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Pythonで視覚化[PyGWalker編].ipnyb
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Pythonで視覚化[PyGWalker編].ipnyb
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["gxBzFXzgxEha"],"authorship_tag":"ABX9TyP5B/T3Zu+WmJk2uScKMFoU"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **PyGWalker**"],"metadata":{"id":"w7fZYfedx8jP"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"fz3-B7E1xrli"}},{"cell_type":"markdown","source":["PyGWalkerは、Jupyter Notebookのデータ解析とデータ可視化を支援するライブラリ。\n","- pandasのデータフレームを**Tableauスタイルのユーザインタフェース**に変換することで、視覚的な探索を可能にしたもの。\n","- PyGWalker：Python binding of Graphic Walker（通称\"Pig Walker\"）\n","- Jupyter NotebookとGraphic Walkerを統合し、Tableauとは異なるタイプのオープンソースの代替となるもの。ドラッグ＆ドロップの簡単な操作でデータ分析や視覚的な探索が実行できる。\n","\n","※Tableauは、膨大なデータの収集・分析・加工が専門家でなくてもできるBI(ビジネス・インテリジェンス)ツールのひとつ。Tableauは2003年にスタンフォード大学のプロジェクトから誕生。BIやデータドリブンな意思決定を支援するツールとして広く利用されている。\n","\n","###**手順**\n","- 「1．インストール」の「▷」をクリックしてください。（インストールが実行されます）。\n","- Select_Datasetのドロップダウンメニュー(dataset:)よりデータセット\n","を選択してください。（任意のcsvデータを読込む場合は Upload を選択してください）\n","- Load dataset の「▷」をクリックしてください。（データセットが読み込まれます）。\n","- PyGWalker の「▷」をクリックしてください。\n","\n","***\n","## **csvデータ読み込み時の注意点**\n","- **csvデータの表形式は以下としてください。**\n","\n","|説明変数1|説明変数2|説明変数3|…|説明変数n|…|目的変数|\n","|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n","|data |data|data |…|data |…|data |\n","|data |data|data |…|data |…|data |\n","|･ |･|･ |…|･ |…|･ |\n","|･ |･|･ |…|･ |…|･ |\n","|･ |･|･ |…|･ |…|･ |\n","\n","- [**注意**] csvデータは文字コードを「UTF-8」としてください。"],"metadata":{"id":"Y11bEn9Ox4Hk"}},{"cell_type":"markdown","source":["## **1．インストール**"],"metadata":{"id":"gxBzFXzgxEha"}},{"cell_type":"code","source":["!pip install pygwalker"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Bk2Dp_xd7rgq","executionInfo":{"status":"ok","timestamp":1678247925498,"user_tz":-540,"elapsed":10549,"user":{"displayName":"Atsu Mae","userId":"10059848880947889702"}},"outputId":"490dfa2a-6480-4f66-d572-385c74b8aa15"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting pygwalker\n"," Downloading pygwalker-0.1.4.8-py3-none-any.whl (727 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m727.4/727.4 KB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: ipython in /usr/local/lib/python3.8/dist-packages (from pygwalker) (7.9.0)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from pygwalker) (3.1.2)\n","Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (57.4.0)\n","Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (4.4.2)\n","Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (4.8.0)\n","Collecting jedi>=0.10\n"," Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m51.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (0.7.5)\n","Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (2.0.10)\n","Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (2.6.1)\n","Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (5.7.1)\n","Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython->pygwalker) (0.2.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.8/dist-packages (from jinja2->pygwalker) (2.1.2)\n","Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython->pygwalker) (0.8.3)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->pygwalker) (0.2.6)\n","Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->pygwalker) (1.15.0)\n","Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython->pygwalker) (0.7.0)\n","Installing collected packages: jedi, pygwalker\n","Successfully installed jedi-0.18.2 pygwalker-0.1.4.8\n"]}]},{"cell_type":"markdown","source":["## **2．データセット読込み**"],"metadata":{"id":"7avNQxplxTWh"}},{"cell_type":"code","source":["#@title Select_Dataset { run: \"auto\" }\n","#@markdown **<font color= \"Crimson\">注意</font>：かならず 実行する前に 設定してください。**</font>\n","\n","dataset = 'Upload' #@param ['Boston_housing :regression', 'Diabetes :regression', 'Breast_cancer :binary','Titanic :binary', 'Titanic(seaborn) :binary', 'Iris :classification', 'Loan_prediction :binary','wine :classification', 'Occupancy_detection :binary', 'Upload']"],"metadata":{"id":"kYtA-M52ZFGQ","cellView":"form","executionInfo":{"status":"ok","timestamp":1678247925499,"user_tz":-540,"elapsed":9,"user":{"displayName":"Atsu Mae","userId":"10059848880947889702"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["#@title Load dataset\n","\n","#ライブラリインポート\n","#import numpy as np #数値計算ライブラリ\n","import pandas as pd #データを効率的に扱うライブラリ\n","import seaborn as sns #視覚化ライブラリ\n","import warnings #警告を表示させないライブラリ\n","warnings.simplefilter('ignore')\n","\n","'''\n","dataset（ドロップダウンメニュー）で選択したデータセットを読込み、データフレーム（df）に格納。\n","目的変数は、データフレームの最終列とし、FEATURES、TARGET、X、yを指定した後、データフレーム\n","に関する情報と先頭5列を表示。\n","任意のcsvデータを読込む場合は、datasetで'Upload'を選択。\n","\n","'''\n","\n","#任意のcsvデータ読込み及びデータフレーム格納、\n","if dataset =='Upload':\n"," from google.colab import files\n"," uploaded = files.upload()#Upload\n"," target = list(uploaded.keys())[0]\n"," df = pd.read_csv(target)\n","\n","#Diabetes データセットの読込み及びデータフレーム格納、\n","elif dataset == \"Diabetes :regression\":\n"," from sklearn.datasets import load_diabetes\n"," diabetes = load_diabetes()\n"," df = pd.DataFrame(diabetes.data, columns = diabetes.feature_names)\n"," df['target'] = diabetes.target\n","\n","#Breast_cancer データセットの読込み及びデータフレーム格納、\n","elif dataset == \"Breast_cancer :binary\":\n"," from sklearn.datasets import load_breast_cancer\n"," breast_cancer = load_breast_cancer()\n"," df = pd.DataFrame(breast_cancer.data, columns = breast_cancer.feature_names)\n"," #df['target'] = breast_cancer.target #目的変数をカテゴリー数値とする時\n"," df['target'] = breast_cancer.target_names[breast_cancer.target]\n","\n","#Titanic データセットの読込み及びデータフレーム格納、\n","elif dataset == \"Titanic :binary\":\n"," data_url = \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\"\n"," df = pd.read_csv(data_url)\n"," #目的変数 Survived をデータフレーム最終列に移動\n"," X = df.drop(['Survived'], axis=1)\n"," y = df['Survived'] \n"," df = pd.concat([X, y], axis=1) #X,yを結合し、dfに格納\n","\n","#Titanic(seaborn) データセットの読込み及びデータフレーム格納、\n","elif dataset == \"Titanic(seaborn) :binary\":\n"," df = sns.load_dataset('titanic')\n"," #重複データをカットし、目的変数 alive をデータフレーム最終列に移動\n"," X = df.drop(['survived','pclass','embarked','who','adult_male','alive'], axis=1)\n"," y = df['alive'] #目的変数データ\n"," df = pd.concat([X, y], axis=1) #X,yを結合し、dfに格納\n","\n","#iris データセットの読込み及びデータフレーム格納、\n","elif dataset == \"Iris :classification\":\n"," from sklearn.datasets import load_iris\n"," iris = load_iris()\n"," df = pd.DataFrame(iris.data, columns = iris.feature_names)\n"," #df['target'] = iris.target #目的変数をカテゴリー数値とする時\n"," df['target'] = iris.target_names[iris.target]\n","\n","#wine データセットの読込み及びデータフレーム格納、\n","elif dataset == \"wine :classification\":\n"," from sklearn.datasets import load_wine\n"," wine = load_wine()\n"," df = pd.DataFrame(wine.data, columns = wine.feature_names)\n"," #df['target'] = wine.target #目的変数をカテゴリー数値とする時\n"," df['target'] = wine.target_names[wine.target]\n","\n","#Loan_prediction データセットの読込み及びデータフレーム格納、 \n","elif dataset == \"Loan_prediction :binary\":\n"," data_url = \"https://github.com/shrikant-temburwar/Loan-Prediction-Dataset/raw/master/train.csv\"\n"," df = pd.read_csv(data_url)\n","\n","#Occupancy_detection データセットの読込み及びデータフレーム格納、 \n","elif dataset =='Occupancy_detection :binary':\n"," data_url = 'https://raw.githubusercontent.com/hima2b4/Auto_Profiling/main/Occupancy-detection-datatest.csv'\n"," df = pd.read_csv(data_url)\n"," df['date'] = pd.to_datetime(df['date']) #[date]のデータ型をdatetime型に変更\n","\n","#Boston データセットの読込み及びデータフレーム格納 \n","else:\n"," from sklearn.datasets import load_boston\n"," boston = load_boston()\n"," df = pd.DataFrame(boston.data, columns = boston.feature_names)\n"," df['target'] = boston.target\n","\n","#FEATURES、TARGET、X、yを指定 \n","FEATURES = df.columns[:-1] #説明変数のデータ項目を指定\n","TARGET = df.columns[-1] #目的変数のデータ項目を指定\n","X = df.loc[:, FEATURES] #FEATURESのすべてのデータをXに格納\n","y = df.loc[:, TARGET] #TARGETのすべてのデータをyに格納\n","\n","#データフレーム表示\n","df.info(verbose=True) #データフレーム情報表示（verbose=Trueで表示数制限カット）\n","df.head() #データフレーム先頭5行表示"],"metadata":{"id":"rwv_xazpmwbC","cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":448},"executionInfo":{"status":"ok","timestamp":1678247937519,"user_tz":-540,"elapsed":12027,"user":{"displayName":"Atsu Mae","userId":"10059848880947889702"}},"outputId":"d44db41c-a873-4c36-c41e-a927b23035cf"},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <input type=\"file\" id=\"files-f4a18cd9-57ff-4ad8-9c8b-c62c6ba18e8a\" name=\"files[]\" multiple disabled\n"," style=\"border:none\" />\n"," <output id=\"result-f4a18cd9-57ff-4ad8-9c8b-c62c6ba18e8a\">\n"," Upload widget is only available when the cell has been executed in the\n"," current browser session. Please rerun this cell to enable.\n"," </output>\n"," <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","// http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n"," const element = document.createElement('span');\n"," element.textContent = text;\n"," for (const key of Object.keys(styleAttributes)) {\n"," element.style[key] = styleAttributes[key];\n"," }\n"," return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n"," const steps = uploadFilesStep(inputId, outputId);\n"," const outputElement = document.getElementById(outputId);\n"," // Cache steps on the outputElement to make it available for the next call\n"," // to uploadFilesContinue from Python.\n"," outputElement.steps = steps;\n","\n"," return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n"," const outputElement = document.getElementById(outputId);\n"," const steps = outputElement.steps;\n","\n"," const next = steps.next(outputElement.lastPromiseValue);\n"," return Promise.resolve(next.value.promise).then((value) => {\n"," // Cache the last promise value to make it available to the next\n"," // step of the generator.\n"," outputElement.lastPromiseValue = value;\n"," return next.value.response;\n"," });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n"," const inputElement = document.getElementById(inputId);\n"," inputElement.disabled = false;\n","\n"," const outputElement = document.getElementById(outputId);\n"," outputElement.innerHTML = '';\n","\n"," const pickedPromise = new Promise((resolve) => {\n"," inputElement.addEventListener('change', (e) => {\n"," resolve(e.target.files);\n"," });\n"," });\n","\n"," const cancel = document.createElement('button');\n"," inputElement.parentElement.appendChild(cancel);\n"," cancel.textContent = 'Cancel upload';\n"," const cancelPromise = new Promise((resolve) => {\n"," cancel.onclick = () => {\n"," resolve(null);\n"," };\n"," });\n","\n"," // Wait for the user to pick the files.\n"," const files = yield {\n"," promise: Promise.race([pickedPromise, cancelPromise]),\n"," response: {\n"," action: 'starting',\n"," }\n"," };\n","\n"," cancel.remove();\n","\n"," // Disable the input element since further picks are not allowed.\n"," inputElement.disabled = true;\n","\n"," if (!files) {\n"," return {\n"," response: {\n"," action: 'complete',\n"," }\n"," };\n"," }\n","\n"," for (const file of files) {\n"," const li = document.createElement('li');\n"," li.append(span(file.name, {fontWeight: 'bold'}));\n"," li.append(span(\n"," `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n"," `last modified: ${\n"," file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n"," 'n/a'} - `));\n"," const percent = span('0% done');\n"," li.appendChild(percent);\n","\n"," outputElement.appendChild(li);\n","\n"," const fileDataPromise = new Promise((resolve) => {\n"," const reader = new FileReader();\n"," reader.onload = (e) => {\n"," resolve(e.target.result);\n"," };\n"," reader.readAsArrayBuffer(file);\n"," });\n"," // Wait for the data to be ready.\n"," let fileData = yield {\n"," promise: fileDataPromise,\n"," response: {\n"," action: 'continue',\n"," }\n"," };\n","\n"," // Use a chunked sending to avoid message size limits. See b/62115660.\n"," let position = 0;\n"," do {\n"," const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n"," const chunk = new Uint8Array(fileData, position, length);\n"," position += length;\n","\n"," const base64 = btoa(String.fromCharCode.apply(null, chunk));\n"," yield {\n"," response: {\n"," action: 'append',\n"," file: file.name,\n"," data: base64,\n"," },\n"," };\n","\n"," let percentDone = fileData.byteLength === 0 ?\n"," 100 :\n"," Math.round((position / fileData.byteLength) * 100);\n"," percent.textContent = `${percentDone}% done`;\n","\n"," } while (position < fileData.byteLength);\n"," }\n","\n"," // All done.\n"," yield {\n"," response: {\n"," action: 'complete',\n"," }\n"," };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n"," _uploadFiles,\n"," _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving senti_data.csv to senti_data.csv\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 367 entries, 0 to 366\n","Data columns (total 3 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 text 367 non-null object \n"," 1 label 367 non-null object \n"," 2 score 367 non-null float64\n","dtypes: float64(1), object(2)\n","memory usage: 8.7+ KB\n"]},{"output_type":"execute_result","data":{"text/plain":[" text label score\n","0 バカなやつだな。それは揚水発電というんだよ。コストは蓄電池と同じぐらい高く、もう適地がない。... NEGATIVE 0.9380\n","1 本気で止める気あるの？『三村知事は「私的な立場での発言」と前置きした上で』青森県・三村知事「... NEUTRAL 0.5551\n","2 【値上げラッシュ】急激に進んだ円安背景に、値上げは2万品目、最大28%の値上げ／ネット「再エ... NEGATIVE 0.5487\n","3 「１５０万円の太陽光パネルは１５年で元が取れるというが、発電の価値は５０万円だけ。残り１００... NEUTRAL 0.5683\n","4 もうやめれ。再生可能エネルギーで自然は再生不可能になるやんけ。 NEGATIVE 0.9841"],"text/html":["\n"," <div id=\"df-556074a7-ffaf-465f-b2ef-8aef6c8a7172\">\n"," <div class=\"colab-df-container\">\n"," <div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>text</th>\n"," <th>label</th>\n"," <th>score</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>バカなやつだな。それは揚水発電というんだよ。コストは蓄電池と同じぐらい高く、もう適地がない。...</td>\n"," <td>NEGATIVE</td>\n"," <td>0.9380</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>本気で止める気あるの？『三村知事は「私的な立場での発言」と前置きした上で』青森県・三村知事「...</td>\n"," <td>NEUTRAL</td>\n"," <td>0.5551</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>【値上げラッシュ】急激に進んだ円安背景に、値上げは2万品目、最大28%の値上げ／ネット「再エ...</td>\n"," <td>NEGATIVE</td>\n"," <td>0.5487</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>「１５０万円の太陽光パネルは１５年で元が取れるというが、発電の価値は５０万円だけ。残り１００...</td>\n"," <td>NEUTRAL</td>\n"," <td>0.5683</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>もうやめれ。再生可能エネルギーで自然は再生不可能になるやんけ。</td>\n"," <td>NEGATIVE</td>\n"," <td>0.9841</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>\n"," <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-556074a7-ffaf-465f-b2ef-8aef6c8a7172')\"\n"," title=\"Convert this dataframe to an interactive table.\"\n"," style=\"display:none;\">\n"," \n"," <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n"," width=\"24px\">\n"," <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n"," <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n"," </svg>\n"," </button>\n"," \n"," <style>\n"," .colab-df-container {\n"," display:flex;\n"," flex-wrap:wrap;\n"," gap: 12px;\n"," }\n","\n"," .colab-df-convert {\n"," background-color: #E8F0FE;\n"," border: none;\n"," border-radius: 50%;\n"," cursor: pointer;\n"," display: none;\n"," fill: #1967D2;\n"," height: 32px;\n"," padding: 0 0 0 0;\n"," width: 32px;\n"," }\n","\n"," .colab-df-convert:hover {\n"," background-color: #E2EBFA;\n"," box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n"," fill: #174EA6;\n"," }\n","\n"," [theme=dark] .colab-df-convert {\n"," background-color: #3B4455;\n"," fill: #D2E3FC;\n"," }\n","\n"," [theme=dark] .colab-df-convert:hover {\n"," background-color: #434B5C;\n"," box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n"," filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n"," fill: #FFFFFF;\n"," }\n"," </style>\n","\n"," <script>\n"," const buttonEl =\n"," document.querySelector('#df-556074a7-ffaf-465f-b2ef-8aef6c8a7172 button.colab-df-convert');\n"," buttonEl.style.display =\n"," google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n"," async function convertToInteractive(key) {\n"," const element = document.querySelector('#df-556074a7-ffaf-465f-b2ef-8aef6c8a7172');\n"," const dataTable =\n"," await google.colab.kernel.invokeFunction('convertToInteractive',\n"," [key], {});\n"," if (!dataTable) return;\n","\n"," const docLinkHtml = 'Like what you see? Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n"," </div>\n"," "]},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","source":["## **3．PyGWalker実行**"],"metadata":{"id":"oaEpMteP8RDT"}},{"cell_type":"code","source":["#@title PyGWalker\n","import pygwalker as pyg\n","gwalker = pyg.walk(df)"],"metadata":{"cellView":"form","id":"N2PajFgj8c7Q","colab":{"base_uri":"https://localhost:8080/","height":871,"output_embedded_package_id":"1KR_3kkquDfZhetmrBUetKyjpVYNHvXJX"},"executionInfo":{"status":"ok","timestamp":1678247940212,"user_tz":-540,"elapsed":2697,"user":{"displayName":"Atsu Mae","userId":"10059848880947889702"}},"outputId":"d1398098-2a4c-42a6-8a7a-a00b1e3cda1b"},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","source":["## **参考**\n","\n","- [**公式**](https://github.com/Kanaries/pygwalker)\n"],"metadata":{"id":"hS721EClUYOg"}}]}