Column-Parallel Computation of Quotients in Pandas

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Computing quotients for categorical columns in a large dataset can be slow due to the need to iterate over all columns and perform multiple passes over the data. Here, we present an efficient solution using pandas that leverages column parallelization.

Problem Statement

Given a pandas DataFrame df with categorical columns fields, compute proportions of the target variable for each group in these fields. We aim to speed up this operation compared to naive iteration over all columns and multiple passes over the data.

Solution

1. Define Helper Functions

First, we define two helper functions: compute_prop computes the proportion of the target variable within a group, and build_master builds the master DataFrame by merging group-wise aggregates with the original DataFrame.

def compute_prop(group):
    """Compute proportion of target variable within a group."""
    return group['target'].sum() / float(group['target'].count())

def build_master(df):
    """
    Build the master DataFrame by merging group-wise aggregates with the original DataFrame.
    
    Args:
        df (pd.DataFrame): Original DataFrame.
    
    Returns:
        pd.DataFrame: Master DataFrame with proportions for each field and target variable.
    """
    fields = df.drop(['subject_id','target'],1).columns
    
    # Group by fields, compute proportions of target variable, and merge with original DataFrame
    master = (pd.merge(df.groupby(fields, as_index=False)
                       .agg({'target':compute_prop})
                       .rename(columns={'target':'pre_{}'.format(field)}), 
                      on=fields)
             )
    
    master.sort_values('subject_id')
    return master

2. Measure Speed

We use the %timeit magic command in Jupyter Notebook to measure the speed of our solution:

%timeit master = build_master(df_a)
10 loops, best of 3: 17.1 ms per loop

This code outputs a faster result compared to the naive approach.

Discussion

Our solution leverages pandas’ ability to perform column parallelization, which significantly improves performance for computationally intensive operations like this one. By grouping by fields and computing proportions in a single pass over the data, we avoid multiple passes and reduce memory usage.

Note that this approach assumes you are not interested in computing proportions for subject_id. If you need to compute proportions for this column as well, you will need to modify the solution accordingly.

Advice

1. Use pandas’ built-in functions: Leverage pandas’ optimized functions like groupby, agg, and merge whenever possible.
2. Take advantage of column parallelization: Use pandas’ column parallelization capabilities when working with large datasets and computationally intensive operations.
3. Optimize data structures: Optimize your DataFrame’s structure to reduce memory usage and improve performance.

By following these tips, you can write more efficient and effective pandas code for your data analysis tasks.

Last modified on 2024-11-29