Log10 Based For Violin Plot With Improved Column Finding

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# 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)

# Select columns 6 through 10 for analysis
selected_columns = df.iloc[:, start_column_position:end_column_position + 1]

# Apply a log10 transformation to the data
log_selected_columns = np.log10(selected_columns)

# Create a violin plot to visualize log10-transformed data
plt.figure(figsize=(10, 6))
sns.violinplot(data=log_selected_columns, inner="stick")
plt.title('Violin Plot for Log10-Transformed Selected Columns')
plt.ylabel('Log10(Values)')
plt.xticks(rotation=45)
plt.tight_layout()

# Show the plot
plt.show()