Pandas Reorder Categories Working with NaN
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When working with categorical data in pandas, it’s common to need to reorder the categories. However, when dealing with missing or null values (NaN), things can get a bit tricky. In this article, we’ll explore how to use pandas’ reorder_categories method along with other techniques to work with NaN values in your categorical column.
Understanding Pandas Categorical Data
Before we dive into the details of working with NaN values, let’s quickly review what pandas categorical data is all about. When you create a categorical column using astype('category'), pandas creates an object-oriented representation of the categories, which allows for easy manipulation and analysis.
Here’s an example:
import pandas as pd
# Create a sample dataframe with a categorical column
df = pd.DataFrame({'Category': ['Moderate', 'Liberal', 'Somewhat Conservative', 'Somewhat liberal', 'Very Liberal', 'Very Conservative', 'Conservative', None]})
# Convert the 'Category' column to categorical data type
df['Category'] = df['Category'].astype('category')
Working with NaN Values in Categorical Data
When you try to reorder categories that include NaN values, pandas will throw an error because NaN is not a valid category. This makes sense, as NaN represents missing or null data, which doesn’t fit into any specific category.
Here’s an example:
import pandas as pd
# Create a sample dataframe with a categorical column and NaN value
df = pd.DataFrame({'Category': ['Moderate', 'Liberal', 'Somewhat Conservative', 'Somewhat liberal', 'Very Liberal', 'Very Conservative', 'Conservative', None]})
# Try to reorder categories that include NaN values
order = ['Very Liberal', 'Liberal', 'Somewhat liberal', 'Moderate', 'Somewhat Conservative', 'Conservative', 'Very Conservative']
df['Category'] = df['Category'].astype('category')
df['Category'] = df['Category'].cat.reorder_categories(order, ordered=True)
# This will throw an error because NaN is not a valid category
Solution: Using add_categories to Add Missing Non-NA Categories
One way to work around this issue is to use the add_categories method to add missing non-NA categories before reordering. This approach ensures that all categories, including NA/NaN values, are included in the ordering process.
Here’s an example:
import pandas as pd
import numpy as np
# Create a sample dataframe with a categorical column and NaN value
df = pd.DataFrame({'Category': ['Moderate', 'Liberal', 'Somewhat Conservative', 'Somewhat liberal', 'Very Liberal', 'Very Conservative', 'Conservative', None]})
# Define the order of categories
order = ['Very Liberal', 'Liberal', 'Somewhat liberal', 'Moderate', 'Somewhat Conservative', 'Conservative', 'Very Conservative']
# Add missing non-NA categories using add_categories
df['Category'] = df['Category'].astype('category')
df['Category'] = df['Category'].cat.add_categories(set(order).difference(df['Category'].cat.categories))
# Reorder categories
df['Category'] = df['Category'].cat.reorder_categories(order, ordered=True)
Solution: Using sort_values to Specify NaN Position
Another approach is to use the sort_values method with the na_position='last' parameter. This allows you to specify a position for NaN values in the ordering process.
Here’s an example:
import pandas as pd
# Create a sample dataframe with a categorical column and NaN value
df = pd.DataFrame({'Category': ['Moderate', 'Liberal', 'Somewhat Conservative', 'Somewhat liberal', 'Very Liberal', 'Very Conservative', 'Conservative', None]})
# Define the order of categories
order = ['Very Liberal', 'Liberal', 'Somewhat liberal', 'Moderate', 'Somewhat Conservative', 'Conservative', 'Very Conservative']
# Sort values with NaN at the end
df['Category'] = df['Category'].astype('category')
df['Category'] = df['Category'].sort_values(na_position='last')
# Reorder categories
df['Category'] = df['Category'].cat.reorder_categories(order, ordered=True)
Using Placeholders for Orderable NaN Values
If you really want an orderable NaN value, you can use a placeholder string like 'NAN' and set it as a category. This approach allows you to keep the NaN values in the ordering process while still making them comparable.
Here’s an example:
import pandas as pd
# Create a sample dataframe with a categorical column and NaN value
df = pd.DataFrame({'Category': ['Moderate', 'Liberal', 'Somewhat Conservative', 'Somewhat liberal', 'Very Liberal', 'Very Conservative', 'Conservative']})
# Define the order of categories and use a placeholder for NaN values
order = ['Very Liberal', 'Liberal', 'Somewhat liberal', 'Moderate', 'Somewhat Conservative', 'Conservative', 'NAN']
# Add missing non-NA categories using add_categories
df['Category'] = df['Category'].astype('category')
df['Category'] = df['Category'].cat.add_categories(set(order).difference(df['Category'].cat.categories))
# Reorder categories
df['Category'] = df['Category'].cat.reorder_categories(order, ordered=True)
Conclusion
Working with NaN values in pandas categorical data can be tricky, but there are ways to overcome these challenges. By using techniques like adding missing non-NA categories and specifying NaN positions, you can successfully reorder your categories while keeping your data consistent and accurate.
In this article, we’ve explored how to use pandas’ reorder_categories method along with other techniques to work with NaN values in your categorical column. We’ve covered topics such as adding missing non-NA categories using add_categories, specifying NaN positions using sort_values, and even using placeholders for orderable NaN values.
Whether you’re working with a small dataset or a large-scale application, mastering these techniques will help you to efficiently and effectively manage your data while ensuring that it remains accurate and consistent.
Last modified on 2024-06-23