Counting Values in Several Columns

Introduction

In this article, we will explore how to count values in several columns of a pandas DataFrame. The problem at hand is to take a DataFrame with multiple columns and transform it into a long format where each row represents a unique combination of column values. We can then use the value_counts function from pandas to count the occurrences of each value in each column.

Problem Statement

Given a DataFrame with multiple columns, we want to transform it into a long format where each row represents a unique combination of column values and count the number of occurrences for each value in each column.

Test Data

Let’s start by creating some test data:

# Test data
df = pd.DataFrame({'date': ['05-24-2015','06-24-2015'],
             'KPI_01': ['green','orange'],
             'KPI_02': ['green','red'],
             'KPI_03': ['red',np.nan]
             })
df.set_index('date', inplace=True)

# Transforming to long format
df.reset_index(inplace=True)
long = pd.melt(df, id_vars=['date'])

# Pivoting data
pivoted = pd.pivot_table(long, index='date', columns=['value'], aggfunc='count', fill_value=0)
# Dropping unnecessary level
pivoted.columns = pivoted.columns.droplevel()

Original DataFrame

            KPI_01 KPI_02 KPI_03
date                            
2015-05-24   green  green    red
2015-06-24  orange    red    NaN

Desired Output

We want the output to be:

value       green  orange  red
date                          
2015-05-24      2       0    1
2015-06-24      0       1    1

Current Solution

One way to solve this problem is by using the apply function from pandas and then value_counts. However, we should note that this approach can be slow for large DataFrames.

# Current solution
df.apply(pd.Series.value_counts(axis=1)).fillna(0)

Slow Approach

As mentioned earlier, the apply function is not ideal due to its slow performance. This is because it applies a function element-wise across the entire DataFrame.

Why is the apply approach slow?

The apply function in pandas works by iterating over each row of the DataFrame and applying the given function to that row. This can lead to slow performance, especially for large DataFrames.

In our case, we are trying to count the occurrences of each value in multiple columns. The apply function is not designed to handle this operation efficiently.

Alternative Approach

A better approach is to use the value_counts function directly on each column separately. This can be done using a list comprehension:

# Alternative solution
value_counts_list = [df[column].value_counts().fillna(0) for column in df.columns]

This will return a list of Series, where each Series represents the value counts for a specific column.

Combining Value Counts into a DataFrame

We can combine these value counts into a single DataFrame using the pd.concat function:

# Combining value counts into a DataFrame
df_value_counts = pd.DataFrame({column: value_counts_list[i] for i, column in enumerate(df.columns) if df[column].notnull().all()})

This will return a DataFrame where each row represents a unique combination of values from multiple columns.

Full Code Example

Here’s the full code example:

import pandas as pd
import numpy as np

# Test data
df = pd.DataFrame({'date': ['05-24-2015','06-24-2015'],
             'KPI_01': ['green','orange'],
             'KPI_02': ['green','red'],
             'KPI_03': ['red',np.nan]
             })
df.set_index('date', inplace=True)

# Transforming to long format
df.reset_index(inplace=True)
long = pd.melt(df, id_vars=['date'])

# Combining value counts into a DataFrame
value_counts_list = [long[column].value_counts().fillna(0) for column in long.columns if 'value' not in column]
df_value_counts = pd.DataFrame({column: value_counts_list[i] for i, column in enumerate(long.columns) if 'value' not in column})

# Displaying the result
print(df_value_counts)

Conclusion

In this article, we explored how to count values in several columns of a pandas DataFrame. We discussed the original problem and provided two solutions: one using the apply function from pandas and another using list comprehension. The alternative approach is more efficient and scalable for large DataFrames.

We also demonstrated the full code example, which combines value counts into a single DataFrame.

Last modified on 2024-01-04