Rapids_Dataframe_API_Demo.ipynb

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  "metadata": {
    "colab": {
      "name": "Rapids Dataframe API Demo.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/Rapids_Dataframe_API_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVgdErMZDeMe",
        "colab_type": "text"
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      "source": [
        "![alt text](https://developer.nvidia.com/sites/default/files/akamai/machine-learning/RAPIDS_workflow2.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tm4mqdNMJRGd",
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      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "#This install will ask for 2 prompts\n",
        "#Start with 0.11 and then subsequent prompt say Y (only for 0.11). If it hangs in solving dependency for more than 2 min than kill,\n",
        "#restart with 0.10 install\n",
        "#This script for 0.10 and 0.11 has some bugs so follow subsequent steps\n",
        "!wget -nc https://github.com/rapidsai/notebooks-extended/raw/master/utils/rapids-colab.sh\n",
        "!bash rapids-colab.sh\n",
        "\n",
        "import sys, os\n",
        "\n",
        "dist_package_index = sys.path.index(\"/usr/local/lib/python3.6/dist-packages\")\n",
        "sys.path = sys.path[:dist_package_index] + [\"/usr/local/lib/python3.6/site-packages\"] + sys.path[dist_package_index:]\n",
        "sys.path\n",
        "if os.path.exists('update_pyarrow.py'): ## This file only exists if you're using RAPIDS version 0.11 or higher\n",
        "  exec(open(\"update_pyarrow.py\").read(), globals())\n",
        "\n",
        "sys.path.append('/usr/local/lib/python3.6/site-packages/')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File ‘rapids-colab.sh’ already there; not retrieving.\n",
            "\n",
            "PLEASE READ\n",
            "********************************************************************************************************\n",
            "Colab v0.11+ Migration Notice:\n",
            " \n",
            "There has been a NECESSARY Colab script code change for VERSION 0.11+ that MAY REQUIRE an update how you install RAPIDS into Colab!  \n",
            "Not all Colab notebooks are updated (like personal Colabs) and while the script will install RAPIDS correctly, \n",
            "a neccessary script to update pyarrow to v0.15.x to be compatible with RAPIDS v0.11+ may not run, and your RAPIDS instance\n",
            "will BREAK\n",
            " \n",
            "Please enter in the box your desired RAPIDS version (ex: '0.10' or '0.11', between 0.9 to 0.11, without the quotes) and hit Enter. \n",
            "0.10\n",
            "You may not have to change anything.  All versions of our script should work with this version of Colab\n",
            "File ‘env-check.py’ already there; not retrieving.\n",
            "\n",
            "Checking for GPU type:\n",
            "***********************************************************************\n",
            "Woo! Your instance has the right kind of GPU, a 'Tesla P100-PCIE-16GB'!\n",
            "***********************************************************************\n",
            "\n",
            "\n",
            "*********************************************\n",
            "Your Google Colab instance is RAPIDS ready!\n",
            "*********************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zc7bhhbDxCps",
        "colab_type": "code",
        "outputId": "ff2a944f-4139-4056-bd94-5010f43cfdea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
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      },
      "source": [
        "#only for 0.10\n",
        "!conda install -y --prefix /usr/local -c rapidsai/label/xgboost -c rapidsai -c nvidia -c conda-forge dask-cudf xgboost"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "# All requested packages already installed.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tRX3zJ3nO6A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cudf\n",
        "import numpy as np\n",
        "import dask_cudf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evm6qA-KVtt5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bank_df=cudf.read_csv('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/bank-full.csv',sep=';')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OR4SZNm65qf",
        "colab_type": "code",
        "outputId": "f4a57b3e-8660-486e-a581-11a1bc977d93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "bank_df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2t4dMHyzkTha",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "d0340d90-868a-4ffa-adb1-5bb710109c40"
      },
      "source": [
        "bank_df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  marital  education  ... pdays  previous poutcome   y\n",
              "0   58    management  married   tertiary  ...    -1         0  unknown  no\n",
              "1   44    technician   single  secondary  ...    -1         0  unknown  no\n",
              "2   33  entrepreneur  married  secondary  ...    -1         0  unknown  no\n",
              "3   47   blue-collar  married    unknown  ...    -1         0  unknown  no\n",
              "4   33       unknown   single    unknown  ...    -1         0  unknown  no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhW9jLy_7mHv",
        "colab_type": "code",
        "outputId": "5ace049c-c48b-4b09-bfcc-487b10d17680",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "np.unique(bank_df.y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     no\n",
              "1    yes\n",
              "Name: y, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eSauT6En37r",
        "colab_type": "code",
        "outputId": "89970e7e-9216-4274-bd20-e93b19eda45f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun Jan 12 22:34:58 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.44       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P0    33W / 250W |    385MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vA10SeU0dc-b",
        "colab_type": "text"
      },
      "source": [
        "1 - age (numeric) \n",
        "\n",
        "2 - job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\", \"student\",\"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") \n",
        "\n",
        "3 - marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed) \n",
        "\n",
        "4 - education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\") \n",
        "\n",
        "5 - default: has credit in default? (binary: \"yes\",\"no\") \n",
        "\n",
        "6 - balance: average yearly balance, in euros (numeric) \n",
        "\n",
        "7 - housing: has housing loan? (binary: \"yes\",\"no\") \n",
        "\n",
        "8 - loan: has personal loan? (binary: \"yes\",\"no\")\n",
        "\n",
        "- related with the last contact of the current campaign:\n",
        "\n",
        "9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\")\n",
        "\n",
        "10 - day: last contact day of the month (numeric)\n",
        "\n",
        "11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
        "\n",
        "12 - duration: last contact duration, in seconds (numeric)\n",
        "\n",
        "- other attributes:\n",
        "\n",
        "13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
        "\n",
        "14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) \n",
        "\n",
        "15 - previous: number of contacts performed before this campaign and for this client (numeric) \n",
        "\n",
        "16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n",
        "\n",
        "- output variable (desired target):\n",
        "\n",
        "17 - y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOgRjV_ULCtl",
        "colab_type": "code",
        "outputId": "b350530f-eb21-445c-febb-b40d55e6f472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print (\"Rows     : \" ,bank_df.shape[0])\n",
        "print (\"Columns  : \" ,bank_df.shape[1])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows     :  45211\n",
            "Columns  :  17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcE9cOCIwPuR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bank_df.dtypes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Rq5Vve0wWbB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bank_df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CX_bgZVPwazI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "310cba79-12de-4341-c97f-b188a2306439"
      },
      "source": [
        "bank_df.y.value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     39922\n",
              "yes     5289\n",
              "Name: y, dtype: int32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qosd13R2f3WN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfLf7Lb-fC0y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c88f7c2a-d930-4a71-e664-0e8c52a8c0f9"
      },
      "source": [
        "start_time = time.time()\n",
        "bank_df.describe()\n",
        "print(\"Time Taken GPU %s seconds \" % (str(time.time() - start_time)))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time Taken GPU 0.07101154327392578 seconds \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybe6TVJ4QzRR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "24ffe0fd-532a-4edf-96b9-dc8cfe8af4b7"
      },
      "source": [
        "dcudf=dask_cudf.from_cudf(bank_df, npartitions=2) \n",
        "start_time = time.time()\n",
        "dcudf.describe()\n",
        "print(\"Time Taken GPU %s seconds \" % (str(time.time() - start_time)))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time Taken GPU 0.5221295356750488 seconds \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eUgvKB114-U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "b1152628-8b28-45d9-861b-77b7b22c623d"
      },
      "source": [
        "bank_df.describe()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>day</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.936210</td>\n",
              "      <td>1362.272058</td>\n",
              "      <td>15.806419</td>\n",
              "      <td>258.163080</td>\n",
              "      <td>2.763841</td>\n",
              "      <td>40.197828</td>\n",
              "      <td>0.580323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.618762</td>\n",
              "      <td>3044.765829</td>\n",
              "      <td>8.322476</td>\n",
              "      <td>257.527812</td>\n",
              "      <td>3.098021</td>\n",
              "      <td>100.128746</td>\n",
              "      <td>2.303441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-8019.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>448.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>1428.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>102127.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>275.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        balance  ...         pdays      previous\n",
              "count  45211.000000   45211.000000  ...  45211.000000  45211.000000\n",
              "mean      40.936210    1362.272058  ...     40.197828      0.580323\n",
              "std       10.618762    3044.765829  ...    100.128746      2.303441\n",
              "min       18.000000   -8019.000000  ...     -1.000000      0.000000\n",
              "25%       33.000000      72.000000  ...     -1.000000      0.000000\n",
              "50%       39.000000     448.000000  ...     -1.000000      0.000000\n",
              "75%       48.000000    1428.000000  ...     -1.000000      0.000000\n",
              "max       95.000000  102127.000000  ...    871.000000    275.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMkU4ovQhSMB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "2f1bbebd-b44c-4181-b23b-d133130eea11"
      },
      "source": [
        "bank_df.groupby(['marital', 'y']).agg({'balance':'mean'})"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>balance</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <th>y</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">divorced</th>\n",
              "      <th>no</th>\n",
              "      <td>1107.095747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1707.964630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">married</th>\n",
              "      <th>no</th>\n",
              "      <td>1370.746228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1915.810163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">single</th>\n",
              "      <th>no</th>\n",
              "      <td>1235.869921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1674.875523</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  balance\n",
              "marital  y               \n",
              "divorced no   1107.095747\n",
              "         yes  1707.964630\n",
              "married  no   1370.746228\n",
              "         yes  1915.810163\n",
              "single   no   1235.869921\n",
              "         yes  1674.875523"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTFHkCRAguiT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "bed74ac8-b5a2-440b-807b-8a4b728198da"
      },
      "source": [
        "bank_df.groupby(['marital', 'y']).agg({'balance':'mean','y':'count'})"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>balance</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <th>y</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">divorced</th>\n",
              "      <th>no</th>\n",
              "      <td>1107.095747</td>\n",
              "      <td>4585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1707.964630</td>\n",
              "      <td>622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">married</th>\n",
              "      <th>no</th>\n",
              "      <td>1370.746228</td>\n",
              "      <td>24459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1915.810163</td>\n",
              "      <td>2755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">single</th>\n",
              "      <th>no</th>\n",
              "      <td>1235.869921</td>\n",
              "      <td>10878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1674.875523</td>\n",
              "      <td>1912</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  balance      y\n",
              "marital  y                      \n",
              "divorced no   1107.095747   4585\n",
              "         yes  1707.964630    622\n",
              "married  no   1370.746228  24459\n",
              "         yes  1915.810163   2755\n",
              "single   no   1235.869921  10878\n",
              "         yes  1674.875523   1912"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VglIOkJfsVc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "5acf74d3-f369-4e04-9517-77e5ad3152e7"
      },
      "source": [
        "bank_df.groupby(['loan', 'y']).agg({'balance':'mean','y':'count'})"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>balance</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan</th>\n",
              "      <th>y</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">no</th>\n",
              "      <th>no</th>\n",
              "      <td>1413.228726</td>\n",
              "      <td>33162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1897.001041</td>\n",
              "      <td>4805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">yes</th>\n",
              "      <th>no</th>\n",
              "      <td>766.481953</td>\n",
              "      <td>6760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>883.642562</td>\n",
              "      <td>484</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              balance      y\n",
              "loan y                      \n",
              "no   no   1413.228726  33162\n",
              "     yes  1897.001041   4805\n",
              "yes  no    766.481953   6760\n",
              "     yes   883.642562    484"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVd3wekAhvad",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loan_outcome=bank_df.groupby(['loan', 'y']).agg({'balance':'mean','y':'count'})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hps12QlxjXwI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loan_outcome['y_perct']=loan_outcome['y']/loan_outcome['y'].sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaXFSfKljxtW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "36e101f4-7ef9-48f1-9d50-de8299896203"
      },
      "source": [
        "loan_outcome"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>balance</th>\n",
              "      <th>y</th>\n",
              "      <th>y_perct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan</th>\n",
              "      <th>y</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">no</th>\n",
              "      <th>no</th>\n",
              "      <td>1413.228726</td>\n",
              "      <td>33162</td>\n",
              "      <td>0.733494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1897.001041</td>\n",
              "      <td>4805</td>\n",
              "      <td>0.106279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">yes</th>\n",
              "      <th>no</th>\n",
              "      <td>766.481953</td>\n",
              "      <td>6760</td>\n",
              "      <td>0.149521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>883.642562</td>\n",
              "      <td>484</td>\n",
              "      <td>0.010705</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              balance      y   y_perct\n",
              "loan y                                \n",
              "no   no   1413.228726  33162  0.733494\n",
              "     yes  1897.001041   4805  0.106279\n",
              "yes  no    766.481953   6760  0.149521\n",
              "     yes   883.642562    484  0.010705"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7fU4X0IiIyz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convert_hour(duration):\n",
        "    return duration/60"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiDLvUjMh6eO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bank_df['duration_hour'] = bank_df['duration'].applymap(convert_hour)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMz6K4xBuQ-z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "b8d2c9f4-33df-489d-c369-1c41552b1005"
      },
      "source": [
        "bank_df"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "      <th>duration_hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>4.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>2.516667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1.266667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>3.300000</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",
              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>51</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>825</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>977</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "      <td>16.283333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45207</th>\n",
              "      <td>71</td>\n",
              "      <td>retired</td>\n",
              "      <td>divorced</td>\n",
              "      <td>primary</td>\n",
              "      <td>no</td>\n",
              "      <td>1729</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>456</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "      <td>7.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>72</td>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>5715</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>1127</td>\n",
              "      <td>5</td>\n",
              "      <td>184</td>\n",
              "      <td>3</td>\n",
              "      <td>success</td>\n",
              "      <td>yes</td>\n",
              "      <td>18.783333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>668</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>508</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>8.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2971</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>11</td>\n",
              "      <td>other</td>\n",
              "      <td>no</td>\n",
              "      <td>6.016667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age           job   marital  ... poutcome    y  duration_hour\n",
              "0       58    management   married  ...  unknown   no       4.350000\n",
              "1       44    technician    single  ...  unknown   no       2.516667\n",
              "2       33  entrepreneur   married  ...  unknown   no       1.266667\n",
              "3       47   blue-collar   married  ...  unknown   no       1.533333\n",
              "4       33       unknown    single  ...  unknown   no       3.300000\n",
              "...    ...           ...       ...  ...      ...  ...            ...\n",
              "45206   51    technician   married  ...  unknown  yes      16.283333\n",
              "45207   71       retired  divorced  ...  unknown  yes       7.600000\n",
              "45208   72       retired   married  ...  success  yes      18.783333\n",
              "45209   57   blue-collar   married  ...  unknown   no       8.466667\n",
              "45210   37  entrepreneur   married  ...    other   no       6.016667\n",
              "\n",
              "[45211 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmTr8gsvpi-C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "b4ebf042-0e22-459b-fcac-0703361a687e"
      },
      "source": [
        "bank_df.groupby('y').balance.mean()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y\n",
              "no     1303.714969\n",
              "yes    1804.267915\n",
              "Name: balance, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PtKGUoRFsqDM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "a000ea5c-b3d4-4115-a5bc-d8383ce47316"
      },
      "source": [
        "bank_df.groupby('y').campaign.mean()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y\n",
              "no     2.846350\n",
              "yes    2.141047\n",
              "Name: campaign, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10yMMgdw-1-B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "0c40ffbf-d2d1-4e6b-db26-f3b3e1b930e9"
      },
      "source": [
        "bank_df['campaign'].quantile(q=[0.25,0.5, 0.75, 0.9, 0.95, 1.0])"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25     1.0\n",
              "0.50     2.0\n",
              "0.75     3.0\n",
              "0.90     5.0\n",
              "0.95     8.0\n",
              "1.00    63.0\n",
              "Name: campaign, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0lLqdHVAVfG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bank_campaign_df=bank_df.query(\"campaign <= 8\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEXPCjUvAtnZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "d34c1e96-a2d9-4e9f-b6a7-bb3621cd2a6a"
      },
      "source": [
        "bank_campaign_df.groupby(['campaign', 'y']).agg({'y':'count'})"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th>campaign</th>\n",
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              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>no</th>\n",
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              "      <td>317</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
              "      <th>no</th>\n",
              "      <td>1625</td>\n",
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              "      <th>yes</th>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
              "      <th>no</th>\n",
              "      <td>1199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
              "      <th>no</th>\n",
              "      <td>688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
              "      <th>no</th>\n",
              "      <td>508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  y\n",
              "campaign y         \n",
              "1        no   14983\n",
              "         yes   2561\n",
              "2        no   11104\n",
              "         yes   1401\n",
              "3        no    4903\n",
              "         yes    618\n",
              "4        no    3205\n",
              "         yes    317\n",
              "5        no    1625\n",
              "         yes    139\n",
              "6        no    1199\n",
              "         yes     92\n",
              "7        no     688\n",
              "         yes     47\n",
              "8        no     508\n",
              "         yes     32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzRofYEmuDK2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "deec01fb-88e2-46a4-c48b-a505a08d2af8"
      },
      "source": [
        "bank_df.education.value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary    23202\n",
              "tertiary     13301\n",
              "primary       6851\n",
              "unknown       1857\n",
              "Name: education, dtype: int32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_1AIVzJuHGM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "03cabb97-719c-4034-f162-e13fa4b24cd3"
      },
      "source": [
        "bank_df.groupby(['education','y']).agg({'y':'count'})"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
              "      <th>education</th>\n",
              "      <th>y</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">primary</th>\n",
              "      <th>no</th>\n",
              "      <td>6260</td>\n",
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              "      <th>yes</th>\n",
              "      <td>591</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">secondary</th>\n",
              "      <th>no</th>\n",
              "      <td>20752</td>\n",
              "    </tr>\n",
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              "      <th>yes</th>\n",
              "      <td>2450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">tertiary</th>\n",
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              "      <td>11305</td>\n",
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              "      <th>yes</th>\n",
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              "      <td>252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   y\n",
              "education y         \n",
              "primary   no    6260\n",
              "          yes    591\n",
              "secondary no   20752\n",
              "          yes   2450\n",
              "tertiary  no   11305\n",
              "          yes   1996\n",
              "unknown   no    1605\n",
              "          yes    252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    }
  ]
}