Python Data Science Handbook: 4 Comprehensive Aspects

Preetipadma Khandavilli • Last Modified: December 29th, 2022

Python Data Science Handbook

Data is regarded as a crucial asset that can empower the Digital Economy. The capacity to extract information from an unprecedented flow of data has become essential to corporate success. Several programming languages are used to develop codes for Data Science Models. Among them, the most widely used programming language is Python.

This general-purpose language is ideal for Data Scientists since it can handle Data Wrangling, perform Data Visualization, build Machine Learning Models, and implement Deep Learning techniques. Very few Data Scientists are aware of Python’s capabilities. This has led to the rise of having a Python Data Science Handbook.

As its popularity is increasing regularly, the need for a guide/handbook is also increasing. A Python Data Science Handbook plays an important role in understanding the key applications of Python in the field of Data Science.

This article will be similar to a Python Data Science Handbook and will give a brief overview of how you can use Python and Data Science to solve real-world problems. It will also help you explore various concepts, libraries, and tools related to Python. Read along how you can use this Python Data Science Handbook for your organization.

Table of Contents


Before diving deep into what a Python Data Science Handbook should have, it is important to address some important prerequisites. Some of these are:

  • Familiarity with Big Data.
  • Understanding of various Programming Languages.

Having these prerequisites in check, you can proceed further to understand the major topics a Python Data Science Handbook must cover.

What is Data Science?

Python Data Science Handbook
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Data Science is an umbrella term for many scientific techniques for discovering hidden patterns in raw data. It is a combination of Data Cleaning, Analysis, and Preparation. Many organizations depend on Data Science to extract insights from Structured and Unstructured Data and use it for optimizations of several Business Operations.

Data Science helps business leaders comprehend the information contained within the data, derive correlations and conclusions from information, and distinguish what is crucial for the business that can be further analyzed to drive better business decisions.

Apart from improving decision-making, it also enables measuring and quantifying Returns on Investments (ROI). If leveraged using the right tools, Data Science can help businesses to have a competitive advantage, re-engineer processes and enhance risk controls. 

At present, both tech-savvy organizations and digital non-natives are being benefited from data-driven models across all disciplines. This only expands on the need for a Python Data Science Handbook.

In a Corporate Ecosystem, Data Science as a whole provides an amazing connection between Business Analysts, Data Engineers, Data Scientists, and Developers. You will get to know more about the essentials of Data Science in this Python Data Science Handbook.

To know more about Data Science, please visit this link.

What is Python?

Python logo
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Python is a high-level, open-source, structured general-purpose programming language that can be used for a variety of tasks. This includes Web Development, Data Research, Software Prototyping, and many more. Developed by Guido Van Rossum and released in 1989, the name Python was inspired by the BBC comedy program “Monty Python’s Flying Circus”. This interactive and object-oriented programming language is similar to PERL or Ruby. 

Python also offers extensive library support for Data Analytics, Visualization, Numerical Computation, and Machine Learning. Some popular examples include Pandas, NumPy, SciPy, among many others. This availability of third-party open-source libraries in Python assists Data Scientists in working with data seamlessly.

Python was selected TIOBE’s Programming Language of the Year in 2007, 2010, and 2018, an honor that is given to a Programming Language with the highest popularity gain throughout the year. Due to its Flexible and Dynamic nature in the field of Data Science, having a Python Data Science Handbook will give you hands-on experience in this field.

You will get to know more about the advantages of Python over other Programming Languages in this Python Data Science Handbook.

To know more about Python, please visit this link.

Features of Python that Make it a Popular Choice Among Data Scientists

Python houses a variety of features that make it a popular choice among Data Scientists. Some of those features include:

  • Scalability: Unlike other Programming Languages such as Java and R, which struggle to handle huge amounts of data, Python manages massive amounts of data with ease. When compared to other programming languages, it can assist in solving problems that other programming languages are unable to address.
  • Data Science Libraries: Its vast range of libraries can cater to most of the Data Science processes. Python tools such as Python Scrapy and BeautifulSoup, for example, can assist with Data Extraction from the Internet, while Python Seaborn and Matplotlib can assist with Data Visualization and Graphical Representation.
  • Ease of Learning: Anyone can adapt to the syntax of Python Programming. This means Data Scientists can quickly master Python coding, then engage in Machine Learning projects. Its English script, easy phrase structure, and code readability allow programmers and non-programmers to learn or understand the Python language with ease.
  • Wider Community Support: Python is an open-source Programming Language that comes with an abundance of tools and superb documentation. Moreover, it has one of the largest communities in the programming and Data Science circles. Python Developers can easily get solutions to their data problems and share exciting updates with the community.
  • Flexibility and Versatility: Python supports a wide range of Data Structures like dictionaries, sets, tuples, lists, and more to work with strenuous data structures.

It is a combination of these features that creates the need to have a Python Data Science Handbook very handy.

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Popular Python Libraries in Various Domains of Data Science

Python Libraries
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A Python library is a file that contains a collection of helpful code with basic functions, variables, and classes that are widely used in Data Science projects across many organizations, sectors, and industries. In a Python Data Science Handbook, you will look at some of the popular Python libraries for supporting different Data Science activities. Some of these include:

Data Processing and Modeling 


It is an abbreviated form for Numerical Python and is the most fundamental library for scientific computing in Python. It aids in the manipulation of massive, multi-dimensional arrays by providing a wide range of mathematical functions to act on the matrices.

It facilitates the processing of arrays that hold values of the same data type and simplifies array math operations, including Vectorization.


The next popular Python library in any Python Data Science Handbook is Pandas. It is an excellent library for Data Processing and Modeling. Data scientists use it for exploring, cleaning, converting, and displaying datasets since it enables easy-to-use Data Structures that allow manipulation of data between in-memory and external data formats like CSV, JSON, Microsoft Excel, SQL, etc.


Another important Python library in any Python Data Science Handbook is SciPy. It stands for Scientific Python and is basically a library built on NumPy, implying that they are used together. To conduct high-level routines in Linear Algebra, Interpolation, Fourier Transforms computations and optimizations. SciPy includes task-specific sub-modules such as scipy.linalg, scipy.fftpack, and others. Its extensive documentation makes the work with this library really easy.

Data Visualization


Matplotlib is one of the Python libraries that has to be in every Python Data Science Handbook. It’s a MATLAB-inspired 2D plotting package for Visualization. With only a few lines of code, Matplotlib can produce high-quality two-dimensional figures such as Bar Charts, Distribution Plots, Histograms, Scatterplots, and many more.

Matplotlib has interactive features like Zooming, Panning, and Saving the Graph in graphics format. Overall, it helps businesses with Descriptive Analysis and clearly Visualizing the data. 


Seaborn is another Matplotlib-based Python Library that provides functionalities for Statistical Graphics. Python also offers libraries for Data Visualization which are not based on Matplotlib. For instance, Bokeh is a great tool for integrating JavaScript widgets to create Dynamic and Scalable Visualizations within browsers.

Machine Learning and Deep Learning


As Machine Learning and Data Science are related, Scikit-learn is another popular Python Library in the Python Data Science Handbook. This Python library is primarily used to implement and code Machine Learning Algorithms. It is used for Data Mining tasks like Classification, Regression, Clustering, and Dimensionality Reduction tasks. It is also used for Model Tuning, Data Preprocessing.

Data Scientists can use Scikit-Learn to develop models for specific functionalities such as Image Processing. Built on SciPy, users must install both NumPy and SciPy before they start using SciKit. It further offers a variety of methods for evaluating a trained model, including the Confusion Matrix, ROC curve, and many more.


Another popular Python library in any Python Data Science Handbook is Keras. It is an open-source high-level Deep Learning Python library used to build Neural Networks and Models. Unlike Scikit-learn, which is an end-to-end Machine Learning Framework, it was developed primarily for experimentation with Deep Neural Networks.

Keras offers users the flexibility to use TensorFlow or Theano as a backend. Keras has successfully simplified the synthesis of Artificial Neural Networks (ANNs).  


Another popular Python library in any Python Data Science Handbook is TensorFlow. This is a Deep Learning Framework developed by Google as an engine behind their environment for training Neural Networks. Google used it to build its applications like Google Translate.

TensorFlow can also improve computation on both CPU and GPU. Theano, on the other hand, is a powerful library, which is essentially a mathematical compiler that combines the native libraries like BLAS and C compiler to run on GPU and CPU.

Python also offers NLTK, which is a Suite of Libraries used for Symbolic and Statistical tasks involved in Natural Language Processing (NLP). It is useful for solving problems based on Text Analytics, Sentiment Analysis, analyzing Linguistic Structure, etc. In addition, Surprise is another Python library that allows the designing of Python-Based Recommendation Engines.

A detailed Python Data Science Handbook covers all these libraries in detail along with some code for further clarity.


This article gave a Comprehensive Analysis similar to a Python Data Science Handbook. It gave an insight into various tools and libraries that are essential in solving real-world problems. Having a Python Data Science Handbook is crucial in today’s world as many aspects of Data Science can be interlinked with each other in a matter of minutes.

Python is a highly versatile programming language. It has enabled Data Scientists to accomplish a lot of work in less time, from developing an efficient model that can simplify the complexity of large data sets to providing quick results and solutions. As businesses move to a data-centric profile, Python will continue to be an indispensable tool. A Python Data Science Handbook helps establish this goal.

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