'crime' 'drama' 'thriller']  \n",
       "3                ['adventure' 'mystery' 'sci-fi']  \n",
       "4  ['action' 'comedy' 'crime' 'drama' 'thriller']  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/movie_titles_metadata.tsv\",sep='\\t',names=['Serial No','Movie Name','Release Year','Rating','Votes','Genres'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "768deb55-5870-4c65-b0dc-558718e207b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "761082ef-467a-455c-be78-a2542f32d416",
   "metadata": {},
   "source": [
    "#### index_col Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f559365-10dc-4b91-9632-4d1d0ea658a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enrollee_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29725</th>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11561</th>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33241</th>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city  city_development_index gender      relevent_experience  \\\n",
       "enrollee_id                                                                     \n",
       "8949         city_103                   0.920   Male  Has relevent experience   \n",
       "29725         city_40                   0.776   Male   No relevent experience   \n",
       "11561         city_21                   0.624    NaN   No relevent experience   \n",
       "33241        city_115                   0.789    NaN   No relevent experience   \n",
       "666          city_162                   0.767   Male  Has relevent experience   \n",
       "\n",
       "            enrolled_university education_level major_discipline experience  \\\n",
       "enrollee_id                                                                   \n",
       "8949              no_enrollment        Graduate             STEM        >20   \n",
       "29725             no_enrollment        Graduate             STEM         15   \n",
       "11561          Full time course        Graduate             STEM          5   \n",
       "33241                       NaN        Graduate  Business Degree         <1   \n",
       "666               no_enrollment         Masters             STEM        >20   \n",
       "\n",
       "            company_size    company_type last_new_job  training_hours  target  \n",
       "enrollee_id                                                                    \n",
       "8949                 NaN             NaN            1              36     1.0  \n",
       "29725              50-99         Pvt Ltd           >4              47     0.0  \n",
       "11561                NaN             NaN        never              83     0.0  \n",
       "33241                NaN         Pvt Ltd        never              52     1.0  \n",
       "666                50-99  Funded Startup            4               8     0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/aug_train.csv\",index_col='enrollee_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e52c655d-e4e5-4633-b33c-2805aaa06b6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "253439a9-8070-4e65-bcee-75e0d4a458f2",
   "metadata": {},
   "source": [
    "#### Header parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d03e047-20d8-4a28-98a9-449f79825e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Gender</th>\n",
       "      <th>University</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>Male</td>\n",
       "      <td>ITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>Kasur</td>\n",
       "      <td>Male</td>\n",
       "      <td>ITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>Female</td>\n",
       "      <td>Fast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID    City  Gender University\n",
       "0                                \n",
       "1  101  Lahore    Male        ITU\n",
       "2  102   Kasur    Male        ITU\n",
       "3  103  Lahore  Female       Fast"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/test.csv\",header=1,index_col='0')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dae9cf0-e0df-4129-8635-8cb082cd7c0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "766db8b0-6507-4c11-88a2-fc03f76a18da",
   "metadata": {},
   "source": [
    "#### use_cols Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "616b6967-d906-4454-a90a-44d281e680db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batter</th>\n",
       "      <th>strike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>125.977972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>122.840842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>136.401577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>126.964594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>132.535312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batter  strike_rate\n",
       "0    V Kohli   125.977972\n",
       "1   S Dhawan   122.840842\n",
       "2  DA Warner   136.401577\n",
       "3  RG Sharma   126.964594\n",
       "4   SK Raina   132.535312"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/batter.csv\",usecols=['batter','strike_rate'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ec0052-a20b-4069-9801-3e9e39b70b54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f879e4a2-afef-4457-ad46-d9cdb348c56e",
   "metadata": {},
   "source": [
    "#### Skiprows Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d461f3b-e753-4a3c-a5c3-adda3b113e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Gender</th>\n",
       "      <th>University</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>Male</td>\n",
       "      <td>ITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "      <td>Kasur</td>\n",
       "      <td>Male</td>\n",
       "      <td>ITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>103</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>Female</td>\n",
       "      <td>Fast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   ID    City  Gender University\n",
       "0  1  101  Lahore    Male        ITU\n",
       "1  2  102   Kasur    Male        ITU\n",
       "2  3  103  Lahore  Female       Fast"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/test.csv\",skiprows=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9491061d-e21a-482b-8f12-0b99bc28bfab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c6b981fd-289c-4597-bf4c-06c890065965",
   "metadata": {},
   "source": [
    "#### nrows Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c4865a66-0a31-4215-8bdb-93b0626381ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batter</th>\n",
       "      <th>runs</th>\n",
       "      <th>avg</th>\n",
       "      <th>strike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>6634</td>\n",
       "      <td>36.251366</td>\n",
       "      <td>125.977972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>6244</td>\n",
       "      <td>34.882682</td>\n",
       "      <td>122.840842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>5883</td>\n",
       "      <td>41.429577</td>\n",
       "      <td>136.401577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>5881</td>\n",
       "      <td>30.314433</td>\n",
       "      <td>126.964594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>5536</td>\n",
       "      <td>32.374269</td>\n",
       "      <td>132.535312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batter  runs        avg  strike_rate\n",
       "0    V Kohli  6634  36.251366   125.977972\n",
       "1   S Dhawan  6244  34.882682   122.840842\n",
       "2  DA Warner  5883  41.429577   136.401577\n",
       "3  RG Sharma  5881  30.314433   126.964594\n",
       "4   SK Raina  5536  32.374269   132.535312"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/batter.csv\",nrows=10)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc2af011-15e8-4401-8911-b1e95a2887e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a0e74156-db39-46a5-b7cc-bd13759a9ac3",
   "metadata": {},
   "source": [
    "#### Encoding Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1a381121-9a6b-4913-b676-b830d6dbb786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3        6318506                    Ooma           162  Mandaluyong City   \n",
       "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
       "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
       "\n",
       "                           Cuisines  ...          Currency Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
       "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
       "3                   Japanese, Sushi  ...  Botswana Pula(P)                No   \n",
       "4                  Japanese, Korean  ...  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "3                  No                No                   No           4   \n",
       "4                  No                No                   No           4   \n",
       "\n",
       "   Aggregate rating  Rating color Rating text Votes  \n",
       "0               4.8    Dark Green   Excellent   314  \n",
       "1               4.5    Dark Green   Excellent   591  \n",
       "2               4.4         Green   Very Good   270  \n",
       "3               4.9    Dark Green   Excellent   365  \n",
       "4               4.8    Dark Green   Excellent   229  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/zomato.csv\",encoding='latin-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f8e4e43-45da-42e5-b8af-177f56a2ac33",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "59aea3d0-d7b0-44ee-b878-d896a739c6c8",
   "metadata": {},
   "source": [
    "#### Skiping bad lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "16c13013-01dd-488f-a240-3e933c082e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Image-URL-S</th>\n",
       "      <th>Image-URL-M</th>\n",
       "      <th>Image-URL-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "      <td>2001</td>\n",
       "      <td>HarperFlamingo Canada</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperPerennial</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0374157065</td>\n",
       "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0393045218</td>\n",
       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ISBN                                         Book-Title  \\\n",
       "0  0195153448                                Classical Mythology   \n",
       "1  0002005018                                       Clara Callan   \n",
       "2  0060973129                               Decision in Normandy   \n",
       "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4  0393045218                             The Mummies of Urumchi   \n",
       "\n",
       "            Book-Author  Year-Of-Publication                   Publisher  \\\n",
       "0    Mark P. O. Morford                 2002     Oxford University Press   \n",
       "1  Richard Bruce Wright                 2001       HarperFlamingo Canada   \n",
       "2          Carlo D'Este                 1991             HarperPerennial   \n",
       "3      Gina Bari Kolata                 1999        Farrar Straus Giroux   \n",
       "4       E. J. W. Barber                 1999  W. W. Norton &amp; Company   \n",
       "\n",
       "                                         Image-URL-S  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-M  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-L  \n",
       "0  http://images.amazon.com/images/P/0195153448.0...  \n",
       "1  http://images.amazon.com/images/P/0002005018.0...  \n",
       "2  http://images.amazon.com/images/P/0060973129.0...  \n",
       "3  http://images.amazon.com/images/P/0374157065.0...  \n",
       "4  http://images.amazon.com/images/P/0393045218.0...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#on_bad_lines = 'skip','warn','error'\n",
    "df = pd.read_csv(\"Datasets/BX-Books.csv\",sep=';',encoding='latin-1',on_bad_lines='skip')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a28098e-338c-44fd-b6a9-5fc45551a2fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9888dc6e-bf0a-4ae6-bc7f-8cdffdb39138",
   "metadata": {},
   "source": [
    "#### dtype parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "538a01c3-562a-4b2b-b538-caa076345859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batter</th>\n",
       "      <th>runs</th>\n",
       "      <th>avg</th>\n",
       "      <th>strike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>6634.0</td>\n",
       "      <td>36.251366</td>\n",
       "      <td>125.977972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>6244.0</td>\n",
       "      <td>34.882682</td>\n",
       "      <td>122.840842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>5883.0</td>\n",
       "      <td>41.429577</td>\n",
       "      <td>136.401577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>5881.0</td>\n",
       "      <td>30.314433</td>\n",
       "      <td>126.964594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>5536.0</td>\n",
       "      <td>32.374269</td>\n",
       "      <td>132.535312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batter    runs        avg  strike_rate\n",
       "0    V Kohli  6634.0  36.251366   125.977972\n",
       "1   S Dhawan  6244.0  34.882682   122.840842\n",
       "2  DA Warner  5883.0  41.429577   136.401577\n",
       "3  RG Sharma  5881.0  30.314433   126.964594\n",
       "4   SK Raina  5536.0  32.374269   132.535312"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/batter.csv\",dtype={'runs':'float'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1decb2-a4c1-472b-9afb-50e7c223d816",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0f36f94d-5302-4fee-80cb-993ad56a91c6",
   "metadata": {},
   "source": [
    "#### na_values paramter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "38d578e8-9b19-4936-9a55-05a0d5857d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   enrollee_id      city  city_development_index gender  \\\n",
       "0         8949  city_103                   0.920   Male   \n",
       "1        29725   city_40                   0.776   Male   \n",
       "2        11561   city_21                   0.624    NaN   \n",
       "3        33241  city_115                   0.789    NaN   \n",
       "4          666  city_162                   0.767   Male   \n",
       "\n",
       "       relevent_experience enrolled_university education_level  \\\n",
       "0  Has relevent experience       no_enrollment             NaN   \n",
       "1   No relevent experience       no_enrollment             NaN   \n",
       "2   No relevent experience    Full time course             NaN   \n",
       "3   No relevent experience                 NaN             NaN   \n",
       "4  Has relevent experience       no_enrollment         Masters   \n",
       "\n",
       "  major_discipline experience company_size    company_type last_new_job  \\\n",
       "0              NaN        >20          NaN             NaN            1   \n",
       "1              NaN         15        50-99         Pvt Ltd           >4   \n",
       "2              NaN          5          NaN             NaN        never   \n",
       "3  Business Degree         <1          NaN         Pvt Ltd        never   \n",
       "4              NaN        >20        50-99  Funded Startup            4   \n",
       "\n",
       "   training_hours  target  \n",
       "0              36     1.0  \n",
       "1              47     0.0  \n",
       "2              83     0.0  \n",
       "3              52     1.0  \n",
       "4               8     0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/aug_train.csv\",na_values=['Graduate','STEM'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9f3a00c-c01c-424a-9bac-b780e218c16b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1cccbef5-2799-4f93-9d6c-13064f4898cc",
   "metadata": {},
   "source": [
    "#### Loading dataset in chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "afe66b0e-3576-4bda-ba2b-ab0261f29880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 14)\n",
      "(4000, 14)\n",
      "(4000, 14)\n",
      "(4000, 14)\n",
      "(3158, 14)\n"
     ]
    }
   ],
   "source": [
    "dfs = pd.read_csv(\"Datasets/aug_train.csv\",chunksize=4000)\n",
    "for chunk in dfs:\n",
    "    print(chunk.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12585090-52c1-4b31-8d2c-c5cf96015330",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2b32ffde-34e1-4c50-a8fa-b4c8d7cb668f",
   "metadata": {},
   "source": [
    "#### Opening excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "70733c8f-09be-44a4-b385-115755e5ff02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rollno</th>\n",
       "      <th>gender</th>\n",
       "      <th>group</th>\n",
       "      <th>age</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>urdu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>28.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>33.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>90</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>44.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rollno  gender    group   age  math  english  urdu\n",
       "0       1  female  group B  28.0  72.0       72  74.0\n",
       "1       2  female  group C  33.0  69.0       90  88.0\n",
       "2       3  female  group B  21.0   NaN       95  93.0\n",
       "3       4    male  group A  44.0  47.0       57  44.0\n",
       "4       5    male  group C  54.0  76.0       78   NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Datasets/classmarks.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f1bd344-f06f-4878-ab8a-f233b5d90d0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "65917a24-aafa-4017-b6c8-15fff0d15be8",
   "metadata": {},
   "source": [
    "#### Opening differnt sheets in a excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6da80790-d519-4ba2-b575-b16fb58f1aee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDO10</td>\n",
       "      <td>13.65</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.012741</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>57.6588</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>343.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDF32</td>\n",
       "      <td>16.35</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068024</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>196.4426</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1977.4260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDN22</td>\n",
       "      <td>18.85</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.138190</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>250.8724</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3775.0860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DRJ59</td>\n",
       "      <td>11.65</td>\n",
       "      <td>low fat</td>\n",
       "      <td>0.019356</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>39.1164</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>308.9312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           NCD19         8.93          Low Fat         0.000000   \n",
       "1           FDO10        13.65          Regular         0.012741   \n",
       "2           FDF32        16.35          Low Fat         0.068024   \n",
       "3           FDN22        18.85          Regular         0.138190   \n",
       "4           DRJ59        11.65          low fat         0.019356   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0              Household   53.8614            OUT013   \n",
       "1            Snack Foods   57.6588            OUT013   \n",
       "2  Fruits and Vegetables  196.4426            OUT013   \n",
       "3            Snack Foods  250.8724            OUT013   \n",
       "4            Hard Drinks   39.1164            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1987        High               Tier 3   \n",
       "1                       1987        High               Tier 3   \n",
       "2                       1987        High               Tier 3   \n",
       "3                       1987        High               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1           994.7052  \n",
       "1  Supermarket Type1           343.5528  \n",
       "2  Supermarket Type1          1977.4260  \n",
       "3  Supermarket Type1          3775.0860  \n",
       "4  Supermarket Type1           308.9312  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Datasets/big_mart_sales_with_multiple_sheets.xlsx\",sheet_name='1987')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e523f0d-de84-4a9a-906c-48349d335dcc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8263725b-e60c-4a82-a7f7-ad753d1f96d3",
   "metadata": {},
   "source": [
    "#### Reading text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "422fa115-aff9-4e72-a7d3-a75b719ccc7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   enrollee_id      city  city_development_index gender  \\\n",
       "0         8949  city_103                   0.920   Male   \n",
       "1        29725   city_40                   0.776   Male   \n",
       "2        11561   city_21                   0.624    NaN   \n",
       "3        33241  city_115                   0.789    NaN   \n",
       "4          666  city_162                   0.767   Male   \n",
       "\n",
       "       relevent_experience enrolled_university education_level  \\\n",
       "0  Has relevent experience       no_enrollment        Graduate   \n",
       "1   No relevent experience       no_enrollment        Graduate   \n",
       "2   No relevent experience    Full time course        Graduate   \n",
       "3   No relevent experience                 NaN        Graduate   \n",
       "4  Has relevent experience       no_enrollment         Masters   \n",
       "\n",
       "  major_discipline experience company_size    company_type last_new_job  \\\n",
       "0             STEM        >20          NaN             NaN            1   \n",
       "1             STEM         15        50-99         Pvt Ltd           >4   \n",
       "2             STEM          5          NaN             NaN        never   \n",
       "3  Business Degree         <1          NaN         Pvt Ltd        never   \n",
       "4             STEM        >20        50-99  Funded Startup            4   \n",
       "\n",
       "   training_hours  target  \n",
       "0              36     1.0  \n",
       "1              47     0.0  \n",
       "2              83     0.0  \n",
       "3              52     1.0  \n",
       "4               8     0.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/aug_train_txt.txt\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5583da35-6289-464b-9d0f-dda18afa227a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "796e2658-3528-4aa5-8f4b-10fea19d1cab",
   "metadata": {},
   "source": [
    "#### Opening JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7af85a51-c7de-495e-891a-11ac0b30b804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10259</td>\n",
       "      <td>greek</td>\n",
       "      <td>[romaine lettuce, black olives, grape tomatoes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25693</td>\n",
       "      <td>southern_us</td>\n",
       "      <td>[plain flour, ground pepper, salt, tomatoes, g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20130</td>\n",
       "      <td>filipino</td>\n",
       "      <td>[eggs, pepper, salt, mayonaise, cooking oil, g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22213</td>\n",
       "      <td>indian</td>\n",
       "      <td>[water, vegetable oil, wheat, salt]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13162</td>\n",
       "      <td>indian</td>\n",
       "      <td>[black pepper, shallots, cornflour, cayenne pe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id      cuisine                                        ingredients\n",
       "0  10259        greek  [romaine lettuce, black olives, grape tomatoes...\n",
       "1  25693  southern_us  [plain flour, ground pepper, salt, tomatoes, g...\n",
       "2  20130     filipino  [eggs, pepper, salt, mayonaise, cooking oil, g...\n",
       "3  22213       indian                [water, vegetable oil, wheat, salt]\n",
       "4  13162       indian  [black pepper, shallots, cornflour, cayenne pe..."
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json(\"Datasets/train.json\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26fb6c2e-81f9-4991-bb66-f3805d167015",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9d444429-05d1-4a5e-9750-df10ad4a7469",
   "metadata": {},
   "source": [
    "#### Opening Using API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fd6efce3-2a0f-4c3b-991f-cbb4ee05a132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>provider</th>\n",
       "      <th>WARNING_UPGRADE_TO_V6</th>\n",
       "      <th>terms</th>\n",
       "      <th>base</th>\n",
       "      <th>date</th>\n",
       "      <th>time_last_updated</th>\n",
       "      <th>rates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AED</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>INR</td>\n",
       "      <td>2024-07-05</td>\n",
       "      <td>1720137601</td>\n",
       "      <td>0.0440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFN</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>INR</td>\n",
       "      <td>2024-07-05</td>\n",
       "      <td>1720137601</td>\n",
       "      <td>0.8510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>INR</td>\n",
       "      <td>2024-07-05</td>\n",
       "      <td>1720137601</td>\n",
       "      <td>1.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMD</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>INR</td>\n",
       "      <td>2024-07-05</td>\n",
       "      <td>1720137601</td>\n",
       "      <td>4.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANG</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>INR</td>\n",
       "      <td>2024-07-05</td>\n",
       "      <td>1720137601</td>\n",
       "      <td>0.0214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             provider  \\\n",
       "AED  https://www.exchangerate-api.com   \n",
       "AFN  https://www.exchangerate-api.com   \n",
       "ALL  https://www.exchangerate-api.com   \n",
       "AMD  https://www.exchangerate-api.com   \n",
       "ANG  https://www.exchangerate-api.com   \n",
       "\n",
       "                          WARNING_UPGRADE_TO_V6  \\\n",
       "AED  https://www.exchangerate-api.com/docs/free   \n",
       "AFN  https://www.exchangerate-api.com/docs/free   \n",
       "ALL  https://www.exchangerate-api.com/docs/free   \n",
       "AMD  https://www.exchangerate-api.com/docs/free   \n",
       "ANG  https://www.exchangerate-api.com/docs/free   \n",
       "\n",
       "                                      terms base       date  \\\n",
       "AED  https://www.exchangerate-api.com/terms  INR 2024-07-05   \n",
       "AFN  https://www.exchangerate-api.com/terms  INR 2024-07-05   \n",
       "ALL  https://www.exchangerate-api.com/terms  INR 2024-07-05   \n",
       "AMD  https://www.exchangerate-api.com/terms  INR 2024-07-05   \n",
       "ANG  https://www.exchangerate-api.com/terms  INR 2024-07-05   \n",
       "\n",
       "     time_last_updated   rates  \n",
       "AED         1720137601  0.0440  \n",
       "AFN         1720137601  0.8510  \n",
       "ALL         1720137601  1.1100  \n",
       "AMD         1720137601  4.6500  \n",
       "ANG         1720137601  0.0214  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://api.exchangerate-api.com/v4/latest/INR\"\n",
    "df = pd.read_json(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77e9720a-248a-4090-9c04-f03aa58a29e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "31c7f57f-a79a-4ab1-98b8-f655ddf83ebe",
   "metadata": {},
   "source": [
    "## Exporting Files with Pandas\n",
    "- Export to csv\n",
    "- Export to json\n",
    "- Export to excel\n",
    "- Export to html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30fbabd1-49db-4334-a3db-11d84980b321",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "780a5314-fd98-4d1e-906e-d6b8dfabe723",
   "metadata": {},
   "source": [
    "#### to_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "073d6885-e7f4-401e-8e82-51ebb6013a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          batsman  2017\n",
       "0  AB de Villiers   216\n",
       "1       DA Warner   641\n",
       "2        MS Dhoni   290\n",
       "3       RG Sharma   333\n",
       "4         V Kohli   308"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/batsman_season_record.csv\")\n",
    "temp_df = df.groupby('batsman')['2017'].sum().reset_index()\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8b36c08b-efa0-4a81-8511-95bd021e931e",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df.to_csv(\"Datasets/batsman_2017_record.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213c11f1-4575-4f8a-aba8-99f978ee9ccf",
   "metadata": {},
   "source": [
    "#### to_excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "994b76c1-0492-492e-9b9c-ee6b7b850dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rollno</th>\n",
       "      <th>gender</th>\n",
       "      <th>group</th>\n",
       "      <th>age</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>urdu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>28.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>33.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>90</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>44.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rollno  gender    group   age  math  english  urdu\n",
       "0       1  female  group B  28.0  72.0       72  74.0\n",
       "1       2  female  group C  33.0  69.0       90  88.0\n",
       "2       3  female  group B  21.0   NaN       95  93.0\n",
       "3       4    male  group A  44.0  47.0       57  44.0\n",
       "4       5    male  group C  54.0  76.0       78   NaN"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Datasets/classmarks.xlsx\")\n",
    "df2 = df.copy()\n",
    "df2['total'] = df2[['math','english','urdu']].sum(axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9df0b941-5d5f-42e5-916e-2c51ea9af00c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter(\"Datasets/classmarks_Extended.xlsx\") as writer:\n",
    "    df.to_excel(writer,sheet_name='sheet_1')\n",
    "    df2.to_excel(writer,sheet_name='sheet_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d82d92ba-168a-4d22-9d6d-ea7045dcf108",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b47b967a-7d95-46df-9dc0-a56db1d1f093",
   "metadata": {},
   "source": [
    "#### to_html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "45f3b6c2-60f9-411f-9749-88831d9686b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ballnumber</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ballnumber     1     2      3      4     5     6\n",
       "overs                                           \n",
       "1            9.0  17.0   31.0   39.0  33.0  27.0\n",
       "2           31.0  40.0   49.0   56.0  58.0  54.0\n",
       "3           75.0  61.0   69.0   72.0  58.0  76.0\n",
       "4           60.0  73.0   74.0  103.0  74.0  70.0\n",
       "5           71.0  76.0  111.0   80.0  81.0  71.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/IPL_Ball_by_Ball_2008_2022.csv\")\n",
    "df = df.query('total_run == 6').pivot_table(index='overs',columns='ballnumber',values='total_run',aggfunc='count')\n",
    "df.index = df.index+1\n",
    "df.drop(columns=[7,8,9,10],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1f710cdd-c71a-4ce9-9732-3ba6ae0d438b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_html('Datasets/sixes_heatmap.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbbca7fb-aa15-4603-92de-57b1a13daf74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8b11aea3-5e74-45dc-b3b5-1182d63e4b67",
   "metadata": {},
   "source": [
    "#### to_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "867de317-d391-497a-a507-936a66db8e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>innings</th>\n",
       "      <th>overs</th>\n",
       "      <th>ballnumber</th>\n",
       "      <th>batter</th>\n",
       "      <th>bowler</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>extra_type</th>\n",
       "      <th>batsman_run</th>\n",
       "      <th>extras_run</th>\n",
       "      <th>total_run</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>isWicketDelivery</th>\n",
       "      <th>player_out</th>\n",
       "      <th>kind</th>\n",
       "      <th>fielders_involved</th>\n",
       "      <th>BattingTeam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1312200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>YBK Jaiswal</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1312200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>YBK Jaiswal</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>legbyes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1312200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>YBK Jaiswal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1312200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>YBK Jaiswal</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1312200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>YBK Jaiswal</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  innings  overs  ballnumber       batter          bowler  \\\n",
       "0  1312200        1      0           1  YBK Jaiswal  Mohammed Shami   \n",
       "1  1312200        1      0           2  YBK Jaiswal  Mohammed Shami   \n",
       "2  1312200        1      0           3   JC Buttler  Mohammed Shami   \n",
       "3  1312200        1      0           4  YBK Jaiswal  Mohammed Shami   \n",
       "4  1312200        1      0           5  YBK Jaiswal  Mohammed Shami   \n",
       "\n",
       "   non-striker extra_type  batsman_run  extras_run  total_run  non_boundary  \\\n",
       "0   JC Buttler        NaN            0           0          0             0   \n",
       "1   JC Buttler    legbyes            0           1          1             0   \n",
       "2  YBK Jaiswal        NaN            1           0          1             0   \n",
       "3   JC Buttler        NaN            0           0          0             0   \n",
       "4   JC Buttler        NaN            0           0          0             0   \n",
       "\n",
       "   isWicketDelivery player_out kind fielders_involved       BattingTeam  \n",
       "0                 0        NaN  NaN               NaN  Rajasthan Royals  \n",
       "1                 0        NaN  NaN               NaN  Rajasthan Royals  \n",
       "2                 0        NaN  NaN               NaN  Rajasthan Royals  \n",
       "3                 0        NaN  NaN               NaN  Rajasthan Royals  \n",
       "4                 0        NaN  NaN               NaN  Rajasthan Royals  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/IPL_Ball_by_Ball_2008_2022.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3a4c5e5b-be34-4bc0-9f0c-42bb70196210",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.batsman_run = df.batsman_run+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "56ec3a5d-f9a5-4ee3-85ac-ba61a1598fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json('Datasets/IPL_Ball_by_Ball_2008_2022+1.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9851c662-010c-419f-9d2b-6ed6de61a30b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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