task1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "task1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPevONh0uUuaQCMIe4m2dHR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sweetpand/Algorithms/blob/master/task1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbRlSePAjgxJ",
        "colab_type": "code",
        "outputId": "046449cf-2853-4ebe-d0a6-cd8e1d8b5325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIunX9THjzEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuXr2m2uj24V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from dask.distributed import Client"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1CUZPmJ7FFL",
        "colab_type": "code",
        "outputId": "55d4adea-6c4d-406d-f92c-c22447aab91c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install -U pandas-profiling"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: pandas-profiling in /usr/local/lib/python3.6/dist-packages (2.6.0)\n",
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            "Requirement already satisfied, skipping upgrade: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.4.4)\n",
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            "Requirement already satisfied, skipping upgrade: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (1.4.2)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lY2l1Wz4j8ho",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transactions = pd.read_csv(\n",
        "    '/content/drive/My Drive/N26/transactions.csv'\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g20m6mt5kLrM",
        "colab_type": "code",
        "outputId": "67afa59e-482e-4133-d69b-38e526c175ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "transactions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>ffeefe73-3e88-4c2e-be70-7bcff9cec099</td>\n",
              "      <td>True</td>\n",
              "      <td>23.82</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>81219971-2bdc-4d3f-821c-0c4655234788</td>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>5e01a485-86eb-43d5-9073-64a9ba29f5b2</td>\n",
              "      <td>True</td>\n",
              "      <td>7.17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>f44b4ae4-4a63-4103-8ee6-1c6bd39df2b1</td>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>4686bf44-7fee-4ea2-a98b-5aa60cf4abaf</td>\n",
              "      <td>True</td>\n",
              "      <td>85.64</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             transaction_id  ... transaction_category_id\n",
              "0      711da3f1-74a3-4e65-9ae2-e7ed32e0cb26  ...                       7\n",
              "1      00c84567-3334-425d-a793-5ccd0081c464  ...                       1\n",
              "2      8da67625-1ae4-4a09-8d18-a3051d0007d8  ...                       4\n",
              "3      307392c6-60cc-48cd-b7b4-50a7c6239273  ...                       9\n",
              "4      8cfe414c-efac-4d6d-ae0e-c5348085e2bc  ...                       5\n",
              "...                                     ...  ...                     ...\n",
              "99995  2bfc033e-4e7e-431e-9390-c5a8521c45ae  ...                       5\n",
              "99996  ae853a05-a274-4fba-a9ad-13c472e431ce  ...                       6\n",
              "99997  b6b803e7-6591-40ba-8994-fbbd6260a4ce  ...                       5\n",
              "99998  81219971-2bdc-4d3f-821c-0c4655234788  ...                       0\n",
              "99999  f44b4ae4-4a63-4103-8ee6-1c6bd39df2b1  ...                       3\n",
              "\n",
              "[100000 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNaFnuwck6bY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "users = pd.read_csv(\n",
        "    '/content/drive/My Drive/N26/users.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hk7n7rp6lAsA",
        "colab_type": "code",
        "outputId": "490f67d0-8b51-46c8-f319-60145f0e02ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "users"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>is_active</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68be02e8-4f48-43d0-90ae-9157969aed3e</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>83eb81a7-b8cf-4687-9f15-aead847970ad</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43620fee-e5fd-4181-8e95-99edce6ef8fc</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d740ee2f-b627-4561-a174-dd5f9996a900</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>77025c3e-8830-45e7-ab67-764b8b81fc03</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>1f821182-cdae-47dd-a879-84b33e074688</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>9d7f6887-c011-40cb-a8c0-28b608e80a8e</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>4baea722-7b34-4201-9ac1-aaef28791570</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>d4b8cf37-d28d-4b5d-8c6c-d426083335f5</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>e4c9802a-b36b-48d1-9694-abd0628cb632</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  user_id  is_active\n",
              "0    68be02e8-4f48-43d0-90ae-9157969aed3e       True\n",
              "1    83eb81a7-b8cf-4687-9f15-aead847970ad       True\n",
              "2    43620fee-e5fd-4181-8e95-99edce6ef8fc       True\n",
              "3    d740ee2f-b627-4561-a174-dd5f9996a900       True\n",
              "4    77025c3e-8830-45e7-ab67-764b8b81fc03       True\n",
              "..                                    ...        ...\n",
              "995  1f821182-cdae-47dd-a879-84b33e074688       True\n",
              "996  9d7f6887-c011-40cb-a8c0-28b608e80a8e       True\n",
              "997  4baea722-7b34-4201-9ac1-aaef28791570       True\n",
              "998  d4b8cf37-d28d-4b5d-8c6c-d426083335f5      False\n",
              "999  e4c9802a-b36b-48d1-9694-abd0628cb632       True\n",
              "\n",
              "[1000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GshCbX4a47qB",
        "colab_type": "code",
        "outputId": "e4bf8f02-ed32-4a65-8621-bdef644d7bfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pip install -U pandas-profiling"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: pandas-profiling in /usr/local/lib/python3.6/dist-packages (2.6.0)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib>=3.2.0 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (3.2.1)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: visions[type_image_path]>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: jinja2>=2.11.1 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (2.11.2)\n",
            "Requirement already satisfied, skipping upgrade: requests>=2.23.0 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: htmlmin>=0.1.12 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.1.12)\n",
            "Requirement already satisfied, skipping upgrade: statsmodels>=0.11.1 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.11.1)\n",
            "Requirement already satisfied, skipping upgrade: confuse>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (1.18.3)\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.25.3 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (1.0.3)\n",
            "Requirement already satisfied, skipping upgrade: phik>=0.9.10 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.9.11)\n",
            "Requirement already satisfied, skipping upgrade: missingno>=0.4.2 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.4.2)\n",
            "Requirement already satisfied, skipping upgrade: tqdm>=4.43.0 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (4.46.0)\n",
            "Requirement already satisfied, skipping upgrade: astropy>=4.0 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (4.0.1.post1)\n",
            "Requirement already satisfied, skipping upgrade: tangled-up-in-unicode>=0.0.4 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.0.4)\n",
            "Requirement already satisfied, skipping upgrade: ipywidgets>=7.5.1 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (7.5.1)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=2.4 in /usr/local/lib/python3.6/dist-packages (from visions[type_image_path]>=0.4.1->pandas-profiling) (2.4)\n",
            "Requirement already satisfied, skipping upgrade: attrs>=19.3.0 in /usr/local/lib/python3.6/dist-packages (from visions[type_image_path]>=0.4.1->pandas-profiling) (19.3.0)\n",
            "Requirement already satisfied, skipping upgrade: Pillow; extra == \"type_image_path\" in /usr/local/lib/python3.6/dist-packages (from visions[type_image_path]>=0.4.1->pandas-profiling) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: imagehash; extra == \"type_image_path\" in /usr/local/lib/python3.6/dist-packages (from visions[type_image_path]>=0.4.1->pandas-profiling) (4.1.0)\n",
            "Requirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.11.1->pandas-profiling) (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.23.0->pandas-profiling) (2.9)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.23.0->pandas-profiling) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.23.0->pandas-profiling) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.23.0->pandas-profiling) (2020.4.5.1)\n",
            "Requirement already satisfied, skipping upgrade: patsy>=0.5 in /usr/local/lib/python3.6/dist-packages (from statsmodels>=0.11.1->pandas-profiling) (0.5.1)\n",
            "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.6/dist-packages (from confuse>=1.0.0->pandas-profiling) (3.13)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.25.3->pandas-profiling) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: numba>=0.38.1 in /usr/local/lib/python3.6/dist-packages (from phik>=0.9.10->pandas-profiling) (0.48.0)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.14.1 in /usr/local/lib/python3.6/dist-packages (from phik>=0.9.10->pandas-profiling) (0.14.1)\n",
            "Requirement already satisfied, skipping upgrade: seaborn in /usr/local/lib/python3.6/dist-packages (from missingno>=0.4.2->pandas-profiling) (0.10.1)\n",
            "Requirement already satisfied, skipping upgrade: traitlets>=4.3.1 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.5.1->pandas-profiling) (4.3.3)\n",
            "Requirement already satisfied, skipping upgrade: ipython>=4.0.0; python_version >= \"3.3\" in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.5.1->pandas-profiling) (5.5.0)\n",
            "Requirement already satisfied, skipping upgrade: ipykernel>=4.5.1 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.5.1->pandas-profiling) (4.10.1)\n",
            "Requirement already satisfied, skipping upgrade: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.5.1->pandas-profiling) (3.5.1)\n",
            "Requirement already satisfied, skipping upgrade: nbformat>=4.2.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.5.1->pandas-profiling) (5.0.6)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.0->pandas-profiling) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.4->visions[type_image_path]>=0.4.1->pandas-profiling) (4.4.2)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets in /usr/local/lib/python3.6/dist-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]>=0.4.1->pandas-profiling) (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.1->phik>=0.9.10->pandas-profiling) (0.31.0)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.1->phik>=0.9.10->pandas-profiling) (46.1.3)\n",
            "Requirement already satisfied, skipping upgrade: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.3.1->ipywidgets>=7.5.1->pandas-profiling) (0.2.0)\n",
            "Requirement already satisfied, skipping upgrade: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (1.0.18)\n",
            "Requirement already satisfied, skipping upgrade: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (4.8.0)\n",
            "Requirement already satisfied, skipping upgrade: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (0.7.5)\n",
            "Requirement already satisfied, skipping upgrade: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (2.1.3)\n",
            "Requirement already satisfied, skipping upgrade: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (0.8.1)\n",
            "Requirement already satisfied, skipping upgrade: jupyter-client in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.1->pandas-profiling) (5.3.4)\n",
            "Requirement already satisfied, skipping upgrade: tornado>=4.0 in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.1->pandas-profiling) (4.5.3)\n",
            "Requirement already satisfied, skipping upgrade: notebook>=4.4.1 in /usr/local/lib/python3.6/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (5.2.2)\n",
            "Requirement already satisfied, skipping upgrade: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.5.1->pandas-profiling) (4.6.3)\n",
            "Requirement already satisfied, skipping upgrade: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.5.1->pandas-profiling) (2.6.0)\n",
            "Requirement already satisfied, skipping upgrade: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (0.1.9)\n",
            "Requirement already satisfied, skipping upgrade: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling) (0.6.0)\n",
            "Requirement already satisfied, skipping upgrade: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.5.1->pandas-profiling) (19.0.0)\n",
            "Requirement already satisfied, skipping upgrade: terminado>=0.3.3; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.8.3)\n",
            "Requirement already satisfied, skipping upgrade: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (5.6.1)\n",
            "Requirement already satisfied, skipping upgrade: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (1.4.2)\n",
            "Requirement already satisfied, skipping upgrade: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (3.1.5)\n",
            "Requirement already satisfied, skipping upgrade: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.6.0)\n",
            "Requirement already satisfied, skipping upgrade: entrypoints>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.3)\n",
            "Requirement already satisfied, skipping upgrade: mistune<2,>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.8.4)\n",
            "Requirement already satisfied, skipping upgrade: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.4.4)\n",
            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (20.3)\n",
            "Requirement already satisfied, skipping upgrade: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Df4mKk7Khxbh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transaction_category_id = transactions['transaction_category_id']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1LneCZHixhB",
        "colab_type": "code",
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      ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>e4c9802a-b36b-48d1-9694-abd0628cb632</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>890 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  user_id  is_active\n",
              "0    68be02e8-4f48-43d0-90ae-9157969aed3e       True\n",
              "1    83eb81a7-b8cf-4687-9f15-aead847970ad       True\n",
              "2    43620fee-e5fd-4181-8e95-99edce6ef8fc       True\n",
              "3    d740ee2f-b627-4561-a174-dd5f9996a900       True\n",
              "4    77025c3e-8830-45e7-ab67-764b8b81fc03       True\n",
              "..                                    ...        ...\n",
              "994  4237413d-967a-41a5-827c-bbc272bc528c       True\n",
              "995  1f821182-cdae-47dd-a879-84b33e074688       True\n",
              "996  9d7f6887-c011-40cb-a8c0-28b608e80a8e       True\n",
              "997  4baea722-7b34-4201-9ac1-aaef28791570       True\n",
              "999  e4c9802a-b36b-48d1-9694-abd0628cb632       True\n",
              "\n",
              "[890 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9yr40CWt-4e",
        "colab_type": "code",
        "outputId": "446530c9-ac71-4368-b773-b59e12706d40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "tr_df = pd.merge(transactions, users['user_id'], right_index=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MergeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-c330f7a87953>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtr_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0mindicator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindicator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m     )\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUserWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_specification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;31m# note this function has side effects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_validate_specification\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1184\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft_on\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1186\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mMergeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Must pass left_on or left_index=True\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1187\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m                 \u001b[0;31m# use the common columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMergeError\u001b[0m: Must pass left_on or left_index=True"
          ]
        }
      ]
    }
  ]
}