Mit dem kostenlosen Python-Tool PyGWalker erstellen Sie schnell Visualisierungen für große Datenmengen. Wir zeigen, wie das geht.

PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis - Kanaries/pygwalker

PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis - Kanaries/pygwalker

In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker. We start by preparing the Titanic dataset for large-scale interactive querying. These analysis-ready engineered features reveal the underlying structure of the data while enabling both detailed row-level exploration and high-level aggregated views for deeper insight. Embedding a Tableau-style drag-and-drop interface directly in the notebook enables rapid hypothesis testing, intuitive cohort comparisons, and efficient data-quality inspection, all without the friction of switching between code and visualization tools.

def generate_advanced_dataset(): np.random.seed(42) start_date = datetime(2022, 1, 1) dates = categories = ['Electronics', 'Clothing', 'Home & Garden', 'Sports', 'Books'] products = { 'Electronics': ['Laptop', 'Smartphone', 'Headphones', 'Tablet', 'Smartwatch'], 'Clothing': ['T-Shirt', 'Jeans', 'Dress', 'Jacket', 'Sneakers'], 'Home & Garden': ['Furniture', 'Lamp', 'Rug', 'Plant', 'Cookware'], 'Sports': ['Yoga Mat', 'Dumbbell', 'Running Shoes', 'Bicycle', 'Tennis Racket'], 'Books': ['Fiction', 'Non-Fiction', 'Biography', 'Science', 'History'] } n_transactions = 5000 data = [] for _ in range(n_transactions): date = np.random.choice(dates) category = np.random.choice(categories) product = np.random.choice(productsAI Shorts) base_prices = { 'Electronics': (200, 1500), 'Clothing': (20, 150), 'Home & Garden': (30, 500), 'Sports': (25, 300), 'Books': (10, 50) } price = np.random.uniform(*base_pricesAI Shorts) quantity = np.random.choice(, p=) customer_segment = np.random.choice(['Premium', 'Standard', 'Budget'], p=) age_group = np.random.choice(['18-25', '26-35', '36-45', '46-55', '56+']) region = np.random.choice(['North', 'South', 'East', 'West', 'Central']) month = date.month seasonal_factor = 1.0 if month in : seasonal_factor = 1.5 elif month in : seasonal_factor = 1.2 revenue = price * quantity * seasonal_factor discount = np.random.choice(, p=) marketing_channel = np.random.choice(['Organic', 'Social Media', 'Email', 'Paid Ads']) base_satisfaction = 4.0 if customer_segment == 'Premium': base_satisfaction += 0.5 if discount > 15: base_satisfaction += 0.3 satisfaction = np.clip(base_satisfaction + np.random.normal(0, 0.5), 1, 5) data.append({ 'Date': date, 'Category': category, 'Product': product, 'Price': round(price, 2), 'Quantity': quantity, 'Revenue': round(revenue, 2), 'Customer_Segment': customer_segment, 'Age_Group': age_group, 'Region': region, 'Discount_%': discount, 'Marketing_Channel': marketing_channel, 'Customer_Satisfaction': round(satisfaction, 2), 'Month': date.strftime('%B'), 'Year': date.year, 'Quarter': f'Q{(date.month-1)//3 + 1}' }) df = pd.DataFrame(data) df['Profit_Margin'] = round(df['Revenue'] * (1 - df['Discount_%']/100) * 0.3, 2) df['Days_Since_Start'] = (df['Date'] - df['Date'].min()).dt.days return df…

PyGWalker: Turn pandas DataFrames into interactive visualizations with drag & drop. No complex coding—explore data faster in Jupyter/Colab!

In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker. We start by preparing the Titanic dataset for large-scale interactive querying. These analysis-ready engineered features reveal the underlying structure of the data while enabling both detailed row-level exploration and high-level aggregated views for deeper insight. Embedding a Tableau-style drag-and-drop interface directly in the notebook enables rapid hypothesis testing, intuitive cohort comparisons, and efficient data-quality inspection, all without the friction of switching between code and visualization tools.

def generate_advanced_dataset(): np.random.seed(42) start_date = datetime(2022, 1, 1) dates = categories = ['Electronics', 'Clothing', 'Home & Garden', 'Sports', 'Books'] products = { 'Electronics': ['Laptop', 'Smartphone', 'Headphones', 'Tablet', 'Smartwatch'], 'Clothing': ['T-Shirt', 'Jeans', 'Dress', 'Jacket', 'Sneakers'], 'Home & Garden': ['Furniture', 'Lamp', 'Rug', 'Plant', 'Cookware'], 'Sports': ['Yoga Mat', 'Dumbbell', 'Running Shoes', 'Bicycle', 'Tennis Racket'], 'Books': ['Fiction', 'Non-Fiction', 'Biography', 'Science', 'History'] } n_transactions = 5000 data = [] for _ in range(n_transactions): date = np.random.choice(dates) category = np.random.choice(categories) product = np.random.choice(productsAI Shorts) base_prices = { 'Electronics': (200, 1500), 'Clothing': (20, 150), 'Home & Garden': (30, 500), 'Sports': (25, 300), 'Books': (10, 50) } price = np.random.uniform(*base_pricesAI Shorts) quantity = np.random.choice(, p=) customer_segment = np.random.choice(['Premium', 'Standard', 'Budget'], p=) age_group = np.random.choice(['18-25', '26-35', '36-45', '46-55', '56+']) region = np.random.choice(['North', 'South', 'East', 'West', 'Central']) month = date.month seasonal_factor = 1.0 if month in : seasonal_factor = 1.5 elif month in : seasonal_factor = 1.2 revenue = price * quantity * seasonal_factor discount = np.random.choice(, p=) marketing_channel = np.random.choice(['Organic', 'Social Media', 'Email', 'Paid Ads']) base_satisfaction = 4.0 if customer_segment == 'Premium': base_satisfaction += 0.5 if discount > 15: base_satisfaction += 0.3 satisfaction = np.clip(base_satisfaction + np.random.normal(0, 0.5), 1, 5) data.append({ 'Date': date, 'Category': category, 'Product': product, 'Price': round(price, 2), 'Quantity': quantity, 'Revenue': round(revenue, 2), 'Customer_Segment': customer_segment, 'Age_Group': age_group, 'Region': region, 'Discount_%': discount, 'Marketing_Channel': marketing_channel, 'Customer_Satisfaction': round(satisfaction, 2), 'Month': date.strftime('%B'), 'Year': date.year, 'Quarter': f'Q{(date.month-1)//3 + 1}' }) df = pd.DataFrame(data) df['Profit_Margin'] = round(df['Revenue'] * (1 - df['Discount_%']/100) * 0.3, 2) df['Days_Since_Start'] = (df['Date'] - df['Date'].min()).dt.days return df…

Graphic Walker 소개 Graphic Walker는 Tableau 스타일의 데이터를 시각화하면서도 가볍고 손쉽게 앱에 내장할 수 있는 React 컴포넌트 기반 오픈소스 도구로, 드래그-앤-드롭(Drag-and-Drop) 인터페이스와 자연어 질의(Natural Language Query)를 기반으로 데이터를 분석하고 패턴을 시각화할 수 있습니다. 주요 목적은 무거운 BI 플랫폼이 아니라 쉽게 임베드할 수 있는 경량 플러그인으로서 데이터 탐색을 지원하는 것입니다. Graphic Walker의 주요 특징 직관적인 인터페이스: 드래그 앤 드롭을 통해 데이터 시각화 생성. 자동 데이터 분석: 데이터 패턴의 원인을 설명하거나 분석 결과를 제안. 웹 환경에서의 활용성: Web Worker를 활용한 클라이언트 사이드 데이터 처리. 다양한 시각화 지원: 차트 유형(바, 라인, 스캐터, 지역 등) 및 그래픽 렌더링 구성 가능. 사용자 지정 가능: React 컴포넌트로 앱에 쉽게 통합 ...

こんにちは。 株式会社アイデミーでデータサイエンティストの米倉です。 前回ご紹介したPyGWalkerに関連して、RATH（ラース、と読む）というツールが存在することを知りました。 以下は前回記事のリンクです。 https://zenn.dev/aidemy/articles/3aeea1470f1535…

目次はじめにMeetup概要1:OSSに貢献する休憩2:PyGWalker×Streamlit3:共同開発者「Amanda Kelly」からのメッセージまとめはじめにこの投稿はアイ…

An issue in Kanaries Inc Pygwalker before v.0.4.9.9 allows a remote attacker to obtain sensitive information and execute arbitrary code via the redirect_path parameter of the login redirection function.

PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis - Kanaries/pygwalker

PyGWalker: Turn your pandas dataframe into a Tableau-style User Interface for visual analysis - GitHub - Kanaries/pygwalker: PyGWalker: Turn your pandas dataframe into a Tableau-style User Interfac...