Swapping rows in a DataFrame

Python
#1

Swapping rows in a dataframe in Pandas means exchanging the positions of two or more rows in a Pandas dataframe.

We use swapping rows in a dataframe in Pandas for various reasons, including:

  • Reordering data
  • Sorting data
  • Error correction
  • Data manipulation
To swap two rows of a dataframe in Pandas, there are several methods you can use, including:

1. Using the "loc" method:

  • This method involves using the loc method to select the rows to be swapped and then swapping their positions using tuple unpacking.

  • To swap the first and second rows, we use the loc method to select the rows by their labels (0 and 1) and assign them to a reversed copy of themselves using the .values attribute. This effectively swaps the two rows in place.

  • The syntax for selecting rows using loc is df.loc[label], where label can be a single label or a list of labels.

  • Using the loc method to swap rows in a DataFrame can be useful when you need to manipulate rows based on their labels.

Example:

2. Using the "iloc" method:

  • The syntax for selecting rows using iloc is df.iloc[position], where position can be a single integer or a list of integers.

  • Using the iloc method to swap rows in a DataFrame can be useful when you need to manipulate rows based on their integer positions.

Example:

3. Using the "reindex" method:

  • This method involves using the reindex method to create a new dataframe with the rows swapped.

  • To swap rows, we use the reindex method to change the order of the rows according to the given index labels [1, 0, 2].

  • The syntax for using the reindex method to swap rows is to pass a list of index labels that corresponds to the new order of the rows.

  • Using the reindex method to swap rows in a DataFrame can be useful when you need to reorder the rows based on a specific order.

Example:

All of these methods will swap the positions of the two specified rows in the dataframe. However, they differ in their implementation and may have different performance characteristics depending on the size of the dataframe and the number of rows to be swapped.