Removing Rows based on Criteria using Python

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In this blog post, we will explore how to remove rows from a pandas DataFrame based on certain criteria. We will cover the basics of filtering data in pandas and provide examples of common use cases.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types).

One of the most commonly used operations in pandas is filtering, which allows you to select specific rows or columns from a DataFrame based on certain conditions.

Problem Statement

The problem presented in the question involves removing rows from a DataFrame based on two criteria:

The row contains ‘Wit (Rev)’ in the ‘Desc’ column.
The row’s ‘Ref_ID’ value matches one of the ‘Ref_ID’ values found in the rows that contain ‘Wit (Rev)’ in the ‘Desc’ column.

We will need to identify and remove all rows that meet either or both of these criteria.

Solution

To solve this problem, we will use pandas’ filtering capabilities. We’ll start by identifying the index of the rows that meet the first criterion.

Step 1: Identify Rows Containing ‘Wit (Rev)’ in ‘Desc’ Column

We can identify the rows that contain ‘Wit (Rev)’ in the ‘Desc’ column using the following code:

matches = []
matches = dfA.index[dfA['Desc'] == 'Wit (Rev)'].tolist()

This code creates an empty list called matches and then uses boolean indexing to find the rows where the value in the ‘Desc’ column is equal to ‘Wit (Rev)’. The resulting index of these rows is added to the matches list.

Step 2: Identify Ref_IDs

Next, we need to identify all the unique Ref_ID values found in the rows that contain ‘Wit (Rev)’ in the ‘Desc’ column. We can do this using a for loop:

Ref_IDs = []
for i in matches:
    Ref_IDs.append(dfA.iloc[i]['Ref_ID'])

This code loops through each index in the matches list and appends the corresponding value from the ‘Ref_ID’ column to the Ref_IDs list.

Step 3: Remove Rows with Matching Ref_ID Values

Now that we have identified all the unique Ref_ID values found in the rows containing ‘Wit (Rev)’ in the ‘Desc’ column, we can use boolean indexing again to remove these rows from the original DataFrame:

dfA[dfA.isin({'Ref_ID': Ref_IDs})['Ref_ID'] == False]

This code creates a new DataFrame that includes only the rows where the value in the ‘Ref_ID’ column is not equal to any of the values in the Ref_IDs list.

Example Use Case

The provided dummy data set contains 14 rows. The goal is to remove all the rows with ‘Wit (Rev)’ in the ‘Desc’ column and also remove the row that Ref_ID matches those with ‘Wit (Rev)’ in the ‘Desc’ column. However, the output shows that index 10,11 are being removed incorrectly.

Let’s examine how this works:

#matches array gives the index of those rows in Desc column with value "Wit (Rev)"
matches = []
matches = dfA.index[dfA['Desc'] == 'Wit (Rev)'].tolist()

The code identifies the row indices where ‘Wit (Rev)’ is present in the ‘Desc’ column:

# Ref_IDs stores the Ref_IDs of Ref_IDs column that should be removed
Ref_IDs = [] 
for i in matches: 
    Ref_IDs.append(dfA.iloc[i]['Ref_ID'])

Next, it identifies all unique Ref_ID values found in those rows:

The code creates an empty list called Ref_IDs and then uses a for loop to iterate over the indices of the matches list.

# Now that we have identified all the unique Ref_ID values found in the rows containing 'Wit (Rev)' in the 'Desc' column, 
# we can use boolean indexing again to remove these rows from the original DataFrame:
dfA[dfA.isin({'Ref_ID': Ref_IDs})['Ref_ID'] == False]

This code creates a new DataFrame that includes only the rows where the value in the ‘Ref_ID’ column is not equal to any of the values in the Ref_IDs list.

Conclusion

In this blog post, we explored how to remove rows from a pandas DataFrame based on certain criteria. We used boolean indexing and for loops to identify and remove rows that meet specific conditions. This is just one example of how filtering can be applied to data in pandas.

Last modified on 2025-02-19