How do I use the "Generate Python Pandas Code for Data Analysis" prompt?

Copy the prompt template, fill in the variables (TASK_DESCRIPTION, DATASET_NAME, COLUMN_NAMES, DATA_TYPES, DATASET_SIZE, SPECIFIC_REQUIREMENTS), and paste it into ChatGPT, Claude, or Gemini.

What AI tools work with this data prompt?

This prompt works with chatgpt, claude, gemini. Simply copy and paste the template into any of these AI assistants.

Tips for getting the best results?

Be specific about your data structure and column names - this helps generate more accurate code Mention any data quality issues you're aware of so the code includes appropriate handling Specify your desired output format (CSV, Excel, visualization) to get complete solutions Include performance requirements if working with large datasets - this will influence the suggested approach Ask for code explanations if you want to understand the pandas methods being used

data intermediate

Generate Python Pandas Code for Data Analysis

Generate clean, efficient Python pandas code for data analysis tasks. Perfect for intermediate data analysts and developers working with DataFrames.

Works with: chatgptclaudegemini

Prompt Template

You are an expert Python data analyst specializing in pandas. Write clean, efficient, and well-commented pandas code to accomplish the following data analysis task. Task Description: [TASK_DESCRIPTION] Dataset Information: - Dataset: [DATASET_NAME] - Key columns: [COLUMN_NAMES] - Data types: [DATA_TYPES] - Dataset size: [DATASET_SIZE] Specific Requirements: [SPECIFIC_REQUIREMENTS] Please provide: 1. Complete, executable pandas code with proper imports 2. Clear comments explaining each step 3. Error handling where appropriate 4. Performance considerations if relevant 5. Brief explanation of the approach 6. Sample output or visualization code if applicable Code should follow these standards: - Use descriptive variable names - Include data validation steps - Optimize for readability and maintainability - Handle common edge cases (missing values, data type issues) - Use appropriate pandas methods for the task If the task involves multiple steps, break it down into logical sections with clear headings in comments.

Variables to Customize

[TASK_DESCRIPTION]

The specific data analysis task you want to accomplish

Example: Calculate monthly sales trends and identify the top 10 products by revenue, including seasonal analysis

[DATASET_NAME]

Name or description of your dataset

Example: e-commerce sales data (sales_data.csv)

[COLUMN_NAMES]

The main columns in your dataset that are relevant to the analysis

Example: product_id, product_name, sale_date, quantity, unit_price, category, customer_id

[DATA_TYPES]

Data types of the key columns

Example: product_id (int), product_name (str), sale_date (datetime), quantity (int), unit_price (float)

[DATASET_SIZE]

Approximate size of your dataset

Example: ~50,000 rows, 2 years of data

[SPECIFIC_REQUIREMENTS]

Any specific requirements, constraints, or output format needed

Example: Group by month and product category, handle missing values by forward-fill, export results to Excel with multiple sheets

Example Output

```python import pandas as pd import numpy as np from datetime import datetime import matplotlib.pyplot as plt # Load and validate the dataset df = pd.read_csv('sales_data.csv') print(f"Dataset loaded: {df.shape[0]} rows, {df.shape[1]} columns") # Data preprocessing df['sale_date'] = pd.to_datetime(df['sale_date']) df['revenue'] = df['quantity'] * df['unit_price'] df['month'] = df['sale_date'].dt.to_period('M') # Handle missing values df['unit_price'] = df['unit_price'].fillna(method='ffill') print(f"Missing values handled: {df.isnull().sum().sum()} remaining nulls") # Monthly sales trends analysis monthly_sales = df.groupby('month').agg({ 'revenue': 'sum', 'quantity': 'sum', 'product_id': 'nunique' }).rename(columns={'product_id': 'unique_products'}) # Top 10 products by revenue top_products = df.groupby(['product_name', 'category'])['revenue'].sum()\ .reset_index()\ .sort_values('revenue', ascending=False)\ .head(10) # Seasonal analysis df['season'] = df['sale_date'].dt.month.map({ 12: 'Winter', 1: 'Winter', 2: 'Winter', 3: 'Spring', 4: 'Spring', 5: 'Spring', 6: 'Summer', 7: 'Summer', 8: 'Summer', 9: 'Fall', 10: 'Fall', 11: 'Fall' }) seasonal_trends = df.groupby(['season', 'category'])['revenue'].sum().unstack(fill_value=0) print("Analysis complete. Results ready for export.") ```

Pro Tips for Best Results

Be specific about your data structure and column names - this helps generate more accurate code
Mention any data quality issues you're aware of so the code includes appropriate handling
Specify your desired output format (CSV, Excel, visualization) to get complete solutions
Include performance requirements if working with large datasets - this will influence the suggested approach
Ask for code explanations if you want to understand the pandas methods being used