Sorting, Searching and Counting in NumPy:

Sorting in Python for Data Science:

It refers to the arrangement of data in a particular format. The sorting algorithm specifies a way of arranging data in a particular order. The most common orders in use are the numerical or lexicographical order. In NumPy we have various functions for sorting operation which is present in the library like lexsort, sort, argsort, etc.

numpy.sort(): It is a function that returns a sorted copy of an array.

Sorting, Searching and Counting in NumPy Python for Data Science

numpy.argsort(): It is a function that returns indices which would sort an array.

Sorting, Searching and Counting in NumPy Python for Data Science

numpy.lexsort(): It is a function that returns an indirect stable sort by using a sequence of keys.

Sorting, Searching and Counting in NumPy Python for Data Science

Searching:

It is a technique or operation which will help us to find the place of a element or value in the list provided. Depending on the element being found or not found, a search is said to successful or unsuccessful. InNumPy various search operations can be perform by using various functions provid in library such as argmin, argmax, nanaargmax, etc.

numpy.argmax(): It is a function which returns the indices of the max element of array in a particular axis.

Sorting, Searching and Counting in NumPy Python for Data Science

numpy.nanargmax(): It is a function that will return the indices of the max element of array in a specified axis while ignoring the NaNs. The result is not trustworthy in case a slice contains only NaNs and Infs.

Sorting, Searching and Counting in NumPy Python for Data Science

numpy.argmin(): It is a function which will return the indices of minimum values along a axis.

Counting:

numpy.count_nonzero(): It will count the number of non zero values in the array.

So, to learn more about sorting in python for data science, you can check this and this as well.

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