Working with DataFrames and Series in Python

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In this article, we will explore how to modify the value of a column in a Pandas DataFrame based on the values in another column using Python.

Problem Statement

We have a DataFrame original_data_set with several columns. Some of these columns end with _mean, while others end with _sum. We want to change the value of the column that ends with _sum into NaN if the corresponding column that ends with _mean is also NaN.

The Challenge

The question poses a challenge because we have a large number of variables, and using a loop might not be the most efficient solution. However, let’s explore some possible approaches to solve this problem.

Approach 1: Using np.where Statement

One approach is to use the np.where statement in NumPy. We can create conditions where if the value in the _mean column is NaN, we set the corresponding value in the _sum column to NaN as well.

holder_set = original_data_set.copy()

for number in range(1,3):
    holder_set['usage_{}_sum'.format(number)] = (
        holder_set['usage_{}_sum'.format(number)]
        .where(holder_set['usage_{}_mean'.format(number)] == np.nan, np.nan
              )
                                                )

However, this approach has a major flaw: it changes all values in the _sum column to NaN regardless of whether the corresponding value in the _mean column is NaN or not. We can see this happening in the provided example output.

Approach 2: Using np.select Statement

Another approach is to use the np.select statement in NumPy. However, in this case, it seems that this approach does not have any effect on the DataFrame.

holder_set = original_data_set.copy()

for number in range(1,3):
    conditions = [holder_set['usage_{}_mean'.format(number)]==np.nan]
    outcome = [np.nan]
    holder_set['usage_{}_sum'.format(number)] = np.select(conditions, outcome, default=holder_set['usage_{}_sum'.format(number)])

Approach 3: Using loc Statement

We can also use the loc statement to achieve our goal. However, in this case, it seems that this approach does not have any effect on the DataFrame either.

holder_set = original_data_set.copy()

for number in range(1,3):
    holder_set.loc[holder_set['usage_{}_mean'.format(number)]==np.nan, 'usage_{}_sum'.format(number)] = 12

Approach 4: Using Series.str.endswith and loc Statements Together

After some research and exploration of the Pandas documentation, we found a way to solve this problem using the Series.str.endswith method. This approach uses vectorized operations to filter out columns that do not end with _mean, then modifies those values in the corresponding _sum columns.

for col in df.columns[df.columns.str.endswith('_mean')]:
    df.loc[df[col].isna(), col.rstrip('_mean') + '_sum'] = np.nan

Explanation and Example Use Case

In this solution, we first find all columns that end with _mean. Then, for each column that ends with _mean, we use the loc statement to filter out rows where the value in that column is NaN. If such a row exists, we set the corresponding value in the _sum column to NaN.

Let’s apply this solution to our example DataFrame:

import pandas as pd
import numpy as np

# Create original DataFrame
df = pd.DataFrame({'customerId': [1, 2], 'usage_1_sum': [100, 200], 'usage_1_mean':[np.nan,100], 'usage_2_sum':[420,330], 'usage_2_mean':[45,np.nan]})

# Apply the solution to the DataFrame
for col in df.columns[df.columns.str.endswith('_mean')]:
    df.loc[df[col].isna(), col.rstrip('_mean') + '_sum'] = np.nan

print(df)

In this example, we create a DataFrame df with columns that end with _mean and _sum. We then apply the solution to these columns by iterating over each column that ends with _mean, filtering out rows where the value in that column is NaN, and setting the corresponding value in the _sum column to NaN.

The resulting output of this code will be:

   customerId  usage_1_sum  usage_1_mean  usage_2_sum  usage_2_mean
0           1          NaN           NaN        420.0          45.0
1           2        200.0         100.0          NaN           NaN

This output shows that the value in the _sum column for row 1 has been successfully changed to NaN based on the corresponding value in the _mean column.

In conclusion, this solution demonstrates how to use Pandas’ vectorized operations and methods like Series.str.endswith to solve common data manipulation problems.

Last modified on 2024-05-29