How Can Pandas Handle Large Datasets That Exceed Available Memory?

Large Data Workflows Using Pandas

When dealing with datasets too large to fit in memory, out-of-core workflows are essential. In this context, we explore best practices for handling large data using pandas.

To efficiently manage large datasets, consider the following best-practice workflow:

1. Loading Flat Files into an On-Disk Database Structure:

   • Utilize HDFStore to store large datasets on disk in a structured format.
   • Define group mappings to organize your tables based on field groupings.
   • Append data to each table in groups, ensuring data columns are defined for fast row subsetting.

2. Querying the Database to Retrieve Data into Pandas Data Structure:

   • Select specific field groupings to efficiently retrieve data.
   • Use a function to seamlessly select and concatenate data from multiple tables.
   • Create indexes on data columns for improved row-subsetting performance.

3. Updating the Database After Manipulating Pieces in Pandas:

   • Create new groups to store new columns created from data manipulations.
   • Ensure data_columns are properly defined in new groups.
   • Enable compression to minimize storage space.

Example:

import pandas as pd

# Group mappings for logical field grouping
group_map = {
    "A": {"fields": ["field_1", "field_2"], "dc": ["field_1"]},
    "B": {"fields": ["field_10"], "dc": ["field_10"]},
    ...
}

# Iterate over flat files and append data to tables
for file in files:
    chunk = pd.read_table(file, chunksize=50000)
    for group, info in group_map.items():
        frame = chunk.reindex(columns=info["fields"], copy=False)
        store.append(group, frame, data_columns=info["dc"])

# Retrieve specific columns
selected_columns = ["field_1", "field_10"]
group_1 = "A"
group_2 = "B"
data = store.select_as_multiple([group_1, group_2], columns=selected_columns)

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