Deleting Rows from a Pandas DataFrame Based on String Containment

In this article, we will explore the process of deleting rows in a pandas DataFrame that contain values from a given list. We’ll examine the use of string containment checks and how to handle multiple strings in the list.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is handling tabular data, such as DataFrames, which can be thought of as two-dimensional labeled data. The DataFrame provides several methods to filter or delete rows based on various conditions.

In this article, we’ll focus on deleting rows that contain values from a specific list. We’ll cover the use of string containment checks and provide examples with multiple strings in the list.

Installing Pandas

Before proceeding with the solution, make sure you have pandas installed in your Python environment. You can install it using pip:

pip install pandas

Creating a DataFrame

Let’s create a sample DataFrame to work with. The following code creates a DataFrame df containing beer names and their respective ratings.

import pandas as pd

# Create a sample DataFrame
data = {
    'Beer': ['Heineken', 'Budweiser', 'Coors Light'],
    'Rating': ['Good', 'Bad', 'Bad']
}
df = pd.DataFrame(data)

print(df)

Output:

Deletion of Rows with String Containment

We’ll use the str.contains function to check if a row contains any value from a given list. The str.contains function returns a boolean Series indicating whether each row meets the condition.

Deleting Single Value from List

Suppose we want to delete rows that contain ‘Light’ in the ‘Beer’ column. We can use the following code:

# Define a single value from the list
value = 'Light'

# Use str.contains to check for containment of the value
df_contain_value = df[df.Beer.str.contains(value, na=False)]

print(df_contain_value)

Output:

Deleting Multiple Values from List

If our list contains multiple values, we can separate them with the | operator. Let’s say we want to delete rows that contain either ‘Light’ or ‘Heineken’. We can use the following code:

# Define multiple values from the list separated by '|'
values = ['Light', 'Heineken']

# Use str.contains to check for containment of any value in the list
df_contain_values = df[~df.Beer.str.contains('|'.join(values), na=False)]

print(df_contain_values)

Output:

Explanation

In the above code snippets, we use str.contains to check for containment of a value. The na=False argument ensures that NaN values are not incorrectly considered as containing the specified value.

When using multiple values from the list, we separate them with the | operator. This allows us to match rows that contain any one of the values in the list.

Handling Null Values

By default, str.contains ignores NaN values. If you want to include or exclude NaN values, you can use the na argument:

• na=False: Ignores NaN values.
• na=True: Includes NaN values as containing the specified value.

For example:

# Define a single value from the list
value = 'Light'

# Use str.contains to check for containment of the value, including NaN values
df_contain_value_including_nan = df[df.Beer.str.contains(value, na=True)]

print(df_contain_value_including_nan)

Output:

Handling Non-String Data

If your DataFrame contains non-string data, such as integers or floats, you may want to preprocess the column before applying str.contains. One common approach is to convert the values to strings using the astype function.

# Convert integer values in the 'Beer' column to string
df['Beer'] = df['Beer'].astype(str)

# Define a single value from the list
value = 'Light'

# Use str.contains to check for containment of the value
df_contain_value = df[df.Beer.str.contains(value, na=False)]

print(df_contain_value)

Output:

Conclusion

Deleting rows from a pandas DataFrame based on string containment is a useful technique for data filtering and cleaning. By using the str.contains function, you can easily check for containment of values in one or more columns.

Remember to handle potential issues with NaN values, non-string data, and preprocessing steps as needed.

Last modified on 2025-01-14