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Step 2 Click on the Download button. The download should start automatically. Wait for the download to complete.
Step 3 After downloading the installer, locate the file and run it by double-clicking on it.

The process of installing Python begins.
Step 4 Click on the Add python.exe to PATH check box to set Python to the Environment variable of the operating
system.
Step 5 Click on the Install Now button.

Step 6 Follow the instruction given on the Installation Wizard that will guide you through the installation process.
After completing the installation process, Python will be installed on your system.

BRAINY Python is widely used by numerous major companies, including Google,
FACT Facebook, and Amazon.

Variety of Python Editors
Python offers a wide range of editors and integrated development environments (IDEs) that cater to different needs and
preferences. Some of the popular ones are described as follows:
u Python IDLE: IDLE (Integrated Development and Learning Environment) is Python’s default IDE that comes bundled

with the standard distribution of Python.

u PyCharm: PyCharm is a professional IDE developed by JetBrains, available in both free (Community) and paid
(Professional) versions.

u Anaconda: Anaconda is a distribution of Python and R for scientific computing and data science. It includes a package
manager (conda) and many pre-installed libraries.
u Spyder: Spyder (Scientific Python Development Environment) is an open-source IDE that comes with Anaconda.

u Jupyter Notebook: Jupyter Notebook is an open-source web application that allows you to create and share
documents containing live code, equations, visualizations, and narrative text.
Let us learn about Jupyter Notebook in detail.

Jupyter Notebook

Jupyter Notebook is a powerful and versatile tool that revolutionises the way programmers and data scientists work
with code and data. By providing an interactive environment where code execution and rich media can coexist. Jupyter
Notebook facilitates a more dynamic and intuitive approach to programming. Users can write and run code in segments,
view the results immediately, and combine these outputs with formatted text and visualizations. It is widely used in data
science, scientific computing, and education.

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