Raincloud Plots

import pandas as pd
import matplotlib.pyplot as plt
import ptitprince as pt  # Import ptitprince for raincloud plots
import seaborn as sns  # Import Seaborn for color palettes

# Replace 'B5 segmentSummary (1).csv' with the actual CSV file path
file_path = 'B5 segmentSummary (1).csv'

# Define the range of column positions you want to analyze (columns 6 through 10)
start_column_position = 6  # Corresponds to column 6
end_column_position = 9  # Corresponds to column 10

# Read the CSV file into a Pandas DataFrame
df = pd.read_csv(file_path)

# Create a Seaborn color palette for differentiation
colors = sns.color_palette('husl', n_colors=end_column_position - start_column_position + 1)

# Create a figure with subplots for Raincloud plots
plt.figure(figsize=(12, 12))

# Iterate through the selected columns and create Raincloud plots with different colors
for i, col in enumerate(df.columns[start_column_position:end_column_position + 1]):
    plt.subplot(2, 2, i + 1)  # Create subplots for Raincloud plot
    pt.RainCloud(data=df, x=col, orient='h', width_viol=0.6, width_box=0.4, palette=[colors[i]])

    plt.title(f'Raincloud Plot for {col}')

# Adjust layout
plt.tight_layout()

# Show the plots
plt.show()