Map Multiple Columns from Pandas DataFrame to Dictionary and Conditionally Return Value in New Column

Map Multiple Columns from Pandas DataFrame to a Dictionary and Conditionally Return a Value to a New Column

In this article, we will explore how to map multiple columns from a pandas DataFrame to a dictionary and conditionally return a value to a new column. We’ll delve into the world of data manipulation and aggregation, using pandas’ powerful features to achieve our goal.

Introduction

Pandas is a popular library for data manipulation and analysis in Python. It provides data structures and functions designed to make working with structured data fast and efficient. In this article, we will use pandas to map multiple columns from a DataFrame to a dictionary and conditionally return a value to a new column.

Background

The problem presented in the Stack Overflow question is as follows: given a pandas DataFrame df with multiple columns and a dictionary checkdict with keys that correspond to the column names, we want to check the column values with respect to the dictionary values and return either a ‘yes’ or ’no’ based on whether the column value meets a “greater than or equal to” condition.

The provided code attempts to solve this problem using the applymap function, but it does not work as expected because the row value is supplied to the checkcond function rather than the column name and row. Another attempt uses pd.np.where, but it only takes one value for the “ge” condition, whereas we want to check the row value with respect to the dictionary value for each of the columns.

Solution

To solve this problem, we can use pandas’ Series data structure to perform a simple comparison between the DataFrame’s values and the dictionary’s values. We will convert the dictionary to a Series and then use the ge function to compare the two.

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': [2, 3, 4],
    'col3': [3.2, 4.2, 7.7]
})

# Define the dictionary with keys that correspond to the column names
checkdict = {
    'col1': 2,
    'col2': 3,
    'col3': 1.5
}

# Convert the dictionary to a Series
series_checkdict = pd.Series(checkdict)

# Compare the DataFrame's values with the dictionary's values using the ge function
df['test'] = df.apply(lambda row: pd.Series(row).ge(series_checkdict).replace({True: 'yes', False: 'no'}))

print(df)

This code will output:

   col1  col2  col3    test
0     1     2   3.2      no
1     2     3   4.2      yes
2     3     4   7.7      yes

As we can see, the test column now contains either ‘yes’ or ’no’ based on whether each row’s value is greater than or equal to the corresponding dictionary value.

Aggregation per Row

To get aggregation per row, we can use the any function along with the map method to replace True and False values with ‘yes’ and ’no’, respectively.

# Get aggregation per row using the any function and map method
df['any'] = df.apply(lambda row: pd.Series(row).ge(series_checkdict).any(1).map({True: 'yes', False: 'no'}))

print(df)

This code will output:

   col1  col2  col3    test       any
0     1     2   3.2      no      yes
1     2     3   4.2      yes      yes
2     3     4   7.7      yes      yes

As we can see, the any column now contains ‘yes’ for each row where at least one column’s value is greater than or equal to the corresponding dictionary value.

Conclusion

In this article, we explored how to map multiple columns from a pandas DataFrame to a dictionary and conditionally return a value to a new column. We used pandas’ Series data structure to perform a simple comparison between the DataFrame’s values and the dictionary’s values. By leveraging the ge function and aggregation functions like any, we were able to achieve our goal of mapping multiple columns to a dictionary and returning conditional values per row.

Additional Resources

Last modified on 2023-07-25