Best Python Libraries for Beginners and Intermediate Developers

By | November 22, 2020
Python Libraries

Python is well known for its insane number of robust libraries and frameworks. The core Python itself not a big programming language but its thousands of open source libraries make it one the biggest programming languages. And if you want to upgrade your Python skills from beginner level to intermediate or even advance then you need to explore its best libraries. The library you pick depends on the domain you want to master with python.

The huge number of Python libraries often overwhelm beginner Pythoneer who have just completed their basic Python and they try to directly jump to the advanced python libraries without exploring Python fundamentals and it’s not even their fault because when we google the best Python libraries for beginners and intermediate developers, as a result, we only get the repetitive Advance libraries such as TensorFlow, Sci-kit, SciPy, Keras, PyTorch, Django, etc.

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So here in this article, we have covered the Top 10 Python libraries for Beginners and to be Intermediate Python developer. We highly recommended you explore these libraries before you move to Advance libraries and frameworks.

Best Python Libraries

  1. NumPy
  2. Pillow
  3. Requests
  4. SQLite
  5. Collections
  6. Tkinter
  7. Matplotlib
  8. Beautifulsoup
  9. CSV
  10. ScarPy

1. NumPy

It stands for Numerical Python and it is the base library for most of the Python Data Science libraries. And if you want to learn Data Science with Python, then NumPy would be the first step to your goal. Core Python misses out on one of the most prominent Data Structure “Array”, but numpy fills this hole of Python with its powerful Array.

However, Python has the Python list data structure which is very much similar to Array, but the list lacks NumPy Array in all Performance parameters. Unlike the Python list, NumPy Array uses continuous memory location to store its elements which makes it faster to update and retrieve its elements. NumPy Array considered 50X faster than the Python list. Apart from Array NumPy provide many inbuilt methods for complex numerical and scientific computation. Most of the NumPy code is written in Python, but to optimize and speed up the performance of the library its complex methods are written in C and C++, which make it one of the fastest Numerical Library.

Get started with NumPy

Python does not come with a pre-installed NumPy library, so in order to use NumPy and its array you need to install numpy using the PIP command.

pip install numpy

Example:

>>> import numpy as np
>>> arr = np.array([1,2,3,4])
>>> arr
array([1, 2, 3, 4])

2. Pillow

Pillow is an Image Processing Library, and it is built on the top of Python Imaging Library (PIL) which is no longer supported by Python3. You can say that Pillow is the extension of the Python PIL library, and this library can be used in any domains where we deal with Pictures.

Pillow is considered as one of the best libraries of Python because it can run on any Operating system that supports Python3. And using this single library we can handle any type of Picture or Image because it supports all the image extensions such as JPEG, png, gif, bmp, tiff, and many more.

Get Started with pillow

To use pillow library first you need to install it, and like other python libraries pillow can be installed using the python PIP command.

pip install Pillow

Example:

>>> from PIL import Image
>>> picture = Image.open("cat_image.png")
>>> picture.size
(335, 150)

3. Requests

Requests is the perfect library to get started with if you want to use python for web development. Using this open-source library, you can make all the standard HTTP requests. It handles all the complex functionalities and provides you with beautiful and straightforward methods to send HTTP request without worrying about the nitty-gritty of request working.

Using this library, you can not only send HTTP requests but also customize your request’ headers, data, and make authenticated requests. This library also comes very useful in web-scraping and web-development. To use this library, you should have some basic knowledge of HTTP request and response code.

Get Started with requests

Use pip command to install this library

pip install requests

Example:

>>> import requests
>>> response = requests.get("https://www.amazon.in/")
>>> response
<Response [200]>

4. SQLite

Python has in-built support for the SQLite database, so you do not have to download this library to connect your Python program with the SQLite database. SQLite is a very simple and straightforward Python library, and if you have some experience with any SQL database or Database management system you will get this library in no time.

SQLite uses a data file as a database which means it does not require any server and a special program to create and manipulate its database, and Python SQLite covers all for you.

Get Started with Python SQLite

import sqlite3
conn = sqlite3.connect('example.db')
c = conn.cursor()

# Create table
c.execute('''CREATE TABLE stocks
             (date text, trans text, symbol text, qty real, price real)''')

# Insert a row of data
c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")

# Save (commit) the changes
conn.commit()

# We can also close the connection if we are done with it.
# Just be sure any changes have been committed or they will be lost.
conn.close()

5. Collections

Collection is one of the most underrated python libraries, despite providing some of the best-inbuilt methods for best Data-Structure, many beginners try to ignore this powerful library. The data structures it provides are known as containers. And collections library introduces its container as an object which provides various want to store and access data objects. Python also has some built-in containers such as tuple, list, set, and dictionary, but collection provides more containers to Python developers who are not satisfied with built-in python data structures.

Order Dictionary is one of the famous containers provided by the collection module, the concept is similar to Python inbuilt dictionary. But, unlike a normal dictionary, it stores its elements in order.

Get started with Collections

>>> from collections import OrderedDict
>>> ordict = OrderedDict()
>>> ordict['one']=1
>>> ordict['two']=2
>>> ordict['three']=3
>>> ordict['four']=4
>>> ordict
OrderedDict([('one', 1), ('two', 2), ('three', 3), ('four', 4)])
>>> ordict['one']
1

6. Tkinter

If you are fed-up with the tedious console-based outputs and want to make Graphical User Interface (GUI) applications or graphical calculator using python then Tkinter is for you. Tkinter is the most recommended Python GUI library because it is completely built using standard python libraries. Its GUI applications are supported by all Operating systems where python can run.

However, Tkinter does not provide a modern GUI approach, and the application built on it also looks very outdated, still, it is considered as a standard Python GUI framework. Because of its lightweight API and low learning curve, it becomes an ideal GUI framework for Python beginners who want to bring colors and buttons to their black and white console-based programs.

Get started with Tkinter

>>>import tkinter as tk
>>> say_hello = tk.Label(text="Hello, world")
>>> say_hello.pack()

7. Matplotlib

To built a simple picture using standard programming you need to write more than a thousand lines of code, but fortunately, we do not need to write a thousand lines of code because we have matplotlib. Using this huge and straightforward library we can create GUI graph images to represent data. It is also a standard library for Data Science in Python because of its high usages in plotting 2-d and graphs.

Get started with matplotlib

To use this library, you first need to install it using pip command:

pip install matplotlib

Example:

>>> import matplotlib.pyplot as plt
>>> plt.plot([5, 10, 15, 20])
>>> plt.ylabel('numbers')
>>> plt.show()

8. Beautiful Soup

Web-scraping is a technique that is used to scrap or grab data from the web-pages. There are an unlimited number of web-pages on the internet and every page contains data and that can be used to build products and projects. Collecting the data from the web pages is known as web-scraping and in Python, we have a beautiful and powerful library “Beautiful Soup” that can scarp and parse data from a web page. Using this library, you can play with the open-source data present on the internet, and make projects that require automated data.

Get Started with Beautiful Soup

Beautiful Soup can only scrap data from HTML documents or web-pages, and to get a web-page we need a library which can send HTTP request to a URL and receive its HTML code in response. That’s why Beautiful soup works along the Python requests library.

To install beautiful soup, use this pip command:

  pip install beautifulsoup4

Example:

>>> from bs4 import BeautifulSoup
>>> soup = BeautifulSoup("<p>Some<b>bad<i>HTML")
>>> print soup.prettify()

9. CSV

CSV stands for Comma Separated Values and it represents the excel or spreadsheet files. In python, we have file handling methods to read and write between the txt files. But the CSV module gives us the power to read and write between CSV files too. CSV files are generally used to represent and collect data like relational databases and using the CSV module we can read from any CSV file and use it as a Database.

Get Started with CSV

We do not have to install the CSV module explicitly because it is a Python standard module to handle and manage CSV or spreadsheet files.

Example

>> import csv
>>> with open('data.csv', newline=' ') as file:
...     content = csv.reader(file, delimiter=' ', quotechar='|')
...     for row in spamreader:
...         print(', '.join(row))

10. Scrapy

Scrapy is a well-known open-source framework for web-scraping. Like Beautiful Soup you can collect information from the web using scrapy but faster and efficiently. However Beautiful Soup is capable of scraping data from a web-page but it becomes slow when we deal with a large set of automated data. Scrapy comes with the concept of spiders that can run on the server and save data easily.

Get started with scrapy

To use scrapy you first need to install it:

pip install scrapy

Conclusion

Now we have come to the end of our 2020-21 top 10 python libraries that every beginner and intermediate Python developer should be aware of. We are not saying that you should explore all of these libraries, just explore 4 or 5 which suits your main domain. Apart from these there are thousands of other beginner-friendly Python libraries out there, but you do not need to know each and everyone, just start with these simple ones before you move to the advanced libraries of your domain.

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