Filling Missing Values in Categorical Data based on Other Columns

As a data analyst or scientist, you’ve likely encountered situations where your dataset contains missing values, making it challenging to perform analysis or modeling. In this article, we’ll explore how to fill missing values in categorical data based on other columns using Python and the Pandas library.

Understanding Missing Values

Before diving into filling missing values, let’s understand what they represent. Missing values can arise due to various reasons such as:

• Data entry errors
• Non-response or incomplete information
• Measurement errors
• Data quality issues

Missing values can have a significant impact on the accuracy of your analysis and models.

Exploring Pandas and DataFrames

To fill missing values in categorical data, we’ll use Python’s Pandas library. A Pandas DataFrame is a two-dimensional table of data with rows and columns. It provides various methods for manipulating and analyzing data.

Here’s an example of creating a DataFrame:

import pandas as pd

# Create a sample DataFrame
data = {
    'type': ['food', 'food', 'food', 'drink', 'drink'],
    'price': [19.0, 12.0, 21.0, 21.0, 10.5],
    'product': ['apple', 'egg', None, 'wine', 'soda']
}

df = pd.DataFrame(data)

print(df)

Displaying the DataFrame with Missing Values

Let’s display the DataFrame with missing values:

import pandas as pd

# Create a sample DataFrame
data = {
    'type': ['food', 'food', 'food', 'drink', 'drink'],
    'price': [19.0, 12.0, 21.0, 21.0, 10.5],
    'product': ['apple', 'egg', None, 'wine', 'soda']
}

df = pd.DataFrame(data)

# Display the DataFrame with missing values
print(df)

This will output:

Using Merge as of to Fill Missing Values

One approach to fill missing values in categorical data is by using the merge_asof function from Pandas.

The idea is to create a temporary DataFrame that contains all the rows from the original DataFrame, along with their corresponding prices and product types. We’ll then use this temporary DataFrame to find the closest match for each row with a missing value.

Here’s an example of how you can do this:

import pandas as pd

# Create a sample DataFrame
data = {
    'type': ['food', 'food', 'food', 'drink', 'drink'],
    'price': [19.0, 12.0, 21.0, 21.0, 10.5],
    'product': ['apple', 'egg', None, 'wine', 'soda']
}

df = pd.DataFrame(data)

# Reset the index and sort by price
tmp = df.reset_index().sort_values(by='price')

# Find rows with missing values in product
m = df['product'].isna()

# Merge as of to find closest match for each row with a missing value
s = (pd.merge_asof(tmp.loc[m, ['type', 'index', 'price']],
                   tmp.loc[~m, ['type', 'price', 'product']],
                   on='price', by='type', direction='nearest')
       .set_index('index')['product']
    )

# Fill missing values with the closest match
df.loc[s.index, 'product'] = s

print(df)

This will output:

Understanding the Code

Here’s a breakdown of what the code is doing:

• We first create a temporary DataFrame tmp that contains all the rows from the original DataFrame, along with their corresponding prices and product types.
• We then find rows with missing values in the product column using the isna() function.
• Next, we use the merge_asof function to merge the rows with missing values with the rows without missing values based on price. The on='price' parameter specifies that we want to match on the ‘price’ column, and the by='type' parameter specifies that we want to match based on the ’type’ column.
• We then use the set_index('index') function to set the index of the merged DataFrame back to the original index.
• Finally, we fill missing values with the closest match using the resulting product column.

Conclusion

In this article, we explored how to fill missing values in categorical data based on other columns using Python and the Pandas library. We discussed various approaches to handling missing values, including using merge as of to find the closest match for each row with a missing value.

Last modified on 2024-09-05