Python for Analytical Skills

UNIT I: Introduction to Python:

  • Python versus Java

Java is a statically typed and compiled language, and Python is a dynamically typed and interpreted language. This single difference makes Java faster at runtime and easier to debug, but Python is easier to use and easier to read. (Ref:

  • Python Interpreter and it’s Environment

Translator (Converting High level language such as english to computer readable form because computer can understand binary(0 or1) language only)

a) Compiler : IT reads all program then convert whole code into machine level language. ( Example : C programming, C++, Core Java)

b) Interpreter :

Its reads line by line and convert line by line into machine level language. (Example : Python3) : it helps to run the program.

c) Assembler : (covert Assembly level language to machine level language )

  • Python installation

Step to run python program in command prompt:

    1. Open Notebook

    2. Write python code in notebook file

    3. Save File with .py extension in any folder ( Example :

    4. Open Command Propmpt

    5. Locate folder where python file is saved( for example : cd C:\Users\rkmis\OneDrive\Desktop\Python-File

    6. run the code using below command


  • Python basics:

    1. variables

    2. operators

    3. Strings,

    4. Conditional and

    5. Control Statements,

    6. loops;

  • Data structures: lists and dictionaries;

  • functions: global functions,

  • local functions,

  • lambda functions and methods.

UNIT II: Object Oriented Programming Concepts:

  • Class,

  • object,

  • constructor, destructor and inheritance;

  • Modules & Packages,

  • File Input and Output,

  • Catching exceptions to deal with bad data,

  • Multithreading, Database Connectivity. 8

UNIT III: Numpy:

  • Creating Arrays, Arrays Operations, Multidimensional Arrays.

  • Arrays transformation, Array Concatenation, Array Math Operations, Multidimensional Array and its Operations, Vector and Matrix.

  • Visualization: Visualization with matplotlib, Figures and subplots, Labeling and arranging figures, Outputting graphics.

UNIT IV: Pandas:

  • Manipulating data from CSV,

  • Excel, HDF5, and SQL databases,

  • Data analysis and modelling with Pandas,

  • Time-series analysis with Pandas, Using Pandas, the Python data analysis library, Series and Data Frames,

  • Grouping, aggregating and applying, Merging and joining.


1. McKinney Wes, "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython", O'Reilly Media, 2012.

2. Hauck Trent, "Instant Data Intensive Apps with Pandas How-To", Packt Publishing Ltd, 2013.

3. Beazley David M.,"Advanced Python Programming", Pearson Education,2009.

4. Chun Wesley , Core Python Programming, 3rd Edition,Prentice Hall Professional, 2012.

5. Telles Matt "Python Power!: The Comprehensive Guide", Cengage Learning, 2008.

6. McKinney Wes & PyData Development Team, "pandas: powerful Python data analysis toolkit", Release 0.13.1, Feb 2014.



Outcomes: -

  • Learning core data types of python.

  • Learning conditional and looping operations in python. -

  • Able to work with Object-oriented concepts and Database connectivity in python.

  • Able to analyze data using Pandas and Numpy.

  • Able to visualize the data using seaborn and matplotlib.