ssrjkk Pandas
Data manipulation and analysis library for Python.
Quick Start
import pandas as pd
df = pd.read_csv('data.csv')
df.info(); df.describe(); df['category'].value_counts()
Selection & Grouping
df[df['price'] > 100] # Filter
df.groupby('category').agg({'price': ['mean', 'std'], 'quantity': 'sum'})
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True).resample('M').mean() # Time series
When to Use
- CSV/Excel data analysis
- Data cleaning and transformation
- Time series analysis
- ETL pipeline development
Validation
- DataFrame operations execute correctly
- GroupBy aggregations return expected values
- Missing values handled properly