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Earlier in this tutorial we have worked with very small amounts of data in our examples, just to understand the different concepts.
In the real world, the data sets are much bigger, but it can be difficult to gather real world data, at least at an early stage of a project.
To create big data sets for testing, we use the Python module NumPy, which comes with a number of methods to create random data sets, of any size.
Example
Create an array containing 250 random floats between 0 and 5:
import numpy
x = numpy.random.uniform(0.0, 5.0, 250)
print(x)
To visualize the data set we can draw a histogram with the data we collected.
We will use the Python module Matplotlib to draw a histogram
Example
Draw a histogram:
import
import matplotlib.pyplot as plt
x = numpy.random.uniform(0.0, 5.0, 250)
plt.hist(x, 5)
plt.show()
We use the array from the example above to draw a histogram with 5 bars.
The first bar represents how many values in the array are between 0 and 1.
The second bar represents how many values are between 1 and 2.
Which gives us this result: