| import numpy as np
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| import matplotlib.pyplot as plt
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| def hist_of_a_sample_normal_distribution():
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| # Write your functionality below
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| # Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
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| fig = plt.figure(figsize=(9, 7))
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| # Create an axis, associated with figure fig, using add_subploat. Name it as ax.
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| ax = fig.add_subplot(111)
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| # Set random see to 100 using the expression np.random.seed(100)
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| np.random.seed(100)
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| # Create a normal distribution dist_arr of 1000 values, with mean 35 and standard deviation 3.0. Use the hist function.
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| dist_arr = 35 + 3.0*np.random.randn(1000)
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| # Label X-Axis as dist_arr, Label Y-Axis as 'Bin Count'.
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| # Set title as Histogram of a Single Dataset.
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| ax.set(title="Histogram of a Single Dataset", ylabel='Bin Count', xlabel='dist_arr')
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| ax.hist(dist_arr, bins=35)
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| return fig
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| def boxplot_of_four_normal_distribution():
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| # Write your functionality below.
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|
|
| # Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
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| fig = plt.figure(figsize=(9, 7))
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|
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| # Create an axis, associated with figure fig, using add_subploat. Name it as ax.
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| ax = fig.add_subplot(111)
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| # Set random see to 100 using the expression np.random.seed(100)
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| np.random.seed(100)
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| # Create a normal distribution arr_1 of 2000 values, with mean 35 and standard deviation 6.0. Use np.random.randn.
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| arr_1 = 35 + 6.0*np.random.randn(2000)
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| # Create a normal distribution arr_2 of 2000 values, with mean 25 and standard deviation 4.0. Use np.random.randn.
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| arr_2 = 25 + 4.0*np.random.randn(2000)
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| # Create a normal distribution arr_3 of 2000 values, with mean 45 and standard deviation 8.0. Use np.random.randn.
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| arr_3 = 45 + 8.0*np.random.randn(2000)
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| # Create a normal distribution arr_4 of 2000 values, with mean 55 and standard deviation 10.0. Use np.random.randn.
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| arr_4 = 55 + 10.0*np.random.randn(2000)
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| # Create a list labels with elements ['arr_1', 'arr_2', 'arr_3', 'arr_4']
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| labels = ['arr_1', 'arr_2', 'arr_3', 'arr_4']
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| # Draw a Boxplot arr_1, arr_2, arr_3, arr_4 with notches and label it using the labels list. USe the boxplot function.
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| # Choose 'o' symbol for outlier, and fill color inside boxes by setting patch_artist argument to True.
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| norml_dist_list = [arr_1, arr_2, arr_3, arr_4]
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| ax.boxplot(norml_dist_list, labels=labels, notch=True, sym='o', patch_artist=True)
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| # Label X_Axes as 'Dataset', Label Y-Axes as 'Value'. Set Title as "Box Plot of Multiple Dataset".
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| ax.set(title="Box Plot of Multiple Dataset", ylabel='Value', xlabel='Dataset')
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| return fig
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