Improved Gaussian Fit That Can Take Columns

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

# 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 7 through 10)
start_column_position = 6  # Corresponds to column 7
end_column_position = 9   # Corresponds to column 10

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

# Select columns 7 through 10 for analysis
selected_columns = df.iloc[:, start_column_position:end_column_position + 1]

# Create a figure with subplots for each Gaussian distribution
plt.figure(figsize=(12, 6))

# Define the x-axis range for the plots
x_range = np.linspace(selected_columns.min().min(), selected_columns.max().max(), 1000)

# Iterate through the selected columns and analyze them
for i, col in enumerate(selected_columns.columns):
    plt.subplot(2, 2, i + 1)  # Create subplots in a 2x2 grid
    target_column = selected_columns[col]
    mean = target_column.mean()
    std_dev = target_column.std()

    # Fit a Gaussian distribution
    fitted_data = norm.pdf(x_range, mean, std_dev)

    # Plot the Gaussian distribution
    plt.plot(x_range, fitted_data, 'b-', linewidth=2, label='Fitted Gaussian')

    # Plot the original data as a histogram
    plt.hist(target_column, bins=30, density=True, alpha=0.7, color='gray', label='Data Histogram')

    # Add labels and title with the target column name
    plt.xlabel('Values')
    plt.ylabel('Frequency')
    plt.title(f'Gaussian Fit for {col}')

    # Show the mean shift
    plt.axvline(mean, color='red', linestyle='dashed', linewidth=2, label='Mean Shift')

    # Add a legend
    plt.legend()

# Adjust layout
plt.tight_layout()

# Show the plot
plt.show()