Optimize DataFrame Memory by Downcasting and Category Dtypes in Pandas

Question

A DataFrame consumes 4GB of RAM. Demonstrate how to inspect memory usage with `df.info(memory_usage='deep')`, downcast 64-bit numbers to 32/16-bit, and convert repeated string columns into `category` dtypes.

Accepted Answer

Optimal Solution:
import pandas as pd

def optimize_memory(df: pd.DataFrame) -> pd.DataFrame:
    for col in df.columns:
        col_type = df[col].dtype
        
        # Downcast floats
        if col_type == 'float64':
            df[col] = pd.to_numeric(df[col], downcast='float')
        # Downcast integers
        elif col_type == 'int64':
            df[col] = pd.to_numeric(df[col], downcast='integer')
        # Convert low-cardinality strings to category
        elif col_type == 'object' and df[col].nunique() / len(df) < 0.5:
            df[col] = df[col].astype('category')
            
    return df

Technical Explanation:
64-bit ints and floats consume 8 bytes per cell. Downcasting to int16 or float32 cuts size by 50-75%. Converting low-cardinality strings to categories replaces raw string pointers with small integer lookup codes.

user_id	gender	age	rating
1001	Male	28	4.5
1002	Female	34	4.8