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SETTING UP THE NUMPY LIBRARY ENVIRONMENT IN A PYTHON PROGRAM
Shodmonkulov Mirjalol Turonovich
Teacher, Department of Informatics and Digital Educational Technologies, Jizzakh State
Pedagogical University
Email address:
Abstract:
This article describes how to configure the Numpy library environment for working with
arrays in the graphics mode of the Python programming language and plotting graphs of
mathematical functions.
Keywords:
Data science, data, analysis, Numpy, engineer, array, python, matplotlib, library.
Numpy is a library for the Python programming language, used in almost all disciplines and
fields. The Numpy library is considered the universal standard for working with numerical data in
Python. Numpy users range from novice coders to experienced researchers engaged in cutting-edge
scientific research and development.
The Numpy library covers working with multidimensional arrays and matrix data. It provides
efficient ways to work with the same n-dimensional array object, the ndarray. NumPy allows you to
perform various mathematical operations on arrays at high speed. It is a Python library that provides
a range of procedures for multidimensional array objects, various derivative objects, and fast
operations on arrays, including mathematical, logical, shape manipulation, sorting, selection, and
insertion/extraction.
At the heart of the NumPy package is the ndarray object. This involves n-dimensional arrays of the
same data type, with many operations performed in compiled code for performance. There are
several important differences between NumPy arrays and standard Python sequences:
NumPy arrays, unlike Python lists (which can grow dynamically), have a fixed size when created.
Resizing an array creates a new array and deletes the original.
• The elements in a NumPy array must all be of the same data type, and therefore the same size in
memory. Exception: Python (including NumPy) can have arrays of objects, which allows you to
create arrays of elements of different sizes.
• NumPy arrays facilitate advanced mathematical and other operations on large amounts of data.
Typically, such operations are performed more efficiently and with less code than can be done using
Python's built-in arrays.
• A large number of scientific and mathematical packages based on Python use NumPy arrays;
although they typically support Python-serial input, they convert such input to NumPy arrays before
processing, and often output NumPy arrays. In other words, to effectively use much of today's
scientific/mathematical software based on Python,
(perhaps most) it is not enough to know how to use Python's built-in sequence types - you also need
to know how to use NumPy arrays [1].
Installing Python and the NumPy library
Step 1. Installing Python
The first step in the NumPy installation process is to install Python. Before you can get NumPy, you
need to have the latest version of Python installed on your system. Read and follow the steps below
to learn how to install Python on Windows [2].
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Figure
1.
Downloading
Python
from
the
official
Python
website
Figure 2. Installing Python.exe
So, when we go to the official website python.org, the program option that corresponds to our
operating system and device will appear on the main screen. The latest version of the program will
have the latest changes. Then it is better to choose the latest version. Right now, one of the screens
offers us the python 3.13.2 version, so we will install this version on the device.
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Figure 3. Now click on the install option.
During the installation process, we will see a window like the one above and select the
required options from this window and select “Install Now” and the installation process will begin.
After this process, if we see that the installation has been completed successfully, it means that the
program has been installed on our device. We can close the window by clicking the “Close” button.
Finally, as shown, you will receive a message that the installation was successful. Click the Close
option
Step 2. Installing Numpy
Our next step will be to install the Numpy library on the device. In this case, we will search for
“Command Prompt” in the computer search box and run it as an administrator. The sequence
continues as follows.
Figure
4.
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Figure 4. Press the Windows key, enter the command line and click Run as Administrator. When
launched, we issue the pip install numpy command to install the Numpy library. After a while, it
will automatically start loading the library itself. We can observe the installation process as follows.
To check if Numpy is installed, we issue the numpy -version command in this “black window”. It
will then display the installed version on the screen. We see that numpy 1.22.3 is displayed on the
screen.
Figure 5.
To
start
the
NumPy
installation,
type
pip
install
numpy
and
press
Enter.
Figure 6.
The download and installation of the NumPy package will start and finish automatically. You will
see the message: numpy (-version) installed successfully
Step 3: Verify NumPy installation In the next step, we can see where Numpy is installed and
detailed information about the installation. To do this, we can enter the command pip show numpy
in the "black window".
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Figure 7. Launch the command prompt and type pip show numpy and press Enter to verify that
NumPy
is
part
of
the
Python
packages
Figure 8. The output shows you where the NumPy version is stored on your system Step 4: Import
the NumPy package You can import the NumPy library to verify whether it was installed
successfully or not. To do the same, read and follow the next steps [4].
Figure 9. Type Python at the command prompt and press Enter to create a Python environment.
In the browser, we will try to import the Numpy library to check whether it is installed or not. To
import, we will need to issue the import numpy command. And we have successfully imported the
numpy library.
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Figure 10. Now type import numpy as the np command and press Enter.
You have now successfully imported the NumPy library package and can use its functions and
classes for various NumPy objects [3].
References:
1.
Python for Data Analysis – Wes McKinney
Gabor Szabo, “1000 Python Examples”, 2020, 140-165 pages
2.
Васильев А.Н., “Python на примерах. Практический курс по программированию”,
Наука и техника, Санкт-Петербург, 2016, 235- 243стр.
3.
Eshtemirov S. Nazarov F. Algoritmlash va dasturlash asoslari. O‘quv qo‘llanma. Samarqand
2019. -208 b.
4.
Sh.A. Mengliyev, O.A. Abdug‘aniev, S.Q. Shonazarov, D. Sh. To‘rayev “Python dasturlash
tili”, Termiz-2021
