Python For Data Analysis: A Complete Beginner Guide to Wrangling & Analyzing Data Using Python (Code tutorials Book 5) by Berg Craig

Author:Berg, Craig [Berg, Craig]
Language: eng
Format: epub
Published: 2020-06-15T16:00:00+00:00

The above code produces a two-dimensional array with two rows and four columns that we can identify by looking at the number of square brackets of the array.

The best and fastest way to create Numpy arrays is by using the Numpy built-in random data generating functions. One of them is randon.randn shown above.

The most common built-in Numpy function for generating random data is the arrange() function. The Numpy arrange function is similar to the python arange function. In the arange function, we pass the start, step and step size to generate evenly spaced values within the given interval.

For example:

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