Introduction to Data Visualization : Datasaurus Dozen

import pandas as pd
import matplotlib.pyplot as plt

df=pd.read_csv('datasaurus.csv')
# print first 5 rows
df.head()

# Group the dataset by the 'group' column
grouped_data = df.groupby('dataset')
# Calculate summary statistics for each group
summary_stats = grouped_data.agg({
    'x': ['mean', 'median', 'std'],
    'y': ['mean', 'median', 'std']
})


# Print the summary statistics for each group
print(summary_stats)

# List of unique dataset names
datasets = df['dataset'].unique()

# Create subplots with multiple columns (e.g., 4 columns per row)
num_cols = 4
num_rows = len(datasets) // num_cols + (len(datasets) % num_cols > 0)

# Create subplots
fig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 10))

# Flatten the axs array to iterate over subplots
axs = axs.flatten()

# Iterate through datasets and create scatter plots
for i, dataset in enumerate(datasets):
    ax = axs[i]
    
    # Filter data for the current dataset
    subset_data = df[df['dataset'] == dataset]
    
    # Plot the scatter plot
    ax.scatter(subset_data['x'], subset_data['y'])
    
    # Set title for the subplot
    ax.set_title(f'Dataset: {dataset}')
    
    # Add labels to the axes
    ax.set_xlabel('X-axis')
    ax.set_ylabel('Y-axis')

# Remove any empty subplots
for i in range(len(datasets), num_rows * num_cols):
    fig.delaxes(axs[i])

# Adjust layout
plt.tight_layout()

# Show the plots
plt.show()