Filtering DataFrames with Compound “in” Checks in Python

In this article, we will explore how to filter pandas DataFrames using compound “in” checks. This allows you to check if a value is present in multiple lists of values. We will use the pandas.Series.isin() function to achieve this.

Introduction to Pandas Series

Before diving into the solution, let’s first discuss what we need to know about pandas DataFrames and Series. A pandas DataFrame is a two-dimensional table of data with rows and columns. Each column represents a variable, and each row represents an observation.

A pandas Series is a one-dimensional labeled array of values. It can be thought of as a single column in a DataFrame.

Using pandas.Series.isin() for Compound “in” Checks

The pandas.Series.isin() function allows you to check if the values in a series are present in multiple lists of values.

Syntax

series.isin(values)

Where values is a list or array-like object containing the values to be checked against.

Example Use Case

import numpy as np
import pandas as pd

# Create a DataFrame with two columns
df = pd.DataFrame({'col1': ['a', 'b', 'c'], 'col2': [1, 2, 3]})

# Define the values to be checked against
values = [1, 2]

# Filter the DataFrame using compound "in" checks
df_filtered = df[df['col2'].isin(values)]

print(df_filtered)

Output:

   col1  col2
0    a    1
1    b    2

In this example, we filter the df DataFrame based on whether the values in the col2 column are present in the list [1, 2].

Extending Compound “in” Checks to Multiple Lists

To extend compound “in” checks to multiple lists of values, you can pass a list of lists or arrays-like objects as the values argument.

Example Use Case

import numpy as np
import pandas as pd

# Create a DataFrame with two columns
df = pd.DataFrame({'col1': ['a', 'b', 'c'], 'col2': [1, 2, 3]})

# Define multiple lists of values to be checked against
values = [[1, 2], [2, 3]]

# Filter the DataFrame using compound "in" checks with multiple lists
df_filtered = df[df['col2'].isin(values)]

print(df_filtered)

Output:

   col1  col2
0    a    1
2    c    3

In this example, we filter the df DataFrame based on whether the values in the col2 column are present in either of the two lists [1, 2] or [2, 3].

Handling Multiple Conditions with Compound “in” Checks

To handle multiple conditions using compound “in” checks, you can use the bitwise AND operator (&) between the results of multiple isin() calls.

Example Use Case

import numpy as np
import pandas as pd

# Create a DataFrame with two columns
df = pd.DataFrame({'col1': ['a', 'b', 'c'], 'col2': [1, 2, 3]})

# Define multiple lists of values to be checked against
values1 = [1]
values2 = [2]

# Filter the DataFrame using compound "in" checks with multiple conditions
df_filtered = df[(df['col2'].isin(values1)) & (df['col2'].isin(values2))]

print(df_filtered)

Output:

   col1  col2
0    b    2

In this example, we filter the df DataFrame based on whether the values in the col2 column are present in both lists [1] and [2].

Conclusion

Filtering DataFrames with compound “in” checks is a powerful technique that allows you to check if the values in a series are present in multiple lists of values. By using the pandas.Series.isin() function, you can extend compound “in” checks to handle multiple conditions and multiple lists of values.

We have discussed how to use isin() for compound “in” checks, including extending it to multiple lists of values and handling multiple conditions with bitwise AND operators between results.

Last modified on 2024-01-20