Counting Values of Multiple Columns with Different Categories

In this article, we will explore how to count the values of multiple columns in a Pandas DataFrame that have different categories. We’ll use real-life examples and code snippets to illustrate the concepts.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One common task when working with data is to perform counting operations on specific columns or groups of columns. In this article, we will show you how to count values across multiple columns with different categories using various Pandas functions and techniques.

The Problem

Suppose we have a DataFrame df with 16 categories, but not all categories are present in the columns we need to count. We want to calculate the frequency of each category across these columns. How can we achieve this?

Solution Using GroupBy and Count

One way to solve this problem is by using the groupby function, which allows us to group data based on specific columns or categories.

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame with different categories
data = {
    'Tipo_Diagnosticos_Secundarios_2': ['Enfermedades del sistema circulatorio', 'Lesiones y envenenamientos', 'Neoplasias'],
    'Tipo_Diagnosticos_Secundarios_3': ['Trastornos mentales', 'Sintomas, signos y estados mal definidos']
}
df = pd.DataFrame(data)

# Use groupby and count to calculate the frequency of each category
grouped_df = df.groupby(['Tipo_Diagnosticos_Secundarios_2', 'Tipo_Diagnosticos_Secundarios_3']).size().reset_index(name='counts')
print(grouped_df)

Output:

Solution Using stack and value_counts

Another way to solve this problem is by using the stack function, which allows us to pivot data from a MultiIndex to a single column. Then, we can use value_counts to count the values in each category.

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame with different categories
data = {
    'Tipo_Diagnosticos_Secundarios_2': ['Enfermedades del sistema circulatorio', 'Lesiones y envenenamientos', 'Neoplasias'],
    'Tipo_Diagnosticos_Secundarios_3': ['Trastornos mentales', 'Sintomas, signos y estados mal definidos']
}
df = pd.DataFrame(data)

# Use stack and value_counts to calculate the frequency of each category
stacked_df = df.filter(like='Tipo_Diagnosticos_Secundarios').stack().value_counts()
print(stacked_df)

Output:

Solution Using filter and ngroup

The ngroup function is similar to groupby, but it returns a unique integer for each group. We can use this function in combination with the filter function to achieve our goal.

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame with different categories
data = {
    'Tipo_Diagnosticos_Secundarios_2': ['Enfermedades del sistema circulatorio', 'Lesiones y envenenamientos', 'Neoplasias'],
    'Tipo_Diagnosticos_Secundarios_3': ['Trastornos mentales', 'Sintomas, signos y estados mal definidos']
}
df = pd.DataFrame(data)

# Use filter and ngroup to calculate the frequency of each category
grouped_df = df.filter(like='Tipo_Diagnosticos_Secundarios').ngroup()
print(grouped_df)

Output:

Conclusion

In this article, we explored how to count values of multiple columns with different categories in a Pandas DataFrame. We used various functions and techniques such as groupby and count, stack and value_counts, and filter and ngroup. By choosing the right approach for our problem, we can efficiently calculate the frequency of each category across specific columns.

Additional Resources

• Pandas documentation
• Groupby function
• Stack and value_counts functions

Last modified on 2023-12-19