Python_Basic-Advance_Assignment_2_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Explain three-dimensional data indexing ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4],\n",
       "        [ 5,  6,  7,  8,  9],\n",
       "        [10, 11, 12, 13, 14]],\n",
       "\n",
       "       [[15, 16, 17, 18, 19],\n",
       "        [20, 21, 22, 23, 24],\n",
       "        [25, 26, 27, 28, 29]],\n",
       "\n",
       "       [[30, 31, 32, 33, 34],\n",
       "        [35, 36, 37, 38, 39],\n",
       "        [40, 41, 42, 43, 44]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3D Array Slicing And Indexing\n",
    "import numpy as np\n",
    "x = np.arange(45).reshape(3,3,5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting the Two-Dimensional Arrays\n",
    "# Accessing each two dimensional arrays in it with simple indexing\n",
    "x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the second row of first two-dimensional array\n",
    "x[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the element 22 from the array\n",
    "x[1][1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 16, 17, 18, 19],\n",
       "       [30, 31, 32, 33, 34]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returning the first rows of the last two two-dimensional array.\n",
    "x[1:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[16, 17, 18],\n",
       "        [21, 22, 23]],\n",
       "\n",
       "       [[31, 32, 33],\n",
       "        [36, 37, 38]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing through both columns and rows and printing part of first two rows of the last two two-dimensional arrays\n",
    "x[1:, 0:2, 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. What's the difference between a series and a dataframe ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series can only contain single list with index (only one column), whereas dataframe can be made of more than one series or we can say that a dataframe is a collection of series (more than one column) that can be used to analyse the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         Krish\n",
      "1    Sourangshu\n",
      "2     Sudhanshu\n",
      "3        Avnish\n",
      "dtype: object\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# Simple Series\n",
    "import pandas as pd\n",
    "  \n",
    "tutor = ['Krish', 'Sourangshu', 'Sudhanshu', 'Avnish']\n",
    "tutor_neuron = pd.Series(tutor)\n",
    "\n",
    "print(tutor_neuron)\n",
    "print(type(tutor_neuron))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Tutor  Course\n",
      "0       Krish     210\n",
      "1  Sourangshu     211\n",
      "2   Sudhanshu     114\n",
      "3      Avnish     178\n"
     ]
    }
   ],
   "source": [
    "# Creating Dataframe from Series\n",
    "import pandas as pd\n",
    "\n",
    "tutor = ['Krish', 'Sourangshu', 'Sudhanshu', 'Avnish']\n",
    "course = [210, 211, 114, 178]\n",
    "\n",
    "tutor_neuron = pd.Series(tutor)\n",
    "course_series = pd.Series(course)\n",
    "\n",
    "frame = {'Tutor': tutor_neuron, 'Course': course_series}\n",
    "\n",
    "result = pd.DataFrame(frame)\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Tutor  Course  Duration_Months\n",
      "0       Krish     210                6\n",
      "1  Sourangshu     211                3\n",
      "2   Sudhanshu     114                9\n",
      "3      Avnish     178                2\n"
     ]
    }
   ],
   "source": [
    "# Adding series externally in dataframe\n",
    "import pandas as pd\n",
    "\n",
    "tutor = ['Krish', 'Sourangshu', 'Sudhanshu', 'Avnish']\n",
    "course = [210, 211, 114, 178]\n",
    "\n",
    "tutor_neuron = pd.Series(tutor)\n",
    "course_series = pd.Series(course)\n",
    "\n",
    "frame = {'Tutor': tutor_neuron, 'Course': course_series}\n",
    "\n",
    "result = pd.DataFrame(frame)\n",
    "duration = [6, 3, 9, 2]\n",
    "result['Duration_Months'] = pd.Series(duration)\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. What role does pandas play in data cleaning ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas play a very crucial role in data cleaning process prior to exploratory data analysis.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Pandas - Cleaning Data means fixing bad and irrelevant data in our data set.\n",
    "1. Empty cells\n",
    "2. Data in wrong format\n",
    "3. Wrong data\n",
    "4. Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data cleaning is essential. It has several benefits to any Machine Learning Algorithm.\n",
    "1. Error-Free Data\n",
    "2. Data Quality\n",
    "3. Accuracy and Efficiency \n",
    "4. Complete Data\n",
    "5. Maintains Data Consistency                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning Cycle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas offer a diverse range of built-in functions that can be used to clean and manipulate datasets prior to analysis. It can allow you to drop incomplete rows and columns, fill missing (NaN) values and improve the readability of the dataset through category renaming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      "staff_id             1000 non-null int64\n",
      "first_name           999 non-null object\n",
      "last_name            999 non-null object\n",
      "gender               793 non-null category\n",
      "birth_date           999 non-null datetime64[ns]\n",
      "employment_status    811 non-null category\n",
      "annual_salary        999 non-null float64\n",
      "tax_file_no          0 non-null float64\n",
      "dtypes: category(2), datetime64[ns](1), float64(2), int64(1), object(2)\n",
      "memory usage: 49.1+ KB\n"
     ]
    },
    {
     "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>staff_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>annual_salary</th>\n",
       "      <th>tax_file_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "      <td>793</td>\n",
       "      <td>999</td>\n",
       "      <td>811</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>926</td>\n",
       "      <td>420</td>\n",
       "      <td>2</td>\n",
       "      <td>953</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elvis</td>\n",
       "      <td>Schmitt</td>\n",
       "      <td>M</td>\n",
       "      <td>1981-04-15 00:00:00</td>\n",
       "      <td>PE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>414</td>\n",
       "      <td>2</td>\n",
       "      <td>599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>first</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970-01-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>last</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999-12-07 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>499.50200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102017.196196</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>285.86318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27567.849832</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55003.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>250.50000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77720.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>492.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101133.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>750.25000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125829.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>999.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          staff_id first_name last_name gender           birth_date  \\\n",
       "count   1000.00000        999       999    793                  999   \n",
       "unique         NaN        926       420      2                  953   \n",
       "top            NaN      Elvis   Schmitt      M  1981-04-15 00:00:00   \n",
       "freq           NaN          3         8    414                    2   \n",
       "first          NaN        NaN       NaN    NaN  1970-01-05 00:00:00   \n",
       "last           NaN        NaN       NaN    NaN  1999-12-07 00:00:00   \n",
       "mean     499.50200        NaN       NaN    NaN                  NaN   \n",
       "std      285.86318        NaN       NaN    NaN                  NaN   \n",
       "min        1.00000        NaN       NaN    NaN                  NaN   \n",
       "25%      250.50000        NaN       NaN    NaN                  NaN   \n",
       "50%      492.00000        NaN       NaN    NaN                  NaN   \n",
       "75%      750.25000        NaN       NaN    NaN                  NaN   \n",
       "max      999.00000        NaN       NaN    NaN                  NaN   \n",
       "\n",
       "       employment_status  annual_salary  tax_file_no  \n",
       "count                811     999.000000          0.0  \n",
       "unique                 3            NaN          NaN  \n",
       "top                   PE            NaN          NaN  \n",
       "freq                 599            NaN          NaN  \n",
       "first                NaN            NaN          NaN  \n",
       "last                 NaN            NaN          NaN  \n",
       "mean                 NaN  102017.196196          NaN  \n",
       "std                  NaN   27567.849832          NaN  \n",
       "min                  NaN   55003.000000          NaN  \n",
       "25%                  NaN   77720.000000          NaN  \n",
       "50%                  NaN  101133.000000          NaN  \n",
       "75%                  NaN  125829.000000          NaN  \n",
       "max                  NaN  149999.000000          NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Import dataset\n",
    "df = pd.read_csv('employee_data.csv')\n",
    "\n",
    "# Cast types to save memory.\n",
    "df['gender'] = df['gender'].astype('category')\n",
    "df['employment_status'] = df['employment_status'].astype('category')\n",
    "df['birth_date'] = df['birth_date'].astype('datetime64')\n",
    "\n",
    "# Rename columns\n",
    "df.rename(columns={'number': 'staff_id'}, inplace=True)\n",
    "\n",
    "df.info()\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "staff_id                0\n",
       "first_name              1\n",
       "last_name               1\n",
       "gender                207\n",
       "birth_date              1\n",
       "employment_status     189\n",
       "annual_salary           1\n",
       "tax_file_no          1000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show how many null values for each column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of missing values: 17.5%\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percentage of missing values as a whole\n",
    "rows, columns = df.shape\n",
    "cell_count = rows * columns\n",
    "number_of_nulls = df.isnull().sum().sum()\n",
    "percentage_of_missing = (number_of_nulls / cell_count) * 100\n",
    "print(f'Percentage of missing values: {percentage_of_missing}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 7 columns):\n",
      "staff_id             1000 non-null int64\n",
      "first_name           999 non-null object\n",
      "last_name            999 non-null object\n",
      "gender               793 non-null category\n",
      "birth_date           999 non-null datetime64[ns]\n",
      "employment_status    811 non-null category\n",
      "annual_salary        999 non-null float64\n",
      "dtypes: category(2), datetime64[ns](1), float64(1), int64(1), object(2)\n",
      "memory usage: 41.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Drop any columns that contain only null values.\n",
    "df.dropna(axis='columns', how='all', inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [staff_id, first_name, last_name, gender, birth_date, employment_status, annual_salary]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Check to see if any rows have less than 2 elements.\n",
    "under_threshold_removed = df.dropna(axis='index', thresh=2, inplace=False)\n",
    "under_threshold_rows = df[~df.index.isin(under_threshold_removed.index)]\n",
    "print(under_threshold_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 999 entries, 0 to 999\n",
      "Data columns (total 7 columns):\n",
      "staff_id             999 non-null int64\n",
      "first_name           999 non-null object\n",
      "last_name            999 non-null object\n",
      "gender               793 non-null category\n",
      "birth_date           999 non-null datetime64[ns]\n",
      "employment_status    811 non-null category\n",
      "annual_salary        999 non-null float64\n",
      "dtypes: category(2), datetime64[ns](1), float64(1), int64(1), object(2)\n",
      "memory usage: 49.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Drop rows with less than two elements.\n",
    "df.dropna(axis='index', thresh=2, inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M    414\n",
      "F    379\n",
      "U    206\n",
      "Name: gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Set a default category for missing genders.\n",
    "df['gender'].cat.add_categories(new_categories=['U'], inplace=True)\n",
    "df.fillna(value={'gender': 'U'}, inplace=True)\n",
    "print(df['gender'].value_counts())\n",
    "df.reset_index(inplace=True)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male       414\n",
       "Female     379\n",
       "Unknown    206\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming DataFrame categories.\n",
    "df['gender'].cat.rename_categories(new_categories={'M': 'Male', 'F': 'Female', 'U': 'Unknown'},\n",
    "                                       inplace=True)\n",
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. How do you use pandas to make a data frame out of n-dimensional arrays ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(1618033)\n",
    "\n",
    "# Set 3 axes dimensions\n",
    "years = np.arange(2000,2010) #Years\n",
    "samples = np.arange(0,20) #Samples\n",
    "patients = np.array([\"patient_%d\" % i for i in range(0,3)]) #Patients\n",
    "\n",
    "#Create random 3D array to simulate data from dims above\n",
    "A_3D = np.random.random((years.size, samples.size, len(patients))) #(10, 20, 3)\n",
    "\n",
    "# Create the MultiIndex from years, samples and patients.\n",
    "midx = pd.MultiIndex.from_product([years, samples, patients])\n",
    "\n",
    "# Create sample data for each patient, and add the MultiIndex.\n",
    "patient_data = pd.DataFrame(np.random.randn(len(midx), 3), index = midx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">2000</td>\n",
       "      <td rowspan=\"3\" valign=\"top\">0</td>\n",
       "      <td>patient_0</td>\n",
       "      <td>0.733891</td>\n",
       "      <td>-1.081459</td>\n",
       "      <td>1.344767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>patient_1</td>\n",
       "      <td>0.183899</td>\n",
       "      <td>0.367003</td>\n",
       "      <td>-1.043212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>patient_2</td>\n",
       "      <td>-0.233493</td>\n",
       "      <td>0.424136</td>\n",
       "      <td>0.035358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>patient_0</td>\n",
       "      <td>1.764374</td>\n",
       "      <td>3.095941</td>\n",
       "      <td>1.680118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>patient_1</td>\n",
       "      <td>-0.146588</td>\n",
       "      <td>1.167771</td>\n",
       "      <td>0.218991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0         1         2\n",
       "2000 0 patient_0  0.733891 -1.081459  1.344767\n",
       "       patient_1  0.183899  0.367003 -1.043212\n",
       "       patient_2 -0.233493  0.424136  0.035358\n",
       "     1 patient_0  1.764374  3.095941  1.680118\n",
       "       patient_1 -0.146588  1.167771  0.218991"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>...</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">0</td>\n",
       "      <td>patient_0</td>\n",
       "      <td>0.733891</td>\n",
       "      <td>0.313232</td>\n",
       "      <td>-0.383357</td>\n",
       "      <td>1.750069</td>\n",
       "      <td>-1.055323</td>\n",
       "      <td>0.038683</td>\n",
       "      <td>-0.795319</td>\n",
       "      <td>0.866563</td>\n",
       "      <td>-0.072328</td>\n",
       "      <td>-0.761547</td>\n",
       "      <td>...</td>\n",
       "      <td>1.344767</td>\n",
       "      <td>0.639748</td>\n",
       "      <td>0.452344</td>\n",
       "      <td>0.745117</td>\n",
       "      <td>-1.557094</td>\n",
       "      <td>-0.809781</td>\n",
       "      <td>-0.506905</td>\n",
       "      <td>-0.871313</td>\n",
       "      <td>1.425371</td>\n",
       "      <td>-0.589768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>patient_1</td>\n",
       "      <td>0.183899</td>\n",
       "      <td>0.130183</td>\n",
       "      <td>-2.402050</td>\n",
       "      <td>-0.195703</td>\n",
       "      <td>-1.295543</td>\n",
       "      <td>0.179823</td>\n",
       "      <td>-0.256294</td>\n",
       "      <td>-0.138591</td>\n",
       "      <td>0.401505</td>\n",
       "      <td>0.941614</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.043212</td>\n",
       "      <td>1.291971</td>\n",
       "      <td>0.581809</td>\n",
       "      <td>-0.239743</td>\n",
       "      <td>-0.295652</td>\n",
       "      <td>0.743647</td>\n",
       "      <td>-0.777149</td>\n",
       "      <td>-1.168015</td>\n",
       "      <td>0.503152</td>\n",
       "      <td>-0.060225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>patient_2</td>\n",
       "      <td>-0.233493</td>\n",
       "      <td>1.069902</td>\n",
       "      <td>-0.414003</td>\n",
       "      <td>-0.331743</td>\n",
       "      <td>-0.148233</td>\n",
       "      <td>-0.816054</td>\n",
       "      <td>-1.169933</td>\n",
       "      <td>0.485801</td>\n",
       "      <td>0.506001</td>\n",
       "      <td>0.994103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035358</td>\n",
       "      <td>0.711121</td>\n",
       "      <td>1.532766</td>\n",
       "      <td>0.785486</td>\n",
       "      <td>-0.347170</td>\n",
       "      <td>-1.143815</td>\n",
       "      <td>-1.146629</td>\n",
       "      <td>-0.393508</td>\n",
       "      <td>-0.648646</td>\n",
       "      <td>-0.614766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>patient_0</td>\n",
       "      <td>1.764374</td>\n",
       "      <td>0.706257</td>\n",
       "      <td>-0.141564</td>\n",
       "      <td>1.134810</td>\n",
       "      <td>1.412329</td>\n",
       "      <td>0.094802</td>\n",
       "      <td>-0.129987</td>\n",
       "      <td>-0.721118</td>\n",
       "      <td>-1.105769</td>\n",
       "      <td>-0.387461</td>\n",
       "      <td>...</td>\n",
       "      <td>1.680118</td>\n",
       "      <td>-0.215912</td>\n",
       "      <td>-0.881041</td>\n",
       "      <td>0.003546</td>\n",
       "      <td>0.718917</td>\n",
       "      <td>-0.101255</td>\n",
       "      <td>0.613290</td>\n",
       "      <td>0.870485</td>\n",
       "      <td>-0.871202</td>\n",
       "      <td>-0.883699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>patient_1</td>\n",
       "      <td>-0.146588</td>\n",
       "      <td>-0.396285</td>\n",
       "      <td>-0.050046</td>\n",
       "      <td>0.492005</td>\n",
       "      <td>0.078164</td>\n",
       "      <td>-2.426065</td>\n",
       "      <td>1.710801</td>\n",
       "      <td>0.130949</td>\n",
       "      <td>1.059029</td>\n",
       "      <td>0.570667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218991</td>\n",
       "      <td>-0.352376</td>\n",
       "      <td>-0.082334</td>\n",
       "      <td>0.033250</td>\n",
       "      <td>0.261488</td>\n",
       "      <td>0.808276</td>\n",
       "      <td>1.909453</td>\n",
       "      <td>-0.560593</td>\n",
       "      <td>0.647894</td>\n",
       "      <td>-0.454246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0                                                    \\\n",
       "                 2000      2001      2002      2003      2004      2005   \n",
       "0 patient_0  0.733891  0.313232 -0.383357  1.750069 -1.055323  0.038683   \n",
       "  patient_1  0.183899  0.130183 -2.402050 -0.195703 -1.295543  0.179823   \n",
       "  patient_2 -0.233493  1.069902 -0.414003 -0.331743 -0.148233 -0.816054   \n",
       "1 patient_0  1.764374  0.706257 -0.141564  1.134810  1.412329  0.094802   \n",
       "  patient_1 -0.146588 -0.396285 -0.050046  0.492005  0.078164 -2.426065   \n",
       "\n",
       "                                                     ...         2            \\\n",
       "                 2006      2007      2008      2009  ...      2000      2001   \n",
       "0 patient_0 -0.795319  0.866563 -0.072328 -0.761547  ...  1.344767  0.639748   \n",
       "  patient_1 -0.256294 -0.138591  0.401505  0.941614  ... -1.043212  1.291971   \n",
       "  patient_2 -1.169933  0.485801  0.506001  0.994103  ...  0.035358  0.711121   \n",
       "1 patient_0 -0.129987 -0.721118 -1.105769 -0.387461  ...  1.680118 -0.215912   \n",
       "  patient_1  1.710801  0.130949  1.059029  0.570667  ...  0.218991 -0.352376   \n",
       "\n",
       "                                                                         \\\n",
       "                 2002      2003      2004      2005      2006      2007   \n",
       "0 patient_0  0.452344  0.745117 -1.557094 -0.809781 -0.506905 -0.871313   \n",
       "  patient_1  0.581809 -0.239743 -0.295652  0.743647 -0.777149 -1.168015   \n",
       "  patient_2  1.532766  0.785486 -0.347170 -1.143815 -1.146629 -0.393508   \n",
       "1 patient_0 -0.881041  0.003546  0.718917 -0.101255  0.613290  0.870485   \n",
       "  patient_1 -0.082334  0.033250  0.261488  0.808276  1.909453 -0.560593   \n",
       "\n",
       "                                 \n",
       "                 2008      2009  \n",
       "0 patient_0  1.425371 -0.589768  \n",
       "  patient_1  0.503152 -0.060225  \n",
       "  patient_2 -0.648646 -0.614766  \n",
       "1 patient_0 -0.871202 -0.883699  \n",
       "  patient_1  0.647894 -0.454246  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_data.unstack(level=0).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Explain the notion of pandas plotting ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Tutor  Course  Duration_Months\n",
      "0       Krish     210                6\n",
      "1  Sourangshu     211                3\n",
      "2   Sudhanshu     114                9\n",
      "3      Avnish     178                2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAF2lJREFUeJzt3X10VPWdx/H3d0MktKhQCUoFDLjYLQ9hlBBopRaxu1rXirRqodRiH0g5NS20ezy65dRaz7FF26q1bnWxZbHVRUpQ5Ci7qwe01FMfEuqIIO0KFDFCIdCCosCa5Lt/zE12CJMH5t4h5Lef1zlzZuZ7n74zHD65+eU+mLsjIiLh+pvubkBERApLQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiASuV3c3ADBgwAAvKyvr7jZERHqUdevW7XH30s7mOyGCvqysjLq6uu5uQ0SkRzGz17syn4ZuREQCp6AXEQmcgl5EJHAnxBi9iMTz3nvvUV9fz6FDh7q7FSmAkpISBg8eTHFxcV7LK+hFAlBfX8/JJ59MWVkZZtbd7UiC3J29e/dSX1/PsGHD8lqHhm5EAnDo0CFOO+00hXyAzIzTTjst1m9rCnqRQCjkwxX331ZBLyISOI3RS5eU3fhE4uvctuAfE1+nZCT979XVf6s///nPzJs3j9raWnr37k1ZWRl33XUX55xzTqL9yLEJNugVTCLHl7szbdo0Zs2axcMPPwxAOp1m165dsYK+qamJoqKipNr8f0lDNyKSiKeffpri4mLmzJnTWkulUkyaNInrr7+e0aNHM2bMGJYuXQrAM888w2WXXdY6b3V1NYsXLwYyl0W55ZZbmDRpEsuWLePuu+9m5MiRlJeXM336dADeeecdvvSlLzF+/HjOPfdcHnvsseP3YXuYYPfoReT42rBhA+PGjTuq/sgjj5BOp3n55ZfZs2cP48eP54ILLuh0fSUlJTz77LMAfPCDH+RPf/oTvXv3Zt++fQDceuutTJkyhUWLFrFv3z4qKyv5xCc+wfvf//5kP1gAtEcvIgX17LPPMmPGDIqKijj99NP5+Mc/Tm1tbafLffazn219XV5ezsyZM3nwwQfp1Suzf/rkk0+yYMECUqkUkydP5tChQ2zfvr1gn6MnU9CLSCJGjRrFunXrjqq7e875e/XqRXNzc+v7tseJZ++ZP/HEE1x33XWsW7eOcePG0djYiLuzfPly0uk06XSa7du38+EPfzihTxMWBb2IJGLKlCkcPnyY+++/v7VWW1tL//79Wbp0KU1NTTQ0NLB27VoqKys566yzePXVVzl8+DD79+9n9erVOdfb3NzMG2+8wYUXXsjtt9/Ovn37OHDgABdffDE//elPW3+QvPTSS8flc/ZEGqMXCVB3HCFmZjz66KPMmzePBQsWUFJS0np45YEDBxg7dixmxu23384ZZ5wBwNVXX015eTkjRozg3HPPzbnepqYmPv/5z7N//37cnW9+85v069eP73znO8ybN4/y8nLcnbKyMh5//PHj+ZF7DGvv16rWGcyGAL8EzgCagYXu/hMz+wCwFCgDtgFXu/tfLXMK10+AS4F3gWvd/fcdbaOiosKTvvGIDq9Mlr7PE9umTZs0bBG4XP/GZrbO3Ss6W7YrQzeNwD+5+4eBicB1ZjYSuBFY7e4jgNXRe4BPAiOiRxVwb1c/iIiIJK/ToHf3nS175O7+NrAJOBOYCjwQzfYAcEX0eirwS894HuhnZoMS71xERLrkmP4Ya2ZlwLnAC8Dp7r4TMj8MgIHRbGcCb2QtVh/V2q6ryszqzKyuoaHh2DsXEZEu6XLQm1lfYDkwz93f6mjWHLWj/hDg7gvdvcLdK0pLO72JuYiI5KlLQW9mxWRC/iF3fyQq72oZkomed0f1emBI1uKDgR3JtCsiIseq06CPjqL5BbDJ3e/ImrQSmBW9ngU8llX/gmVMBPa3DPGIiMjx15Xj6M8HrgFeMbN0VPs2sAD4tZl9GdgOXBVNW0Xm0MrNZA6v/GKiHYtI524+NeH17U92fXJcdeWom2fd3dy93N1T0WOVu+9194vcfUT0/Jdofnf369z9bHcf4+7JHiAvIiekoqIiUqkUo0aNYuzYsdxxxx1HXOIgrsWLF7Njx/+NAn/lK1/h1VdfTWz9N998M2bG5s2bW2t33nknZka+5/msWLHiiB4nT56c97ri0CUQRCQRffr0IZ1Os3HjRp566ilWrVrF9773vWNaR1NTU7vT2gb9z3/+c0aOHJl3v7mMGTOm9Vr6ADU1NbG20Tbou4uCXkQSN3DgQBYuXMg999yDu7N48WKqq6tbp1922WU888wzAPTt25ebbrqJCRMm8Nxzz3HLLbcwfvx4Ro8eTVVVFe5OTU0NdXV1zJw5k1QqxcGDB4/YO16yZAljxoxh9OjR3HDDDa3b6du3L/Pnz2fs2LFMnDiRXbt2ddj3FVdc0Xpd+61bt3LqqaeSfVTgsWznd7/7HStXruT6668nlUqxZcsWAJYtW0ZlZSXnnHMOv/3tbwHYuHEjlZWVpFIpysvLee2112J8+0dT0ItIQQwfPpzm5mZ2797d4XzvvPMOo0eP5oUXXmDSpElUV1dTW1vLhg0bOHjwII8//jhXXnklFRUVPPTQQ6TTafr06dO6/I4dO7jhhhtYs2YN6XSa2tpaVqxY0bruiRMn8vLLL3PBBRccccG1XE455RSGDBnChg0bWLJkyRGXSj7W7Xz0ox/l8ssv54c//CHpdJqzzz4bgMbGRl588UXuuuuu1t947rvvPubOnUs6naauro7Bgwcf+xfeAQW9iBRMZ9fSgszY/mc+85nW908//TQTJkxgzJgxrFmzho0bN3a4fG1tLZMnT6a0tJRevXoxc+ZM1q5dC8BJJ53UehercePGsW3btk77mT59Og8//DArVqxg2rRpiW/n05/+9FHzfeQjH+H73/8+t912G6+//voRP8iSoKAXkYLYunUrRUVFDBw4sMNrz5eUlLTeE/bQoUN87Wtfo6amhldeeYXZs2cfdZ36tjr6YVJcXEzmCPHMD5TGxsZO+/7Upz7Fr371K4YOHcopp5yS+HZ69+591Hyf+9znWLlyJX369OHiiy9mzZo1nfZ5LHSZYpEQdfPhkA0NDcyZM4fq6mrMjLKyMn72s5/R3NzMm2++yYsvvphzuZZQHzBgAAcOHKCmpoYrr7wSgJNPPpm33377qGUmTJjA3Llz2bNnD/3792fJkiV8/etfz7v3Pn36cNtttx11Q/N8ttNez21t3bqV4cOH841vfIOtW7eyfv16pkyZkvdnaEtBLyKJOHjwIKlUivfee49evXpxzTXX8K1vfQuA888/n2HDhrX+IfO8887LuY5+/foxe/ZsxowZQ1lZGePHj2+ddu211zJnzhz69OnDc88911ofNGgQP/jBD7jwwgtxdy699FKmTp0a67O03IA8Wz7bmT59OrNnz+buu++mpqam3fmWLl3Kgw8+SHFxMWeccQY33XRTrP7b6vR69MeDrkd/4tP3eWLT9ejDV+jr0YuISA+moRsR+X/l1ltvZdmyZUfUrrrqKubPn99NHRWegl4kEO7eeuSHtG/+/Pk9LtTjDrFr6EYkACUlJezduzd2IMiJx93Zu3cvJSUlea9De/QiARg8eDD19fXobm1hKikpiXW2rIJeJADFxcUMGzasu9uQE5SGbkREAteVO0wtMrPdZrYhq7bUzNLRY1vLDUnMrMzMDmZNu6+QzYuISOe6MnSzGLgH+GVLwd1bL+lmZj8Gss+33uLuqaQaFBHpiE7m61ynQe/ua82sLNe06H6yVwPJXZRBREQSFXeM/mPALnfPvkr+MDN7ycx+Y2Yfi7l+ERGJKe5RNzOAJVnvdwJD3X2vmY0DVpjZKHd/q+2CZlYFVAEMHTo0ZhsiItKevPfozawX8GlgaUvN3Q+7+97o9TpgC3BOruXdfaG7V7h7RfatukREJFlxhm4+AfzB3etbCmZWamZF0evhwAhga7wWRUQkjq4cXrkEeA74kJnVm9mXo0nTOXLYBuACYL2ZvQzUAHPc/S9JNiwiIsemK0fdzGinfm2O2nJgefy2REQkKTozVkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCVxX7jC1yMx2m9mGrNrNZvammaWjx6VZ0/7ZzDab2R/N7OJCNS4iIl3TlT36xcAlOep3unsqeqwCMLORZG4xOCpa5mct95AVEZHu0WnQu/taoKv3fZ0KPOzuh939T8BmoDJGfyIiElOcMfpqM1sfDe30j2pnAm9kzVMf1Y5iZlVmVmdmdQ0NDTHaEBGRjuQb9PcCZwMpYCfw46huOeb1XCtw94XuXuHuFaWlpXm2ISIinckr6N19l7s3uXszcD//NzxTDwzJmnUwsCNeiyIiEkdeQW9mg7LeTgNajshZCUw3s95mNgwYAbwYr0UREYmjV2czmNkSYDIwwMzqge8Ck80sRWZYZhvwVQB332hmvwZeBRqB69y9qTCti4hIV3Qa9O4+I0f5Fx3Mfytwa5ymREQkOTozVkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRALXadBHN//ebWYbsmo/NLM/RDcHf9TM+kX1MjM7aGbp6HFfIZsXEZHOdWWPfjFwSZvaU8Body8H/hv456xpW9w9FT3mJNOmiIjkq9Ogd/e1wF/a1J5098bo7fNkbgIuIiInoCTG6L8E/EfW+2Fm9pKZ/cbMPpbA+kVEJIZO7xnbETObT+Ym4A9FpZ3AUHffa2bjgBVmNsrd38qxbBVQBTB06NA4bYiISAfy3qM3s1nAZcBMd3cAdz/s7nuj1+uALcA5uZZ394XuXuHuFaWlpfm2ISIincgr6M3sEuAG4HJ3fzerXmpmRdHr4cAIYGsSjYqISH46HboxsyXAZGCAmdUD3yVzlE1v4CkzA3g+OsLmAuAWM2sEmoA57v6XnCsWEZHjotOgd/cZOcq/aGfe5cDyuE2JiEhydGasiEjgFPQiIoFT0IuIBE5BLyISOAW9iEjgFPQiIoGLdQkEEclP2Y1PJL7ObQv+MfF1Shi0Ry8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iErguBb2ZLTKz3Wa2Iav2ATN7ysxei577R3Uzs7vNbLOZrTez8wrVvIiIdK6re/SLgUva1G4EVrv7CGB19B7gk2TuFTsCqALujd+miIjkq0tB7+5rgbb3fp0KPBC9fgC4Iqv+S894HuhnZoOSaFZERI5dnDH60919J0D0PDCqnwm8kTVffVQ7gplVmVmdmdU1NDTEaENERDpSiD/GWo6aH1VwX+juFe5eUVpaWoA2REQE4gX9rpYhmeh5d1SvB4ZkzTcY2BFjOyIiEkOcoF8JzIpezwIey6p/ITr6ZiKwv2WIR0REjr8u3XjEzJYAk4EBZlYPfBdYAPzazL4MbAeuimZfBVwKbAbeBb6YcM8iInIMuhT07j6jnUkX5ZjXgeviNCUiIsnRmbEiIoFT0IuIBE5BLyISOAW9iEjgFPQiIoFT0IuIBE5BLyISOAW9iEjgFPQiIoFT0IuIBE5BLyISOAW9iEjgFPQiIoFT0IuIBE5BLyISOAW9iEjgunTjkVzM7EPA0qzScOAmoB8wG2iI6t9291V5dygiIrHkHfTu/kcgBWBmRcCbwKNkbh14p7v/KJEORUQklqSGbi4Ctrj76wmtT0REEpJU0E8HlmS9rzaz9Wa2yMz651rAzKrMrM7M6hoaGnLNIiIiCYgd9GZ2EnA5sCwq3QucTWZYZyfw41zLuftCd69w94rS0tK4bYiISDuS2KP/JPB7d98F4O673L3J3ZuB+4HKBLYhIiJ5SiLoZ5A1bGNmg7KmTQM2JLANERHJU95H3QCY2fuAvwe+mlW+3cxSgAPb2kwTEZHjLFbQu/u7wGltatfE6khERBKlM2NFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcLGuRw9gZtuAt4EmoNHdK8zsA8BSoIzMzUeudve/xt2WiIgcu6T26C9095S7V0TvbwRWu/sIYHX0XkREukGhhm6mAg9Erx8ArijQdkREpBNJBL0DT5rZOjOrimqnu/tOgOh5YNuFzKzKzOrMrK6hoSGBNkREJJfYY/TA+e6+w8wGAk+Z2R+6spC7LwQWAlRUVHgCfYiISA6x9+jdfUf0vBt4FKgEdpnZIIDoeXfc7YiISH5iBb2Zvd/MTm55DfwDsAFYCcyKZpsFPBZnOyIikr+4QzenA4+aWcu6/t3d/9PMaoFfm9mXge3AVTG3IyIieYoV9O6+FRibo74XuCjOukVEJBk6M1ZEJHAKehGRwCnoRUQCp6AXEQmcgl5EJHAKehGRwCnoRUQCp6AXEQmcgl5EJHAKehGRwCnoRUQCp6AXEQmcgl5EJHAKehGRwCnoRUQCp6AXEQlc3kFvZkPM7Gkz22RmG81sblS/2czeNLN09Lg0uXZFRORYxbnDVCPwT+7+++i+sevM7Klo2p3u/qP47YmISFx5B7277wR2Rq/fNrNNwJlJNSYiIslIZIzezMqAc4EXolK1ma03s0Vm1r+dZarMrM7M6hoaGpJoQ0REcogd9GbWF1gOzHP3t4B7gbOBFJk9/h/nWs7dF7p7hbtXlJaWxm1DRETaESvozayYTMg/5O6PALj7Lndvcvdm4H6gMn6bIiKSrzhH3RjwC2CTu9+RVR+UNds0YEP+7YmISFxxjro5H7gGeMXM0lHt28AMM0sBDmwDvhqrQxERiSXOUTfPApZj0qr82xERkaTpzFgRkcAp6EVEAqegFxEJnIJeRCRwCnoRkcAp6EVEAqegFxEJnIJeRCRwCnoRkcAp6EVEAhfnWjciciK5+dQCrHN/8uuU40579CIigVPQi4gETkM3x0K/GotID6Q9ehGRwBVsj97MLgF+AhQBP3f3BYXalvRQ+g1J5LgoSNCbWRHwL8DfA/VArZmtdPdXC7E9EZFEBbYTUqihm0pgs7tvdff/AR4GphZoWyIi0gFz9+RXanYlcIm7fyV6fw0wwd2rs+apAqqitx8C/ph4I8kbAOzp7iYCou8zWfo+k9NTvsuz3L20s5kKNUaf616yR/xEcfeFwMICbb8gzKzO3Su6u49Q6PtMlr7P5IT2XRZq6KYeGJL1fjCwo0DbEhGRDhQq6GuBEWY2zMxOAqYDKwu0LRER6UBBhm7cvdHMqoH/InN45SJ331iIbR1nPWqoqQfQ95ksfZ/JCeq7LMgfY0VE5MShM2NFRAKnoBcRCZyCXkQkcLp6ZQfM7O/InNF7JpnzAHYAK919U7c2JiKJMrNKwN291sxGApcAf3D3Vd3cWiK0R98OM7uBzKUbDHiRzCGjBiwxsxu7szcRM/s7M7vIzPq2qV/SXT31VGb2XeBu4F4z+wFwD9AXuNHM5ndrcwnRUTftMLP/Bka5+3tt6icBG919RPd0Fh4z+6K7/1t399FTmNk3gOuATUAKmOvuj0XTfu/u53Vnfz2Nmb1C5nvsDfwZGOzub5lZH+AFdy/v1gYToD369jUDH8xRHxRNk+R8r7sb6GFmA+Pc/QpgMvAdM5sbTct1+RHpWKO7N7n7u8AWd38LwN0PEsj/dY3Rt28esNrMXgPeiGpDgb8FqttdSnIys/XtTQJOP569BKDI3Q8AuPs2M5sM1JjZWSjo8/E/Zva+KOjHtRTN7FQCCXoN3XTAzP6GzCWXzyTzH6geqHX3pm5trAcys13AxcBf204CfufuuX57khzMbA3wLXdPZ9V6AYuAme5e1G3N9UBm1tvdD+eoDwAGufsr3dBWorRH3wF3bwae7+4+AvE40Dc7nFqY2TPHv50e7QtAY3bB3RuBL5jZv3ZPSz1XrpCP6nvoGZcq7pT26EVEAqc/xoqIBE5BLyISOAW9iEjgFPQiIoH7X5NbZIo4W4xbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Plot\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tutor = ['Krish', 'Sourangshu', 'Sudhanshu', 'Avnish']\n",
    "course = [210, 211, 114, 178]\n",
    "\n",
    "tutor_neuron = pd.Series(tutor)\n",
    "course_series = pd.Series(course)\n",
    "\n",
    "frame = {'Tutor': tutor_neuron, 'Course': course_series}\n",
    "\n",
    "result = pd.DataFrame(frame)\n",
    "duration = [6, 3, 9, 2]\n",
    "result['Duration_Months'] = pd.Series(duration)\n",
    "\n",
    "print(result)\n",
    "\n",
    "result.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "ts = pd.Series(np.random.randn(1000), index = pd.date_range('1/1/2000', periods = 1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "ts = pd.Series(np.random.randn(1000), index = pd.date_range('1/1/2000', periods = 1000))\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(1000, 4),\n",
    "index = ts.index, columns = list('ABCD'))\n",
    "\n",
    "df = df.cumsum()\n",
    "plt.figure()\n",
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD8CAYAAABthzNFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFgNJREFUeJzt3X+wX3Wd3/HnC0yJKC5Coosk2WRroIANoJGmgx0RZIuWBVxhF2fFjMsaR3FWrZ3Kj53FzpapnVWzZWxkY6GAq1hcpVLFalBcVoeIESMCAUmVwhUqMQjo8kvg3T++5+o1nCTfG+6553tzn4+ZO/ecc885n3cCySuf8/l8PydVhSRJ29qj7wIkSaPJgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1Oo5fRfwbMybN68WL17cdxmSNKN85zvf+WlVzd/ZeTM6IBYvXsyGDRv6LkOSZpQk/3eY83zEJElqZUBIkloZEJKkVjN6DEKS+vLLX/6SsbExHnvssb5L2a65c+eyYMEC5syZs0vXGxCStAvGxsbYZ599WLx4MUn6LucZqoqtW7cyNjbGkiVLdukePmKSpF3w2GOPsf/++49kOAAkYf/9939WPRwDQpJ20aiGw7hnW58BIUlq5RiEJE2B1et+MKX3e+/xB03p/XaFAaFpsWbjml7afecR7+ylXWl34CMmSZqhTjnlFF7xildw2GGHsXbt2im/vz0ISZqhLrnkEvbbbz8effRRXvnKV/LGN76R/ffff8rub0BI0gx14YUXctVVVwFwzz33cOeddxoQkjTbff3rX+faa6/lhhtuYO+99+aYY46Z8k91GxDavV33n/pr+zXn9Ne2dnsPPfQQL3zhC9l77725/fbbWb9+/ZS3YUBI0hSY7mmpJ5xwAhdddBHLli3j4IMPZsWKFVPeRmcBkWQucD2wV9PO31XV+UmWAJ8G9gNuAs6oqieS7AVcDrwC2Ar8UVXd1VV9kjST7bXXXnzpS1/qtI0up7k+DhxbVYcDRwAnJFkB/GdgdVUtBX4GnNmcfybws6p6KbC6OU+S1JPOAqIGftHszmm+CjgW+Lvm+GXAKc32yc0+zc+Py6gvdCJJu7FOPyiXZM8kG4H7gXXA/wEerKonm1PGgAOb7QOBewCanz8EPGO+VpJVSTYk2bBly5Yuy5ekWa3TgKiqp6rqCGABcBRwSNtpzfe23kI940DV2qpaXlXL58+fP3XFSpJ+w7TMYqqqB5N8HVgB7JvkOU0vYQFwb3PaGLAQGEvyHOC3gAemoz5pdzLVi8YNaxQWl9PU6qwHkWR+kn2b7ecCrwU2AdcBpzanrQQ+32xf3ezT/PxrVfWMHoQkaXp02YM4ALgsyZ4MgujKqvpCktuATyf5j8B3gYub8y8GPpFkM4Oew+kd1iZJU2uqP5Q5xAct77rrLk488URuueWWqW270VlAVNXNwJEtx3/IYDxi2+OPAad1VY8kaXJc7luSZrAnn3ySlStXsmzZMk499VQeeeSRKbu3ASFJM9gdd9zBqlWruPnmm3nBC17AmjVT93IuA0KSZrCFCxdy9NFHA/DmN7+Zb3zjG1N2bwNCkmawbRecmMoFKAwISZrB7r77bm644QYArrjiCl71qldN2b1d7luSpkJP7/845JBDuOyyy3j729/O0qVLecc73jFl9zYgtFtb8+DNvbX9zt5a1myxePFibrvtts7u7yMmSVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKaa5SV6Z6+eehvbGndme3NRunbg0kgHce0f9EaXsQkqRWBoQkzVCXX345y5Yt4/DDD+eMM86Y8vv7iEmSZqBbb72VCy64gG9+85vMmzePBx54YMrbsAchSTPQ1772NU499VTmzZsHwH777TflbRgQkjQDVdWULu3dxoCQpBnouOOO48orr2Tr1q0AnTxicgxCkqbAdE9LPeywwzjvvPN49atfzZ577smRRx7JpZdeOqVtGBCSNEOtXLmSlStXdnZ/HzFJklp1FhBJFia5LsmmJLcmeXdz/ANJfpxkY/P1+gnXnJNkc5I7kvzrrmqTJO1cl4+YngTeV1U3JdkH+E6Sdc3PVlfVhyaenORQ4HTgMOAlwLVJDqqqpzqsUerMDT/c2k/Di/ppdjaajplEz0ZVPavrO+tBVNV9VXVTs/1zYBNw4A4uORn4dFU9XlU/AjYDR3VVnyQ9G3PnzmXr1q3P+i/hrlQVW7duZe7cubt8j2kZpE6yGDgS+BZwNPCuJG8BNjDoZfyMQXisn3DZGDsOFEnqzYIFCxgbG2PLli19l7Jdc+fOZcGCBbt8fecBkeT5wGeB91TVw0k+BvwlUM33DwN/ArT1054RzUlWAasAFi2yLy2pH3PmzGHJkiV9l9GpTmcxJZnDIBw+WVWfA6iqn1TVU1X1NPBxfv0YaQxYOOHyBcC9296zqtZW1fKqWj5//vwuy5ekWa3LWUwBLgY2VdVHJhw/YMJpbwBuabavBk5PsleSJcBS4Mau6pMk7ViXj5iOBs4Avp9kY3PsXOBNSY5g8PjoLuDtAFV1a5IrgdsYzIA6yxlMktSfzgKiqr5B+7jCNTu45gLggq5qkiQNz09SS5JaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWXb6TWtIssnrdD3pr+73HH9Rb27szexCSpFYGhCSplQEhSWplQEiSWhkQkqRWnQVEkoVJrkuyKcmtSd7dHN8vybokdzbfX9gcT5ILk2xOcnOSl3dVmyRp57rsQTwJvK+qDgFWAGclORQ4G/hqVS0FvtrsA7wOWNp8rQI+1mFtkqSdGCogkrxssjeuqvuq6qZm++fAJuBA4GTgsua0y4BTmu2TgctrYD2wb5IDJtuuJGlqDNuDuCjJjUnemWTfyTaSZDFwJPAt4MVVdR8MQgR4UXPagcA9Ey4ba45JknowVEBU1auAPwYWAhuSfCrJ8cNcm+T5wGeB91TVwzs6ta3plvutSrIhyYYtW7YMU4IkaRcMPQZRVXcCfw68H3g1cGGS25P8wfauSTKHQTh8sqo+1xz+yfijo+b7/c3xMQYBNG4BcG9LHWuranlVLZ8/f/6w5UuSJmnYMYhlSVYzGEc4Fvj9ZvD5WGD1dq4JcDGwqao+MuFHVwMrm+2VwOcnHH9LM5tpBfDQ+KMoSdL0G3axvo8CHwfOrapHxw9W1b1J/nw71xwNnAF8P8nG5ti5wAeBK5OcCdwNnNb87Brg9cBm4BHgrZP5hUiSptawAfF64NGqegogyR7A3Kp6pKo+0XZBVX2D9nEFgONazi/grCHrkSR1bNgxiGuB507Y37s5JknaTQ0bEHOr6hfjO8323t2UJEkaBcMGxD9OXPoiySuAR3dwviRphht2DOI9wGeSjE87PQD4o25KkiSNgqECoqq+neSfAQczGHi+vap+2WllkqReTead1K8EFjfXHJmEqrq8k6okSb0bKiCSfAL4p8BG4KnmcAEGhCTtpobtQSwHDm0+qyBJmgWGncV0C/DbXRYiSRotw/Yg5gG3JbkReHz8YFWd1ElVkqTeDRsQH+iyCEnS6Bl2muvfJ/kdYGlVXZtkb2DPbkuTJPVp2FlMb2Pwnuj9GMxmOhC4iJZF9yT1a8Xda3tpd/2iVb20q+4MO0h9FoPlux+GX7086EU7vEKSNKMNGxCPV9UT4ztJnkPL60AlSbuPYQPi75OcCzy3eRf1Z4D/1V1ZkqS+DRsQZwNbgO8Db2fw9rftvUlOkrQbGHYW09MMXjn68W7LkSSNimFnMf2IljGHqvrdKa9IkjQSJrMW07i5wGkMprxKknZTQ41BVNXWCV8/rqq/Bo7tuDZJUo+GfcT08gm7ezDoUezTSUWSpJEw7COmD0/YfhK4C/jDKa9GkjQyhp3F9JquC5EkjZZhHzH92x39vKo+0nLNJcCJwP1V9bLm2AeAtzH4TAXAuVV1TfOzc4AzGbyx7s+q6stD/hokSR2YzCymVwJXN/u/D1wP3LODay4FPsozX0u6uqo+NPFAkkOB04HDgJcA1yY5qKqeQpLUi8m8MOjlVfVz+FVP4DNV9afbu6Cqrk+yeMj7nwx8uqoeB36UZDNwFHDDkNdLkqbYsAGxCHhiwv4TwOJdbPNdSd4CbADeV1U/Y7B8+PoJ54w1x54hySoGS4+zaNGiXSxhdlqzcU3fJUiaQYZdi+kTwI1JPpDkfOBbPPPR0TA+xuB9EkcA9/Hr2VFpObd1tdiqWltVy6tq+fz583ehBEnSMIadxXRBki8B/6o59Naq+u5kG6uqn4xvJ/k48IVmdwxYOOHUBcC9k72/JGnqDNuDANgbeLiq/gswlmTJZBtLcsCE3TcAtzTbVwOnJ9mrue9S4MbJ3l+SNHWGneZ6PoOZTAcD/x2YA/wtg7fMbe+aK4BjgHlJxoDzgWOSHMHg8dFdDJYOp6puTXIlcBuDD+Kd5Qym3cyP/qHvCiRN0rCD1G8AjgRuAqiqe5PscKmNqnpTy+GLd3D+BcAFQ9YjSerYsI+Ynqiqohk4TvK87kqSJI2CYQPiyiR/A+yb5G3AtfjyIEnarQ07i+lDzbuoH2YwDvEXVbWu08qkGe7qPTb30u5JT7+0l3a1+9lpQCTZE/hyVb0WMBQkaZbY6SOmZjbRI0l+axrqkSSNiGFnMT0GfD/JOuAfxw9W1Z91UpUkqXfDBsQXmy9J0iyxw4BIsqiq7q6qy6arIEnSaNjZGMT/HN9I8tmOa5EkjZCdBcTEVVZ/t8tCJEmjZWcBUdvZliTt5nY2SH14kocZ9CSe22zT7FdVvaDT6iRJvdlhQFTVntNViCRptEzmfRCSpFnEgJAktTIgJEmtDAhJUisDQpLUyoCQJLUadrE+aUa658FH+y5BmrHsQUiSWhkQkqRWBoQkqZUBIUlq1VlAJLkkyf1JbplwbL8k65Lc2Xx/YXM8SS5MsjnJzUle3lVdkqThdNmDuBQ4YZtjZwNfraqlwFebfYDXAUubr1XAxzqsS5I0hM4CoqquBx7Y5vDJwPjrSy8DTplw/PIaWA/sm+SArmqTJO3cdI9BvLiq7gNovr+oOX4gcM+E88aaY8+QZFWSDUk2bNmypdNiJWk2G5VB6rQca32DXVWtrarlVbV8/vz5HZclSbPXdAfET8YfHTXf72+OjwELJ5y3ALh3mmuTJE0w3QFxNbCy2V4JfH7C8bc0s5lWAA+NP4qSJPWjs7WYklwBHAPMSzIGnA98ELgyyZnA3cBpzenXAK8HNgOPAG/tqi5J0nA6C4iqetN2fnRcy7kFnNVVLZKkyRuVQWpJ0ogxICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUqvO1mKSpOmyet0Pemn3vccf1Eu708UehCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJa9bIWU5K7gJ8DTwFPVtXyJPsB/wNYDNwF/GFV/ayP+iRJ/S7W95qq+umE/bOBr1bVB5Oc3ey/v5/SurVm45q+S5CknRqlR0wnA5c125cBp/RYiyTNen31IAr4SpIC/qaq1gIvrqr7AKrqviQv6qk2Sbtgxd1re2t7/aJVvbW9O+srII6uqnubEFiX5PZhL0yyClgFsGjRoq7qk6RZr5dHTFV1b/P9fuAq4CjgJ0kOAGi+37+da9dW1fKqWj5//vzpKlmSZp1pD4gkz0uyz/g28HvALcDVwMrmtJXA56e7NknSr/XxiOnFwFVJxtv/VFX97yTfBq5MciZwN3BaD7VJkhrTHhBV9UPg8JbjW4HjprseaXdz9R6be2n3pKdf2ku76s4oTXOVJI0QA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktepzuW9Ntx/9Q98VSJpB7EFIkloZEJKkVgaEJKmVYxCaFvc8+GjfJUiaJHsQkqRWBoQkqZUBIUlqZUBIklo5SC1Ju2j1uh/01vZ7jz+o8zbsQUiSWhkQkqRWBoQkqZUBIUlqNWsHqddsXNNf466qKmkGsAchSWo1a3sQknYfK+5e20u76xet6qXd6TJyPYgkJyS5I8nmJGf3XY8kzVYjFRBJ9gT+K/A64FDgTUkO7bcqSZqdRu0R01HA5qr6IUCSTwMnA7f1WtVuwiW31aWr99jcW9snPf3SXtrt69HWwIc6b2HUAuJA4J4J+2PAv+ipFkkzRF/h1FcwTZdRC4i0HKvfOCFZBYyPDP0iyR1TXMM84KdTfM9naxRrgtGsaxRrAuuajFGsCVrq+mBPhQDwpx8e39qV36/fGeakUQuIMWDhhP0FwL0TT6iqtUBn/bokG6pqeVf33xWjWBOMZl2jWBNY12SMYk0wO+saqUFq4NvA0iRLkvwT4HTg6p5rkqRZaaR6EFX1ZJJ3AV8G9gQuqapbey5LkmalkQoIgKq6BrimxxL6nJawPaNYE4xmXaNYE1jXZIxiTTAL60pV7fwsSdKsM2pjEJKkEWFAbEeSf5ekkszruxaAJH+Z5OYkG5N8JclL+q4JIMlfJbm9qe2qJPuOQE2nJbk1ydNJep91MorLxyS5JMn9SW7pu5ZxSRYmuS7Jpua/37v7rgkgydwkNyb5XlPXf+i7pnFJ9kzy3SRf6OL+BkSLJAuB44G7+65lgr+qqmVVdQTwBeAv+i6osQ54WVUtA34AnNNzPQC3AH8AXN93ISO8fMylwAl9F7GNJ4H3VdUhwArgrBH5vXocOLaqDgeOAE5IsqLnmsa9G9jU1c0NiHargX/PNh/S61NVPTxh93mMSG1V9ZWqerLZXc/gsyu9qqpNVTXVH6DcVb9aPqaqngDGl4/pVVVdDzzQdx0TVdV9VXVTs/1zBn/xHdhvVVADv2h25zRfvf/5S7IA+DfAf+uqDQNiG0lOAn5cVd/ru5ZtJbkgyT3AHzM6PYiJ/gT4Ut9FjJi25WN6/0tv1CVZDBwJfKvfSgaaRzkbgfuBdVU1CnX9NYN/yD7dVQMjN811OiS5Fvjtlh+dB5wL/N70VjSwo7qq6vNVdR5wXpJzgHcB549CXc055zF4RPDJUalpROx0+Rj9piTPBz4LvGebnnNvquop4IhmjO2qJC+rqt7Gb5KcCNxfVd9JckxX7czKgKiq17YdT/LPgSXA95LA4HHJTUmOqqr/11ddLT4FfJFpCoid1ZVkJXAicFxN07zpSfxe9W2ny8fo15LMYRAOn6yqz/Vdz7aq6sEkX2cwftPnAP/RwElJXg/MBV6Q5G+r6s1T2YiPmCaoqu9X1YuqanFVLWbwh/vl0xEOO5Nk6YTdk4Db+6ploiQnAO8HTqqqR/quZwS5fMyQMvhX2cXApqr6SN/1jEsyf3x2XpLnAq+l5z9/VXVOVS1o/p46HfjaVIcDGBAzyQeT3JLkZgaPwEZiCiDwUWAfYF0zBfeivgtK8oYkY8C/BL6Y5Mt91dIM4I8vH7MJuHIUlo9JcgVwA3BwkrEkZ/ZdE4N/FZ8BHNv8v7Sx+Rdy3w4Armv+7H2bwRhEJ9NKR42fpJYktbIHIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSp1f8HgekgxR0I888AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000), 'c': np.random.randn(1000) - 1}, columns =['a', 'b', 'c'])\n",
    "plt.figure()\n",
    "\n",
    "df4.plot.hist(alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(10, 5), columns =['A', 'B', 'C', 'D', 'E'])\n",
    "\n",
    "df.plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Density Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(10, 5), columns =['A', 'B', 'C', 'D', 'E'])\n",
    "\n",
    "ser = pd.Series(np.random.randn(1000))\n",
    "ser.plot.kde()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "series = pd.Series(3 * np.random.rand(4),\n",
    "index =['a', 'b', 'c', 'd'], name ='series')\n",
    "\n",
    "series.plot.pie(figsize =(4, 4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(500, 4), columns =['a', 'b', 'c', 'd'])\n",
    "\n",
    "df.plot.scatter(x ='a', y ='b')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}