Anscombe's Quartet Analysis¶

In [ ]:

# Import necessary libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import io
import requests

In [7]:

# Load the dataset
url = "data/anscombe.csv"
data = pd.read_csv(url)

# Display the first few rows of the dataset
data.head()

Out[7]:

	dataset	x	y
0	I	10.0	8.04
1	I	8.0	6.95
2	I	13.0	7.58
3	I	9.0	8.81
4	I	11.0	8.33

In [4]:

# Create a function to plot Anscombe's quartet with a dark theme
def plot_anscombe(data):
    # Set the dark background style
    plt.style.use('dark_background')
    
    # Set up the figure and axes
    fig, axs = plt.subplots(2, 2, figsize=(12, 10))
    fig.suptitle("Anscombe's Quartet", fontsize=16, color='white')

    # Loop through each dataset in Anscombe's quartet
    for i, dataset in enumerate(['I', 'II', 'III', 'IV']):
        subset = data[data['dataset'] == dataset]
        x = subset['x']
        y = subset['y']
        
        # Create a scatter plot
        axs[i // 2, i % 2].scatter(x, y, color='cyan')
        axs[i // 2, i % 2].plot(x, np.poly1d(np.polyfit(x, y, 1))(x), color='orange')
        axs[i // 2, i % 2].set_title(f"Dataset {dataset}", color='white')
        axs[i // 2, i % 2].set_xlabel("x", color='white')
        axs[i // 2, i % 2].set_ylabel("y", color='white')
        axs[i // 2, i % 2].tick_params(colors='white')  # Change tick color to white
    
    plt.tight_layout(rect=[0, 0, 1, 0.96])
    plt.show()

This Jupyter Notebook is designed to demonstrate the capabilities of JupyterLite in the context of data science, specifically using popular libraries such as NumPy, Pandas, and Matplotlib. The focus of the analysis is on Anscombe's quartet, a well-known dataset that illustrates the importance of data visualization in statistical analysis.

In [ ]:

# Call the function to plot the data
plot_anscombe(data)

In [ ]:

AnscombeQuartet

Anscombe's Quartet Analysis¶