Removing Middle Rows from a Pandas DataFrame

When working with dataframes, it’s not uncommon to need to manipulate the data by removing certain rows or keeping only specific subsets. In this post, we’ll explore how to remove the middle rows from a pandas dataframe, specifically when you want to keep the head and tail.

Understanding the Problem

Imagine you have a dataframe df with various columns such as ‘Location’, ‘ID’, ‘Item’, ‘Qty’, and ‘Time’. The ‘Item’ column is used for grouping purposes. You can create groups based on this column, perform some operations within each group, and then keep only specific rows from the grouped data.

The problem arises when you want to remove the middle rows of a group, leaving only the first and last rows as desired.

Solution Overview

To achieve this, we will utilize the groupby.nth method provided by pandas. This function allows us to select the first (0) or last (-1) row within each group based on our criteria (in this case, removing the middle rows).

Here is a step-by-step guide:

1. Group By: We’ll use the groupby method to divide the dataframe into groups based on the ‘Item’ column.
2. Select First and Last Rows: Use nth([0,-1]) to select the first (0) and last (-1) row within each group.
3. Reset Index: Finally, use reset_index() to convert the resulting dataframe back into a standard format with an index column.

Step-by-Step Solution

Here’s how you can implement this using Python:

import pandas as pd

# Sample DataFrame Creation
data = {
    'Location': ['381', '761', '494', '455', '730', '761', '494', '424', '487', '514', '587', '654'],
    'ID': [202546661, 202547268, 202546857, 202546771, 202547225, 202547268, 202546857, 202546723, 202546848, 202546891, 202547004, 202547101],
    'Item': ['995820', '995820', '995822', '999810', '999810', '999825', '999942', '999942', '999942', '999942', '999942', '999942'],
    'Qty': [1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1],
    'Time': ['06:55:07', '07:12:44', '06:58:30', '06:56:52', '07:11:57', '07:13:04', '06:58:52', '06:55:36', '06:57:47', '06:59:23', '07:01:03', '07:01:42']
}

df = pd.DataFrame(data)

# Group By Item and Select First and Last Rows
df = df.groupby('Item').nth([0,-1]).reset_index()

print(df)

Explanation

In the code snippet above, we first create a sample dataframe df with columns ‘Location’, ‘ID’, ‘Item’, ‘Qty’, and ‘Time’. We then apply the steps mentioned earlier to manipulate this dataframe.

Here’s how it works:

• groupby('Item'): Divide the dataframe into groups based on the ‘Item’ column.
• .nth([0,-1]): Select the first (0) and last (-1) row within each group.
• .reset_index(): Convert the resulting dataframe back into a standard format with an index column.

Real-World Applications

This technique can be applied in various real-world scenarios, such as:

• Analyzing sales data across different products to identify top-selling items
• Examining weather patterns within specific regions using geographical coordinates as the grouping criterion
• Comparing performance metrics of different teams or departments based on their respective job roles

Last modified on 2025-04-01