Day9.ipynb

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        "<a href=\"https://colab.research.google.com/github/Abhigyan76/Movie-Recommendation-System/blob/main/Day9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "Return the Index label if some condition is satisfied over a column in Pandas Dataframe"
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      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],\n",
        "                   'Product':['Umbrella', 'Mattress', 'Badminton', 'Shuttle'],\n",
        "                   'Last_Price':[1200, 1500, 1600, 352],\n",
        "                   'Updated_Price':[1250, 1450, 1550, 400],\n",
        "                   'Discount':[10, 10, 10, 10]})"
      ],
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.index=['Item 1','Item 2','Item 3','Item 4']"
      ],
      "metadata": {
        "id": "tP4AINZ_WnUM"
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "print(df)"
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          "text": [
            "             Date    Product  Last_Price  Updated_Price  Discount\n",
            "Item 1  10/2/2011   Umbrella        1200           1250        10\n",
            "Item 2  11/2/2011   Mattress        1500           1450        10\n",
            "Item 3  12/2/2011  Badminton        1600           1550        10\n",
            "Item 4  13/2/2011    Shuttle         352            400        10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merge DataFrames on Multiple Columns"
      ],
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        "id": "PeFDNXynXSBo"
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      "source": [
        "df = pd.DataFrame({'Courses': [\"Spark\",\"PySpark\",\"Python\",\"pandas\",\"Java\"],\n",
        "                    'Fee' : [20000,25000,30000,24000,40000],\n",
        "                    'Duration':['30day','40days','60days','55days','50days']})\n",
        "\n",
        "df1 = pd.DataFrame({'Courses': [\"Java\",\"PySpark\",\"Python\",\"pandas\",\"Hyperion\"],\n",
        "                    'Fee': [20000,25000,30000,24000,40000],\n",
        "                    'Percentage':['10%','20%','25%','20%','10%']})"
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      "source": [
        "merged_df=pd.merge(df,df1)"
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      "execution_count": 7,
      "outputs": []
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              "   Courses    Fee Duration Percentage\n",
              "0  PySpark  25000   40days        20%\n",
              "1   Python  30000   60days        25%\n",
              "2   pandas  24000   55days        20%"
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        "df2=pd.merge(df,df1,on=[\"Courses\",\"Fee\"])"
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              "   Courses    Fee Duration Percentage\n",
              "0  PySpark  25000   40days        20%\n",
              "1   Python  30000   60days        25%\n",
              "2   pandas  24000   55days        20%"
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              "      <td>pandas</td>\n",
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            "text/plain": [
              "   Courses    Fee Duration Percentage\n",
              "0    Spark  20000    30day        NaN\n",
              "1  PySpark  25000   40days        20%\n",
              "2   Python  30000   60days        25%\n",
              "3   pandas  24000   55days        20%\n",
              "4     Java  40000   50days        NaN"
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              "      <th>2</th>\n",
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              "      <td>pandas</td>\n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Complete Merge DataFrames on Multiple Columns"
      ],
      "metadata": {
        "id": "1gkb_FEPZwaw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Courses': [\"Spark\",\"PySpark\",\"Python\",\"pandas\",\"Java\"],\n",
        "                    'Fee' : [20000,25000,30000,24000,40000],\n",
        "                    'Duration':['30day','40days','60days','55days','50days']})\n",
        "\n",
        "df1 = pd.DataFrame({'Courses': [\"Java\",\"PySpark\",\"Python\",\"pandas\",\"Hyperion\"],\n",
        "                    'Fee': [20000,25000,30000,24000,40000],\n",
        "                    'Percentage':['10%','20%','25%','20%','10%']})"
      ],
      "metadata": {
        "id": "-syeGY5dY2x2"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)\n",
        "print(df1)"
      ],
      "metadata": {
        "id": "sH8Wsl-6Z1OJ",
        "outputId": "b16dcd9c-1db6-4ef6-99d1-cc3e42fc94c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Courses    Fee Duration\n",
            "0    Spark  20000    30day\n",
            "1  PySpark  25000   40days\n",
            "2   Python  30000   60days\n",
            "3   pandas  24000   55days\n",
            "4     Java  40000   50days\n",
            "    Courses    Fee Percentage\n",
            "0      Java  20000        10%\n",
            "1   PySpark  25000        20%\n",
            "2    Python  30000        25%\n",
            "3    pandas  24000        20%\n",
            "4  Hyperion  40000        10%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Merging\n",
        "merged_df=pd.merge(df,df1)\n",
        "#conclusion - pd.merge() use for the merging columns"
      ],
      "metadata": {
        "id": "I6SI04AgZ63i"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas Iterate Over Columns of DataFrame"
      ],
      "metadata": {
        "id": "Y2cv0bGHa3O2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "technologies = [\n",
        "            (\"Spark\", 22000,'30days',1000.0),\n",
        "            (\"PySpark\",25000,'50days',2300.0),\n",
        "            (\"Hadoop\",23000,'55days',1500.0)\n",
        "            ]"
      ],
      "metadata": {
        "id": "Gu_6snEFaF7_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df:\n",
        "  print(df[column]) #for get item itterate over column\n",
        "\n"
      ],
      "metadata": {
        "id": "2Llla2HLa_Ia",
        "outputId": "cb79116f-43e2-4830-845a-87677581cdb8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      Spark\n",
            "1    PySpark\n",
            "2     Python\n",
            "3     pandas\n",
            "4       Java\n",
            "Name: Courses, dtype: object\n",
            "0    20000\n",
            "1    25000\n",
            "2    30000\n",
            "3    24000\n",
            "4    40000\n",
            "Name: Fee, dtype: int64\n",
            "0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Use getitem ([]) to iterate over columns in pandas DataFrame\n",
        "for column in df:\n",
        "  print(df[column].values)"
      ],
      "metadata": {
        "id": "7a3dD_8wdKP0",
        "outputId": "d9552dfd-db21-4835-a37c-7b0a204de770",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Spark' 'PySpark' 'Python' 'pandas' 'Java']\n",
            "[20000 25000 30000 24000 40000]\n",
            "['30day' '40days' '60days' '55days' '50days']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterate over columns using DataFrame.iteritems()\n",
        "for (colname,colval) in df.iteritems():\n",
        "  print(colname,colval.values)"
      ],
      "metadata": {
        "id": "0n40MSJ2dbGv",
        "outputId": "3f6b282f-9a6b-4481-a8db-5a605e0f8ac5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Courses ['Spark' 'PySpark' 'Python' 'pandas' 'Java']\n",
            "Fee [20000 25000 30000 24000 40000]\n",
            "Duration ['30day' '40days' '60days' '55days' '50days']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-7864a61e15ad>:2: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
            "  for (colname,colval) in df.iteritems():\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use iteritems()\n",
        "for name in df.iteritems():\n",
        "  print(name)"
      ],
      "metadata": {
        "id": "WJUl3kRCd_79",
        "outputId": "52db3889-a6df-4f06-92d7-76d01f264dee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Courses', 0      Spark\n",
            "1    PySpark\n",
            "2     Python\n",
            "3     pandas\n",
            "4       Java\n",
            "Name: Courses, dtype: object)\n",
            "('Fee', 0    20000\n",
            "1    25000\n",
            "2    30000\n",
            "3    24000\n",
            "4    40000\n",
            "Name: Fee, dtype: int64)\n",
            "('Duration', 0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-e24d0f803527>:2: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
            "  for name in df.iteritems():\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#iterate over columns in pandas DataFrame using enumerate() \n",
        "# enumerate() function allows to keep the track of the no. of iternations\n",
        "for (index,colname) in enumerate(df):\n",
        "  print(index,df[colname].values)\n",
        "\n"
      ],
      "metadata": {
        "id": "nOemcOBUeg3V",
        "outputId": "09fa01ef-94b5-4b52-c0c4-a36651ec1b3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 ['Spark' 'PySpark' 'Python' 'pandas' 'Java']\n",
            "1 [20000 25000 30000 24000 40000]\n",
            "2 ['30day' '40days' '60days' '55days' '50days']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using enumerate()\n",
        "for (index,column) in enumerate(df):\n",
        "  print(index,df[column])"
      ],
      "metadata": {
        "id": "iGGU9KSMfdDV",
        "outputId": "f2b25259-1f91-4cf7-e108-f2fb16f44fe4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0      Spark\n",
            "1    PySpark\n",
            "2     Python\n",
            "3     pandas\n",
            "4       Java\n",
            "Name: Courses, dtype: object\n",
            "1 0    20000\n",
            "1    25000\n",
            "2    30000\n",
            "3    24000\n",
            "4    40000\n",
            "Name: Fee, dtype: int64\n",
            "2 0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using enumerate() & Numpy.asarray()\n",
        "for (index,column) in enumerate(df):\n",
        "  print(index,np.asarray(df[column]))"
      ],
      "metadata": {
        "id": "KIIkp8otgCpH",
        "outputId": "d2dfbdb5-6a2a-44ab-af85-40b8893ffbb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 ['Spark' 'PySpark' 'Python' 'pandas' 'Java']\n",
            "1 [20000 25000 30000 24000 40000]\n",
            "2 ['30day' '40days' '60days' '55days' '50days']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use DataFrame.columns()\n",
        "for columns in df.columns[1]:\n",
        "  print(df[column])"
      ],
      "metadata": {
        "id": "nrFh61zHi-HR",
        "outputId": "53b9a303-da16-4fd8-e918-e8c2928c2237",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object\n",
            "0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object\n",
            "0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterate over all the columns in reversed order\n",
        "for columns in df.columns[:-1]:\n",
        "  print(df[columns])"
      ],
      "metadata": {
        "id": "h3QmDeyrjXGF",
        "outputId": "1b83b3f8-6e2f-42a1-e2ce-3264c5e78e52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      Spark\n",
            "1    PySpark\n",
            "2     Python\n",
            "3     pandas\n",
            "4       Java\n",
            "Name: Courses, dtype: object\n",
            "0    20000\n",
            "1    25000\n",
            "2    30000\n",
            "3    24000\n",
            "4    40000\n",
            "Name: Fee, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the indices of all columns\n",
        "for indix,column in enumerate(df.columns):\n",
        "  print(indix,column)"
      ],
      "metadata": {
        "id": "QtytoiiQjlb5",
        "outputId": "bfa4ada4-e021-42a0-a43c-aed446c84f65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 Courses\n",
            "1 Fee\n",
            "2 Duration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use DataFrame.transpose().iterrows()\n",
        "for column,column_name in df.transpose().iterrows():\n",
        "  print(column_name)"
      ],
      "metadata": {
        "id": "YPq2JSYvj7Tv",
        "outputId": "dd44b1e2-c31b-420a-af88-fbf646ad89af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      Spark\n",
            "1    PySpark\n",
            "2     Python\n",
            "3     pandas\n",
            "4       Java\n",
            "Name: Courses, dtype: object\n",
            "0    20000\n",
            "1    25000\n",
            "2    30000\n",
            "3    24000\n",
            "4    40000\n",
            "Name: Fee, dtype: object\n",
            "0     30day\n",
            "1    40days\n",
            "2    60days\n",
            "3    55days\n",
            "4    50days\n",
            "Name: Duration, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas – Replace NaN Values with Zero in a Column"
      ],
      "metadata": {
        "id": "QHV8w_zhkuYJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "technologies = {\n",
        "    'Courses':[\"Spark\",\"PySpark\",\"Hadoop\"],\n",
        "    'Fee' :[20000,25000, np.nan],\n",
        "    'Duration':[np.nan,'40days','35days'],\n",
        "    'Discount':[1000,np.nan,1500]\n",
        "               }"
      ],
      "metadata": {
        "id": "ZFy-_gmHkEAr"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(technologies)\n",
        "df"
      ],
      "metadata": {
        "id": "0YJ6nD7Wnnsf",
        "outputId": "3b23ca43-0a35-4a09-e342-3c3c236a84af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Courses      Fee Duration  Discount\n",
              "0    Spark  20000.0      NaN    1000.0\n",
              "1  PySpark  25000.0   40days       NaN\n",
              "2   Hadoop      NaN   35days    1500.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb323cf2-a126-44d6-a8ce-7a74f1230b6f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Courses</th>\n",
              "      <th>Fee</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Spark</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PySpark</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>40days</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hadoop</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35days</td>\n",
              "      <td>1500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb323cf2-a126-44d6-a8ce-7a74f1230b6f')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb323cf2-a126-44d6-a8ce-7a74f1230b6f 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-fb323cf2-a126-44d6-a8ce-7a74f1230b6f');\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Repalce NaN with zero on all columns \n",
        "df2=df.fillna(0)\n",
        "df2"
      ],
      "metadata": {
        "id": "ZkKAn4fQnurA",
        "outputId": "6ba7e80f-8c39-434a-9a01-e9b64a0e24da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Courses      Fee Duration  Discount\n",
              "0    Spark  20000.0        0    1000.0\n",
              "1  PySpark  25000.0   40days       0.0\n",
              "2   Hadoop      0.0   35days    1500.0"
            ],
            "text/html": [
              "\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>Courses</th>\n",
              "      <th>Fee</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Spark</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PySpark</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>40days</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hadoop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35days</td>\n",
              "      <td>1500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d71407c2-5151-4972-bb2b-8a47d45824eb')\"\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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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d71407c2-5151-4972-bb2b-8a47d45824eb 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-d71407c2-5151-4972-bb2b-8a47d45824eb');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Repalce NaN with zero inplace\n",
        "df.fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "3c2ZSrK8n6do"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "zR725LLPoJ-E",
        "outputId": "dad80df0-e65b-45e3-a52b-96252dc15464",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Courses      Fee Duration  Discount\n",
              "0    Spark  20000.0        0    1000.0\n",
              "1  PySpark  25000.0   40days       0.0\n",
              "2   Hadoop      0.0   35days    1500.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4ef90c0-ab64-48fe-afaa-e485861d7d58\">\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>Courses</th>\n",
              "      <th>Fee</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Discount</th>\n",
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              "      <th>0</th>\n",
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              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PySpark</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>40days</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hadoop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35days</td>\n",
              "      <td>1500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4ef90c0-ab64-48fe-afaa-e485861d7d58')\"\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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              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a4ef90c0-ab64-48fe-afaa-e485861d7d58 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-a4ef90c0-ab64-48fe-afaa-e485861d7d58');\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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace on single column\n",
        "df['Fee']=df['Fee'].fillna(0)\n",
        "df"
      ],
      "metadata": {
        "id": "pfajPSe5oLQW",
        "outputId": "9b9a42da-68cd-4fb6-f65e-dab3e55154f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Courses      Fee Duration  Discount\n",
              "0    Spark  20000.0        0    1000.0\n",
              "1  PySpark  25000.0   40days       0.0\n",
              "2   Hadoop      0.0   35days    1500.0"
            ],
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              "\n",
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              "      <th></th>\n",
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              "      <th>0</th>\n",
              "      <td>Spark</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PySpark</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>40days</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hadoop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35days</td>\n",
              "      <td>1500.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-220d3721-5d61-433e-a7fd-db8e56d28a3a')\"\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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              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-220d3721-5d61-433e-a7fd-db8e56d28a3a 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-220d3721-5d61-433e-a7fd-db8e56d28a3a');\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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m3hOSxFLoYlZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j9JDZwoGotOF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}