Pandas Cheat Sheet

Pandas Cheat Sheet

Let me give you a quick reference for the most common Pandas operations. Bookmark this page — you'll come back to it constantly.

Reading & Writing Data

df = pd.read_csv('file.csv')
df = pd.read_excel('file.xlsx')
df = pd.read_json('file.json')

df.to_csv('output.csv', index=False)
df.to_excel('output.xlsx', index=False)
    

Quick Overview

df.head()          # First 5 rows
df.tail()          # Last 5 rows
df.info()          # Column types, non-null counts
df.describe()      # Statistical summary
df.shape           # (rows, columns)
df.columns         # Column names
df.dtypes          # Data types per column
    

Selection

df['column']              # Single column
df[['col1', 'col2']]     # Multiple columns
df.iloc[0:5]              # Rows by position
df.loc[0:5]               # Rows by label
df[df['col'] > 10]        # Conditional filter
    

Data Cleaning

df.dropna()               # Remove missing values
df.fillna(0)              # Fill missing with 0
df.drop_duplicates()      # Remove duplicates
df.rename(columns={'old': 'new'})
df['col'] = df['col'].astype(int)
    

Aggregation & Merging

df.groupby('col').agg({'val': 'mean'})
pd.merge(df1, df2, on='key')
pd.concat([df1, df2])     # Stack DataFrames
    

One thing that confused me at first was the difference between `merge` and `concat`. `merge` is for combining on a key column (like SQL joins). `concat` is for stacking DataFrames on top of each other.

Key Takeaways

• Keep this cheat sheet handy for quick reference
• Reading/writing data is just one function call away
• Use `info()` and `describe()` for quick data overviews
• Master selection, cleaning, and aggregation to handle any data task