Pandas memory optimization tips

pandas is a popular python library for data manipulation and analysis. however, working with large datasets in pandas can lead to high memory usage. in this tutorial, we will explore some memory optimization tips to efficiently manage memory usage when working with pandas dataframes.

1. use the correct data types: one of the most effective ways to reduce memory usage in pandas is to use the appropriate data types for your columns. for example, using int8 instead of int64 for integer columns, or using category data type for columns with a limited number of unique values can significantly reduce memory usage.

2. convert object data type to categorical: if a column contains repetitive values, converting it to a categorical data type can reduce memory usage. categorical data type stores unique values only once and references them, saving memory.

5. delete unnecessary columns: remove columns that are not needed for your analysis to free up memory. use the `drop()` function to delete columns from the dataframe.

here is an example code demonstrating some of these memory optimization tips:

by following these memory optimization tips, you can efficiently manage memory usage when working with large datasets in pandas.

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