Pandas memory optimization tips

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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.

3. load data in chunks: when working with large datasets, consider loading the data in smaller chunks using the `chunksize` parameter in `pd.read_csv()`. this can help reduce memory usage by processing the data in smaller portions.

4. use `to_numeric()` for numeric columns: if a column contains numeric data but is stored as an object data type, use the `pd.to_numeric()` function to convert it to a numeric data type. this can reduce memory usage and improve performance.

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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