14_Python_Basics_Pandas_Manupulating_Data.ipynb

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        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "source": [
        "# Adding new column to existing DataFrame in Pandas\n",
        "\n",
        "Method #1: By declaring a new list as a column. \n",
        "\n",
        "\n"
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        "# Import pandas package\n",
        "import pandas as pd\n",
        "\n",
        "# Define a dictionary containing Students data\n",
        "data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],\n",
        "\t\t'Height': [5.1, 6.2, 5.1, 5.2],\n",
        "\t\t'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}\n",
        "\n",
        "# Convert the dictionary into DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "print(df)\n",
        "print(\"*********************************************\")\n",
        "# Declare a list that is to be converted into a column\n",
        "cities = ['Delhi', 'Bangalore', 'Chennai', 'Patna']\n",
        "\n",
        "# Using 'Address' as the column name\n",
        "# and equating it to the list\n",
        "df['Address'] = cities\n",
        "\n",
        "# Observe the result\n",
        "df\n"
      ],
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            "     Name  Height Qualification\n",
            "0     Jai     5.1           Msc\n",
            "1  Princi     6.2            MA\n",
            "2  Gaurav     5.1           Msc\n",
            "3    Anuj     5.2           Msc\n",
            "*********************************************\n"
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              "     Name  Height Qualification    Address\n",
              "0     Jai     5.1           Msc      Delhi\n",
              "1  Princi     6.2            MA  Bangalore\n",
              "2  Gaurav     5.1           Msc    Chennai\n",
              "3    Anuj     5.2           Msc      Patna"
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        "Method #2: Using Dataframe.assign() method\n",
        "\n",
        "This method will create a new dataframe with a new column added to the old dataframe."
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        "# Import pandas package\n",
        "import pandas as pd\n",
        "\n",
        "# Define a dictionary containing Students data\n",
        "data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],\n",
        "\t\t'Height': [5.1, 6.2, 5.1, 5.2],\n",
        "\t\t'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}\n",
        "\n",
        "\n",
        "# Convert the dictionary into DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Using 'Address' as the column name and equating it to the list\n",
        "df2 = df.assign(address = ['Delhi', 'Bangalore', 'Chennai', 'Patna'])\n",
        "\n",
        "# Observe the result\n",
        "df2\n"
      ],
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              "     Name  Height Qualification    address\n",
              "0     Jai     5.1           Msc      Delhi\n",
              "1  Princi     6.2            MA  Bangalore\n",
              "2  Gaurav     5.1           Msc    Chennai\n",
              "3    Anuj     5.2           Msc      Patna"
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              "      <td>Msc</td>\n",
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      "source": [
        "# Delete rows/columns from DataFrame using Pandas.drop()\n",
        "\n",
        "Syntax:\n",
        "DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)\n",
        "\n",
        "Parameters:\n",
        "\n",
        "labels: String or list of strings referring row or column name.\n",
        "\n",
        "axis: int or string value, 0 ‘index’ for Rows and 1 ‘columns’ for Columns.\n",
        "\n",
        "index or columns: Single label or list. index or columns are an alternative to axis and cannot be used together.\n",
        "\n",
        "level: Used to specify level in case data frame is having multiple level index.\n",
        "\n",
        "inplace: Makes changes in original Data Frame if True.\n",
        "\n",
        "errors: Ignores error if any value from the list doesn’t exists and drops rest of the values when errors = ‘ignore’\n",
        "\n",
        "Return type: Dataframe with dropped values"
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      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/My Mentorship/nba.csv\",index_col =\"Name\")"
      ],
      "metadata": {
        "id": "wpR0-WFNNgSg"
      },
      "execution_count": 12,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Example #1: Dropping Rows by index label\n",
        "#In his code, A list of index labels is passed and the rows corresponding to those labels are dropped using .drop() method.\n",
        "\n",
        "# importing pandas module\n",
        "import pandas as pd\n",
        "\n",
        "# dropping passed values\n",
        "data.drop([\"Avery Bradley\", \"John Holland\", \"R.J. Hunter\"],axis=0, inplace = True)\n",
        "\n",
        "# display\n",
        "data"
      ],
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              "                         Team  Number Position   Age Height  Weight  \\\n",
              "Name                                                                  \n",
              "Jae Crowder    Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
              "Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
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              "...                       ...     ...      ...   ...    ...     ...   \n",
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              "Raul Neto           Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
              "Tibor Pleiss        Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
              "Jeff Withey         Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
              "NaN                       NaN     NaN      NaN   NaN    NaN     NaN   \n",
              "\n",
              "                 College      Salary  \n",
              "Name                                  \n",
              "Jae Crowder    Marquette   6796117.0  \n",
              "Jonas Jerebko        NaN   5000000.0  \n",
              "Amir Johnson         NaN  12000000.0  \n",
              "Jordan Mickey        LSU   1170960.0  \n",
              "Kelly Olynyk     Gonzaga   2165160.0  \n",
              "...                  ...         ...  \n",
              "Shelvin Mack      Butler   2433333.0  \n",
              "Raul Neto            NaN    900000.0  \n",
              "Tibor Pleiss         NaN   2900000.0  \n",
              "Jeff Withey       Kansas    947276.0  \n",
              "NaN                  NaN         NaN  \n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>455 rows × 8 columns</p>\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Example #2 : Dropping columns with column name\n",
        "\n",
        "In his code, Passed columns are dropped using column names. axis parameter is kept 1 since 1 refers to columns.\n",
        "\n"
      ],
      "metadata": {
        "id": "bijdhExMPAld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing pandas module\n",
        "import pandas as pd\n",
        "\n",
        "# making data frame from csv file\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/My Mentorship/nba.csv\", index_col =\"Name\" )\n",
        "\n",
        "# dropping passed columns\n",
        "data.drop([\"Team\", \"Weight\"], axis = 1, inplace = True)\n",
        "\n",
        "# display\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "RqBiwknVOdey",
        "outputId": "3cf32e2c-b16b-4589-834f-ffc977fbd6d9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "               Number Position   Age Height            College     Salary\n",
              "Name                                                                     \n",
              "Avery Bradley     0.0       PG  25.0    6-2              Texas  7730337.0\n",
              "Jae Crowder      99.0       SF  25.0    6-6          Marquette  6796117.0\n",
              "John Holland     30.0       SG  27.0    6-5  Boston University        NaN\n",
              "R.J. Hunter      28.0       SG  22.0    6-5      Georgia State  1148640.0\n",
              "Jonas Jerebko     8.0       PF  29.0   6-10                NaN  5000000.0\n",
              "...               ...      ...   ...    ...                ...        ...\n",
              "Shelvin Mack      8.0       PG  26.0    6-3             Butler  2433333.0\n",
              "Raul Neto        25.0       PG  24.0    6-1                NaN   900000.0\n",
              "Tibor Pleiss     21.0        C  26.0    7-3                NaN  2900000.0\n",
              "Jeff Withey      24.0        C  26.0    7-0             Kansas   947276.0\n",
              "NaN               NaN      NaN   NaN    NaN                NaN        NaN\n",
              "\n",
              "[458 rows x 6 columns]"
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pandas DataFrame.truncate\n",
        "\n",
        "Pandas DataFrame.truncate() function is used to truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.\n",
        "\n",
        "Example #1: Use DataFrame.truncate() function to truncate some entries before and after the passed labels of the given dataframe."
      ],
      "metadata": {
        "id": "qXJ6GStITnLS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# Creating the DataFrame\n",
        "df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],\n",
        "\t\t\t\t'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],\n",
        "\t\t\t\t'Age':[14, 25, 55, 8, 21]})\n",
        "\n",
        "# Create the index\n",
        "index_ = pd.date_range('2010-10-09 08:45', periods = 5, freq ='H')\n",
        "\n",
        "# Set the index\n",
        "df.index = index_\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdOP7iEoPGnS",
        "outputId": "b5a52bc6-40d1-4e8e-80bc-6da74a404757"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     Weight    Name  Age\n",
            "2010-10-09 08:45:00      45     Sam   14\n",
            "2010-10-09 09:45:00      88  Andrea   25\n",
            "2010-10-09 10:45:00      56    Alex   55\n",
            "2010-10-09 11:45:00      15   Robin    8\n",
            "2010-10-09 12:45:00      71     Kia   21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will use DataFrame.truncate() function to truncate the entries before ‘2010-10-09 09:45:00’ and after ‘2010-10-09 11:45:00’ in the given dataframe."
      ],
      "metadata": {
        "id": "NlsU6i-9UCuo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# return the truncated dataframe\n",
        "result = df.truncate(before = '2010-10-09 09:45:00', after = '2010-10-09 11:45:00')\n",
        "\n",
        "# Print the result\n",
        "print(result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67iMYvrjT1WA",
        "outputId": "dfd307b5-9716-433e-abdf-fb3e84e21bf5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     Weight    Name  Age\n",
            "2010-10-09 09:45:00      88  Andrea   25\n",
            "2010-10-09 10:45:00      56    Alex   55\n",
            "2010-10-09 11:45:00      15   Robin    8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Working with Missing Data in Pandas\n",
        "\n",
        "Missing Data can occur when no information is provided for one or more items or for a whole unit. Missing Data is a very big problem in a real-life scenarios. Missing Data can also refer to as NA(Not Available) values in pandas. In DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed. For Example, Suppose different users being surveyed may choose not to share their income, some users may choose not to share the address in this way many datasets went missing.\n",
        "\n",
        "\n",
        "In Pandas missing data is represented by two value:\n",
        "\n",
        "**None:** None is a Python singleton object that is often used for missing data in Python code.\n",
        "\n",
        "**NaN :** NaN (an acronym for Not a Number), is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation\n",
        "\n",
        "Pandas treat None and NaN as essentially interchangeable for indicating missing or null values. To facilitate this convention, there are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame :\n",
        "\n",
        "isnull()\n",
        "\n",
        "notnull()\n",
        "\n",
        "dropna()\n",
        "\n",
        "fillna()\n",
        "\n",
        "replace()\n",
        "\n",
        "interpolate()"
      ],
      "metadata": {
        "id": "hJk2hRK7VHXa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Checking for missing values using isnull() and notnull()\n",
        "In order to check missing values in Pandas DataFrame, we use a function isnull() and notnull(). Both function help in checking whether a value is NaN or not. These function can also be used in Pandas Series in order to find null values in a series.\n",
        "\n",
        "##Checking for missing values using isnull()\n",
        "In order to check null values in Pandas DataFrame, we use isnull() function this function return dataframe of Boolean values which are True for NaN values."
      ],
      "metadata": {
        "id": "ZlSQgAziWOXd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        "\n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "\t\t'Second Score': [30, 45, 56, np.nan],\n",
        "\t\t'Third Score':[np.nan, 40, 80, 98]}\n",
        "\n",
        "# creating a dataframe from list\n",
        "df = pd.DataFrame(dict)\n",
        "print(df)\n",
        "print(\"*****************************\")\n",
        "\n",
        "# using isnull() function\n",
        "df.isnull()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "-KQDsIl4UGaw",
        "outputId": "f7a61435-55e1-4441-a833-1cdefe525e54"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   First Score  Second Score  Third Score\n",
            "0        100.0          30.0          NaN\n",
            "1         90.0          45.0         40.0\n",
            "2          NaN          56.0         80.0\n",
            "3         95.0           NaN         98.0\n",
            "*****************************\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score\n",
              "0        False         False         True\n",
              "1        False         False        False\n",
              "2         True         False        False\n",
              "3        False          True        False"
            ],
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "code",
      "source": [
        "df.isnull().sum() # find the sum of null values column wise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98lIKlRuWTO-",
        "outputId": "da081c70-ce15-445a-dadc-a7b57379bd33"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "First Score     1\n",
              "Second Score    1\n",
              "Third Score     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
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    {
      "cell_type": "code",
      "source": [
        "df.info() # info method to get non null count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sT_40m9MXE6z",
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      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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   First Score   3 non-null      float64\n",
            " 1   Second Score  3 non-null      float64\n",
            " 2   Third Score   3 non-null      float64\n",
            "dtypes: float64(3)\n",
            "memory usage: 224.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking for missing values using notnull()\n",
        "In order to check null values in Pandas Dataframe, we use notnull() function this function return dataframe of Boolean values which are False for NaN values."
      ],
      "metadata": {
        "id": "UFk3JnP9Xu67"
      }
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    {
      "cell_type": "code",
      "source": [
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        "\n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "\t\t'Second Score': [30, 45, 56, np.nan],\n",
        "\t\t'Third Score':[np.nan, 40, 80, 98]}\n",
        "\n",
        "# creating a dataframe using dictionary\n",
        "df = pd.DataFrame(dict)\n",
        "\n",
        "# using notnull() function\n",
        "df.notnull()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "DBpURsUGXHy0",
        "outputId": "f6dbbafc-40a8-4882-e9b1-73c22b6eab03"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score\n",
              "0         True          True        False\n",
              "1         True          True         True\n",
              "2        False          True         True\n",
              "3         True         False         True"
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filling missing values using fillna(), replace() and interpolate()\n",
        "In order to fill null values in a datasets, we use fillna(), replace() and interpolate() function these function replace NaN values with some value of their own. All these function help in filling a null values in datasets of a DataFrame. Interpolate() function is basically used to fill NA values in the dataframe but it uses various interpolation technique to fill the missing values rather than hard-coding the value."
      ],
      "metadata": {
        "id": "NV3IlmT-X-pr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"Code #1: Filling null values with a single value\"\n",
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        "\n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "\t\t'Second Score': [30, 45, 56, np.nan],\n",
        "\t\t'Third Score':[np.nan, 40, 80, 98]}\n",
        "\n",
        "# creating a dataframe from dictionary\n",
        "df = pd.DataFrame(dict)\n",
        "\n",
        "# filling missing value using fillna()\n",
        "df.fillna(0)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "pjCmgO0PXzDA",
        "outputId": "d2b4ac2e-5aeb-4278-d9bf-8af36b71f16f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score\n",
              "0        100.0          30.0          0.0\n",
              "1         90.0          45.0         40.0\n",
              "2          0.0          56.0         80.0\n",
              "3         95.0           0.0         98.0"
            ],
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              "\n",
              "  <div id=\"df-d2c4a5cb-b317-4582-bbab-bb01a1bf7919\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>First Score</th>\n",
              "      <th>Second Score</th>\n",
              "      <th>Third Score</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>100.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>90.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2c4a5cb-b317-4582-bbab-bb01a1bf7919')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-d2c4a5cb-b317-4582-bbab-bb01a1bf7919 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d2c4a5cb-b317-4582-bbab-bb01a1bf7919');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"Code #2: Filling null values with the previous ones\"\n",
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        "\n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "\t\t'Second Score': [30, 45, 56, np.nan],\n",
        "\t\t'Third Score':[np.nan, 40, 80, 98]}\n",
        "\n",
        "# creating a dataframe from dictionary\n",
        "df = pd.DataFrame(dict)\n",
        "\n",
        "# filling a missing value with\n",
        "# previous ones\n",
        "df.fillna(method ='pad')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_KSSGQg2YKvs",
        "outputId": "043bd002-9d22-4cd1-945a-26cd7ca14d36"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score\n",
              "0        100.0          30.0          NaN\n",
              "1         90.0          45.0         40.0\n",
              "2         90.0          56.0         80.0\n",
              "3         95.0          56.0         98.0"
            ],
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              "\n",
              "  <div id=\"df-d296565c-3da3-4f78-8421-27d9ac03a329\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>First Score</th>\n",
              "      <th>Second Score</th>\n",
              "      <th>Third Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>90.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>90.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>95.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d296565c-3da3-4f78-8421-27d9ac03a329')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d296565c-3da3-4f78-8421-27d9ac03a329');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
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          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"Code #3: Filling null value with the next ones\"\n",
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        "\n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "\t\t'Second Score': [30, 45, 56, np.nan],\n",
        "\t\t'Third Score':[np.nan, 40, 80, 98]}\n",
        "\n",
        "# creating a dataframe from dictionary\n",
        "df = pd.DataFrame(dict)\n",
        "\n",
        "# filling null value using fillna() function\n",
        "df.fillna(method ='bfill')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "4KyJgMfqYyOS",
        "outputId": "73acbbc8-5ab8-4073-bfc9-bda574ac64d3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score\n",
              "0        100.0          30.0         40.0\n",
              "1         90.0          45.0         40.0\n",
              "2         95.0          56.0         80.0\n",
              "3         95.0           NaN         98.0"
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              "      <div>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>95.0</td>\n",
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              "      <td>80.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>95.0</td>\n",
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              "      <td>98.0</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-b41a1883-39a1-42cc-a1fa-cb5e0c894156');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Code #4: Filling null values in CSV File\n",
        "\n"
      ],
      "metadata": {
        "id": "m9pW51T5aNCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# making data frame from csv file\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/My Mentorship/employees.csv\")\n",
        "\n",
        "# Printing the first 10 to 24 rows of\n",
        "# the data frame for visualization\n",
        "data[10:25]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "ewu3p-qzY_lW",
        "outputId": "7fd99ecc-78e0-486a-d54b-95898b3fa80b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
              "10     Louise  Female   8/12/1980         9:01 AM   63241   15.132   \n",
              "11      Julie  Female  10/26/1997         3:19 PM  102508   12.637   \n",
              "12    Brandon    Male   12/1/1980         1:08 AM  112807   17.492   \n",
              "13       Gary    Male   1/27/2008        11:40 PM  109831    5.831   \n",
              "14   Kimberly  Female   1/14/1999         7:13 AM   41426   14.543   \n",
              "15    Lillian  Female    6/5/2016         6:09 AM   59414    1.256   \n",
              "16     Jeremy    Male   9/21/2010         5:56 AM   90370    7.369   \n",
              "17      Shawn    Male   12/7/1986         7:45 PM  111737    6.414   \n",
              "18      Diana  Female  10/23/1981        10:27 AM  132940   19.082   \n",
              "19      Donna  Female   7/22/2010         3:48 AM   81014    1.894   \n",
              "20       Lois     NaN   4/22/1995         7:18 PM   64714    4.934   \n",
              "21    Matthew    Male    9/5/1995         2:12 AM  100612   13.645   \n",
              "22     Joshua     NaN    3/8/2012         1:58 AM   90816   18.816   \n",
              "23        NaN    Male   6/14/2012         4:19 PM  125792    5.042   \n",
              "24       John    Male    7/1/1992        10:08 PM   97950   13.873   \n",
              "\n",
              "   Senior Management             Team  \n",
              "10              True              NaN  \n",
              "11              True            Legal  \n",
              "12              True  Human Resources  \n",
              "13             False            Sales  \n",
              "14              True          Finance  \n",
              "15             False          Product  \n",
              "16             False  Human Resources  \n",
              "17             False          Product  \n",
              "18             False  Client Services  \n",
              "19             False          Product  \n",
              "20              True            Legal  \n",
              "21             False        Marketing  \n",
              "22              True  Client Services  \n",
              "23               NaN              NaN  \n",
              "24             False  Client Services  "
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              "      <td>111737</td>\n",
              "      <td>6.414</td>\n",
              "      <td>False</td>\n",
              "      <td>Product</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Diana</td>\n",
              "      <td>Female</td>\n",
              "      <td>10/23/1981</td>\n",
              "      <td>10:27 AM</td>\n",
              "      <td>132940</td>\n",
              "      <td>19.082</td>\n",
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              "      <th>19</th>\n",
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              "      <th>20</th>\n",
              "      <td>Lois</td>\n",
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              "      <td>4/22/1995</td>\n",
              "      <td>7:18 PM</td>\n",
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              "      <td>4.934</td>\n",
              "      <td>True</td>\n",
              "      <td>Legal</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Matthew</td>\n",
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              "      <td>9/5/1995</td>\n",
              "      <td>2:12 AM</td>\n",
              "      <td>100612</td>\n",
              "      <td>13.645</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Now we are going to fill all the null values in Gender column with “No Gender”\n",
        "\n",
        "# filling a null values using fillna()\n",
        "data[\"Gender\"].fillna(\"No Gender\", inplace = True)\n",
        "\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "NQRecQI9aWnZ",
        "outputId": "2f3723b7-8017-49ff-ae2b-dd86153c5189"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    First Name     Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
              "0      Douglas       Male    8/6/1993        12:42 PM   97308    6.945   \n",
              "1       Thomas       Male   3/31/1996         6:53 AM   61933    4.170   \n",
              "2        Maria     Female   4/23/1993        11:17 AM  130590   11.858   \n",
              "3        Jerry       Male    3/4/2005         1:00 PM  138705    9.340   \n",
              "4        Larry       Male   1/24/1998         4:47 PM  101004    1.389   \n",
              "..         ...        ...         ...             ...     ...      ...   \n",
              "995      Henry  No Gender  11/23/2014         6:09 AM  132483   16.655   \n",
              "996    Phillip       Male   1/31/1984         6:30 AM   42392   19.675   \n",
              "997    Russell       Male   5/20/2013        12:39 PM   96914    1.421   \n",
              "998      Larry       Male   4/20/2013         4:45 PM   60500   11.985   \n",
              "999     Albert       Male   5/15/2012         6:24 PM  129949   10.169   \n",
              "\n",
              "    Senior Management                  Team  \n",
              "0                True             Marketing  \n",
              "1                True                   NaN  \n",
              "2               False               Finance  \n",
              "3                True               Finance  \n",
              "4                True       Client Services  \n",
              "..                ...                   ...  \n",
              "995             False          Distribution  \n",
              "996             False               Finance  \n",
              "997             False               Product  \n",
              "998             False  Business Development  \n",
              "999              True                 Sales  \n",
              "\n",
              "[1000 rows x 8 columns]"
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              "      <td>Larry</td>\n",
              "      <td>Male</td>\n",
              "      <td>1/24/1998</td>\n",
              "      <td>4:47 PM</td>\n",
              "      <td>101004</td>\n",
              "      <td>1.389</td>\n",
              "      <td>True</td>\n",
              "      <td>Client Services</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>Henry</td>\n",
              "      <td>No Gender</td>\n",
              "      <td>11/23/2014</td>\n",
              "      <td>6:09 AM</td>\n",
              "      <td>132483</td>\n",
              "      <td>16.655</td>\n",
              "      <td>False</td>\n",
              "      <td>Distribution</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Phillip</td>\n",
              "      <td>Male</td>\n",
              "      <td>1/31/1984</td>\n",
              "      <td>6:30 AM</td>\n",
              "      <td>42392</td>\n",
              "      <td>19.675</td>\n",
              "      <td>False</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Russell</td>\n",
              "      <td>Male</td>\n",
              "      <td>5/20/2013</td>\n",
              "      <td>12:39 PM</td>\n",
              "      <td>96914</td>\n",
              "      <td>1.421</td>\n",
              "      <td>False</td>\n",
              "      <td>Product</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>Larry</td>\n",
              "      <td>Male</td>\n",
              "      <td>4/20/2013</td>\n",
              "      <td>4:45 PM</td>\n",
              "      <td>60500</td>\n",
              "      <td>11.985</td>\n",
              "      <td>False</td>\n",
              "      <td>Business Development</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>Albert</td>\n",
              "      <td>Male</td>\n",
              "      <td>5/15/2012</td>\n",
              "      <td>6:24 PM</td>\n",
              "      <td>129949</td>\n",
              "      <td>10.169</td>\n",
              "      <td>True</td>\n",
              "      <td>Sales</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42ef3870-0891-42c0-bfd9-0971419de8cc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      gap: 12px;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42ef3870-0891-42c0-bfd9-0971419de8cc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-42ef3870-0891-42c0-bfd9-0971419de8cc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"Code #5: Filling a null values using replace() method\"\"\"\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/My Mentorship/employees.csv\")\n",
        "\n",
        "data.replace(to_replace = np.nan, value = -99) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "p-9NZCN5aglR",
        "outputId": "f10372d8-f889-4e0a-e54b-24ee4d87f9cf"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
              "0      Douglas    Male    8/6/1993        12:42 PM   97308    6.945   \n",
              "1       Thomas    Male   3/31/1996         6:53 AM   61933    4.170   \n",
              "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
              "3        Jerry    Male    3/4/2005         1:00 PM  138705    9.340   \n",
              "4        Larry    Male   1/24/1998         4:47 PM  101004    1.389   \n",
              "..         ...     ...         ...             ...     ...      ...   \n",
              "995      Henry     -99  11/23/2014         6:09 AM  132483   16.655   \n",
              "996    Phillip    Male   1/31/1984         6:30 AM   42392   19.675   \n",
              "997    Russell    Male   5/20/2013        12:39 PM   96914    1.421   \n",
              "998      Larry    Male   4/20/2013         4:45 PM   60500   11.985   \n",
              "999     Albert    Male   5/15/2012         6:24 PM  129949   10.169   \n",
              "\n",
              "    Senior Management                  Team  \n",
              "0                True             Marketing  \n",
              "1                True                   -99  \n",
              "2               False               Finance  \n",
              "3                True               Finance  \n",
              "4                True       Client Services  \n",
              "..                ...                   ...  \n",
              "995             False          Distribution  \n",
              "996             False               Finance  \n",
              "997             False               Product  \n",
              "998             False  Business Development  \n",
              "999              True                 Sales  \n",
              "\n",
              "[1000 rows x 8 columns]"
            ],
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Douglas</td>\n",
              "      <td>Male</td>\n",
              "      <td>8/6/1993</td>\n",
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              "    <tr>\n",
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              "      <td>Thomas</td>\n",
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              "      <td>3/31/1996</td>\n",
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              "      <td>4/23/1993</td>\n",
              "      <td>11:17 AM</td>\n",
              "      <td>130590</td>\n",
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              "    <tr>\n",
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              "      <td>Larry</td>\n",
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              "      <td>1/24/1998</td>\n",
              "      <td>4:47 PM</td>\n",
              "      <td>101004</td>\n",
              "      <td>1.389</td>\n",
              "      <td>True</td>\n",
              "      <td>Client Services</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>Henry</td>\n",
              "      <td>-99</td>\n",
              "      <td>11/23/2014</td>\n",
              "      <td>6:09 AM</td>\n",
              "      <td>132483</td>\n",
              "      <td>16.655</td>\n",
              "      <td>False</td>\n",
              "      <td>Distribution</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Phillip</td>\n",
              "      <td>Male</td>\n",
              "      <td>1/31/1984</td>\n",
              "      <td>6:30 AM</td>\n",
              "      <td>42392</td>\n",
              "      <td>19.675</td>\n",
              "      <td>False</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Russell</td>\n",
              "      <td>Male</td>\n",
              "      <td>5/20/2013</td>\n",
              "      <td>12:39 PM</td>\n",
              "      <td>96914</td>\n",
              "      <td>1.421</td>\n",
              "      <td>False</td>\n",
              "      <td>Product</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>Larry</td>\n",
              "      <td>Male</td>\n",
              "      <td>4/20/2013</td>\n",
              "      <td>4:45 PM</td>\n",
              "      <td>60500</td>\n",
              "      <td>11.985</td>\n",
              "      <td>False</td>\n",
              "      <td>Business Development</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>Albert</td>\n",
              "      <td>Male</td>\n",
              "      <td>5/15/2012</td>\n",
              "      <td>6:24 PM</td>\n",
              "      <td>129949</td>\n",
              "      <td>10.169</td>\n",
              "      <td>True</td>\n",
              "      <td>Sales</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3cf7e73-af3e-4a2c-a343-1e40d08971e5')\"\n",
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              "\n",
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              "          document.querySelector('#df-d3cf7e73-af3e-4a2c-a343-1e40d08971e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3cf7e73-af3e-4a2c-a343-1e40d08971e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"Code #6: Using interpolate() function to fill the missing values using linear method.\"\n",
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\t\n",
        "# Creating the dataframe\n",
        "df = pd.DataFrame({\"A\":[12, 4, 5, None, 1],\n",
        "\t\t\t\t\"B\":[None, 2, 54, 3, None],\n",
        "\t\t\t\t\"C\":[20, 16, None, 3, 8],\n",
        "\t\t\t\t\"D\":[14, 3, None, None, 6]})\n",
        "\t\n",
        "# Print the dataframe\n",
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3yHzGXXXaxit",
        "outputId": "9f707e83-e6fa-4d44-f36b-b4cb86e48bfa"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      A     B     C     D\n",
              "0  12.0   NaN  20.0  14.0\n",
              "1   4.0   2.0  16.0   3.0\n",
              "2   5.0  54.0   NaN   NaN\n",
              "3   NaN   3.0   3.0   NaN\n",
              "4   1.0   NaN   8.0   6.0"
            ],
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              "      <th>D</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>14.0</td>\n",
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              "    <tr>\n",
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              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>2</th>\n",
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              "      <td>54.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
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    {
      "cell_type": "code",
      "source": [
        "# to interpolate the missing values\n",
        "df.interpolate(method ='linear', limit_direction ='forward')\n",
        "\"\"\"As we can see the output, values in the first row could not get filled as the direction of filling of values is forward \n",
        "and there is no previous value which could have been used in interpolation.\"\"\""
      ],
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          "height": 206
        },
        "id": "A8fdqBH8bCay",
        "outputId": "ce167a7c-6b21-4c75-da65-dcaa6964da13"
      },
      "execution_count": 31,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      A     B     C     D\n",
              "0  12.0   NaN  20.0  14.0\n",
              "1   4.0   2.0  16.0   3.0\n",
              "2   5.0  54.0   9.5   4.0\n",
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              "\n",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dropping missing values using dropna()\n",
        "In order to drop a null values from a dataframe, we used dropna() function this function drop Rows/Columns of datasets with Null values in different ways.\n",
        "\n",
        "Code #1: Dropping rows with at least 1 null value."
      ],
      "metadata": {
        "id": "3pjpIQmhbkEi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        "\n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        "\n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "\t\t'Second Score': [30, np.nan, 45, 56],\n",
        "\t\t'Third Score':[52, 40, 80, 98],\n",
        "\t\t'Fourth Score':[np.nan, np.nan, np.nan, 65]}\n",
        "\n",
        "# creating a dataframe from dictionary\n",
        "df = pd.DataFrame(dict)\n",
        "\t\n",
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "uoI0jQ-pbHq1",
        "outputId": "0408f445-985b-461d-cf04-51d3fae414d7"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score  Fourth Score\n",
              "0        100.0          30.0           52           NaN\n",
              "1         90.0           NaN           40           NaN\n",
              "2          NaN          45.0           80           NaN\n",
              "3         95.0          56.0           98          65.0"
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              "      <th>Fourth Score</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>80</td>\n",
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              "      <th>3</th>\n",
              "      <td>95.0</td>\n",
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              "      <td>98</td>\n",
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              "</table>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "mGNM1iVdbsd6",
        "outputId": "0371a06b-4889-4e82-8d01-241990c3203b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score  Fourth Score\n",
              "3         95.0          56.0           98          65.0"
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"Code #2: Dropping rows if all values in that row are missing.\"\n",
        "df.dropna(how = 'all')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "vRchY9bHb0x6",
        "outputId": "d7b7e228-079d-4d27-b260-b1b88ae3f7ae"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   First Score  Second Score  Third Score  Fourth Score\n",
              "0        100.0          30.0           52           NaN\n",
              "1         90.0           NaN           40           NaN\n",
              "2          NaN          45.0           80           NaN\n",
              "3         95.0          56.0           98          65.0"
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"Code #3: Dropping columns with at least 1 null value.\"\n",
        "df.dropna(axis = 1)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "qL_iBZb4b92x",
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      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Third Score\n",
              "0           52\n",
              "1           40\n",
              "2           80\n",
              "3           98"
            ],
            "text/html": [
              "\n",
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              "\n",
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              "</style>\n",
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              "      <td>80</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-56dfea47-b5bb-47c9-90f6-20acc1478942 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56dfea47-b5bb-47c9-90f6-20acc1478942');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Code #4: Dropping Rows with at least 1 null value in CSV file\n",
        "\n"
      ],
      "metadata": {
        "id": "-padoUGwcOkm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/My Mentorship/employees.csv\")\n",
        "# making new data frame with dropped NA values \n",
        "new_data = data.dropna(axis = 0, how ='any') \n",
        "    \n",
        "print(new_data.head())\n",
        "\"Now we compare sizes of data frames so that we can come to know how many rows had at least 1 Null value\"\n",
        "print(\"Old data frame length:\", len(data))\n",
        "print(\"New data frame length:\", len(new_data)) \n",
        "print(\"Number of rows with at least 1 NA value: \", (len(data)-len(new_data)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x578fytNcIJC",
        "outputId": "e65ad0d0-4c2a-407d-ec1e-22fc1b6974c5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  First Name  Gender Start Date Last Login Time  Salary  Bonus %  \\\n",
            "0    Douglas    Male   8/6/1993        12:42 PM   97308    6.945   \n",
            "2      Maria  Female  4/23/1993        11:17 AM  130590   11.858   \n",
            "3      Jerry    Male   3/4/2005         1:00 PM  138705    9.340   \n",
            "4      Larry    Male  1/24/1998         4:47 PM  101004    1.389   \n",
            "5     Dennis    Male  4/18/1987         1:35 AM  115163   10.125   \n",
            "\n",
            "  Senior Management             Team  \n",
            "0              True        Marketing  \n",
            "2             False          Finance  \n",
            "3              True          Finance  \n",
            "4              True  Client Services  \n",
            "5             False            Legal  \n",
            "Old data frame length: 1000\n",
            "New data frame length: 764\n",
            "Number of rows with at least 1 NA value:  236\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MN3BC5-KcYXp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}