Top 10 Python Libraries that you must know in 2020
Top 10 Python Libraries
Do you know what is Python and the top 10 Python libraries one must know in this 2020? Here is a detailed information on what is Python, Python Library and the top 10 Python Libraries you must know in 2020. Python is one of the top-rated programming languages which has a quick evolution in Machine Learning and Artificial Intelligence. In the past few years, we can see a big gain of popularity with Python. Most of the developers feels easy to use the Python syntax to implement multifaceted algorithms with flexibility. Python popularity has become high because it is used in Data Science & Machine learning. Also, Python contains number of libraries which is open for any type of development. Web development, Game development, scripting, Data Science concepts, and many more are possible through Python. Python machine learning libraries are preferred by many people to perform implementations on Machine Learning Algorithms.
So what is Python Library?
A collection of pre-written code which you can easily import in the code to augment some additional functionality is called Library. Using libraries, writing the code from the scratch is not required, instead as the pre-written code, classes and functions or classes are already made available for the coders to enhance your code. There are huge number of Python libraries for different development and below we are discussing the top 10 Python Libraries you must know in 2020.
Numpy is one of the highly famed Machine Learning library in Python. NumPy is internally used by TensorFlow and few other libraries to perform various operations on Tensors. One of the most important features of NumPy is Array interface. Developers feels that NumPy is very easy to use and interactive as well. You can perform the complex mathematical implementations in a simple method and there are many open source contributions with this library.
Hope you all are aware of speedy improvement on Machine Learning and we can see many Python developers building python libraries for Machine learning. In addition, libraries are specially built for scientific and analytical computing. Merging all these bits and pieces of codes and standardization process resulted in SciPy library. SciPy library is standing out as a fundamental tool for achieving scientific, mathematical, and engineering calculations. Though there are multiple Python libraries, people are fond of using SciPy library just because of three main reasons. First reason to be quick computational power.
In a very short term, SciPy is good at dealing with integration, statistics, linear algebra, numerical interpolation, etc. The speed of ML models and integration is seen to be at very good speed. The next reason for opting SciPy is this library is easy to understand and the whole bunch of features are helping the developers to create machine learning models. The most important motive of SciPy is to give higher quality and quicker execution of computing operations.
The developers who are working on a Machine Learning Python project use the library TensorFlow. Having an association with Brain team, TensorFlow library was developed by Google. Most of the Google applications make use of TensorFlow for machine learning. To write fresh and new algorithms which involves a huge number of tensor operations, TensorFlow library is used. N-dimensional matrices available on tensors represents your data in an effective method. TensorFlow utilizes the techniques like XLA for rapid linear algebra operations.
Scikit-learn library was developed by David Cournapeau in the year 2007, as part of the Google Summer of Code project. Public release of Scikit-learn happened on January 2010. NumPy and SciPy are mainly used to develop Scikit-learn and this has become the most widespread Python machine learning library to develop algorithms for Machine Learning. Scikit-learn contains a broad variety of supervised and unsupervised learning algorithms which finely works on a reliable Python interface. For data analysis and data mining, you can use this library. The different functions that a Scikit-learn library can handle are clustering, pre-processing, classification, dimensionality reduction, regression, and model selection.
PyTorch is the principal library of machine learning Python which permits developers to accomplish tensor computations, construct active computational graphs, and analyze gradients mechanically. PyTorch affords amusing APIs to resolve application problems with regards to neural networks. PyTorch, an open source library which is written in C was introduced in 2017. This library has received a huge popularity and appealing more and more machine learning developers. PyTorch library is intensely integrated into Python and not bound into massive C++ framework. Applications like Natural Language Processing make use of PyTorch library.
Pandas library built upon NumPy, is one of the most popular machine learning libraries which affords high-level data structures and a broad range of tools to perform analysis. A developer names Wes Mckinney initiated the creation of this library in the year 2008 and you will be able to translate multifaceted operations with data just with single or two commands. There are numerous inbuilt approaches in Python for merging data, grouping, and filtering, and time-series functionality. Presently, we can see minimum count of panda’s library releases but with hundreds of enhancements, unique features, API changes and bug fixes. When Pandas are used with other tools and libraries which guarantees an improved functionality and fine volume of flexibility.
Always you cannot expect the results of Machine Learning model predictions to be right. In such cases, Eli5 helps a lot for the developers and aids to overcome this challenge. Eli5 is a combination of machine learning model debug and visualization and algorithm working process can be tracked. This Eli5 library plays an energetic role when we see some dependencies with various other Python packages. Eli5 is used in heritage applications and of great help in executing new methodologies in numerous arenas.
One of the relaxed and cooles libraries of Python is Keras. To express neural network, Keras provides a simpler mechanism. Through Keras, you can get some of the finest services like processing datasets, compiling models, graphs visualization, and few more.
TensorFlow or Theano are used by Keras in the backend internally. Keras is moderately deliberate when compared to few other machine learning libraries as it generates a computational graph via back-end infrastructure. You can get portable models in Keras. Do you know that you all are using Keras in your daily life? Yes, we constantly use Uber, Netflix, Uber, Instacart, Square, and few others which are working through Keras Library. Most of the startups are making use of Keras as it focuses on Deep Learning. In addition, huge scientific organizations are adopting Keras.
Matplotlib Python Library helps to build numerous diagrams and graphs like Non-Cartesian coordinates Graphs, histograms, scatterplots, two-dimensional diagrams and still more are there. You can get many plotting libraries to work with the combination of matplotlib library. A recent release happened with Matplotlib which contains the new changes on sizes, legends, colours, font and style. On creating a colorblind- friendly colour cycle, a great improvement is applied to the colour cycle adding to the appearance improvement like automatic axes legends alignment.
Fundamentally, Matplotlib is utilized with SciPy and NumPy libraries. The reason for such process is the important task of Matplotlib is for different filtered data, plotting of 2D graphs are possible. Initially, SciPy and NumPy or any other library is used data sorting and later make use of this library to get an appropriate graphical data view. Analysing the data using Matplotlib seems to be in fun-filled method and hence even a non-programmer can read the data.
Statsmodels is a wonderful Python library with which a developer can get various chances to accomplish the statistical model’s assessment, statistical test, statistical data analysis etc. Statsmodels is constructed using various numerical libraries like SciPy, NumPy, Pandas and also uses Patsy. Different options in plotting and implement various process in machine learning are made possible through Statsmodels library. The StatsModels library is inspiring and budding uninterruptedly with fresh opportunities in the recent days.
Feature summary of Top 10 Python Library
The below table gives a great idea on each Python library features.
|S. No||Python Library||Features|
|1.||NumPy||a. Innate: Coding are easy, and concepts are simple to understand
b. Computing capabilities: Using NumPy, developers can deal with matric computation, linear algebra, and random number generation
|2.||SciPy||a. SciPy is developed using NumPy
b. SciPy submodules has many functions and they are documented in a well-planned manner
|3.||TensorFlow||a. TensorFlow is flexible in operability
b. Using TensorFlow, you can simply visualize every part of the graph
c. TensorFlow is one of the best open sources and hence anyone can make use of it
|4.||Scikit-Learn||a. Cross-validation feature of Scikit-Learn helps to give a check on supervised models accuracy
b. You can simply extract text and images using Scikit-Learn
c. Spread of algorithms are huge
|5.||PyTorch||a. A rich ecosystem of libraries and tools are built by a live community where there are numerous developers and researchers. This feature extends the PyTorch library and support various development departments.
b. A new hybrid front-end is made available to for easy usage and suppleness in excited mode.
|6.||Pandas||a. The whole manipulating data process is easy
b. Pandas supports various operations like sorting, visualizations, iterations, re-indexing, concatenations and aggregations
|7.||Eli5||a. Eli5 is used in mathematical applications which requires a lot of computations in a short duration
b. Other libraries supported by Eli5 are Scikit-Learn, XGBoost and Lightning. These libraries are to perform different tasks and help in various fields
|8.||Keras||a. Keras Library is flexible, communicative and suitable for innovative research
b. A complete Python-based framework is available in Keras and hence debugging and exploring becomes simple for the developers
c. You can run keras in both CPU and GPU in a smooth way
|9.||Matplotlib||a. Matplotlib is easy to learn
b. Possible to plot various graphs such as pie graph, bar graph
c. Enjoy with Matplotlib as it is completely fun filled
|10.||StatsModels||a. On successful installation of StatsModels, few functions and models are loaded for the users
b. You can create two design matrices namely, endogenous and exogenous
You can kick start your career in Python by learning all these top 10 libraries and get more and more opportunities. In this 2020, though there are many technologies and tools available in the market for various purpose, it is pretty sure that you will never end up in zero knowledge after reading this active information on Python. As Python plays a major role in Machine learning and Data Science, there would be a promising future for any individual to overcome the current market challenges. This should be a very good chance to learn the Top 10 Python Library you must know in 2020.