How to Subset Pandas DataFrames Using a List of Values?

Subsetting Pandas Dataframes Based on a List of Values

In data analysis, it's often necessary to retrieve specific rows from a dataframe based on predefined criteria. Pandas provides various methods for subsetting dataframes, including the ability to select rows based on a list of values.

Utilizing isin() Method

To subset a Pandas dataframe based on a list of values, you can employ the isin() method, as demonstrated below:

import pandas as pd

# Create a Pandas dataframe
df = pd.DataFrame({'A': [5, 6, 3, 4], 'B': [1, 2, 3, 5]})

# Define a list of values to filter by
list_of_values = [3, 6]

# Subset dataframe based on the list
y = df[df['A'].isin(list_of_values)]

print(y)

Output:

   A  B
1  6  2
2  3  3

The isin() method allows you to filter rows where the specified column values match any value in the provided list.

Negating Selection with ~

In certain scenarios, you may need to exclude rows based on the list of values. To achieve this, you can use the ~ operator along with isin(), as illustrated below:

import pandas as pd

# Create a Pandas dataframe
df = pd.DataFrame({'A': [5, 6, 3, 4], 'B': [1, 2, 3, 5]})

# Define a list of values to exclude
list_of_values = [3, 6]

# Subset dataframe excluding the list
z = df[~df['A'].isin(list_of_values)]

print(z)

Output:

   A  B
0  5  1
3  4  5

The ~ operator negates the selection, ensuring that rows with values not in the specified list are displayed.

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