How can I optimize memory usage in Pandas with NumPy?
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How can I optimize memory usage in Pandas with NumPy?
You can optimize memory usage in Pandas by using NumPy data types, reducing the size of your DataFrame, and using efficient operations.
What specific NumPy data types should I use?
Consider using 'float32' instead of 'float64' for floating-point numbers and 'int32' instead of 'int64' for integers.
Are there any functions in Pandas that can help with this?
Yes, you can use the astype() method to change the data types of your DataFrame columns to more memory-efficient types.
What about using categorical data?
Using categorical data can significantly reduce memory usage for columns with a limited number of unique values. You can convert a column to 'category' type.
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