Sorting Pandas Index: How to Obtain Integer Indices That Would Sort the Index in Python

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💡 Problem Formulation: When working with Pandas DataFrames in Python, oftentimes we need to sort the index and get the integer indices that would sort the DataFrame’s index. For example, given a DataFrame with a non-sequential index of [3, 1, 2], the desired output for sorting indices would be [1, 2, 0], indicating the positions the original indices would take after sorting.

Method 1: Using argsort() Method

This method involves using the argsort() function from NumPy, which returns the indices that would sort an array. When applied to a Pandas DataFrame’s index, it gives us those sorting indices. This method directly reflects the underlying numerical sorting.

Here’s an example:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [4, 5, 6]}, index=[3, 1, 2])
sort_indices = np.argsort(df.index)
print("Sorted indices:", sort_indices)

The output:

Sorted indices: [1 2 0]

This code snippet creates a DataFrame with a simple non-sequential index. The numpy.argsort() function is then used to obtain the indices that sort the DataFrame’s index. The indices are printed out, showing the order in which the original indices would be sorted.

Method 2: Using DataFrame.sort_index() Method

A straightforward approach in Pandas is to use the sort_index() method on a DataFrame, followed by the .index attribute and .get_indexer() method for the original index. This method is a Pandas-centric solution, utilizing its built-in functions thoroughly.

Here’s an example:

df = pd.DataFrame({'A': [4, 5, 6]}, index=[3, 1, 2])
sorted_df = df.sort_index()
sort_indices = sorted_df.index.get_indexer(df.index)
print("Sorted indices:", sort_indices)

The output:

Sorted indices: [1 2 0]

In this example, the sort_index() method creates a new DataFrame with indices sorted, and then the get_indexer() method is used to map the original indices to their sorted positions. The resulting indices are the positions each original index would be in after sorting.

Method 3: Using the Index.argsort() Method

Pandas Index objects have an argsort() method similar to NumPy’s, providing a way to get index sortation inline with the DataFrame’s index. This method leverages Pandas inner workings and stays within the Pandas ecosystem.

Here’s an example:

df = pd.DataFrame({'A': [4, 5, 6]}, index=[3, 1, 2])
sort_indices = df.index.argsort()
print("Sorted indices:", sort_indices)

The output:

Sorted indices: [1 2 0]

Here, df.index.argsort() is applied directly on the DataFrame’s Index object, returning an array of indices that sort the index. It’s a cleaner approach since there’s no need to import NumPy specifically for this functionality.

Method 4: Using Series.searchsorted() Method

When the DataFrame’s index is guaranteed to be unique and sorted, the searchsorted() method on a Series can be a clever option, finding the indices where the current index values should be inserted to maintain order.

Here’s an example:

sorted_index = pd.Index([1, 2, 3])
df = pd.DataFrame({'A': [4, 5, 6]}, index=[3, 1, 2])
sort_indices = sorted_index.searchsorted(df.index)
print("Sorted indices:", sort_indices)

The output:

Sorted indices: [2 0 1]

This snippet assumes we are working with a sorted Index object (sorted_index). We use the searchsorted() method to find out where in the sorted index the current index values would fit to keep the order, effectively giving us the sort indices.

Bonus One-Liner Method 5: Using Comprehension with sorted()

Python’s built-in sorted() function can be used in a one-liner list comprehension to get the sortation indices. Efficient for small to medium-sized DataFrames, this method shouldn’t be overlooked due to its simplicity.

Here’s an example:

df = pd.DataFrame({'A': [4, 5, 6]}, index=[3, 1, 2])
sort_indices = [df.index.get_loc(i) for i in sorted(df.index)]
print("Sorted indices:", sort_indices)

The output:

Sorted indices: [1 2 0]

By employing list comprehension, we sort the index and then use get_loc() to find the position of each index value, which gives us the sorted indices in a compact, one-line fashion.

Summary/Discussion

  • Method 1: NumPy’s argsort(). Universal and quick. Not Pandas-specific, which might be a downside in a pure Pandas context.
  • Method 2: sort_index() and get_indexer(). Purely Pandas-based and plays nicely within Pandas’ methods. Slightly verbose compared to Method 1.
  • Method 3: Index.argsort(). Stays within the Pandas library, intuitive and does not require additional imports. May be less known to some users.
  • Method 4: Series.searchsorted(). Suitable for unique and pre-sorted indices, ensures original index integrity. Can be cumbersome with unsorted/non-unique indices.
  • Bonus Method 5: Comprehension with sorted(). Simple and Pythonic. Becomes less efficient with larger datasets, due to the DataFrame index lookup.