Counting Frequency of a Number in a Column While Matching Text in Another Column

As data analysts and scientists, we often encounter datasets that require complex data manipulation. In this article, we will explore how to count the frequency of a specific number in one column while also matching certain text values in another column.

Problem Statement

The problem presented is a common one in data analysis: taking a dataset with two columns of interest and finding the frequency of a particular value in one column that matches specific text values in the other column. The provided sample dataset has three columns: “Year”, “Course”, and “Modul” (with an alias “module”). We want to count how many times the number 4 appears in the “Q1” or “Q2” column for students enrolled in “CS1203”.

Solution Overview

There are a couple of ways to approach this problem. The first method involves using boolean indexing with the & operator, which allows us to select rows where both conditions are met. This method is efficient but can be somewhat cumbersome if we want to count frequencies across multiple columns.

The second approach uses the melt function from pandas, which transforms a dataset from wide format to long format. This method provides more flexibility when dealing with multiple columns and allows us to easily apply transformations or aggregations.

Boolean Indexing Approach

Boolean indexing is a powerful feature in pandas that enables us to select rows based on conditional logic. In this case, we want to find the students who enrolled in “CS1203” and have either a Q1 score of 4 or a Q2 score of 4.

Here’s how you can achieve this using boolean indexing:

import pandas as pd

# Load the dataset from the clipboard
df = pd.read_clipboard()

# Filter rows where module is CS1203 and either q1 or q2 is 4
filtered_df = df[(df['module'] == 'CS1203') & (df['q1'] == 4) | (df['q2'] == 4)]

# Print the filtered dataset
print(filtered_df)

# Count the number of rows in the filtered dataset
count = len(filtered_df)
print(count)

Melt Approach

The melt function is another useful tool in pandas that transforms a wide format dataset into long format. In this case, we want to pivot our data so that “Q1” and “Q2” become separate columns.

Here’s how you can achieve this using the melt approach:

import pandas as pd

# Load the dataset from the clipboard
df = pd.read_clipboard()

# Pivot the data so that 'q' becomes a new column
pivoted_df = df.melt(id_vars=['year', 'course', 'module'], value_name='q')

# Filter rows where module is CS1203 and q is 4
filtered_df = pivoted_df[pivoted_df['module'] == 'CS1203' & pivoted_df['q'] == 4]

# Print the filtered dataset
print(filtered_df)

# Count the number of rows in the filtered dataset
count = len(filtered_df)
print(count)

Choosing Between Methods

When deciding between boolean indexing and the melt approach, consider the following factors:

Data structure: If your data is already in long format or you want to perform further aggregations on other columns, the melt approach might be more suitable.
Readability: Boolean indexing can result in more concise code but may also lead to less readable conditional logic.
Performance: In most cases, boolean indexing will be faster since it directly accesses rows without transforming the data.

Conclusion

In conclusion, counting the frequency of a number in one column while matching certain text values in another column is a common problem in data analysis. Both boolean indexing and the melt approach have their strengths and weaknesses. By choosing the right method based on your specific use case and dataset structure, you can efficiently solve this type of problem.

In our example, we explored how to count the frequency of “4” in either Q1 or Q2 columns for students enrolled in module CS1203 using boolean indexing and the melt approach. These techniques are versatile and powerful tools that can help you tackle more complex data analysis tasks.

Last modified on 2025-04-16