In the realm of statistics, a categorical variable is characterized by multiple categories without any inherent order. For instance, a binary variable, such as a yes/no query, is a categorical variable with two categories (yes and no) devoid of any intrinsic sequence. Categorical variables epitomize data types that can be segmented into various groups. Common examples encompass race, gender, age bracket, and educational qualification.
Understanding Correlation
Correlation is a statistical metric that delineates the degree to which two variables are linearly interconnected, signifying their synchronized change at a consistent rate. It's a prevalent instrument for elucidating uncomplicated associations without inferring causality. The correlation coefficient numerically expresses this relationship, oscillating between -1.0 and 1.0. A positive correlation indicates that as one variable fluctuates, the other follows suit in the same direction. Conversely, a negative correlation signifies that the variables move in opposing directions. A zero correlation denotes an absence of any linear association.
Determining the Correlation of Categorical Variables
Unlike other data types, such as numerical or boolean, conventional methods in pandas cannot be employed to produce a correlation matrix for categorical variables. To ascertain the correlation of categorical variables, we utilize a library named dython.
Dython: A Brief Overview
Dython is an ensemble of data analysis tools tailored for Python 3.x, designed to offer profound insights into your dataset. The library's core tenets are ease of use, functionality, and lucidity. Dython effortlessly discerns categorical from numerical features, computes a pertinent measure of association for each feature, and visually represents it in an intuitive heatmap.
Installation
To integrate Dython into your environment, utilize the following commands:
pip install dythonor
conda install -c conda-forge dythonDelving into the Dataset
For our analysis, we'll harness the Pokemon dataset.
URL ='https://raw.githubusercontent.com/adamerose/datasets/master/pokemon.csv'
df= pd.read_csv(URL)Identifying Categorical Variables
With the identify_nominal_columns function from the Dython library, we can effortlessly pinpoint the categorical variables in our dataset.
from dython.nominal import identify_nominal_columns
categorical_features = identify_nominal_columns(df)For the Pokemon dataset, the identified categorical features are 'Name', 'Type 1', and 'Type 2'.
Generating the Correlation Matrix and Visualization
To craft the correlation matrix, we employ the associations function from Dython.
complete_correlation = associations(df, filename= 'complete_correlation.png', figsize=(10,10))This function computes the correlation or strength of association of features in the dataset, encompassing both categorical and continuous features.
Correlation Matrix for Categorical Variables
To exclusively generate a correlation matrix for categorical variables, we first filter them into a distinct dataframe.
selected_column = df[categorical_features]
categorical_df = selected_column.copy()
categorical_correlation = associations(categorical_df, filename= 'categorical_correlation.png', figsize=(10,10))Conclusion
In this guide, we delved into the nuances of categorical variables and correlation matrices. We explored the methodology to determine the correlation matrix of categorical variables, which is instrumental in pinpointing features optimal for model training.
FAQs
- What is a categorical variable?
- A categorical variable is a type of variable that can take on one of a limited, and usually fixed, number of possible values.
- How is correlation different for categorical variables?
- For categorical variables, traditional correlation measures like Pearson's don't apply. Instead, measures like Cramer's V or Theil’s U are used.
- What is Dython?
- Dython is a Python library designed for data analysis, especially for determining correlations among features.
