21 NumPy Functions That Will Boost Your Data Analysis Process

21 NumPy Functions That Will Boost Your Data Analysis Process

21 NumPy Functions That Will Boost Your Data Analysis Process. In this post, we will go over 20 functions and methods that will boost your data analysis process.

Note: All images created by the author unless stated otherwise.

Everything about data science starts with data and it comes in various formats. Numbers, images, texts, x-rays, sound, and video recordings are just some examples of data sources. Whatever the format data comes in, it needs to be converted to an array of numbers to be analyzed.

One of the foremost tools to handle arrays of numbers is NumPy which is a scientific computing package for Python.

In this post, we will go over 20 functions and methods that will boost your data analysis process.

1. Array

It is used to create an array from scratch or convert a list or pandas series object to an array.

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2. Arange

It creates an array in a range with a specified increment.

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The first two arguments are lower and upper bounds (upper is exclusive). The third argument is the step size.

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