This post describes how to create charts related to Principal Component Analysis (PCA). PCA is a famous tool and has lots of application in the field of data visualization. The charts will be create with matplotlib.
We will go over the scree plot (explained variance plot), the correlation circle plot and the visualization of the observations on the new principal components. These charts are the most basics when working with PCA but are an important start.

## About Principal Component Analysis

The principal component analysis (`PCA`) is a widely used dimensionality reduction and data analysis technique in the field of statistics and machine learning. It is used to transform high-dimensional data into a lower-dimensional representation while retaining as much of the original variability as possible. `PCA` achieves this by identifying the directions (principal components) in the data along which there is the most variation.

Representing data in smaller spaces is a technique used for important visualization and statistical analysis tasks:

• clustering (finding groups of similar observations)
• feature significance (which variables are most important in terms of variability?)
• outlier detection (find observations that stand out from the rest)
• exploratory analysis (simply to better understand the dataset)

## Libraries

In order to apply `PCA` to our data, we need to use a library that supports it: in our case, this will be `scikit-learn`.

• matplotlib is used for creating the chart
• `scikit-learn` is used for applying the PCA to our dataset
• `numpy` is used to generate some data
• `pandas` is used to put the data into a dataframe

Don't forget to install `scikit-learn` with the `pip install -U scikit-learn` command.

``````# Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler``````

## Dataset

We're going to create a data set consisting of 4 variables, more or less correlated. We want them to be correlated, because that's what makes a `PCA` relevant in an analysis.

The data are generated using `numpy` random function `np.random.normal()` and `np.random.uniform()`. Then, we store these variables to a `pandas` dataframe.

One important part of PCA is to normalize the data. In practice, it just means applying a z-score transformation and it is really easy.

``````# Create our variables
sample_size = 100
random1 = np.random.uniform(30, 20, sample_size)
random2 = random1*3 + np.random.normal(10, 30, sample_size)
random3 = random2*-3 + np.random.normal(10, 100, sample_size)
random4 = random3*2 + np.random.normal(10, 500, sample_size)

df = pd.DataFrame({'variable1': random1,
'variable2': random2,
'variable3': random3,
'variable4': random4,})

# Scale our data using z-score normalization
scaler = StandardScaler()
data_scaled = scaler.fit_transform(df)
df = pd.DataFrame(data_scaled, columns=df.columns)``````

## Create the PCA object

With `scikit-learn`, it's easy to make a `PCA`. Simply initialize a `PCA` object (and optionnaly specify the number of components you want to keep). As it's difficult to create a visualization on more than 2 axes (3 is possible but more complex), we'll keep only the first 2 dimensions (see graph section below).

``````# Init a PCA object where we will only keep the 2 first principal components
pca = PCA()

# Use the `fit_transform` method from this object to our dataframe
pca_results = pca.fit_transform(df)``````

## Scree plot

A scree plot is actually just a barplot, but usually create for factorial analysis like PCA. In this context, it gives us the information of 'how much inertia each component has?'.

``````# Get the explained variance ratio for each principal component
explained_variance = pca.explained_variance_ratio_

# Set figsize
plt.figure(figsize=(10, 6))

# Create a scree plot to visualize the explained variance
plt.plot(range(1, len(explained_variance) + 1), # x-axis
explained_variance*100, # convert explained variance in percentage
marker='o', # add a marker at each value
)

# Add title and axis label
plt.title('Scree Plot of Explained Variance for Principal Components')
plt.xlabel('Principal Component')
plt.ylabel('Explained Variance (in %)')

# Add label to x-axis
plt.xticks(range(1, len(explained_variance) + 1))

# Add grid in the background
plt.grid(True)

# Display the chart
plt.show()``````

## Visualization of the variables

There is no current function in `scikit-learn` or `matplotlib` that allow us to simply plot the correlation circle plot. This means that we manually add arrows and variable to the plot, using `plt.arrow()` and `plt.annotate()` functions from matplotlib.

By multiplying the transposed matrix (`T()` function) of principal components by the square root (`np.sqrt()` function) of the explained variance ratios, we are scaling the principal component vectors (`loadings`) by the square root of their contribution to the total variance. This scaling ensures that the `loadings` properly represent the relationships between the original variables and the principal components in terms of variance.

``````# Get the explained variance ratios for the selected components
explained_variances = pca.explained_variance_ratio_

# Get the principal component vectors (also known as loadings)

# Set figsize and other layout parameters
plt.figure(figsize=(8, 8))
plt.title('Correlation Circle Plot')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Add variable labels to the plot
for i, feature in enumerate(df.columns):

# Add the name of the variable near the arrow
plt.annotate(feature, # variable name
color='red')

# Add an arrow representing the variable on the new axis
plt.arrow(0, 0,
color='black',
alpha=0.7,
width=0.01,
)

# Fix x-axis between -1 and 1 is important for better visualization
plt.xlim(-1,1)
plt.ylim(-1,1)

# Add grid in the background
plt.grid(True)

# Display the chart
plt.show()``````

## Visualization of the observations

This graph is easy to make, since it's a simple scatter plot. The particularity lies in the fact that the axes are the variables artificially created by `PCA`.

``````# Create a scatter plot to visualize the observations in the 2D PCA space
plt.figure(figsize=(10, 6))
plt.scatter(pca_results[:, 0], # position on the first principal component of the observations
pca_results[:, 1], alpha=0.7) # position on the second principal component of the observations

# Add title and axis label
plt.title('Scatter Plot of Observations in 2D PCA Space')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# (optionally) Add labels to each point based on their index in the original dataframe
for i, txt in enumerate(df.index):
plt.annotate(txt, (pca_results[i, 0], pca_results[i, 1]), fontsize=8)
# This might be useful when doing outlier detection

# Add grid in the background
plt.grid(True)

# Display the chart
plt.show()``````

## Going further

This article explains how to create very important visualizations when working with `PCA`.

If you want to see how to create better scatter plot, barplot or chart with annotations, check out the gallery.

## Contact & Edit

👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥

This page is just a jupyter notebook, you can edit it here. Please help me making this website better 🙏!