Introduction to Data Science On Python Programming Language

What is Data Science?
This is the process of deriving knowledge and insights from a huge and diverse set of data through organizing, processing and analysing the data. It involves many different disciplines like mathematical and statistical modelling, extracting data from it source and applying data visualization techniques. Often it also involves handling big data technologies to gather both structured and unstructured data. Below we will see some example scenarios where Data science is used.

1)System recommendation
As online stores  becomes more prevalent, the e-commerce platforms are able to capture users shopping preferences as well as the performance of various products in the market. This leads to creation of recommendation systems which create models predicting the shoppers needs and show the products the shopper is most likely to buy.

2) Financial Risk management
The financial risk involving loans and credits are better analyzed by using the customers past spend habits, past defaults, other financial commitments and many socioeconomic indicators. These data is gathered from various sources in different formats. Organizing them together and getting insight into customers profile needs the help of Data science. The outcome is minimizing loss for the financial organization by avoiding bad debt.

3)Improving Health Care services
The health care industry deals with a variety of data which can be classified into technical data, financial data, patient information, drug information and legal rules. All this data need to be analyzed in a coordinated manner to produce insights that will save cost both for the health care provider and care receiver while remaining legally compliant.

4)Computer Vision
The advancement in recognizing an image by a computer involves processing large sets of image data from multiple objects of same category. For example, Face recognition. These data sets are modelled, and algorithms are created to apply the model to newer images to get a satisfactory result. Processing of these huge data sets and creation of models need various tools used in Data science.

Efficient Management of Energy
As the demand for energy consumption soars, the energy producing companies need to manage the various phases of the energy production and distribution more efficiently. This involves optimizing the production methods, the storage and distribution mechanisms as well as studying the customers consumption patterns. Linking the data from all these sources and deriving insight seems a daunting task. This is made easier by using the tools of data science.

Python in Data Science
The programming requirements of data science demands a very versatile yet flexible language which is simple to write the code but can handle highly complex mathematical processing. Python is most suited for such requirements as it has already established itself both as a language for general computing as well as scientific computing. More over it is being continuously upgraded in form of new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.

A simple and easy way to learn language which achieves result in fewer lines of code than other similar languages like R. Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
It is cross platform, so the same code works in multiple environments without needing any change. That makes it perfect to be used in a multi-environment setup easily.
It executes faster than other similar languages used for data analysis like R and MATLAB.
Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing very large volume of data transformation, slicing, dicing and visualization.
Most importantly Python has got a very large collection of libraries which serve as special purpose analysis tools. For example – the NumPy package deals with scientific computing and its array needs much less memory than the conventional python list for managing numeric data. And the number of such packages is continuously growing.
Python has packages which can directly use the code from other languages like Java or C. This helps in optimizing the code performance by using existing code of other languages, whenever it gives a better result.
In the subsequent chapters we will see how we can leverage these features of python to accomplish all the tasks needed in the different areas of Data Science. successfully create and run the example code in this tutorial we will need an environment set up which will have both general-purpose python as well as the special packages required for Data science. We will first look as installing the general-purpose python which can be python 2 or python 3. But we will prefer python 2 for this tutorial mainly because of its maturity and wider support of external packages.

Getting Python
The most up-to-date and current source code, binaries, documentation, news, etc., is available on the official website of Python

You can download Python documentation from The documentation is available in HTML, PDF, and PostScript formats.

Installing Python
Python distribution is available for a wide variety of platforms. You need to download only the binary code applicable for your platform and install Python.

If the binary code for your platform is not available, you need a C compiler to compile the source code manually. Compiling the source code offers more flexibility in terms of choice of features that you require in your installation.

Here is a quick overview of installing Python on various platforms −

Unix and Linux Installation
Here are the simple steps to install Python on Unix/Linux machine.

Open a Web browser and go to

Follow the link to download zipped source code available for Unix/Linux.

Download and extract files.

Editing the Modules/Setup file if you want to customize some options.

run ./configure script


make install

This installs Python at standard location /usr/local/bin and its libraries at /usr/local/lib/pythonXX where XX is the version of Python.

Here are the steps involved to installing python on windows.

Open a Web browser and go to

Follow the link for the Windows installer python-XYZ.msi file where XYZ is the version you need to install.

To use this installer python-XYZ.msi, the Windows system must support Microsoft Installer 2.0. Save the installer file to your local machine and then run it to find out if your machine supports MSI.

Run the downloaded file. This brings up the Python install wizard, which is really easy to use. Just accept the default settings, wait until the install is finished, and you are done.

Macintosh Installation
Recent Macs come with Python installed, but it may be several years out of date. See for instructions on getting the current version along with extra tools to support development on the Mac. For older Mac OS's before Mac OS X 10.3 (released in 2003), MacPython is available.

Jack Jansen maintains it and you can have full access to the entire documentation at his website − You can find complete installation details for Mac OS installation.

Setting up PATH
Programs and other executable files can be in many directories, so operating systems provide a search path that lists the directories that the OS searches for executables.

The path is stored in an environment variable, which is a named string maintained by the operating system. This variable contains information available to the command shell and other programs.

The path variable is named as PATH in Unix or Path in Windows (Unix is case sensitive; Windows is not).

In Mac OS, the installer handles the path details. To invoke the Python interpreter from any particular directory, you must add the Python directory to your path.

Setting path at Unix/Linux
To add the Python directory to the path for a particular session in Unix −

In the csh shell − type setenv PATH "$PATH:/usr/local/bin/python" and press Enter.

In the bash shell (Linux) − type export ATH="$PATH:/usr/local/bin/python" and press Enter.

In the sh or ksh shell − type PATH="$PATH:/usr/local/bin/python" and press Enter.

Note − /usr/local/bin/python is the path of the Python directory

Setting path at Windows
To add the Python directory to the path for a particular session in Windows −

At the command prompt − type path %path%;C:\Python and press Enter.

Integrated Development Environment
You can run Python from a Graphical User Interface (GUI) environment as well, if you have a GUI application on your system that supports Python.

Unix − IDLE is the very first Unix IDE for Python.

Windows − PythonWin is the first Windows interface for Python and is an IDE with a GUI.

Macintosh − The Macintosh version of Python along with the IDLE IDE is available from the main website, downloadable as either MacBinary or BinHex'd files.

Installing SciPy Pack
The best way to enable the required packs is to use an installable binary package specific to your operating system. These binaries contain full SciPy stack (inclusive of NumPy, SciPy, matplotlib, IPython, SymPy and nose packages along with core Python).

Anaconda (from is a free Python distribution for SciPy stack. It is also available for Linux and Mac.

Canopy ( is available as free as well as commercial distribution with full SciPy stack for Windows, Linux and Mac.

Python (x,y): It is a free Python distribution with SciPy stack and Spyder IDE for Windows OS. (Downloadable from

Package managers of respective Linux distributions are used to install one or more packages in SciPy stack.

For Ubuntu
sudo apt-get install python-numpy
python-scipy python-matplotlibipythonipythonnotebook python-pandas
python-sympy python-nose
For Fedora
sudo yum install numpyscipy python-matplotlibipython
python-pandas sympy python-nose atlas-devel
Building from Source
Core Python (2.6.x, 2.7.x and 3.2.x onwards) must be installed with distutils and zlib module should be enabled.

GNU gcc (4.2 and above) C compiler must be available.

To install NumPy, run the following command.

Python install
Let us test whether NumPy module is properly installed, try to import it from Python prompt.

If it is not installed, the following error message will be displayed.

Traceback (most recent call last):
   File "<pyshell#0>", line 1, in <module>
      import numpy
ImportError: No module named 'numpy'

Pandas is an open-source Python Library used for high-performance data manipulation and data analysis using its powerful data structures. Python with pandas is in use in a variety of academic and commercial domains, including Finance, Economics, Statistics, Advertising, Web Analytics, and more. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, organize, manipulate, model, and analyse the data.

Below are the some of the important features of Pandas which is used specifically for Data processing and Data analysis work.

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.
Time Series functionality.
Pandas deals with the following three data structures −


These data structures are built on top of Numpy array, making them fast and efficient.
Introduction to Data Science On Python Programming Language Introduction to Data Science On Python Programming Language Reviewed by Joseph on May 11, 2018 Rating: 5

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