Creating a frequency table for each column in a data frame using a loop and storing the results in a list

2 min read 30-09-2024

Creating a frequency table for each column in a data frame using a loop and storing the results in a list

When working with data in Python, especially using the Pandas library, you may encounter situations where you need to analyze the frequency of values in each column of a DataFrame. This can help you understand the distribution of categorical data or the presence of unique values in numerical data. In this article, we will explore how to create frequency tables for each column in a DataFrame by utilizing a loop, and we will store the results in a list for easy access.

Problem Scenario

Let’s consider that we have the following DataFrame containing some sample data:

import pandas as pd

data = {
    'A': ['apple', 'banana', 'apple', 'orange'],
    'B': [1, 2, 2, 3],
    'C': ['red', 'yellow', 'red', 'orange']
}

df = pd.DataFrame(data)

Our goal is to create a frequency table for each of the columns 'A', 'B', and 'C'. We will use a loop to iterate through the columns and compute the frequency tables, storing the results in a list.

Original Code to Solve the Problem

Here’s how you can achieve this:

frequency_tables = []

for column in df.columns:
    freq_table = df[column].value_counts()
    frequency_tables.append(freq_table)

print(frequency_tables)

Analysis and Explanation

Understanding the DataFrame: The sample DataFrame df consists of three columns 'A', 'B', and 'C', each containing different types of data (categorical and numerical). Analyzing the frequency of each unique value will provide insights into how the data is distributed across these columns.
Using a Loop: By using a loop over df.columns, we are able to systematically calculate the frequency table for each column. The value_counts() method is a powerful function provided by Pandas that counts the occurrences of unique values in a column, returning a Series sorted in descending order of frequency.
Storing Results in a List: By appending each frequency table to the list frequency_tables, we can maintain an organized collection of results. This approach is beneficial as it allows for easy access and further manipulation of the frequency tables if needed.

Practical Example

Here’s how the output of the frequency tables will look after running the provided code:

[
    apple      2
    banana     1
    orange     1
    Name: A, dtype: int64,
    
    2    2
    1    1
    3    1
    Name: B, dtype: int64,
    
    red       2
    yellow    1
    orange    1
    Name: C, dtype: int64
]

For column 'A', "apple" appears 2 times while "banana" and "orange" each appear once.
Column 'B' shows that the number '2' occurs twice, while '1' and '3' each occur once.
Lastly, for column 'C', "red" appears twice, while "yellow" and "orange" appear once.

Additional Resources

To delve deeper into working with frequency tables and data analysis using Pandas, consider the following resources:

Pandas Documentation
Kaggle Datasets - Explore various datasets to practice data analysis techniques.
DataCamp Course on Pandas - A structured way to learn Pandas.

Conclusion

Creating frequency tables in a DataFrame using a loop is a straightforward yet powerful technique for data analysis. By systematically processing each column and storing the results in a list, you can effectively gain insights into your dataset's structure and distribution. Leveraging libraries like Pandas allows for efficient data manipulation, making this a valuable skill for any data enthusiast.

By mastering these concepts, you'll be better equipped to handle and analyze real-world data, leading to more informed decisions and insights. Happy coding!