Bar Graph for STD, MEAN, 25% ,50%, 75%, and MAX

import pandas as pd
import matplotlib.pyplot as plt

# 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]

# Calculate summary statistics for the selected columns
summary_statistics = selected_columns.describe()

# Transpose the summary statistics for plotting
summary_statistics = summary_statistics.T

# Extract the statistics you want to plot
statistics_to_plot = ['mean', 'std', 'min', '25%', '50%', '75%', 'max']

# Create a grouped bar plot for the selected statistics
plt.figure(figsize=(12, 6))
x = range(len(summary_statistics.index))
bar_width = 0.15

for i, statistic in enumerate(statistics_to_plot):
    plt.bar(
        [pos + i * bar_width for pos in x],
        summary_statistics[statistic],
        width=bar_width,
        label=statistic
    )

plt.xlabel('Columns')
plt.ylabel('Values')
plt.title('Summary Statistics for Selected Columns')
plt.xticks([pos + 3 * bar_width for pos in x], summary_statistics.index, rotation=45)
plt.legend()
plt.tight_layout()

# Show the plot
plt.show()