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Bar graphs, often known as bar charts, are a common method for displaying categorical data. Matplotlib method
                  plt.bar(), can be used for plotting bar graphs which accept as input parameters a list of categories that appear
                  on the x-axis, and the associated counts that appear on the y-axis




             C T  04     1.   Modify the above code to redraw the above graph using six different colors. As shown above,
                             we use the keyword argument color to function plt.bar() for speciying different color
                             encoding for different bars.
                         2.  Consider the following two lists depicting sales data of four different products:
                             >>> products = ['Product A', 'Product B', 'Product C', 'Product D']
                             >>> sales = [350, 480, 240, 520]
                             Draw the bargraph visualizing the same.



            3.3 Histogram

            Histograms are popular data visualisation tools that offer a graphical depiction of a dataset's distribution. They are
            especially useful for investigating the underlying frequency or count of various values or ranges in a dataset. The
            Matplotlib module in Python includes function plt.hist() for producing and customising histograms.
            3.3.1 CGPA of students

            Suppose, we have CGPA of 10 students and we wish to depict pictorically the distribution of the CGPA values of
            students within different ranges. In the following program, we first prompt the user to provide list of numerical values
            representing the CGPA (Cumulative Grade Point Average) of students as an input. Thereafter, we plot the histogram
            representing frequency distribution of CGPA obtained by students using plt.hist() function. The plt.hist()
            function takes two main arguments: the data to be plotted and the bins specification. The bins define the intervals or
            ranges into which the data will be divided. In our case, we specify the bins as [0, 2, 4, 6, 8, 10], which means the data
            will be grouped into the following intervals: [0, 2), [2, 4), [4, 6), [6, 8), and [8, 10]. The color of the bars in the histogram
            is set to red ("r") for better visibility as shown below (Fig 3.21):

             >>> import matplotlib.pyplot as plt
             >>> cgpa = eval(input('Enter CGPA data to be plotted as histogram: '))
                 Enter data to be plotted as histogram: [2,4,4,4.5,6,6.7,8,8.5,9,9.5,9.9,9]
             >>> plt.hist(cgpa, bins = [0, 2, 4, 6, 8, 10], color="r")
             >>> plt.xlabel('CGPA Range')
             >>> plt.ylabel('Frequency')
             >>> plt.title('Frequency Distribution of CGPA of students')
             >>> plt.grid()
             >>> plt.savefig('CGPAHistogram.png',dpi=900,bbox_inches='tight')
             >>> plt.show()


                  Histograms are popular data visualisation tools that offer a graphical depiction of a dataset's distribution. Python
                  includes function plt.hist() for producing and customising histograms.




             C T  05     Write a program that takes a list of grocery items purchased as an input from the user. The
                         program should plot the frequency distribution of grocery items purchased using a histogram.







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