Removing Rows from a DataFrame Based on Column Values

===========================================================

In this article, we will explore how to remove rows from a Pandas DataFrame based on specific conditions in another column. We’ll use the example provided by Stack Overflow and delve deeper into the concepts of boolean indexing, masking, and data manipulation.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is data structures like DataFrames, which allow us to efficiently work with structured data. In this article, we will focus on how to remove rows from a DataFrame based on conditions specified in another column.

The Problem

We are given a sample DataFrame test and asked to remove rows where the order number is refunded. We want to keep only the rows where the refund amount does not match the order amount.

test = {"number": ['1555','1666','1777', '1888'],
        "order_amount": ['100.00','200.00','-200.00', '300.00'],
        "number_of_refund": ['','','1666', '']
    }

df = pd.DataFrame(test)

The resulting DataFrame should look like this:

Solution

We can solve this problem using boolean indexing with two masks: m1 and m2. Here’s how it works:

Step 1: Define the Masks

First, we define the two masks that will be used to identify rows where the order number is refunded.

# is the row a refund?
m1 = df['number_of_refund'].ne('')
# is the number order matching a refund?
m2 = df['number'].isin(df.loc[m1, 'number_of_refund'])

In this code:

• df['number_of_refund'].ne('') creates a boolean mask where any row with a non-empty value in the number_of_refund column is marked as True.
• df['number'].isin(df.loc[m1, 'number_of_refund']) creates another boolean mask that checks if the order number from each row matches a refund amount. This is done by looking at the rows where m1 is True (i.e., where there’s a non-empty value in number_of_refund) and checking if the number column matches any of those values.

Step 2: Apply the Masks

Now, we apply these masks to remove rows that match either of the conditions.

out = df[~(m1|m2)]

Here:

• ~(m1|m2) creates a boolean mask where each row is marked as True if it does NOT match either condition. This means all rows that do not match either condition are kept.
• The resulting DataFrame out is the original DataFrame with all matching rows removed.

Output

The final output of this code should be:

This is the resulting DataFrame after removing rows where the order number matches a refund amount.

Partial Refunds

The problem statement also mentions handling “partial refunds,” which means we need to update amounts when there’s only a partial match between an order and a refund.

Step 1: Update Amounts

First, convert order_amount column to numeric values. Then, create a mapping of refunds where the key is the number and the value is the corresponding order amount in the number_of_refund column.

df['order_amount'] = pd.to_numeric(df['order_amount'], errors='coerce')

# get mapping of refunds
s = df.loc[m1, ['number', 'order_amount']].set_index('number')['order_amount']

Here:

• pd.to_numeric(df['order_amount'], errors='coerce') converts the order_amount column to numeric values. Non-numeric values are replaced with NaN.
• We use df.loc[m1, ['number', 'order_amount']].set_index('number')['order_amount'] to create a mapping where each refund number becomes a key and its corresponding order amount is mapped as the value.

Step 2: Add Refund Amounts

Now, we add the refund amounts to the original order_amount column using this mapping. If there’s no matching amount (i.e., NaN), we use 0 instead.

# update amounts
df['order_amount'] += df['number'].map(s).fillna(0)

Here:

• We map each number to its corresponding order amount from the s dictionary and add it to the original column.
• When there’s no matching refund (i.e., NaN), we use the fillna(0) method to replace the NaN value with 0.

Step 3: Remove Partial Refunds

Finally, we remove rows where the updated amount equals zero. This corresponds to cases of partial refunds.

# find null values
m2 = df['order_amount'].eq(0)

# keep rows that do not match any mask
df = df[~(m1|m2)]

Here:

• df['order_amount'].eq(0) creates a boolean mask for rows where the updated order amount is zero.
• We then apply another mask to remove these rows from the DataFrame.

Example Usage

To demonstrate how this solution works, we’ll create an example DataFrame with partial refunds and run the same code as before.

test = {"number": ['1555','1666','1777', '1888'],
        "order_amount": ['100.00','200.00','-100.00', '300.00'],
        "number_of_refund": ['','','1666', '']
    }

df = pd.DataFrame(test)

The resulting DataFrame should look like this:

When we run the code with partial refunds:

# is the row a refund?
m1 = df['number_of_refund'].ne('')
# get mapping of refunds
s = df.loc[m1, ['number', 'order_amount']].set_index('number')['order_amount']
# update amounts
df['order_amount'] += df['number'].map(s).fillna(0)
# find null values
m2 = df['order_amount'].eq(0)

# keep rows that do not match any mask
df = df[~(m1|m2)]

The final output should be:

This is the resulting DataFrame after removing rows with partial refunds.

Conclusion

In this article, we discussed how to remove rows from a Pandas DataFrame based on conditions in another column using boolean indexing and data manipulation techniques. We went through two scenarios: one where there are no partial refunds, and another where there are. This solution allows you to efficiently work with DataFrames by applying masks and updating values accordingly.

References

• Pandas Documentation
• Boolean Indexing in Pandas

Last modified on 2023-07-27