Creating New Indicator Columns Based on Values in Another Column

In this tutorial, we will explore how to create new indicator columns based on values present in another column of a pandas DataFrame. We’ll cover the necessary steps and provide explanations for each part.

Introduction

Pandas is a powerful library in Python used extensively for data manipulation and analysis. One common use case involves creating new columns or indicators based on existing data. This can be particularly useful when working with datasets that contain specific patterns, values, or conditions that need to be tracked or identified.

In this tutorial, we’ll demonstrate how to create new indicator columns using the str.contains method from pandas Series. We will use a simple example dataset and illustrate each step in detail.

Background

Before diving into the code, let’s review some of the key concepts involved:

DataFrames: A two-dimensional data structure used to store and manipulate tabular data.
Series: A one-dimensional labeled array of values. It is similar to a list, but it has additional features such as indexing and labeling.
Str.contains method: This method searches for occurrences of a specified value within the elements of a Series.

The Problem

Suppose we have a dataset with a column named “col1” that contains strings related to fruits. We want to create three new columns: “apple”, “pear”, and “peach”, where each cell in these columns is 1 if the corresponding string from “col1” matches the value in the respective column, otherwise it’s 0.

The Solution

We’ll use a simple loop-based approach to achieve this. The key idea here is to iterate through the items in the fruits list and check for each item using the str.contains method on the “col1” Series.

Here’s the code:

import pandas as pd

# Create the DataFrame
fruits = ['apple', 'pear', 'peach']
df = pd.DataFrame({'col1':['i want an apple', 'i hate pears', 'please buy a peach and an apple', 'I want squash']})

print("Original DataFrame:")
print(df)

# Define the fruits list
items = ['apple', 'pear', 'peach']

# Create new columns based on values in col1
for it in items:
    df[it] = df['col1'].str.contains(it, case=False).astype(int)

Explanation

Let’s break down what happens in this code:

We first import the pandas library and create a DataFrame with our sample data.
Then we define a list of fruits that we want to check for.
Next, we use a loop to iterate over each item in the fruits list.
Inside the loop, we use the str.contains method on the “col1” Series to search for occurrences of the current fruit. We pass case=False to make the comparison case-insensitive.
The resulting boolean values are then converted to integers using the astype(int) function and assigned to a new column with the same name as the fruit (e.g., “apple”, “pear”, etc.). If no matches were found, this value will be 0.

Output

The final output of our code should match the desired DataFrame:

                              col1  apple  pear  peach
0                  i want an apple      1     0      0
1                     i hate pears      0     1      0
2  please buy a peach and an apple      1     0      1
3                    I want squash      0     0      0

Conclusion

In this tutorial, we demonstrated how to create new indicator columns based on values present in another column of a pandas DataFrame using the str.contains method. This technique can be applied to a wide range of use cases where you need to identify or track specific patterns or conditions within your data.

By understanding and mastering this technique, you’ll become more proficient in working with pandas DataFrames and unlocking their full potential for efficient data manipulation and analysis.

Last modified on 2024-09-30