Cheat Sheet for Data Cleaning in Python Pandas

1. Data Overview

import pandas as pd
df = pd.read_csv('data.csv')   # Load CSV
df.head(5)                     # First 5 rows
df.info()                      # Data types and null counts
df.describe()                  # Summary statistics

2. Handling Missing Values and Duplicates

df.isnull().sum()              # Count missing values
df.dropna()                    # Remove rows with nulls
df.fillna(0)                   # Fill missing values with zeros
df.duplicated().sum()          # Count duplicates
df.drop_duplicates()           # Remove duplicates

3. Renaming and Type Conversion

df.rename(columns={'old': 'new'})   # Rename column
df.astype({'col': 'int'})           # Change data type
df.columns.str.strip()              # Remove spaces

4. Filtering and Selection

df.loc[df['col'] > 100]             # Filter by condition
df.iloc[0:5]                        # Select by row index
df['col'].isin(['A', 'B'])          # Filter by values

5. Sorting and Grouping

df.sort_values('col')               # Sort ascending
df.groupby('group')['val'].mean()   # Group and aggregate
df['col'].value_counts()            # Count unique values

6. Column Operations

df['new'] = df['col1'] + df['col2']               # New column
df['col'] = df['col'].apply(lambda x: x*2)        # Apply function
df.drop('col', axis=1)                            # Drop column

7. Saving Cleaned Data

df.to_csv('cleaned.csv', index=False)  # Export to CSV

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