Why Python Is Gaining So Many Users – History, Benefits, and How to Get Started

Python development history

1991‑02 – Python 0.9.0 released with classes, functions, exception handling, lists and dictionaries.

1994‑01 – Python 1.0 added lambda, map, filter, reduce (functional programming primitives).

2000‑10 – Python 2.0 introduced list comprehensions, a full garbage‑collector and Unicode support.

2008‑12 – Python 3.0 released, breaking compatibility with 2.x to fix fundamental language flaws.

Python 3.x is the current mainstream; the latest stable release is 3.13. Python 2 reached end‑of‑life in 2020.

Python environment setup

Download the installer from www.python.org for the target OS. On Windows, enable the “Add Python x.x to PATH” option. Verify the installation by running:

python --version
# or
python3 --version

A minimal program to confirm the interpreter works: print("Hello World!") Save the line in a file hello.py and execute with python hello.py (or python3 hello.py).

Conda installation

Conda provides isolated environments and can manage multiple Python versions. Two distribution options exist:

Anaconda – full bundle with many data‑science packages.

Miniconda – lightweight installer. Example for macOS Apple Silicon:

# 1. Create a folder
mkdir -p ~/miniconda3
# 2. Download Miniconda
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh
# 3. Install silently
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
# 4. Remove the script
rm ~/miniconda3/miniconda.sh

Activate the base environment and initialise shell integration:

source ~/miniconda3/bin/activate
conda init --all

Confirm the installation:

conda -V

Conda basic commands

Create a Python 3.8 environment named myenv: conda create --name myenv python=3.8 Activate, verify the interpreter, and deactivate:

conda activate myenv
python --version
conda deactivate

Configure domestic mirrors (China)

Replace the default overseas channels with USTC mirrors to improve download speed:

conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/
conda config --set show_channel_urls yes

Verify the configuration with conda config --show channels. If speed remains insufficient, specify a mirror on the install command, e.g.:

conda install opencv -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/

pip usage

Install packages from PyPI:

# latest version
pip install numpy
# specific version
pip install numpy==1.19.4
# multiple packages at once
pip install numpy matplotlib requests

When multiple Python versions are present, use pip3 for Python 3. To accelerate downloads in China, add the -i option with a domestic mirror:

pip install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple/

Remove a package with:

pip uninstall numpy

Development IDE

PyCharm (JetBrains) is the most widely used Python IDE. Two editions are available:

Professional – full feature set, commercial license.

Community – free, feature‑reduced version sufficient for most development.

Download from https://www.jetbrains.com/pycharm/download/other.html, install, and create a new Python project. During project creation you can select a Conda environment (e.g., myenv) to ensure isolation. The generated project contains a main.py file; run it via the IDE’s Run action.