**๐ก 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 DataFrameโs 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. Itโs 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()`

Pythonโs 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 Pandasโ 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.