10 Leading Python Libraries You Should Know in 2022

Python programming is full of possibilities. It is straightforward and simple, with many excellent libraries and functions that can make tasks much more manageable. Every Python developer must work with popular libraries like OS, pandas, datetime, scikit-learn, seaborn, Tkinter and many more.

In this article, I will discuss ten such Python libraries that you might be interested in. let’s take a closer look at some of the unusual but valuable libraries of Python programming.

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1. Wget

One of the most significant responsibilities of data scientists is data extraction, particularly data extraction from websites. Wget is a free module for downloading non-interactive online files. This module can function in the background even if the user is not signed in because it is non-interactive. As a result, it is ideal for downloading all photos from a website or page.

We usually use this module for automation, where we want to make it continue running in the background.

Installation:

pip install wget

2. Pendulum

The pendulum is a beautiful project if you need to use dates and timings in python projects. The date and time operations are made simpler by this python package. It is capable of totally replacing Python’s native classes.

Installation:

pip install pendulum

If we want to get the current time for any specific timezone, then a single-line code of the pendulum module can do the task as follows.

import pendulum

# Getting current UTC time
utc_time = pendulum.now('UTC')

3. imbalanced-learning

In truth, when the number of samples in each class is almost equal, the classification algorithm performs best, but most datasets are imbalanced in the actual project.

These datasets influence the learning phase and subsequent prediction of the machine learning algorithm. To address such issues, unbalanced learning is being developed. Scikit Learn, a component of the IB project, is compatible with it. When you next encounter an unbalanced dataset, keep this in mind.

Installation:

pip install -U imbalanced-learn

# or

conda install -c conda-forge imbalanced-learn

4. FlashText

In most NLP tasks, substituting words in sentences or removing words from phrases is text data cleaning. This procedure is often performed using regular expressions, but as the number of search keywords approaches thousands, it becomes exceedingly difficult.

The flash text module in Python is based on the flash text algorithm, which gives an appropriate alternative in this case. Flashtext’s runtime is consistent regardless of the number of search queries, which is its most robust feature.

Installation:

pip install flashtext

5. Fuzzywuzzy and Polyfuzz

The name sounds funny, but fuzzywuzzy is a handy library for character matching. It can rapidly implement operations like string matching and token matching. It can also readily match entries from several databases.

Many advanced fuzzy match alternatives are available, like polyfuzz, which uses transformers, Glove, FastText, and Embeddings to do an efficient match.

Installation:

pip install fuzzywuzzy
pip install polyfuzz

If we want to apply fuzzy matching between two lists of text with advanced algorithms like transformers, FastText, Td-IDF, or Embeddings, then the below lines of code can help us.

from polyfuzz.models import TFIDF                                from polyfuzz import PolyFuzz                                                           tfidf = TFIDF(n_gram_range=(3, 3))                              model = PolyFuzz(tfidf)                             model.match(from_list, to_list)

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6. PyFlux

One of the most prevalent difficulties in machine learning is time series analysis. Pyflux is an open-source Python package designed to deal with time series challenges. The module contains several good current time series models, including the Arima, GARCH, and VaR models. Finally, flux offers an effective way for time series modelling. It’s worth a shot.

Installation:

pip install pyflux

7. Ipyvolume

Data Science is about communicating results, and visualisation is a huge help. Ipyvolume is a Python module for viewing 3D visuals in Jupyter notebooks (such as 3D stereograms).

Installation:

# Using pip

pip install ipyvolume

# Using Conda/Anaconda

conda install -c conda-forge ipyvolume

Here is how you can use it.

import ipyvolume as ipv
import numpy as np
x, y, z, u, v, w = np.random.random((6, 1000))*2-1
ipv.quickquiver(x, y, z, u, v, w, size=5)

8. Dash

Dash is a lightweight Python framework for developing web apps. It is built with Flash Plotly.js and React.js. Dash is ideal for creating data visualisation apps. These apps are then viewable via a web browser.

Installation:

pip install dash==0.29.0 # This will install the core dash backend

pip install dash-html-components==0.13.2 # This command will install HTML components

pip install dash-core-components==0.36.0 # Supercharged components

pip install dash-table==3.1.3 # Interactive DataTable component

9. Bashplotlib

Bashlotlib is a Python library and command-line utility for creating simple drawings at the terminal. The visualisation data becomes extremely useful when the user cannot access the GUI.

Installation:

pip install bashplotlib

Here is how you can use it to make an interactive histogram plot.

import numpy as np

from bashplotlib.histogram import plot_hist

rand_nums = np.random.normal(size=700, loc=0, scale=1)

plot_hist(rand_nums, bincount=100)

10. Colorama

Colorama is a Python package that produces colour text on the terminal and command line. It colours and styles terminal output using standard ANSI escape codes. It is cross-platform and works nicely on Windows and Linux.

Installation:

pip install colorama

Here is how to use it.

from colorama import init, Fore, Back

init()

# Fore changes the text's foreground color
print(Fore.BLUE + "These are Blue Letters")

#Back changes the text's background color
print(Back.WHITE + "This is White Background")

Conclusion

Numerous real-world applications frequently employ the approachable Python programming language. It is quickly increasing in the fields of error debugging since it is a high-level, dynamically typed, interpreted language. Furthermore, because Python Libraries are available, users may accomplish various activities without building their code.

As a result, learning about Python and its libraries is critical for any aspiring talent today. These libraries can make your life easier as a developer.

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