Update matplotlib-box-plots.md

contrib/plotting-visualization/matplotlib-box-plots.md

@@ -40,60 +40,97 @@ Syntax - matplotlib.pyplot.boxplot(data,notch=none,vert=none,patch_artist,widths
 ## Implementation of Box Plot in Python
 
 ### Import libraries
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 import matplotlib.pyplot as plt
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 import numpy as np 
 
 ### Creating dataset
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 np.random.seed(10)
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 data = np.random.normal(100, 20, 200) 
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 fig = plt.figure(figsize =(10, 7))
 
 ### Creating plot
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 plt.boxplot(data)
 
 ### show plot
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 plt.show()
 
 ### Implementation of Multiple Box Plot in Python
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 import matplotlib.pyplot as plt  
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 import numpy as np  
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 np.random.seed(10)  
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 dataSet1 = np.random.normal(100, 10, 220)  
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 dataSet2 = np.random.normal(80, 20, 200) 
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 dataSet3 = np.random.normal(60, 35, 220)  
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 dataSet4 = np.random.normal(50, 40, 200)  
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 dataSet = [dataSet1, dataSet2, dataSet3, dataSet4]  
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 figure = plt.figure(figsize =(10, 7))  
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 ax = figure.add_axes([0, 0, 1, 1])  
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 bp = ax.boxplot(dataSet)  
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 plt.show()  
 
 ### Implementation of Box Plot with Outliers (visual representation of the sales distribution for each product, and the outliers highlight months with exceptionally high or low sales)
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 import matplotlib.pyplot as plt
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 import numpy as np
 
 ### Data for monthly sales
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 product_A_sales = [100, 110, 95, 105, 115, 90, 120, 130, 80, 125, 150, 200]
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 product_B_sales = [90, 105, 100, 98, 102, 105, 110, 95, 112, 88, 115, 250]
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 product_C_sales = [80, 85, 90, 78, 82, 85, 88, 92, 75, 85, 200, 95]
 
 ### Introducing outliers 
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 product_A_sales.extend([300, 80])
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 product_B_sales.extend([50, 300])
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 product_C_sales.extend([70, 250])
 
 ### Creating a box plot with outliers
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 plt.boxplot([product_A_sales, product_B_sales, product_C_sales], sym='o')
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 plt.title('Monthly Sales Performance by Product with Outliers')
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 plt.xlabel('Products')
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 plt.ylabel('Sales')
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 plt.show()
 
 ### Implementation of Grouped Box Plot (to compare the exam scores of students from three different classes (A, B, and C))
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 import matplotlib.pyplot as plt
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 import numpy as np
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 class_A_scores = [75, 80, 85, 90, 95]
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 class_B_scores = [70, 75, 80, 85, 90]
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 class_C_scores = [65, 70, 75, 80, 85]
 
 ### Creating a grouped box plot