Sorting Pandas Columns by Number of Unique Groups

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In this article, we will explore how to sort the columns of a pandas DataFrame based on the number of unique groups. We’ll dive into different approaches and discuss their strengths and weaknesses.

Understanding the Problem

The problem at hand is to sort the columns of a pandas DataFrame such that the columns with the fewest unique categories come first. This can be particularly useful when dealing with categorical data, where some categories may appear more frequently than others.

Approach 1: Using nunique() and Indexing

One way to achieve this is by using the nunique() method, which returns a Series of unique values per column. We can sort these values and use them as an index to reorder our DataFrame.

u = df.nunique().sort_values().index
df[u]

This approach works well for small DataFrames, but it may not be the most efficient way to solve this problem for larger datasets.

Approach 2: Using nunique() and Assignment

Another approach is to call nunique() directly on each column and use the result as an index to reorder our DataFrame. This approach avoids calling nunique() multiple times, which can improve performance.

u = df.nunique().sort_values()
df = df[u]

This approach is similar to the previous one but is more concise and easier to read.

Comparison of Approaches

Let’s compare the two approaches in terms of performance and readability.

In general, Approach 2 is a better choice for larger datasets because it avoids calling nunique() multiple times.

Example Use Case

Suppose we have a DataFrame with 80 columns of categorical data. We want to sort the columns by the number of unique categories.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'LotConfig': ['Inside', 'FR2', 'Corner'],
    'Street': ['Pave', 'Pave', 'Grvl'],
    'MSZoning': ['RL', 'RL', 'RL']
})

# Sort columns by number of unique categories
u = df.nunique().sort_values()
df = df[u]

print(df)

This code creates a sample DataFrame with 80 columns of categorical data and sorts the columns by the number of unique categories.

Conclusion

In this article, we explored different approaches to sorting pandas DataFrame columns based on the number of unique groups. We discussed the strengths and weaknesses of each approach and provided example use cases. Ultimately, Approach 2 is a better choice for larger datasets because it avoids calling nunique() multiple times.

Additional Tips and Variations

• When dealing with large datasets, consider using nunique() on a subset of data to reduce memory usage.

• If you need to sort columns in descending order (i.e., fewest unique categories first), use the following code:

u = df.nunique().sort_values(ascending=False)

*   To sort columns by the number of unique categories and then alphabetically, use the following code:

    ```markdown
u = df.nunique().sort_values()
df = df[u].sort_index()

Last modified on 2023-09-20