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Python Libraries for Data Science

Published on Sep 10, 2023


Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales. In July 2018, Van Rossum stepped down as the leader in the language community after 30 years. Python features a dynamic type system and automatic memory management. It supports multiple programmingparadigms,including object-oriented, imperative, functional and procedural, and has a large and comprehensive standard library.

Python interpreters are available for many operating systems. CPython, the reference implementation of Python, is open source software and has a community-based development model, as do nearly all of Python's other implementations. Python and CPython are managed by the non-profit Python Software Foundation.

Python has a simple, easy to learn syntax emphasizes readability hence, it reduces the cost of program maintenance. Also, Python supports modules and packages, which encourages program modularity and code reuse

Advantages of using PYTHON

The diverse application of the Python language is a result of the combination of features which give this language an edge over others. Some of the benefits of programming in Python include:

1. Presence of Third Party Modules:

The Python Package Index (PPI) contains numerous third-party modules that make Python capable of interacting with most of the other languages and platforms.

2. Extensive Support Libraries:

Python provides a large standard library which includes areas like internet protocols, string operations, web services tools and operating system interfaces. Many high use programming tasks have already been scripted into the standard library which reduces length of code to be written significantly.

3. Open Source and Community Development:

Python language is developed under an OSI-approved open source license, which makes it free to use and distribute, including for commercial purposes. Further, its development is driven by the community which collaborates for its code through hosting conferences and mailing lists, and provides for its numerous modules.

4. Learning Ease and Support Available:

Python offers excellent readability and uncluttered simple-to-learn syntax which helps beginners to utilize this programming language. The code style guidelines, PEP 8, provide a set of rules to facilitate the formatting of code. Additionally, the wide base of users and active developers has resulted in a rich internet resource bank to encourage development and the continued adoption of the language.

5. User-friendly Data Structures:

Python has built-in list and dictionary data structures which can be used to construct fast runtime data structures. Further, Python also provides the option of dynamic high-level data typing which reduces the length of support code that is needed.

6. Productivity and Speed:

Python has clean object-oriented design, provides enhanced process control capabilities, and possesses strong integration and text processing capabilities and its own unit testing framework, all of which contribute to the increase in its speed and productivity. Python is considered a viable option for building complex multi-protocol network applications.

Data Science

“Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components:

1) Data exploration & analysis:-.

• Included here: Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library.

2) Data visualization:-

A pretty self-explanatory name. Taking data and turning it into something colorful.

• Included here: Matplotlib; Seaborn; Datashader; others.

3) Classical machine learning:-

Conceptually, we could define this as any supervised or unsupervised learning task that is not deep learning (see below). Scikit-learn is far-and-away the go-to tool for implementing classification, regression, clustering, and dimensionality reduction, while StatsModels is less actively developed but still has a number of useful features.

• Included here: Scikit-Learn, StatsModels.

4) Deep learning:-

This is a subset of machine learning that is seeing a renaissance, and is commonly implemented with Keras, among other libraries. It has seen monumental improvements over the last ~5 years, such as AlexNet in 2012, which was the first design to incorporate consecutive convolutional layers.

• Included here: Keras, TensorFlow, and a whole host of others.

5) Data storage and big data frameworks:-

Big data is best defined as data that is either literally too large to reside on a single machine, or can’t be processed in the absence of a distributed environment. The Python bindings to Apache technologies play heavily here.

• Apache Spark; Apache Hadoop; HDFS; Dask; h5py/pytables.

Most common libraries used in the Data Science


NumPy is the fundamental package for scientific computing with Python. It contains among other things:

• a powerful N-dimensional array object

• sophisticated (broadcasting) functions

• tools for integrating C/C++ and Fortran code

• useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

NumPy is licensed under the BSD license, enabling reuse with few restrictions. The core functionality of NumPy is its "ND array", for n-dimensional array, data structure. These arrays are stride views on memory. In contrast to Python's built-in list data structure (which, despite the name, is a dynamic array), these arrays are homogeneously typed: all elements of a single array must be of the same type. NumPy has built-in support for memory-mapped arrays.

Here is some function that are defined in this NumPy Library.

1. zeros (shape [, dtype, order]) - Return a new array of given shape and type, filled with zeros.

2. array (object [, dtype, copy, order, lubok, ndim]) - Create an array

3. as array (a [, dtype, order]) - Convert the input to an array.

4. As an array (a [, dtype, order]) - Convert the input to an ND array, but pass ND array subclasses through.

Numpy will help you to manage multi-dimensional arrays very efficiently. Maybe you won’t do that directly, but since the concept is a crucial part of data science, many other libraries (well, almost all of them) are built on Numpy. Simply put: without Numpy you won’t be able to use Pandas, Matplotlib, Scipy or Scikit-Learn. That’s why you need it on the first hand.


Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc. In this tutorial, we will learn the various features of Python Pandas and how to use them in practice.

The name Pandas is derived from the word Panel Data – an Econometrics from Multidimensional data.

Key Features of Pandas

• Fast and efficient DataFrame object with default and customized indexing.

• Tools for loading data into in-memory data objects from different file formats.

• Data alignment and integrated handling of missing data.

• Reshaping and pivoting of date sets.

• Label-based slicing, indexing and subsetting of large data sets.

• Columns from a data structure can be deleted or inserted.

• Group by data for aggregation and transformations.

• High performance merging and joining of data


Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. For examples, see the sample plots and thumbnail gallery.


For simple plotting the pyplot module provides a MATLAB-like interface, particularly when combined with IPython. For the power user, you have full control of line styles, font properties, axes properties, etc, via an object-oriented interface or via a set of functions familiar to MATLAB users.

The best and most well-known Python data visualization library is Matplotlib. I wouldn’t say it’s easy to use… But usually if you save for yourself the 4 or 5 most commonly used code blocks for basic line charts and scatter plots, you can create your charts pretty fast.


SciPy is a machine learning library for application developers and engineers. However, you still need to know the difference between SciPy library and SciPy stack. SciPy library contains modules for optimization, linear algebra, integration, and statistics.

Features Of SciPy:-

The main feature of SciPy library is that it is developed using NumPy, and its array makes the most use of NumPy.

In addition, SciPy provides all the efficient numerical routines like optimization, numerical integration, and many others using its specific submodules.
All the functions in all submodules of SciPy are well documented.

2Where Is SciPy Used?

SciPy is a library that uses NumPy for the purpose of solving mathematical functions. SciPy uses NumPy arrays as the basic data structure, and comes with modules for various commonly used tasks in scientific programming. Tasks including linear algebra, integration (calculus), ordinary differential equation solving and signal processing are handled easily by SciPy.


Scikit-learn (formerly scikits. learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Advantages of using Scikit-Learn:

• Scikit-learn provides a clean and consistent interface to tons of different models.

• It provides you with many options for each model, but also chooses sensible defaults.

• Its documentation is exceptional, and it helps you to understand the models as well as how to use them properly.

• It is also actively being developed


Primary Intent: Developing and training deep learning models, deep learning research

Secondary Intent(s): Working with image and text data

Considered to be one of the coolest machine learning Python libraries, Keras offers an easier mechanism for expressing neural networks. It also features great utilities for compiling models, processing datasets, visualizing graphs, and much more.

Written in Python, Keras has the ability to run on top of CNTK, TensorFlow, and Theano. The Python machine learning library is developed with a primary focus on allowing fast experimentation. All Keras models are portable.


• Being completely Python-based makes it easier to debug and explore

• Modular in nature

• Neural network models can be combined for developing more complex models

• Runs smoothly on both CPU and GPU

• Supports almost all models of a neural network, including convolutional, embedding, fully connected, pooling, and recurrent


Primary Intent: Data visualization, making statistical graphics in Python

Secondary Intent(s): None

Basically a data visualization library for Python, Seaborn is built on top of the Matplotlib library. Also, it is closely integrated with Pandas data structures. The Python data visualization library offers a high-level interface for drawing attractive as well as informative statistical graphs.

The main aim of Seaborn is to make visualization a vital part of exploring and understanding data. Its dataset-oriented plotting functions operate on arrays and data-frames containing whole datasets. The library is ideal for examining relationships among multiple variables


• Automatic estimation as well as the plotting of linear regression models

• Comfortable views of the overall structure of complex datasets

• Eases building complex visualizations using high-level abstractions for structuring multi-plot grids

• Options for visualizing bivariate or univariate distributions

• Specialized support for using categorical variables


Primary Intent: Developing, training, and designing deep learning models

Secondary Intent(s): Performing numerical computation

Anybody involved in machine learning projects using Python must have, at least, heard of TensorFlow. Developed by Google, it is an open source symbolic math library for numerical computation using data flow graphs.

The mathematical operations in a typical TensorFlow data flow graph are represented by the graph nodes. The graph edges, on the other hand, represent the multidimensional data arrays, a.k.a. tensors, that flow between the graph nodes.


• Allows training multiple neural networks and multiple GPUs, making models very efficient for large-scale systems

• Easily trainable on CPU and GPU for distributed computing

• Flexibility in its operability, meaning TensorFlow offers the option of taking out the parts that you want and leaving that you don’t

• Great level of community and developer support

• Unlike other data science Python libraries, TensorFlow simplifies the process of visualizing each and every part of the graph


While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions.

Python’s standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs.


• Guttag, John V. (12 August 2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press. ISBN 978-0-262-52962-4.

• "Python Release Python 3.8.2".

• "Python Release Python 3.9.0a5".

• Benjamin (6 April 2020). "Python Insider: Python 2.7.18 release candidate 1 available". Python Insider. Retrieved 14 April 2020. Python 2.7.18 will be the last release of the Python 2.7 series, and thus Python 2.

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