How Can Pandas Efficiently Handle Large Datasets That Don't Fit in Memory?

Handling Large Datasets in Pandas with Workflows

Many real-world applications involve datasets too large to fit in memory. Pandas provides out-of-core support for effectively handling such data. This article discusses best practices for accomplishing core workflows using Pandas.

1. Loading Flat Files into a Permanent, On-Disk Database Structure

Use HDFStore to store large datasets on disk. Iterate through files and append them to HDFStore, using chunk-by-chunk reading to avoid memory issues. Define a group map linking field groups and data columns for efficient selection later.

2. Querying the Database to Retrieve Data

To retrieve data for Pandas data structures, select a group from the HDFStore based on the group map. Optionally, specify desired columns or apply filtering criteria using 'where'.

3. Updating the Database after Manipulating Pieces in Pandas

Create new columns by performing operations on selected columns. To add these new columns to the database, create a new group in the HDFStore and append the new columns, ensuring data column definition.

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