Reordering a Pandas DataFrame Based on Conditions: A Step-by-Step Guide

Reordering a DataFrame Based on Conditions

In this article, we will explore how to reorder a Pandas DataFrame based on certain conditions. We’ll use the info DataFrame from the Stack Overflow question as an example, but you can apply these techniques to any DataFrame.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to reorganize data based on various conditions. In this article, we’ll delve into the process of reordering a DataFrame using Pandas.

Splitting Data

The first step in reordering a DataFrame is to split it into three parts: the rows that meet the condition, the rows that don’t meet the condition, and the original order of the DataFrame.

## Splitting Data
To achieve this, we'll use the `iloc` method to separate the data into three partitions. The first partition will contain the first row (head), the second partition will contain the rows that satisfy the condition, and the third partition will contain the rows that don't meet the condition.

```python
info = pd.DataFrame({
    'Merchant name': ['Boohoo', 'PRETTYLITTLETHING', 'ASOS US', 'PRINCESS POLLY', 'URBAN OUTFITTERS', 'KIM+ONO'],
    'order_cnt': [200, 100, 50, 80, 120, 500],
    'profit': [30, -60, 100, 50, -20, 90],
    'epc': [0.6, -0.4, 1.0, 0.8, -0.1, 0.7]
})

head = info.head(1)
tail = info.iloc[1:]
mask = tail.eval('order_cnt >= 100 and profit >= 0')
pos = tail[mask]
neg = tail[~mask]

Sorting Positive Rows

Next, we’ll sort the positive rows based on the desired criteria (epc) in descending order. This will ensure that the rows with higher epc values come first.

## Sorting Positive Rows
We'll use the `sort_values` method to sort the positive rows. The `ascending=False` parameter ensures that the rows are sorted in descending order based on the epc column.

```python
pos.sort_values('epc', ascending=False)

Concatenating Partitions

After sorting the positive rows, we’ll concatenate the three partitions back together to form the reordered DataFrame.

## Concatenating Partitions
We'll use the `concat` method to combine the head, sorted positive rows, and negative rows into a single DataFrame.

```python
df = pd.concat([head, pos.sort_values('epc', ascending=False), neg])

Adding New Ranks

To get the output as presented in the original question (with both the original and new ranks), we’ll add two new columns to the DataFrame: new_rank and original_rnk.

## Adding New Ranks
We'll use the `range` function to assign a new rank to each row, starting from 1. The `map` method will be used to match the merchant names with their original ranks.

```python
df['new_rank'] = range(1, 7)
df['original_rnk'] = df['Merchant name'].map(ranks.set_index('Merchant name')['original_rnk'])

Sorting by Original Rank

Finally, we’ll sort the DataFrame based on the original_rnk column to ensure that the rows with the same original rank are sorted accordingly.

## Sorting by Original Rank
We'll use the `sort_values` method to sort the DataFrame based on the `original_rnk` column.

```python
df.sort_values('original_rnk')[['Merchant name', 'new_rank', 'original_rnk']]

Conclusion

In this article, we’ve demonstrated how to reorder a Pandas DataFrame based on certain conditions. By splitting the data into three partitions, sorting the positive rows, concatenating the partitions, and adding new ranks, we can achieve the desired output.

Note that this is just one way to approach reordering a DataFrame in Pandas. Depending on your specific use case, you may need to modify or extend these techniques to suit your needs.

Last modified on 2023-11-26