Python Programming Language Introduction: Features, Scope and Libraries

If you open the code behind a banking app, a movie recommendation, a rocket launch dashboard and a school marks sheet, there is a good chance that at least one of them was written in Python. It is the language that a student can pick up in a weekend, and a data scientist can still use ten years later. That range is rare.

Python is a high level, general purpose programming language that is known for code that reads almost like plain English. It was created in the early 1990s, it is free to use, and it runs on Windows, Mac and Linux. In October 2026, the TIOBE index, which ranks languages by how widely they are used, still places Python at number one.

In this guide, we explain what Python is, where it came from and why it has the name it has. We go through its main features, with a short example of each, and its scope, which means the kinds of work it is used for. We then look at the libraries used for data science and visualization, compare Python with other languages, and say plainly where it is weak. We finish with a simple plan to start learning.

What is Python Programming?

Python is a general purpose, high level, interpreted programming language. Each of those words has a meaning.

• General purpose. It is not built for one job. You can use it for websites, data analysis, automation, games and more.
• High level. You write instructions close to how people think, and Python handles the machine details, such as memory.
• Interpreted. You run your code directly with the Python interpreter, without a separate compile step that you have to manage. We explain what happens inside in the features section.

Python supports several styles of programming, including procedural, object oriented and functional. That means you can start with simple step by step scripts, and later move to classes and larger designs, in the same language. The official Python FAQ describes it as an interpreted, interactive, object oriented language that combines remarkable power with very clear syntax.

Your First Python Program

Here is the classic first program. It is a single line.

print("Hello, World!")

Hello, World!

In many other languages, the same program takes four or five lines of setup. That is one reason Python is popular with beginners. Here is a slightly bigger example, with a list and a decision:

marks = [72, 88, 91, 65]
for m in marks:
    if m >= 70:
        print(m, "pass")
    else:
        print(m, "needs work")

72 pass
88 pass
91 pass
65 needs work

Notice that Python uses indentation, the spaces at the start of a line, to show which lines belong together. There are no curly brackets. This forces tidy code, and it is a big part of why Python is easy to read.

A Short History of Python

Where does the Name Come From?

Many people think the name comes from the snake. It does not. Guido van Rossum wanted a name that was short, unique and slightly mysterious, and he named the language after Monty Python’s Flying Circus, a British comedy show from the 1970s.

Who Looks After Python?

Python is open source. The Python Software Foundation, a non profit, holds the rights to it. The license lets you use Python freely, even in commercial products, as long as you keep the copyright notices. New features are proposed in public documents called PEPs, and a new feature release comes out every 12 months. If your company plans to ship Python inside a product, have your legal team read the PSF license text itself.

Features of Python Programming Language

These are the features that matter most. Each one is explained here, with a short example.

1. Easy to Learn and Read

The syntax is short and close to English. The program you saw above is clear even to a beginner. Less typing and fewer symbols mean fewer places to make a mistake.

2. Interpreted, and Compiled to Bytecode

Python first compiles your code to a simpler form called bytecode, and then the interpreter runs the bytecode. You do not see this step or manage it. You can look at the bytecode of a function with the dis module:

import dis

def add(a, b):
    return a + b

dis.dis(add)

LOAD_FAST_LOAD_FAST   1 (a, b)
BINARY_OP             0 (+)
RETURN_VALUE

The exact names and the extra lines change between Python versions, so your screen may look a little different. The point is that your code becomes a list of small steps, and Python runs them one by one.

3. Dynamically Typed

You do not declare the type of a variable. Python works it out from the value, and you can change it later.

x = 10
print(type(x).__name__)
x = "ten"
print(type(x).__name__)

int
str

4. Cross Platform

The same program runs on Windows, Mac and Linux, as long as Python is installed. The official FAQ says Python is portable, and runs on many Unix systems including Linux and macOS, and on Windows.

5. Free and Open Source

Python costs nothing, and its source code is open. A very large community builds extra tools for it and shares them for free.

6. Supports Several Programming Styles

You can write a plain loop, a functional one line, or a class, and all three are normal Python.

marks = [72, 88, 91, 65]

# Procedural
total = 0
for m in marks:
    total += m
print(total / len(marks))

# Functional
print(sum(map(lambda m: m * 2, marks)))

# Object oriented
class Student:
    def __init__(self, name, marks):
        self.name = name
        self.marks = marks

    def average(self):
        return sum(self.marks) / len(self.marks)

print(Student("Asha", marks).average())

79.0
632
79.0

7. A Large Standard Library

Python is said to come with batteries included. Its standard library has ready made tools for maths, dates, files, text, the internet and more, and you do not install anything extra to use them.

import json
import math
from datetime import date

print(math.sqrt(144), date(2026, 10, 9).strftime("%d %B %Y"))
print(json.dumps({"language": "Python", "year": 1991}))

12.0 09 October 2026
{"language": "Python", "year": 1991}

8. Graphical User Interface Support

You can build desktop windows and buttons with Python. The standard library has Tkinter, and outside libraries such as PyQt and wxPython add more.

9. A Huge Ecosystem of Libraries

The thing that makes Python stand out is the number of libraries built on top of it. They are downloaded from the Python Package Index, and installed with pip. More on this below.

Scope of Python Programming Language

Python has a very wide scope. Here is a view of the kinds of work done in each area.

The official Python FAQ names Google, Yahoo and Lucasfilm among the companies that use Python inside their systems, and says several Linux distributions, notably Red Hat, wrote part or all of their installer and system tools in Python.

How Popular Is Python?

Two well known sources give a current picture.

• TIOBE Index, October 2026: Python is ranked number 1, with a rating of 19.23 percent. C is second with 11.33 percent, then C plus plus with 8.87 percent, and Java with 8.41 percent. Python’s rating is lower than a year ago, when it also ranked first, so it leads, but by a smaller margin.
• Stack Overflow Developer Survey 2025: 57.9 percent of all respondents used Python. It ranked fourth overall, and first among people who are learning to code, at 71.8 percent. The survey notes a 7 percentage point rise from 2024 to 2025.

These are two different measures, so they do not contradict each other. TIOBE counts how much a language is searched for and discussed online. The Stack Overflow survey asks developers what they use.

Python Tutorial for Beginners

A video is a good way to see Python running for the first time.

Python Libraries for Data Science and Visualization

Python does not come with built in libraries for machine learning. The libraries below are separate downloads, and you install them with pip. If you try to use one that you have not installed, Python stops with an error:

import not_a_real_library

ModuleNotFoundError: No module named 'not_a_real_library'

Only two of the seven libraries are for visualization. Here they are, grouped by what they actually do.

NumPy

NumPy gives Python fast arrays and the maths to work on them. Almost every other library in this list is built on it, and it is where most data work starts.

SciPy

SciPy adds scientific tools on top of NumPy: statistics, optimisation, signal processing and more. For example, it can tell how strongly two sets of numbers move together.

import numpy as np
from scipy import stats

hours = np.array([1, 2, 3, 4, 5])
score = np.array([35, 48, 62, 73, 85])
print(round(float(stats.pearsonr(hours, score)[0]), 3))

0.999

A value close to 1 means the two move together very strongly.

statsmodels

The name is written as one word, in lower case. It is used for statistical models and tests, such as regression with full statistical reports.

pandas

pandas is a library for working with tables of data. Here it finds the average mark in each course:

import pandas as pd

df = pd.DataFrame({
    "course": ["Python", "Python", "SQL", "SQL", "Excel"],
    "marks": [82, 74, 95, 65, 70],
})
print(df.groupby("course")["marks"].mean())

course
Excel     70.0
Python    78.0
SQL       80.0
Name: marks, dtype: float64

scikit learn

Scikit learn is the main library for classic machine learning. It covers classification, regression, clustering and more. Here is a tiny model that learns the link between study hours and marks, and predicts the mark for 6 hours:

import numpy as np
from sklearn.linear_model import LinearRegression

hours = np.array([[1], [2], [3], [4], [5]])
score = np.array([35, 48, 62, 73, 85])
model = LinearRegression().fit(hours, score)
print(round(float(model.predict([[6]])[0]), 1))

98.1

Matplotlib

Matplotlib is the main library for drawing charts in Python, including line, bar, pie, scatter and 3D plots. See our Matplotlib tutorial for full examples.

Seaborn

Seaborn is built on Matplotlib, and makes statistical charts with less code and better default styling.

A Note on Jupyter Notebook

A notebook is not an IDE. It is a tool where you write code in cells and see the results, charts and notes right under each cell. It is a very popular way to learn and to share data work. An IDE, such as PyCharm, is a full development environment with a debugger and project tools.

Python Compared With Other Languages

No language is best at everything. This is a fair view of where Python stands.

Where Python Is Weak

• Raw speed. For very heavy number crunching in plain Python, languages like C++ or Java are faster. Libraries such as NumPy close the gap, because they do the heavy work in fast code underneath.
• Mobile apps. Python is not the usual choice for phone apps.
• Many threads at once. Python has a global interpreter lock that limits true parallel threads. Python 3.13 added an optional build without it, which is a new area.
• Memory use. Python programs often use more memory than the same program in C.

What is New in Python 3.15

The newest release is Python 3.15, published on 9 October 2026. The python.org release page lists these highlights.

• Two new built in types. The new types are sentinel and frozendict.
• UTF 8 by default. Python now uses UTF 8 as the default text encoding.
• Lazy imports. Explicit lazy imports aim to speed up program startup.
• A faster experimental JIT. The release page says the experimental JIT is 7 to 8 percent faster than the standard interpreter on x86 64 Linux.

You do not need any of these to start. Learn the basics first. These are useful to know, because a beginner who installs the newest version will see them.

How to Start Learning Python

1. Install Python and an editor. See our guide on how to install and run Python, and our PyCharm installation guide.
2. Learn the basics: variables, numbers, strings, comments and operators.
3. Learn the four main data types for collections: lists, tuples, sets and dictionaries.
4. Learn functions, then files, then exception handling.
5. Pick a direction: data analysis with pandas, charts with Matplotlib, web with Flask or Django, or automation.
6. Build small projects. A marks calculator, an expense tracker or a script that renames files will teach you more than another hour of reading.

Complete Script to Run Yourself

Here is the full script used to test every example on this page. Install the libraries first with the command below, and then run the script with Python 3.

pip install numpy pandas scipy scikit-learn matplotlib seaborn statsmodels

import dis
import json
import math
import platform
import sys
from datetime import date

print("Hello, World!")

marks = [72, 88, 91, 65]
for m in marks:
    if m >= 70:
        print(m, "pass")
    else:
        print(m, "needs work")

x = 10
print(type(x).__name__)
x = "ten"
print(type(x).__name__)

total = 0
for m in marks:
    total += m
print(total / len(marks))
print(sum(map(lambda m: m * 2, marks)))

class Student:
    def __init__(self, name, marks):
        self.name = name
        self.marks = marks

    def average(self):
        return sum(self.marks) / len(self.marks)

print(Student("Asha", marks).average())

def add(a, b):
    return a + b

dis.dis(add)

print(math.sqrt(144), date(2026, 10, 9).strftime("%d %B %Y"))
print(json.dumps({"language": "Python", "year": 1991}))
print(platform.system() in ("Windows", "Linux", "Darwin"))
print(sys.version_info >= (3, 9))

for name in ["numpy", "pandas", "scipy", "statsmodels", "sklearn", "matplotlib", "seaborn"]:
    try:
        __import__(name)
        print(name, "imports ok")
    except ImportError:
        print(name, "not installed")

try:
    import not_a_real_library
except ModuleNotFoundError as e:
    print("ModuleNotFoundError:", e)

import numpy as np
import pandas as pd

df = pd.DataFrame({
    "course": ["Python", "Python", "SQL", "SQL", "Excel"],
    "marks": [82, 74, 95, 65, 70],
})
print(df.groupby("course")["marks"].mean())

from sklearn.linear_model import LinearRegression

hours = np.array([[1], [2], [3], [4], [5]])
score = np.array([35, 48, 62, 73, 85])
model = LinearRegression().fit(hours, score)
print(round(float(model.predict([[6]])[0]), 1))

from scipy import stats
print(round(float(stats.pearsonr(hours.ravel(), score)[0]), 3))

Python is popular for a simple reason: you can start writing useful code on day one, and you do not outgrow it. Install it, run the first program from this page, and then change it. Add a loop, break it on purpose, and read the error. The language rewards curiosity. When you are ready, the next steps are variables and data types, then functions, then the libraries for the field you want to work in.