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Python for Data Science – How to Implement Python Libraries

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Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in the IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this article, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.

Before we begin, let me just list out the topics I’ll be covering through the course of this article.

You can go through this Python for data science video lecture where our Python Training expert is discussing each & every nitty-gritty of the technology.

Learn Python for Data Science | Python Data Science Tutorial | Edureka

Why Learn Python For Data Science?

Python is no-doubt the best-suited language for a Data Scientist. I have listed down a few points which will help you understand why people go with Python for Data Science:

    • Python is a free, flexible and powerful open source language
    • Python cuts development time in half with its simple and easy to read syntax
    • With Python, you can perform data manipulation, analysis, and visualization
    • Python provides powerful libraries for Machine learning applications and other scientific computations

And do you know the best part? Data Scientist is one of the highest paid jobs who earn around $130,621 per year as per Indeed.com.

Python Introduction

What is Python - Python For Data Science - Edureka

Python was created by Guido Van Rossum in 1989. It is an interpreted language with dynamic semantics. It is free to access and run on all platforms. Python is:

1) Object Oriented
2) 
High-Level Language
3) 
Easy to Learn
4) 
Procedure Oriented

 Jupyter Installation for Python With Data Science

Let me guide you through the process of installing Jupyter on your system. Just follow the below steps:

Step 1: Go to the link: http://jupyter.org/

Step 2: You can either click on “Try in your browser” or “Install the Notebook”. 

Well, I would recommend you to install Python and Jupyter using Anaconda distribution. Once you have installed Jupyter, it will open on your default browser by typing “Jupyter Notebook” in command prompt. Let us now perform a basic program on Jupyter.


name=input("Enter your Name:")
print("Hello", name)

Now to run this, press “Shift+Enter” and view the output. Refer to the below screenshot:

Firstprogram - Python For Data Science - Edureka

In case you are facing any issues with the installation or Jupyter basics, you can go through the below video. It will also take you to various fundamentals of Python, along with a practical demonstrating the various libraries such as Numpy, Pandas, Matplotlib and Seaborn. Hope you like it! :)

Basics of Python For Data Science

Now is the time when you get your hands dirty in Python programming. But for that, you should have a basic understanding of the following topics:

Variables: Variables refers to the reserved memory locations to store the values. In Python, you don’t need to declare variables before using them or even declare their type. 

Data Types: Python supports numerous data types, which defines the operations possible on the variables and the storage method. The list of data types includes – Numeric, Lists, Strings, tuples, Sets and Dictionary.

Operators: Operators helps to manipulate the value of operands. The list of operators in Python includes- Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, and Identity.

Conditional Statements: Conditional statements helps to execute a set of statements based on a condition. There are namely three conditional statements – If, Elif and Else.

Loops: Loops are used to iterate through small pieces of code. There are three types of loops namely – While, for and nested loops.

Functions: Functions are used to divide your code into useful blocks, allowing you to order the code, make it more readable, reuse it & save some time. 

For more information and practical implementations, you can refer to this blog: Python Tutorial.

Python Libraries For Data Science

This is the part where the actual power of Python with data science comes into the picture. Python comes with numerous libraries for scientific computing, analysis, visualization etc. Some of them are listed below:

    • Numpy – NumPy is a core library of Python for Data Science which stands for ‘Numerical Python’. It is used for scientific computing, which contains a powerful n-dimensional array object and provide tools for integrating C, C++ etc. It can also be used as multi-dimensional container for generic data where you can perform various Numpy Operations and special functions
    • Matplotlib – Matplotlib is a powerful library for visualization in Python. It can be used in Python scripts, shell, web application servers and other GUI toolkits. You can use different types of plots and how multiple plots work using Matplotlib.
    • Scikit-learn – Scikit learn is one of the main attractions, where in you can implement machine learning using Python. It is a free library which contains simple and efficient tools for data analysis and mining purposes. You can implement various algorithm, such as logistic regression, time series algorithm using scikit-learn. It is suggested that you should go through this tutorial video on Scikit-learn to understand machine learning and various techniques before proceeding ahead.
    • Seaborn – Seaborn is a statistical plotting library in Python. So whenever you’re using Python for data science, you will be using matplotlib (for 2D visualizations) and Seaborn, which has its beautiful default styles and a high level interface to draw statistical graphics.
    • Pandas – Pandas is an important library in Python for data science. It is used for data manipulation and analysis.  It is well suited for different data such as tabular, ordered and unordered time series, matrix data etc. 

Demo: Practical Implementation

Problem Statement: You are given a dataset which comprises of comprehensive statistics on a range of aspects like distribution & nature of prison institutions, overcrowding in prisons, type of prison inmates etc. You have to use this dataset to perform descriptive statistics and derive useful insights out of the data. Below are few tasks:

  1. Data loading: Load a dataset “prisoners.csv” using pandas and display the first and last five rows in the dataset. Then find out the number of columns using describe method in Pandas.
  2. Data Manipulation: Create a new column -“total benefitted”, which is the sum of inmates benefitted through all modes. 
  3. Data Visualization: Create a bar plot with each state name on the x-axis and their total benefitted inmates as their bar heights.

Solution:

For data loading, write the below code:


import pandas as pd
import matplotlib.pyplot as plot
%matplotlib inline
file_name = "prisoners.csv"
prisoners = pd.read_csv(file_name)
prisoners

dataset - Python For data science - Edureka

Now to use describe method in Pandas, just type the below statement:


prisoners.describe()

describefunction- python for data science - Edureka

Next in Python with data science article, let us perform data manipulation.


prisoners["total_benefited"]=prisoners.sum(axis=1)

prisoners.head()

datamanipulation - python for data science - Edureka

And finally, let us perform some visualization in Python for data science article. Refer the below code:


import numpy as np
xlabels = prisoners['STATE/UT'].values
plot.figure(figsize=(20, 3))
plot.xticks(np.arange(xlabels.shape[0]), xlabels, rotation = 'vertical', fontsize = 18)
plot.xticks
plot.bar(np.arange(prisoners.values.shape[0]),prisoners['total_benefited'],align = 'edge')

Output – 

dataVisualization - python for data science - Edureka


I hope my blog on “Python for data science” was relevant for you. To get in-depth knowledge, check out our interactive, live-online 
Edureka Python Data Science Certification Training here, that comes with 24*7 support to guide you throughout your learning period.

Got a question for us? Please mention it in the comments section of this “Python for data science” article and we will get back to you as soon as possible.

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