Efficient Count of Distinct Values Across Columns of a DataFrame, Grouped by Rows

In this article, we’ll explore the most efficient way to count distinct values across columns of a DataFrame, grouped by rows in Python using the pandas library.

Introduction

The problem at hand is to find the number of distinct values for each row in a DataFrame, where all columns have the same data type. This can be achieved by various methods, including using the nunique function provided by pandas, applying NumPy reduction functions, or using loops and bitwise operations. In this article, we’ll delve into each approach, discuss their performance, and explore alternative solutions.

Background

The pandas.DataFrame.apply function is a powerful tool for applying arbitrary functions to each row or column of a DataFrame. However, it can be slow for large DataFrames due to the overhead of creating new objects. The nunique function is optimized for large Series (one-dimensional arrays) and provides fast counting capabilities.

Original Attempts

Our initial attempts involved using the apply function with the lambda function to count unique values in each column:

%timeit -n20 testDf.iloc[:,1:].apply(lambda x: len(x.unique()), axis=1)
# 20 loops, best of 3: 721 ms per loop *best so far*

We also tried using np.apply_along_axis with a similar lambda function:

%timeit -n20 np.apply_along_axis(lambda x: len(np.unique(x)), axis=1, arr = testDf.iloc[:,1:].values)
# 20 loops, best of 3: 1.04 s per loop

However, these approaches were slower than expected.

Optimized Solution

The optimized solution involves using the nunique function in combination with the stack and groupby functions:

%timeit testDf.iloc[:, 1:].stack().groupby(level=0).nunique()
10 loops, best of 3: 46.8 ms per loop

%timeit pd.melt(testDf, id_vars ='custId').groupby('custId').value.nunique()
10 loops, best of 3: 47.6 ms per loop

These approaches provide fast counting capabilities and are more efficient than the original attempts.

Alternative Solutions

For smaller DataFrames with a fixed number of columns, using a simple loop to compare each column against others can be faster:

%%timeit
testDf['nunique'] = 1
for col1, col2 in zip(days, days[1:]):
    testDf['nunique'] += ~((testDf[[col2]].values == testDf.ix[:, 'day1':col1].values)).any(axis=1)
100 loops, best of 3: 3.83 ms per loop

However, as the number of columns increases, this approach becomes slower.

Performance Comparison

Here’s a summary of performance comparisons between different approaches:

As expected, the loop-based approach becomes slower with increasing numbers of columns.

Conclusion

In conclusion, for efficient counting of distinct values across columns of a DataFrame, grouped by rows in Python, using the nunique function combined with stack and groupby functions is recommended. For smaller DataFrames or fixed numbers of columns, using a simple loop with bitwise operations can be faster.

Example Use Case

import pandas as pd
import numpy as np

# Generate sample DataFrame
np.random.seed(0)
data = np.random.choice(['A', 'B', 'C'], size=(10000, 3))
df = pd.DataFrame(data, columns=['day1', 'day2', 'day3'])

# Count distinct values across columns
print(df.iloc[:, 1:].stack().groupby(level=0).nunique())

This example demonstrates how to efficiently count distinct values across columns of a DataFrame using the optimized solution.

Last modified on 2023-12-26