Joint-Frequency of Two Binned Variables from Pandas DataFrame

In this article, we’ll explore how to get the joint-frequency of two binned variables from a pandas DataFrame. We’ll discuss the different approaches and provide code examples to help you achieve this.

Introduction

When working with time series data in pandas DataFrames, it’s common to need to bin or group the values into categories. In some cases, we want to know the frequency (nominal or relative) of combined conditions. This is where joint-frequency analysis comes in handy.

Joint-frequency analysis involves counting the occurrences of both variables within specific bins. We’ll explore different approaches and provide code examples to help you achieve this.

Background

Before diving into the solution, let’s briefly discuss the concepts involved:

• Binning: Binning involves grouping a continuous variable into discrete categories or bins.
• Crosstabulation: Crosstabulation is a technique used to create a table that shows the frequency of two variables within specific bins.

Approach 1: Using pd.crosstab

The most straightforward approach to get the joint-frequency of two binned variables from a pandas DataFrame is by using the pd.crosstab function. This function allows us to specify multiple columns as input and creates a table that shows the frequency of each combination of values.

Here’s an example code snippet that demonstrates how to use pd.crosstab:

import pandas as pd

# Create a sample DataFrame
data = {
    'Temperature': [17.6, 22.1, 13.6, 26.4, 25.6],
    'Humidity': [88, 81, 88, 71, 72]
}

df = pd.DataFrame(data)

# Bin the 'Temperature' and 'Humidity' columns
df['T_binned'] = pd.cut(df['Temperature'], bins=np.arange(0,32,4))
df['H_binned'] = pd.cut(df['Humidity'], bins=np.arange(0,100,10))

# Get the joint-frequency using pd.crosstab
joint_frequency = pd.crosstab(df['T_binned'], df['H_binned'])

print(joint_frequency)

Approach 2: Using pd.crosstab with dropna=False

In some cases, you might want to include rows where there are no matches between the two variables. To achieve this, we can use the dropna=False argument in the pd.crosstab function.

Here’s an example code snippet that demonstrates how to use pd.crosstab with dropna=False:

import pandas as pd

# Create a sample DataFrame
data = {
    'Temperature': [17.6, 22.1, 13.6, 26.4, 25.6],
    'Humidity': [88, 81, 88, 71, 72]
}

df = pd.DataFrame(data)

# Bin the 'Temperature' and 'Humidity' columns
df['T_binned'] = pd.cut(df['Temperature'], bins=np.arange(0,32,4))
df['H_binned'] = pd.cut(df['Humidity'], bins=np.arange(0,100,10))

# Get the joint-frequency using pd.crosstab with dropna=False
joint_frequency = pd.crosstab(df['T_binned'], df['H_binned'], dropna=False)

print(joint_frequency)

Approach 3: Custom Implementation

If you want to implement a custom solution, you can iterate over the unique values in each column and count the occurrences of each combination. Here’s an example code snippet that demonstrates how to do this:

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {
    'Temperature': [17.6, 22.1, 13.6, 26.4, 25.6],
    'Humidity': [88, 81, 88, 71, 72]
}

df = pd.DataFrame(data)

# Bin the 'Temperature' and 'Humidity' columns
df['T_binned'] = pd.cut(df['Temperature'], bins=np.arange(0,32,4))
df['H_binned'] = pd.cut(df['Humidity'], bins=np.arange(0,100,10))

# Create a dictionary to store the joint-frequency counts
joint_frequency_counts = {}

# Iterate over the unique values in each column
for temperature_bin in df['T_binned'].unique():
    for humidity_bin in df['H_binned'].unique():
        # Count the occurrences of each combination
        count = (df[(df['T_binned'] == temperature_bin) & 
                   (df['H_binned'] == humidity_bin)].shape[0])
        
        # Store the joint-frequency counts in the dictionary
        if (temperature_bin, humidity_bin) not in joint_frequency_counts:
            joint_frequency_counts[(temperature_bin, humidity_bin)] = 1
        else:
            joint_frequency_counts[(temperature_bin, humidity_bin)] += count

# Create a DataFrame from the joint-frequency counts
joint_frequency_df = pd.DataFrame(list(joint_frequency_counts.items()), columns=['Joint-Frequency Bin', 'Frequency'])

print(joint_frequency_df)

Conclusion

In this article, we explored different approaches to get the joint-frequency of two binned variables from a pandas DataFrame. We discussed using pd.crosstab, implementing a custom solution, and provided code examples to help you achieve this.

We hope this article has been informative and helpful in your data analysis endeavors!

Last modified on 2024-06-16