How can I manage memory usage in Pandas DataFrames?
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How can I manage memory usage in Pandas DataFrames?
You can manage memory usage in Pandas DataFrames by using data types efficiently, dropping unnecessary columns, and using chunking.
What are some specific data types I should consider?
Consider using 'category' for categorical data, 'float32' instead of 'float64', and 'int32' instead of 'int64' when possible.
How can I drop unnecessary columns?
You can drop columns using the drop() method, like this: df.drop(columns=['column_name'], inplace=True).
What is chunking and how does it help?
Chunking allows you to read large datasets in smaller pieces, reducing memory usage. Use pd.read_csv() with the chunksize parameter.
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