How can you reduce memory usage when working with large datasets in pandas?

2 Category Usage

When working with columns that have a limited set of possible values, such as gender or country names, converting them to categorical data types can save memory. Categorical types store the unique values in a column as a dictionary and then reference them using integer codes. This can be done using the astype('category') method. This approach is particularly effective for columns with a high ratio of repeated values to unique values, as the memory footprint becomes much smaller than storing each value individually.

3 Load Less Data

Consider whether you need all the data at once. If not, use chunking by reading only a subset of rows at a time with the read_csv() function's chunksize parameter. Another approach is to selectively load columns that are relevant to your analysis by specifying the usecols parameter. This prevents loading unnecessary data into memory, which can make a significant difference in resource utilization.

4 Sparse Data

Sparse data structures store only non-zero or non-null values, which can lead to massive memory savings when dealing with datasets that contain many such elements. Pandas provides the SparseDataFrame and SparseSeries to handle this kind of data efficiently. By default, these structures only record the locations and values of non-empty cells, which can dramatically reduce memory usage if your dataset is suitable for this format.

6 Data Iteration

Sometimes, you can avoid loading large datasets into memory altogether by iterating through the data and processing it in smaller chunks. Pandas allows for iteration using the iterrows() , itertuples() , or groupby() methods. This technique is especially useful when performing operations that don't require comparing or aggregating data across rows. By processing data incrementally, you maintain a low memory profile throughout your analysis.