Data Visualization with Matplotlib

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Getting Stared with Matplotlib - Creating Histograms using Pyplot

In this assessment, we will learn how to create histograms using Matplotlib's Pyplot.

Histograms are a special form of bar chart where the data represent continuous rather than discrete categories. This means that in a histogram there are no gaps between the columns representing the different categories. Histogram’s data is plotted within a range against its frequency. Histograms are very commonly occurring graphs in probability and statistics and form the basis for various distributions like the normal -distribution, t-distribution, etc.

  • Let us start by importing Pyplot:

    import << your code goes here >> as plt
  • Now let us create a variable x with the data that we will use to plot the histogram. Here we will use NumPy’s random.randn() method which generates data with the properties of a standard normal distribution i.e. mean = 0 and standard deviation = 1, and hence the histogram looks like a normal distribution curve.

    import << your code goes here >> as np
    x = np.random.randn(10000)
  • Next, we will set up the title and the axis labels:

    plt.<< your code goes here >>("Histogram Example")
    plt.<< your code goes here >> ("Random Data")
    plt.<< your code goes here >> ("Frequency")
  • Now we will plot our histogram using the hist method:

    plt.<< your code goes here >>(x, 10)

    We have used 10 here to divide the data into 10 equal strata or bins.

  • And finally, we will show our plot:

    plt.<< your code goes here >>
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