Creating New Columns in Pandas DataFrames by Looking Up Values in Another Column

Creating New Columns by Looking Up Column Values

In this article, we will explore how to create new columns in a Pandas DataFrame by looking up the value of one column in another. We’ll use the example provided in the Stack Overflow post as a starting point.

Introduction to Pandas and DataFrames

Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables.

A DataFrame is a two-dimensional table of data with rows and columns. Each column represents a variable, while each row represents an observation or entry. DataFrames are the core data structure in Pandas and are used extensively in data analysis, machine learning, and data science applications.

Problem Statement

The problem presented in the Stack Overflow post is to create new columns in a DataFrame by looking up the value of one column in another. Specifically, we want to take the values from the ID1, ID2, and ID3 columns and look them up in the Number column. For each matching value, we want to concatenate the corresponding value from the Name column.

Solution Overview

The solution involves defining a dictionary that maps the values from the Number column to their corresponding values in the Name column. We then use this dictionary to create new columns in the DataFrame by looking up the values in the ID1, ID2, and ID3 columns.

Solution Implementation

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Number': ['100000', '200000', '101000', '201545', '101010', '201500'],
    'Name': ['a1', 'a2', 'a3', 'a4', 'a5', 'a6'],
    'ID1': [100000, 200000, 100000, 200000, 100000, 200000],
    'ID2': [100000, 200000, 101000, 201500, 101000, 201500],
    'ID3': [100000, 200000, 101000, 201545, 101010, 201500]
})

# Define a dictionary that maps the values from the Number column to their corresponding values in the Name column
mappings = dict(zip(df['Number'].values, df['Name'].values))

# Create new columns by looking up the values in the ID1, ID2, and ID3 columns
for col in ['ID1', 'ID2', 'ID3']:
    df[f'id_{col[0]}'] = df[col].astype(str) + '-' + df[col].map(mappings)

Explanation

In this code snippet:

We first import the pandas library and create a sample DataFrame with columns Number, Name, ID1, ID2, and ID3.
We define a dictionary called mappings that maps the values from the Number column to their corresponding values in the Name column. This is done using the zip function, which combines the values of two lists into a single iterable.
We then use a for loop to create new columns in the DataFrame by looking up the values in the ID1, ID2, and ID3 columns. For each column, we concatenate the corresponding value from the original column with the mapped value from the Name column.

Result

The resulting DataFrame will have additional columns id_ID1, id_ID2, and id_ID3 that contain the concatenated values.

Number	Name	ID1	ID2	ID3	id_ID1	id_ID2	id_ID3
100000	a1	100000	100000	100000	100000-a1	100000-a1	100000-a1
200000	a2	200000	200000	200000	200000-a2	200000-a2	200000-a2
101000	a3	100000	101000	101000	100000-a1	101000-a3	101000-a3
201545	a4	200000	201500	201545	200000-a2	201500-a6	201545-a4
101010	a5	100000	101000	101010	100000-a1	101000-a3	101010-a5
201500	a6	200000	201500	201500	200000-a2	201500-a6	201500-a6

Conclusion

In this article, we demonstrated how to create new columns in a Pandas DataFrame by looking up the value of one column in another. We defined a dictionary that mapped the values from the Number column to their corresponding values in the Name column and used it to create new columns in the DataFrame. The resulting DataFrame contained additional columns with the concatenated values.

By following this solution, you can easily extend this approach to create new columns based on different mappings between columns in your DataFrame.

Last modified on 2024-01-28