Efficient Techniques to Sort and Retrieve Sorted Indices in Pandas

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💡 Problem Formulation: When working with data in Pandas, it’s common to need to sort the index of a DataFrame or Series. Besides sorting the data itself, sometimes we need to obtain the indices that would sort the index. Consider a Pandas Series with the index out of order. Our objective is to sort this Series by its index and additionally get the index positions that would sort the Series.

Method 1: Using sort_index() and argsort()

This approach uses the sort_index() method to sort the DataFrame or Series by index and the argsort() function from NumPy to return the indices that would sort the index. sort_index() returns a new object with the index sorted, while np.argsort() gives the indices that would sort an array.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a Series with an unordered index
s = pd.Series(data=[2, 1, 4, 3], index=[3, 1, 2, 0])

# Sorting the Series by index
sorted_series = s.sort_index()

# Getting the indices that would sort the index
sort_indices = np.argsort(s.index)

Output:

sorted_series:
0    3
1    1
2    4
3    2
dtype: int64

sort_indices:
array([3, 1, 2, 0])

This example demonstrates creating a Series with a scrambled index and then sorting it using sort_index(). Afterward, the indices that would sort the original index are obtained using NumPy’s argsort() function applied to the index.

Method 2: Using reset_index() and sort_values()

To sort index values while preserving the original index as a column, you can use reset_index() followed by sort_values(). This technique first resets the index, moving it into a column, and then sorts by that column. It returns a DataFrame with sorted values and original indices.

Here’s an example:

# Resetting index and sorting by the former index
reset_sorted = s.reset_index().sort_values(by='index')

# Retrieving the new order of indices
new_indices = reset_sorted['index'].to_numpy()

Output:

reset_sorted:
   index  0
3      0  3
1      1  1
2      2  4
0      3  2

new_indices:
array([0, 1, 2, 3])

After calling reset_index(), the original index becomes a column in the DataFrame, which allows us to sort by this column using sort_values(). The sort order is conserved in a separate array, new_indices, which gives the positions of the original indices.

Method 3: Using sorted() with a Custom Lambda

Python’s built-in sorted() function can sort indexes with a custom lambda function that extracts the indexes. This is a more manual approach but allows for additional flexibility if needed, such as custom sorting logic.

Here’s an example:

# Sorting the index with a lambda function and sorted()
sorted_indices = sorted(range(len(s.index)), key=lambda k: s.index[k])

# Creating the sorted Series
sorted_series_by_lambda = s.iloc[sorted_indices]

Output:

sorted_indices:
[3, 1, 2, 0]

sorted_series_by_lambda:
0    3
1    1
2    4
3    2
dtype: int64

By using the sorted() function with a lambda, we can specify a custom sorting function—here, one that sorts the indices. Then, we use these sorted indices to rearrange the original Series.

Method 4: Combining Series.index and Series.take()

Another option is to use the take() method, which allows you to sort by indices and preserve the original index’s order. The method take() is used to return the elements in the given indices along an axis.

Here’s an example:

# Getting indices that would sort the index
indices = s.index.argsort()

# Using take() to sort by index
sorted_series = s.take(indices)

Output:

sorted_series:
0    3
1    1
2    4
3    2
dtype: int64

By obtaining the sorted indices with argsort(), we can then apply these to the Series using the take() method. This results in a sorted Series while also giving us access to the sort order through indices.

Bonus One-Liner Method 5: Using pandas.Index.get_indexer()

The pandas.Index.get_indexer() method provides an alternative one-liner to retrieve the order of indices needed to sort the index. It returns an array of index positions that shows where the target index should be inserted to maintain order.

Here’s an example:

# Using get_indexer() for a one-liner solution
sorted_order = s.index.get_indexer(s.index.sort_values())

Output:

sorted_order:
array([3, 1, 2, 0])

This one-liner retrieves the positions where the sorted index values need to be placed. Index.get_indexer() is used on the Series index, comparing it to the sorted index, thus providing the sorted order.

Summary/Discussion

  • Method 1: Using sort_index() and argsort(). Strengths: Direct and utilizes well-known Pandas and NumPy methods. Weaknesses: It involves an additional import and understanding of NumPy.
  • Method 2: Using reset_index() and sort_values(). Strengths: Leverages Pandas’ own methods without extra imports. Weaknesses: Can be less intuitive for those unfamiliar with resetting and sorting indices.
  • Method 3: Using sorted() with custom lambda. Strengths: High customization potential and does not rely on Pandas-specific functionality. Weaknesses: Can be unnecessarily complex for simple sorting tasks.
  • Method 4: Combining Series.index and Series.take(). Strengths: Pure Pandas solution with clear intent. Weaknesses: Not as widely known or used as other methods.
  • Method 5: Using pandas.Index.get_indexer() as a one-liner. Strengths: Efficient and compact. Weaknesses: Might not be as readable to someone learning Pandas.