Libraries in Python Explained: List of Important Libraries

What is a library?

A library is a previously combined set of codes that can be iteratively used, hence reducing time. As the term suggests it’s similar to the physical library that holds reusable resources. Python has founded several open source libraries based on the fact that each library has a root source.

What are Python Libraries?

Python has been widely used in the present times being a high-level programming language. The ease of use lies in its syntax which uses a lesser number of codes to express a concept. Therefore, this allows the user to apply python and write programs on both large and small scales. The language supports automatic memory management and has a large standard library.

A Python library defines lines of code that can be reused in other programs. It is basically a collection of modules. Their usefulness lies in the fact that new codes are not required to be written every time the same process is required to run. Libraries in Python play an important role in areas of data science, machine learning, data manipulation applications, etc.  

Python standard library

The life of a programmer becomes easy with the availability of a large number of standard libraries in python. This is mainly because the programmer is not required to keep on writing the codes. For example, a programmer can use the MySQLdb library to connect a MySQL database to a server. The python libraries are mostly written in the C programming language that handles operations like I/O and other core modules.  The standard library consists of more than 200 core modules and around 137,000 python libraries have been developed to date.

Important Python Libraries

1. Matplotlib

This library is used for the plotting of numerical data and used in data analysis. This open-source library is used for publishing high-quality figures like graphs, pie charts, scatterplots, histograms, etc.

2. Pandas

The panda is an open-source library and BSD licensed. The library is widely used in the data science area. They are mostly used for the analysis, manipulation, and cleaning of data. Without the need for switching it to another language like R, panda makes it possible for the easy operations of modelling and data analysis. 

The data used by the libraries in python are:

• Tabular data
• Time series with ordered and unordered data.
• Matrix data labelling rows and columns.
• Unlabeled data
• Any other form of statistical data

Installation of Pandas

The user has to type “pip install pandas” in the command line or type “conda install pandas” if an anaconda has been already installed in the system. Once the installation is done, it can be imported to the IDE by typing the command “import pandas as pd”.

Operations in Panda

A Large number of operations can be carried out in panda:

• Slicing of the data frame
• Merging and joining of data frames
• Concatenation of columns from two data frames
• Changing of index values in a data frame.
• Changing of headers in a column.
• Conversion of data into different formats.

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3. Numpy

Deviating towards the scientific computation areas, NumPy is the most used open-source packages offered by python. It supports large matrices and multidimensional data and has inbuilt mathematical functions for easy computation. The name “NumPy” defines “Numerical Python”. It can be used in linear algebra, random number capability, etc., and can act as a multi-dimensional container for generic data. Python NumPy Array is an object defining N-dimensional array in the form of rows and columns.

NumPy is preferred over lists in python because of:

• Less memory
• Fast
• Convenient

Installation

The installation of the NumPy package is done through typing the command ““pip install numpy” on the command prompt. Importing of the package in the IDE can be done through the command “import numpy as np”. The installation packages on NumPy can be found in the link 

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4. Scipy (Scientific Python)

Scipy is an open-source python library used for scientific computation, data computation, and high-performance computation. A large number of user-friendly routines are present in the library for easy computation. The package is built over the NumPy extension allowing the manipulation and visualization of the data with the availability of high-level commands. Along with the NumPy, Scipy is used for mathematical computation. NumPy allows the sorting, indexing of the array data, while the numerical code is stored in SciPy. 

A large number of subpackages are available in SciPy which are: cluster, constants, fftpack, integrate, interpolate, io, linalg, ndimage, odr, optimize, signal, sparse, spatial, special, and stats. These can be imported from SciPy through “from scipy import subpackage-name”.

However, the core packages of SciPy are NumPy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

5. SQLAlchemy

This library of python is mostly used for accessing information from databases supporting a wide range of databases and layouts. For its easy understanding, SQLAlchemy can be used at the beginner level. A large number of platforms are supported by it like Python 2.5, Jython, and Pypy making a fast communication between the Python language and the database.

The package can be installed from the link

6. Scrapy

Scrapy is an open-source framework in Python for the extraction of data from websites. It is a fast, high-level scraping and web crawling library under the “Scrapinghub ltd”. Scraping multiple pages within a minute, Scrapy is a faster approach for web scraping. 

It can be used for:

• Comparison of prices in web portals for specific products.
• Mining of data for information retrieval.
• Calculation of data in data analysis tools.
• Collection of data and serving it to the information hubs like news portals.

Installation

For the conda environment, installation can be done through the command “conda install -c conda-forge scrapy”. If conda is not installed, then the command “pip install scrapy” is used.

7. BeautifulSoup

Similar to Scrapy, BeautifulSoup is a library under Python programming used for the extraction and collection of information from websites. It has an excellent XML-HTML library for beginners.

8. Scikit- learn

Scikit- learn is an open-source library under the Python programming environment used for machine learning approaches. It supports a wide range of supervised and unsupervised learning algorithms. The library contains popular algorithms along with the packages NumPy, Matplotlib, and SciPy. The famous application of Scikit-learn is in Spotify for music recommendations.

Installation

For installing Scikit-learn, the above packages have to be installed first. Since Scikit-learn is built over the SciPy platform, SciPy needs to be installed first. The installation can then be done through pip.

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9. Ramp

Ramp library is used for rapid prototyping of machine learning models with a simple syntax for exploring algorithms, features, and transformations. It can be used with machine learning packages and statistical tools. It consists of various machine learning and statistical libraries like; pandas, scikit-learn, etc. The collection of these python libraries provides simple syntax that helps in the exploration of features and transformations efficiently.

Details of the Ramp library can be accessed from the link  

10. Seaborn

The package can be used for the visualization of the statistical models. The library is based on Matplotlib and allows the creation of statistical graphics through:

• Comparison of variables through an API based on datasets.
• Easy generation of complex visualization supporting multi-plot grids.
• Comparison of data subsets through Univariate and bivariate visualizations.
• Options of various color palettes to display the patterns.
• Automatic estimation of linear regression and its plotting.

Installation

The following commands can be used for installing Seaborn:

• pip install seaborn
• conda install seaborn (for conda environment)

The installation of the library is followed by the installation of its dependencies: NumPy, SciPy, Matplotlib, and Pandas. Another recommended dependency is the statsmodels.

Any type of dataset can be imported from GIT, through seaborn using the load_dataset() function. The dataset can be viewed through get_dataset_names() function.

11. Statsmodels

Statsmodels is a python library useful in the analysis and estimation of statistical models. The library is incorporated to carry out the statistical tests, etc. providing high-performance outcomes.

12. TensorFlow

TensorFlow is an open-source library used for high-performance numerical computation. It is also used in machine learning approaches and deep learning algorithms. Developed by the researchers of the Google Brain team within the Google AI organization, it is now widely used by researchers from math, physics, and machine learning for complex mathematical computations. TensorFlow is supported by macOS 10.12.6 (Sierra) or later; Windows 7 or above; Ubuntu 16.04 or later; and Raspbian 9.0 or later

13. PyGame

The PyGame package provides an interface to the Simple Directmedia Library (SDL) platform-independent graphic, audio, and input libraries.

Installation

Installation of Python 2.7 is a must before the installation of PyGame. Once Python 2.7 is installed, the official PyGame installer needs to be downloaded. The corresponding files are to be executed.

• The command “import pygame” is required to import the modules required for PyGame. 
• The command “pygame.init()”  is required for the initialization of the required modules for PyGame.
• The function “pygame.display.set_mode((width, height))” will launch a window where the graphical operations are to be performed.
• The command “pygame.event.get()” helps in emptying the events queued up else the events will pile up leading to the risk of the game becoming unresponsive.
• Fir quitting the game “pygame.QUIT” function is used
• The command “pygame.display.flip()” is used for displaying any updates made to the game.

14. PyTorch

PyTorch is a python based library blending two high-level features:

• Tensor computation (like NumPy) with strong GPU acceleration 
• Deep Neural Network platforms provide flexibility and speed.

It was introduced by Facebook in 2017. Some of the features of PyTorch are:

• Support Python and its libraries.
• Used in the development of Facebook for its Deep Learning requirements.
• An easy-to-use API for better usability and understanding.
• At any point of code execution, graphs can be built up dynamically and can be dynamically computed at run-time.
• Easy coding and fast processing.
• Can be executed in GPU machines as it’s supported by CUDA.

Installation

PyTorch can be installed through the command prompt or within an IDE. 

15. Theano

Similar to other libraries used for mathematical operations, Theano enables the user to define, optimize, and evaluate mathematical expressions. It involves large multi-dimensional arrays for efficient mathematical computation. Normal C-based codes become slower considering huge volumes of data. However, with the availability of the library, Theano enables the implementation of code swiftly. Unstable expressions can be recognized and computed, making the library more useful over NumPy. 

16. SymPy

The package is the closest to the Theano library and is used in all symbolic mathematics. With simple code provided by the package, the library can be effectively used for the computer algebra system.  Written in python only, SymPy can be customized and applied in other applications. The source code of the package can be found in GitHub.

17. Caffe2

Caffe2 is a python based framework for deep learning. Some of the features of the Caffe2 package are:

• Supports large-scale distributed training.
• Support for new hardware.
• Applicability to several computations like quantized computation.

The package is compatible with operating systems like MacOSX, Ubuntu, CentOS, Windows, iOS, Android, Raspbian, and Tegra. It can be installed from Pre-Built libraries, built from source, docker images, or Cloud. The installation guide is available 

18. NuPIC

The Library stands for Numenta Platform for Intelligent Computing (NuPIC). It provides a platform for the implementation of the HTM learning algorithm. Future machine learning algorithms can be founded upon this library based on the neocortex. HTM contains time-based continuous learning algorithms and is a detailed computational theory of the neocortex. The algorithms are associated with the storage and recall of spatial and temporal patterns. Problems like anomaly detection, etc. can be solved through the use of NuPIC.

The files can be downloaded from the link “https://pypi.org/project/nupic/”. 

19. Pipenv

The Pipenv was officially included in the python libraries in 2017. It is a python packaging tool solving problems of the workflow. The main purpose of the package is to provide an environment that is easy to set up by the users. It collects all the packaging worlds i.e. bundler, composer, npm, cargo, yarn, etc., and integrates into the python environment. Some of the problems solved by Pipenv are:

• Users no longer have to use the “pip” and “virtualenv” separately to work collectively.
• The users can get a proper insight into the dependency graph.
• Streamline development workflow through .env files.

Installation

• Through the command “$ sudo apt install pipenv” in a Debian Buster.
• Through the command “$ sudo dnf install pipenv” in Fedora.
• Through the command “pkg install py36-pipenv” in FreeBSD.
• Through Pipx using “$ pipx install pipenv”.

20. PyBrain

PyBrain is an open source-library from the available libraries in python used for Machine Learning algorithms for every entry-level student in research. The goal of PyBrain is to offer flexible and easy-to-use algorithms for machine learning tasks. It also provides predefined environments for comparing the algorithms. PyBrain stands for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. Compared to the other machine learning libraries provided by python, PyBrain is fast and easily understandable.

Some of the features of PyBrain are:

1. Networks: A network is defined as modules connected through links. Few networks supported by PyBrain are Feed-Forward Network, Recurrent Network, etc. 

• The network where information is passed from one node to the other in a forward direction is termed the Feed-Forward network. The information won’t travel backward in this type of network. It is one of the first and simplest networks offered by the artificial neural network. The flow of data is from the input nodes to the hidden nodes and lastly to the output nodes.
• Similar to the Feed-Forward nodes are the recurrent nodes, where the information has to be remembered in each step.

   2.  Datasets: Datasets include the data that is to be provided to the networks for the testing, validation, and training of the networks. It              depends on the task to be carried out with machine learning. Two types of datasets are mostly supported by PyBrain i.e.                                  SupervisedDataSet and ClassificationDataSet.

• SupervisedDataSet: These types of datasets are mostly used for supervised learning tasks. The fields in the datasets are the “input” and the “target”.
• ClassificationDataSet: These types of datasets are mostly used for classification tasks. Along with the “input” and the “target” fields, there is an additional field i.e. “class”. The “class’ includes the automated backup of the targets.

   3. Trainer: The data in a neural network gets trained with the training data provided to the networks. To check whether the network is                properly trained, the prediction of test data on that network is analyzed.  Two types of trainer mostly used in PyBrain are:

• Backprop Trainer: the parameters in a network are trained based on the supervised or ClassificationDataSet dataset by back-propagating the errors.
• TrainUntilConvergence: The module is trained until convergence

   4.  Visualization: visualization of the data can be carried out through other frameworks like Mathplotlib, pyplot, etc.

21. MILK

The machine learning package “MILK” in python focuses on the use of available classifiers for the supervised classification. The available classifiers are SVM’s, k-NN, random forests, and decision trees. Along with the classification, MILK helps in the feature selection process. The combination of the classifiers varies on the classification systems. 

• For the unsupervised classification problem, MILK uses the -means clustering and affinity propagation.
• Inputs for MILK vary. Mostly it is optimized for the NumPy arrays, but other forms of inputs can be accepted.
• The codes in MILK are written in C++ which uses low memory and is of high speed.

Installation

Installation code for MILK can be retrieved from Github. The commands used for the installation are “easy_install milk” or “pip install milk”.

More information on the toolkit can be retrieved from the link.

Conclusion

The simple to use python language has been making wide applications in several areas of the real world. With being a high-level, dynamically typed, and interpreted language, the language is rapidly growing itself in the areas of debugging errors. Some of the global applications where python has been increasingly used are YouTube, DropBox, etc. Further, with the availability of libraries in python, the users are able to perform lots of tasks without having to write their own codes.

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