Using or with .isin() on DataFrame

When working with DataFrames in pandas, filtering data based on multiple conditions can be achieved using various methods. In this article, we’ll explore how to use the .isin() function in conjunction with the apply() method to filter rows based on specific values in two columns.

Introduction to .isin()

The .isin() function is used to check if a value exists within a specified set of values. When applied to a Series or DataFrame, it returns a boolean mask indicating whether each element in the Series or column meets the condition.

For example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'values': ['apple', 'banana', 'cherry', 'date']})

# Use .isin() to check if values exist within a set
print(df['values'].isin(['apple', 'pear']))  # [True, False, True, False]

In the context of filtering DataFrames, .isin() can be used to identify rows that contain specific values in one or more columns.

Filtering with .isin() on Two Columns

Let’s revisit the original question: we have a DataFrame df with two columns, currency1 and currency2, where each row may contain currency codes like GBP, USD, EUR, etc. We want to filter out rows that meet certain conditions based on these currencies.

We’ll use the .isin() function in combination with the apply() method to achieve this.

Approach 1: Using .isin() Directly

# Filter rows where currency1 is 'GBP' or 'USD'
filtered_df = df[df['currency1'].isin(['GBP', 'USD'])]

print(filtered_df)

However, this approach has a limitation. Since we want to exclude rows with both currency1 and currency2 having specific values, we can’t directly use .isin() on the combined column.

Approach 2: Using .isin() and Apply

# Filter rows where either currency1 or currency2 is 'GBP' or 'USD'
filtered_df = df[df.apply(lambda x:x.isin(['GBP','USD']).any(1))]

print(filtered_df)

In this approach, we use the apply() method to apply a lambda function to each row. The lambda function checks if either currency1 or currency2 contains ‘GBP’ or ‘USD’ using .isin(). The any(1) parameter returns True if at least one of the conditions is met.

The resulting DataFrame, filtered_df, will contain rows where either currency1 or currency2 (or both) match the specified values.

Explaining the Lambda Function

Let’s break down the lambda function used in Approach 2:

lambda x:x.isin(['GBP','USD']).any(1)

This function takes a Series x as input and returns a boolean value indicating whether any of its elements meet the condition.

Here’s what happens step by step:

1. x.isin(['GBP','USD']): This checks if any element in the Series x contains ‘GBP’ or ‘USD’. The resulting Series will have the same values as x, but with boolean indicators (True or False) indicating whether each value matches.
2. .any(1): This method returns True if at least one of the elements in the previous Series is True.

By combining these two operations, we get a Series that indicates whether each row meets the condition.

Alternative Approaches

While using the apply() method with a lambda function works, there are alternative approaches to achieve the same result:

Approach 3: Using bitwise Operators

# Filter rows where either currency1 or currency2 is 'GBP' or 'USD'
filtered_df = df[(df['currency1'].isin(['GBP','USD'])) | (df['currency2'].isin(['GBP','USD']))]

print(filtered_df)

In this approach, we use the bitwise OR operator (|) to combine two separate conditions: df['currency1'] containing ‘GBP’ or ‘USD’, and df['currency2'] containing ‘GBP’ or ‘USD’. This produces a boolean mask that can be used to filter the DataFrame.

Approach 4: Using numpy’s in1d

import numpy as np

# Filter rows where either currency1 or currency2 is 'GBP' or 'USD'
filtered_df = df[np.in1d(df['currency1'], ['GBP', 'USD']) | np.in1d(df['currency2'], ['GBP', 'USD'])]

print(filtered_df)

This approach uses numpy’s in1d function to check if each element in the Series matches one of the specified values. We then combine these conditions using bitwise OR.

Conclusion

In this article, we explored how to use .isin() on DataFrame columns to filter out rows based on specific values. We discussed four approaches:

1. Directly using .isin() on a single column
2. Using .isin() and apply()
3. Using bitwise operators to combine conditions
4. Using numpy’s in1d

Each approach has its strengths and weaknesses, and the choice of method depends on the specific requirements of your data filtering task.

By understanding how to apply .isin() in various contexts, you’ll become more proficient in working with DataFrames and making informed decisions about data manipulation.

Last modified on 2025-04-14