P#16 Line, Bar, Scatter, Pie charts

MATPLOTLIB...

In this blog, we will see how to plot various chart types using plt.line(), plt.bar(), plt.barh(). plt.scatter(), plt.hist(). 

If you run this code which is self explanatory, you will get this chart. 

def plotline():
    import matplotlib.pyplot as plt
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.plot(langs, students)  # plot
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('line.png')
    plt.show()

plotline()

If you run this code, you will get this chart. 

def plotbar():
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.bar(langs, students, col)  # BAR
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('bar.png')
    plt.show()

plotbar()

def plotbarh():
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.barh(langs, students, color = 'hotpink')  # BARh Pink
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('bar.png')
    plt.show()

plotbarh() plots horizontal bar as shown below.

def plotbarss():
    import numpy as np
    import matplotlib.pyplot as plt
    data = [[30, 25, 50, 20],
            [40, 23, 51, 17],
            [35, 22, 45, 19]]
    X = np.arange(4)
    fig = plt.figure()
    ax = fig.add_axes([0, 0, 1, 1])
    ax.bar(X + 0.00, data[0], color='b', width=0.25)
    ax.bar(X + 0.25, data[1], color='g', width=0.25)
    ax.bar(X + 0.50, data[2], color='r', width=0.25)
    ax.legend(labels=('cse', 'it', 'mech', 'mba'), loc='upper right')
    plt.savefig('bars.png')
    plt.show()

plotbarss() displays the chart as shown below with three bars.

def plotscat():
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.scatter(langs, students)  # Scatter
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('scat.png')
    plt.show()

plotscat()

plt.scatter() wil plot scatter plot as shown below()

def plotscatc():
    np.random.seed(19680801)  # seed the random number generator.
    data = {'a': np.arange(50),
            'c': np.random.randint(0, 50, 50),
            'd': np.random.randn(50)}
    data['b'] = data['a'] + 10 * np.random.randn(50)
    data['d'] = np.abs(data['d']) * 100

    fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
    ax.scatter('a', 'b', c='c', s='d', data=data, marker='*')
    ax.set_xlabel('entry a')
    ax.set_ylabel('entry b');
    plt.savefig('scatcc.png')
    plt.show()

plotscatc()

This will print different size, different colors.

This will print marker different size, different colors. 
marker = “*” sets the plot symbol as *. s = different size of the marker symbol in the plot. 

def plothist():
    import matplotlib.pyplot as plt
    import numpy as np
    x = np.random.normal(170, 10, 250)
    plt.hist(x)
    plt.savefig('hist.png')
    plt.show()

plt.hist() method plots histogram as shown below;

def plotpie():
    import matplotlib.pyplot as plt
    import numpy as np
    y = np.array([32, 40, 60, 80, 100])
    plt.pie(y)
    plt.savefig('pie.png')
    plt.show()

plotpie()

plot.pie() method to plot y.
def plotpie1():
    deg = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    # Creating plot
    fig = plt.figure(figsize=(8, 6))
    plt.pie(students, labels=deg)
    # show plot
    plt.savefig('pie1.png')
    plt.show()
plotpie1()

This is pie plot with labels. That is provide by  plt.pie(students, labels=deg) 

Happy learning with AMET ODL!!!