After a decade, Python has grown as one of the topmost popular programming languages in the world. With more than 137,000 python libraries available right now, picking the one appropriate for your project can be a tiresome task. When you consider a large number of packages and libraries that the community still has to offer, all of which must be combined to make this programming language one suite that you'll rarely reject.
Many programming languages have been reintroduced into companies thanks to Python. Python's success can be attributed to several factors such as:
It has one of the broad collections of libraries of all programming languages.
It’s also known as a beginner-level programming language because of its simplicity and ease.
It is profoundly portable – the sole reason for the huge following of Python.
It has a simple high-level syntax compared to C, Java, and C++.
This blog will guide you in making the best decision for your project, so let's get started with the major Python libraries and packages that you might employ in your project. Starting with machine learning libraries, we'll move to general-purpose libraries...
What is machine learning?
Machine learning (ML) is the study of computer algorithms that learn and improve automatically through the experience without being explicitly programmed. In other words, machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.
For example Traffic Alerts, Face Recognition, Product recommendation while shopping online, Chatbots, Driver-Less Cars are some of the everyday examples of machine learning.
As per the Survey, NumPy, Pandas, and Matplotlib are the most chosen python libraries for machine learning.
What Is Numpy?
NumPy, or Numerical Python, is a library that contains multidimensional array objects as well as a collection of routines for processing those arrays. Besides that, it’s the fundamental package for scientific computing with Python. It contains various features - N-dimensional array objects, sophisticated (broadcasting) functions, tools for integrating C/C++, and Fortran code are few.
TensorFlow and other libraries use Numpy internally for performing various operations on Tensors. Array interface is the best and the most important feature of Numpy.
Interactive: Numpy is a fun and easy-to-use programme.
Mathematics: It simplifies the implementation of complicated mathematical equations.
Intuitive: It makes coding and understanding the concepts very convenient.
A lot of interaction: Because of the open source contribution, it is extensively used.
Use of NumPy library
The library can be utilized to show images, binary raw streams, and sound waves.
Full-stack developers need to have experience with this library for machine learning methods execution.
What is Pandas?
Pandas is a machine learning library in Python that implements high-level data structures and a broad variety of tools for analysis. One of the vast features of this library is the skill to translate complex operations with data utilizing one or two commands. Pandas have many inbuilt methods for grouping, combining data, and filtering, as well as time-series functionality.
Agile and effective DataFrame object with the default and customized indexing.
Tools for storing data into in-memory data objects from various file formats.
Data alignment and integrated handling of missing data.
Reshaping and pivoting of data sets.
Use of Pandas library
Used to group and sort data.
Used to select the best-suited output for the apply method.
It provides support for performing custom types operations.
It is used with other libraries and tools to ensure high functionality and a good amount of flexibility.
What is Matplotlib?
Matplotlib provides a lookalike of MATLAB interface and an attractive user experience. Its learning curve is very smooth, with clear functions. It offers standard GUI toolkits like Qt, wxPython, GTK+, or Tkinter to give programmers an object-oriented API to embed graphs and plots into their applications.
Faster Text Rendering
Developed Image support
Enhanced offset text choice
The use of the Matplotlib library
Used for producing high-quality 2D plotting graphs and figures in multiple formats.
The library provides a state machine-based procedural pylab interface.
types of graphs and plots include error charts, scatter plots, histograms, plots, bar charts with the least lines of coding.
Top general-purpose libraries are:
Pillow & MoviePy
import PIL, import moviepy
Pillow is a branch of the PIL library, a collection of functions that support Python to communicate with images. The same idea applies to MoviePy while operating with videos.
Pillow gives you the ability to work with a broad variety of formats, from the several common ones like PNG, JPEG, and GIF, to the more difficult ones, like PSD. The library further provides functions to change images through filters that can detect edges, smooth, blur, or sharpen the image; these are the methods that can enhance the visual quality.
There are a lot of changes being made in the Scikit-Learn library. One change is the cross-validation feature, giving the ability to use more than one metric. Lots of training methods like logistics regression and nearest bystanders have gained some little improvements. There are a huge number of algorithms – beginning from clustering, factor analysis, principal component analysis to unsupervised neural networks.
Scikit-Learn is used with several algorithms for performing standard machine learning and data mining tasks like reducing dimensionality, classification, regression, clustering, and model selection.
When the tasks get more difficult, you have to use the OpenCV library. This Python library goes deep into image analysis and modification, allowing the user to solve most computer vision tasks with ease. It also operates with video files - it reads videos frame by frame and interprets them as a single or group of images. You can find and track objects in a view or even divide the view between background and foreground.
The biggest imperfection of this library is the lack of proper documentation. The examples on the official site are inadequate to explain the functions, while the community is still growing, so it is hard to find answers to your doubts.
Keras is very fit for data scientists studying to create deep learning models like neural networks. Developed on the head of Theano and TensorFlow, Keras simply helps to develop a neural network. Though, this library is relatively slow to the other libraries since it tends to create a computational graph using back-end infrastructure. It gives the best services for compiling models, processing data, and visualizing graphs. The library is very famous amongst CERN and NASA organizations.
Various companies such as Uber, Yelp, Square, Netflix, and Zocdoc are making use of Keras library.
This is by no means an exhaustive list! Python has been in high demand for a long time, and programmers have enjoyed working with it.
Have you developed something from the libraries? Let us know in the comments section below, we would love to hear that.