material/library/pandas_profiling.ipynb

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            "\u001b[K     |████████████████████████████████| 256kB 23.6MB/s \n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/ce/193e8ddf62d4be643b9b4b20e8e9c63b2f6a20f92778c0410c629f89bdaa/phik-0.11.2.tar.gz (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 32.7MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (1.4.1)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9f/f2/2d5425efe57f6c4e06cbe5e587c1fd16929dcf0eb90bd4d3d1e1c97d1151/pydantic-1.8.2-cp37-cp37m-manylinux2014_x86_64.whl (10.1MB)\n",
            "\u001b[K     |████████████████████████████████| 10.1MB 31.5MB/s \n",
            "\u001b[?25hCollecting htmlmin>=0.1.12\n",
            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: missingno>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (0.4.2)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/c1/24814557f1d22c56d50280771a17307e6bf87b70727d975fd6b2ce6b014a/requests-2.25.1-py2.py3-none-any.whl (61kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 5.9MB/s \n",
            "\u001b[?25hCollecting PyYAML>=5.0.0\n",
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            "\u001b[?25hCollecting tangled-up-in-unicode==0.1.0\n",
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            "\u001b[K     |████████████████████████████████| 3.1MB 25.4MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: seaborn>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (0.11.1)\n",
            "Requirement already satisfied, skipping upgrade: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (1.1.5)\n",
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            "Requirement already satisfied, skipping upgrade: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from pydantic>=1.8.1->pandas-profiling) (3.7.4.3)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=2.4 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.1->pandas-profiling) (2.5.1)\n",
            "Requirement already satisfied, skipping upgrade: bottleneck in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.1->pandas-profiling) (1.3.2)\n",
            "Requirement already satisfied, skipping upgrade: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.1->pandas-profiling) (21.2.0)\n",
            "Collecting multimethod==1.4\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/d0/ce5ad0392aa12645b7ad91a5983d6b625b704b021d9cd48c587630c1a9ac/multimethod-1.4-py2.py3-none-any.whl\n",
            "Collecting imagehash; extra == \"type_image_path\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/18/9dbb772b5ef73a3069c66bb5bf29b9fb4dd57af0d5790c781c3f559bcca6/ImageHash-4.2.0-py2.py3-none-any.whl (295kB)\n",
            "\u001b[K     |████████████████████████████████| 296kB 31.6MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: Pillow; extra == \"type_image_path\" in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.1->pandas-profiling) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (1.3.1)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->pandas-profiling) (2.0.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.4->visions[type_image_path]==0.7.1->pandas-profiling) (4.4.2)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]==0.7.1->pandas-profiling) (1.1.1)\n",
            "Building wheels for collected packages: phik, htmlmin\n",
            "  Building wheel for phik (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for phik: filename=phik-0.11.2-cp37-none-any.whl size=1107413 sha256=5845cb9e5bb2d8e3545f5214367e4b117af5f484ffe80680a7ff9a6c87e4513a\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/a3/b0/f27b1cfe32ea131a3715169132ff6d85653789e80e966c3bf6\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n",
            "Successfully built phik htmlmin\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: phik 0.11.2 has requirement scipy>=1.5.2, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: phik, pydantic, htmlmin, requests, PyYAML, tqdm, tangled-up-in-unicode, multimethod, imagehash, visions, pandas-profiling\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "Successfully installed PyYAML-5.4.1 htmlmin-0.1.12 imagehash-4.2.0 multimethod-1.4 pandas-profiling-3.0.0 phik-0.11.2 pydantic-1.8.2 requests-2.25.1 tangled-up-in-unicode-0.1.0 tqdm-4.60.0 visions-0.7.1\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynH8rZBcIQTc"
      },
      "source": [
        "import pandas as pd\n",
        "import pandas_profiling"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i268-jaaG0_T"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "df = sns.load_dataset(\"penguins\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "ub20k7kfIcaU",
        "outputId": "72ccce89-8b40-4f0b-a2ee-0d57014c6726"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>46.8</td>\n",
              "      <td>14.3</td>\n",
              "      <td>215.0</td>\n",
              "      <td>4850.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>50.4</td>\n",
              "      <td>15.7</td>\n",
              "      <td>222.0</td>\n",
              "      <td>5750.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>45.2</td>\n",
              "      <td>14.8</td>\n",
              "      <td>212.0</td>\n",
              "      <td>5200.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>49.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>213.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>344 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex\n",
              "0    Adelie  Torgersen            39.1  ...              181.0       3750.0    Male\n",
              "1    Adelie  Torgersen            39.5  ...              186.0       3800.0  Female\n",
              "2    Adelie  Torgersen            40.3  ...              195.0       3250.0  Female\n",
              "3    Adelie  Torgersen             NaN  ...                NaN          NaN     NaN\n",
              "4    Adelie  Torgersen            36.7  ...              193.0       3450.0  Female\n",
              "..      ...        ...             ...  ...                ...          ...     ...\n",
              "339  Gentoo     Biscoe             NaN  ...                NaN          NaN     NaN\n",
              "340  Gentoo     Biscoe            46.8  ...              215.0       4850.0  Female\n",
              "341  Gentoo     Biscoe            50.4  ...              222.0       5750.0    Male\n",
              "342  Gentoo     Biscoe            45.2  ...              212.0       5200.0  Female\n",
              "343  Gentoo     Biscoe            49.9  ...              213.0       5400.0    Male\n",
              "\n",
              "[344 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 5
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GU0oW9jMIrMw"
      },
      "source": [
        "report = df.profile_report()"
      ],
      "execution_count": 6,
      "outputs": []
    },
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            "29236ed25c4d4271ba528235a8db9c5a",
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            "886485ee0b794b0fb77f4065b89aaa15",
            "af8efec331f440bba681281cbdcb2413"
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        },
        "id": "ouvvnA-RIxqb",
        "outputId": "e97adb0e-54bd-47b9-96ed-82359bbceb37"
      },
      "source": [
        "report.to_file(\"report.html\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a4ad8d5f3bf24644bff560425ad3180f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Summarize dataset:   0%|          | 0/20 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c56096ef26534923accfbfc20388d886",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "471b958ad79347df8212a102047ac7bc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5e0d16b03c44a619eb170fce7f76970",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8GNW3oJI4DH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}