A Complete Tutorial to Data Science for Newbies with Python!!

Table of Contents:

  1. Basics of Python for Data Analysis
    • Why learn Python for data analysis?
    • Python 2.7 v/s 3.4
    • How to install Python?
    • Running a few simple programs in Python
  2. Python libraries and data structures
    • Python Data Structures
    • Python Iteration and Conditional Constructs
    • Python Libraries
  3. Exploratory analysis in Python using Pandas
    • Introduction to series and data frames
    • Special dataset- Loan Prediction Problem
  4. Data Munging in Python using Pandas
  5. Building a Predictive Model in Python
    • Logistic Regression
    • Decision Tree
    • Random Forest

1. Basics of Python for Data Analysis

Why learn Python for data analysis?

Python has gathered a lot of interest recently as a choice of language for data analysis. I had compared it against SAS & R some time back. Here are some reasons which go in favour of learning Python:

  • Open Source – free to install
  • Awesome online community
  • Very easy to learn
  • Can become a common language for data science and production of web based analytics products.

Needless to say, it still has few drawbacks too:

  • It is an interpreted language rather than compiled language – hence might take up more CPU time. However, given the savings in programmer time (due to ease of learning), it might still be a good choice.

Python 2.7 v/s 3.4

This is one of the most debated topics in Python. You will invariably cross paths with it, specially if you are a beginner. There is no right/wrong choice here. It totally depends on the situation and your need to use. I will try to give you some pointers to help you make an informed choice.

Why Python 2.7?

  1. Awesome community support! This is something you’d need in your early days. Python 2 was released in late 2000 and has been in use for more than 15 years.
  2. Plethora of third-party libraries! Though many libraries have provided 3.x support but still a large number of modules work only on 2.x versions. If you plan to use Python for specific applications like web-development with high reliance on external modules, you might be better off with 2.7.
  3. Some of the features of 3.x versions have backward compatibility and can work with 2.7 version.

Why Python 3.4?

  1. Cleaner and faster! Python developers have fixed some inherent glitches and minor drawbacks in order to set a stronger foundation for the future. These might not be very relevant initially, but will matter eventually.
  2. It is the future! 2.7 is the last release for the 2.x family and eventually everyone has to shift to 3.x versions. Python 3 has released stable versions for past 5 years and will continue the same.

There is no clear winner but I suppose the bottom line is that you should focus on learning Python as a language. Shifting between versions should just be a matter of time. Stay tuned for a dedicated article on Python 2.x vs 3.x in the near future!

How to install Python?

There are 2 approaches to install Python:

  • You can download Python directly from its project site and install individual components and libraries you want
  • Alternately, you can download and install a package, which comes with pre-installed libraries. I would recommend downloading Anaconda. Another option could be Enthought Canopy Express.

Second method provides a hassle free installation and hence I’ll recommend that to beginners. The imitation of this approach is you have to wait for the entire package to be upgraded, even if you are interested in the latest version of a single library. It should not matter until and unless, until and unless, you are doing cutting edge statistical research.

Choosing a development environment

Once you have installed Python, there are various options for choosing an environment. Here are the 3 most common options:

  • Terminal / Shell based
  • IDLE (default environment)
  • iPython notebook – similar to markdown in R

IDLE editor for Python

While the right environment depends on your need, I personally prefer iPython Notebooks a lot. It provides a lot of good features for documenting while writing the code itself and you can choose to run the code in blocks (rather than the line by line execution)

We will use iPython environment for this complete tutorial.

Warming up: Running your first Python program

You can use Python as a simple calculator to start with:


Few things to note

  • You can start iPython notebook by writing “ipython notebook” on your terminal / cmd, depending on the OS you are working on
  • You can name a iPython notebook by simply clicking on the name – UntitledO in the above screenshot
  • The interface shows In [*] for inputs and Out[*] for output.
  • You can execute a code by pressing “Shift + Enter” or “ALT + Enter”, if you want to insert an additional row after.

Before we deep dive into problem solving, lets take a step back and understand the basics of Python. As we know that data structures and iteration and conditional constructs form the crux of any language. In Python, these include lists, strings, tuples, dictionaries, for-loop, while-loop, if-else, etc. Let’s take a look at some of these.

2. Python libraries and Data Structures

Python Data Structures

Following are some data structures, which are used in Python. You should be familiar with them in order to use them as appropriate.

  • Lists – Lists are one of the most versatile data structure in Python. A list can simply be defined by writing a list of comma separated values in square brackets. Lists might contain items of different types, but usually the items all have the same type. Python lists are mutable and individual elements of a list can be changed.

Here is a quick example to define a list and then access it:


  • Strings – Strings can simply be defined by use of single ( ‘ ), double ( ” ) or triple ( ”’ ) inverted commas. Strings enclosed in tripe quotes ( ”’ ) can span over multiple lines and are used frequently in docstrings (Python’s way of documenting functions). \ is used as an escape character. Please note that Python strings are immutable, so you can not change part of strings.


  • Tuples – A tuple is represented by a number of values separated by commas. Tuples are immutable and the output is surrounded by parentheses so that nested tuples are processed correctly. Additionally, even though tuples are immutable, they can hold mutable data if needed.

Since Tuples are immutable and can not change, they are faster in processing as compared to lists. Hence, if your list is unlikely to change, you should use tuples, instead of lists.


  • Dictionary – Dictionary is an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. 


Python Iteration and Conditional Constructs

Like most languages, Python also has a FOR-loop which is the most widely used method for iteration. It has a simple syntax:

for i in [Python Iterable]:

Here “Python Iterable” can be a list, tuple or other advanced data structures which we will explore in later sections. Let’s take a look at a simple example, determining the factorial of a number.

for i in range(1,N+1):
  fact *= i

Coming to conditional statements, these are used to execute code fragments based on a condition. The most commonly used construct is if-else, with following syntax:

if [condition]:
  __execution if true__
  __execution if false__

For instance, if we want to print whether the number N is even or odd:

if N%2 == 0:
  print 'Even'
  print 'Odd'

Now that you are familiar with Python fundamentals, let’s take a step further. What if you have to perform the following tasks:

  1. Multiply 2 matrices
  2. Find the root of a quadratic equation
  3. Plot bar charts and histograms
  4. Make statistical models
  5. Access web-pages

If you try to write code from scratch, its going to be a nightmare and you won’t stay on Python for more than 2 days! But lets not worry about that. Thankfully, there are many libraries with predefined which we can directly import into our code and make our life easy.

For example, consider the factorial example we just saw. We can do that in a single step as:


Off-course we need to import the math library for that. Lets explore the various libraries next.

Python Libraries

Lets take one step ahead in our journey to learn Python by getting acquainted with some useful libraries. The first step is obviously to learn to import them into our environment. There are several ways of doing so in Python:

import math as m
from math import *

In the first manner, we have defined an alias m to library math. We can now use various functions from math library (e.g. factorial) by referencing it using the alias m.factorial().

In the second manner, you have imported the entire name space in math i.e. you can directly use factorial() without referring to math.

Tip: Google recommends that you use first style of importing libraries, as you will know where the functions have come from.

Following are a list of libraries, you will need for any scientific computations and data analysis:

  • NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
  • SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
  • Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
  • Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.
  • Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
  • Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
  • Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
  • Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
  • Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
  • Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
  • SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
  • Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

  • os for Operating system and file operations
  • networkx and igraph for graph based data manipulations
  • regular expressions for finding patterns in text data
  • BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

Now that we are familiar with Python fundamentals and additional libraries, lets take a deep dive into problem solving through Python. Yes I mean making a predictive model! In the process, we use some powerful libraries and also come across the next level of data structures. We will take you through the 3 key phases:

  1. Data Exploration – finding out more about the data we have
  2. Data Munging – cleaning the data and playing with it to make it better suit statistical modeling
  3. Predictive Modeling – running the actual algorithms and having fun

Let’s discuss about these 3 key steps in detail  in the next post. Until then Happy Learning!!


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