Understanding the Issue with Missing Values in Pandas DataFrames

When working with pandas DataFrames, it’s not uncommon to encounter missing values that need to be handled. In this article, we’ll explore a specific scenario where creating a new variable with missing values doesn’t quite behave as expected.

Background on Missing Values in Pandas

In pandas, missing values are represented using the NaN (Not a Number) value. When working with DataFrames, it’s essential to understand how these values are handled and manipulated.

Pandas provides several ways to detect missing values, including:

• Using the .isnull() method
• Checking for NaN values directly

For example:

import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])

print(df['age'].isnull().any())  # Output: False

In this example, we’re checking if there are any missing values in the age column. Since all values are present, the output is False.

Creating Missing Values with np.where

The original code snippet attempts to create a new variable called my_var based on conditions involving the age column:

df['my_var'] = np.where((df['age']>=36) & (df['age']<=42), 'yes',np.where((df['age']>=52) & (df['age']<=73),'no',np.NaN))

However, this approach doesn’t quite produce the expected results. Instead of creating missing values when the conditions are not met, it creates a new variable with non-NaN values.

Let’s take a closer look at what happens in this code:

• df['age']>=36 returns a boolean mask indicating which rows meet the condition.
• df['age']<=42 returns another boolean mask.
• The bitwise AND operator (&) combines these two masks to produce a new mask where both conditions are met.

The first np.where call then uses this combined mask to assign either 'yes' or 'no' values to the my_var variable.

However, when the conditions are not met (i.e., df['age'] is outside the range 36-42), the second np.where call assigns NaN values instead of missing values.

To achieve the desired behavior, we can use a different approach that takes advantage of numpy’s selection capabilities.

Using np.select for Efficient Selection

One way to efficiently create missing values when conditions are not met is by using numpy.select. This function allows us to specify multiple conditions and corresponding values, which can be used to select rows from the DataFrame.

Here’s an example of how we can use np.select to achieve our desired outcome:

import pandas as pd
import numpy as np

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])

conditions = [df['age'].between(36, 42), df['age'].between(52, 73)]
values = ['yes', 'no']
default_value = None

df['my_var'] = np.select(conditions, values, default_value)

In this code:

• We define two conditions: df['age'].between(36, 42) and df['age'].between(52, 73).
• We specify the corresponding values to be assigned when each condition is met ('yes' and 'no', respectively).
• We also set a default value (None) that will be used when neither condition is met.
• Finally, we use np.select to apply these conditions and assign the corresponding values to the my_var variable.

Note that in this example, the default_value parameter specifies what should happen when none of the conditions are true. By setting it to None, we’re effectively creating missing values (i.e., NaN) instead of non-NaN values.

Let’s see how this code compares to our original attempt:

# Original attempt
df['my_var'] = np.where((df['age']>=36) & (df['age']<=42), 'yes',np.where((df['age']>=52) & (df['age']<=73),'no',np.NaN))

print(df['my_var'].isnull().any())  # Output: False

In contrast, our np.select approach correctly produces missing values when the conditions are not met:

# np.select approach
conditions = [df['age'].between(36, 42), df['age'].between(52, 73)]
values = ['yes', 'no']
default_value = None

df['my_var'] = np.select(conditions, values, default_value)

print(df['my_var'].isnull().any())  # Output: True

By using np.select, we can efficiently create missing values when conditions are not met, which is exactly what our original code snippet failed to achieve.

Last modified on 2025-04-09