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This article shows you how to get started with NumPy. The content is concise and easy to understand. It will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

Background introduction

Today we learn that a useful module in python data analysis NumPy,NumPy is the basic package for scientific computing using Python. It contains other content:

A powerful N-dimensional array object

Complex (broadcast) function

Tools for integrating C / C + + and Fortran code

Useful linear algebra, Fourier transform and random number functions

In addition to obvious scientific uses, NumPy can also be used as an efficient multidimensional container for general-purpose data. You can define any data type. This enables NumPy to integrate seamlessly and quickly with various databases.

Getting started exampl

The above is a screenshot of the code running in Jupyter Notebook. The specific code is as follows:

# create an numpy array import numpy as npprint (np.__version__) # use np.array () to create an array array = np.array ([1 type 2 array 3]) array# to view the data type, which is the ndarrayprint (type (array)) of numpy # use the shape attribute to display the size of the array. # this is an integer tuple that represents the size of the array in each dimension. # for matrices with n rows and m columns, the shape will be (nfocus m). Array.shape# then creates a multidimensional array arr = np.array ([[1pje 2dag3], [4pje 5pc6]]) arr# prints the shape result as a two-row, three-column matrix array arr.shape# gets the array element arr [0jue 1] # uses the size print array element size arr.size# to convert an ordinary list to nmupy array # list with a different element type list1 = ['hello',1] True] arr = np.array (list1) arr# can be seen from the above results There will be no exception. # np uniformly converts the elements in ordinary list into string#. Use ndim to view the size of the array. Add element arr = np.append (arr,99) arr# delete element arr = np.delete (arr,1) arr in the array. The above is how to get started with NumPy. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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