Notes

import seaborn as sns
import matplotlib.pyplot as plt

# Set the style of seaborn plot
sns.set(style="whitegrid")

# Create a boxplot to show the distribution of readings for each part number and screw
plt.figure(figsize=(14, 8))
melted_data = pd.melt(data, id_vars=['Sample', 'Partnumber'], value_vars=['R1', 'R2', 'R3', 'R4'], var_name='Screw', value_name='Torque')
sns.boxplot(x='Partnumber', y='Torque', hue='Screw', data=melted_data)
plt.title('Distribution of Torque Readings Across Part Numbers and Screws')
plt.axhline(y=lower_bound, color='r', linestyle='--', label='Lower Acceptable Limit')
plt.axhline(y=upper_bound, color='g', linestyle='--', label='Upper Acceptable Limit')
plt.legend(title='Screw & Acceptable Limits')
plt.show()

# Bar plot for out-of-range counts by part number
plt.figure(figsize=(10, 6))
sns.barplot(x='Partnumber', y='sum', data=out_of_range_summary)
plt.title('Total Out-of-Range Readings by Partnumber')
plt.ylabel('Total Count of Out-of-Range Readings')
plt.xlabel('Partnumber')
plt.show()