How to Subset a Pandas DataFrame Based on Multiple Values?

Subsetting a Pandas DataFrame Based on Multiple Values

In Pandas, subsetting a DataFrame based on a specific value is straightforward, as demonstrated by the following example:

import pandas as pd

# Dataframe initialization
df = pd.DataFrame({'A': [5, 6, 3, 4], 'B': [1, 2, 3, 5]})

# Subset based on a single value
x = df[df['A'] == 3]

However, the challenge arises when selecting rows that match a list of values. Consider the following use case:

# List of values to filter on
list_of_values = [3, 6]

# Subset attempt (incorrect syntax)
y = df[df['A'] in list_of_values]

This syntax will result in an error, as Pandas requires a slightly different syntax to subset based on multiple values.

Solution: Using the isin() Method

The correct way to subset a DataFrame based on a list of values is to use the isin() method. Here's the corrected code:

y = df[df['A'].isin(list_of_values)]

Output:

     A  B
1    6  2
2    3  3

The isin() method takes a list or array of values as input and returns a DataFrame containing rows where the specified column matches any value in the input.

Inverse Selection

To select rows where the column values do not match the supplied list, you can use the ~ operator in conjunction with isin(). For example:

# Inverse subset
z = df[~df['A'].isin(list_of_values)]

Output:

   A  B
0  5  1
3  4  5

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