Utilizing Python for Data Analysis and Visualization
Exploring the Power of Python for Data Analysis as a Beginner
Introduction
Python is an interpreted scripting language created by Guido Van Rossum and released in 1991. Python offers numerous libraries and tools for data analysis, visualisation, data manipulation, and artificial intelligence that are easy to use and accessible to beginners. Python is a general-purpose object-oriented programming language that has various uses, which are listed below:
Data Analysis
Server-side web development
Statistics
Software Development
DevOps
Artificial Intelligence
Python is used for various reasons, which are listed below:
Its syntax allows programmes to be written in fewer lines compared to other programming languages
It works on various platforms, such as Windows, Mac, Linux, etc.
It has a large, vibrant community with various open-source projects
It is a versatile scripting language
It can easily be integrated with other programming languages, such as Java.
In this article, we will be exploring the use of Python in data analysis.
Getting started
Anaconda is an open-source software platform that contains various toolkits and libraries that are used for data analysis. Programming languages such as Python and R can be used on the Anaconda platform. Anaconda is a very popular platform amongst data analysts for the reasons listed below:
It contains over 1000 libraries for data analysis and science tasks
It contains various environments such as Jupyter Notebook, Jupyter Lab, RStudio, etc. which are environments where data analysis projects are carried out
It is simple to install and utilise.
The packages found on Anaconda are regularly updated.
Installation
Anaconda can be downloaded and installed by going to the official website (https://www.anaconda.com/download). Anaconda comes with Python installed, so there is no need for any additional installation, and it does not affect previously installed Python.
Data Visualisation with Python
Creating appealing visualisations with Python is very easy with the use of libraries such as Matplotlib, Seaborn, and Plotly.
Matplotlib
Simple and less complex visualisations such as line plots, scatterplots, etc. can be created using Matplotlib. An example of a line plot being created using Matplotlib is shown below:
In the example above, the Matplotlib library is used to create a simple line plot. The library is imported by using the code (import matplotlib.pyplot as plt) and labelled accordingly using the functions of the Matplotlib library.
Seaborn
Seaborn is a visualisation library built on the Matplotlib library to create more complex visualisations such as pair plots, joint plots, violin plots, heatmaps, etc.
An example of a heat map is created below using the Seaborn library in conjunction with the Pandas and NumPy libraries. The Matplotlib library must be imported to use the Seaborn library.
Plotly
Plotly is a visualisation library that allows for the creation of interactive visualisations, and Plotly also allows for the creation of interactive visualisations on web pages. Below is an example of an interactive scatterplot created using Plotly:
Clicking a point in the scatterplot would display information about the point, as shown in the image above.
Data Manipulation with Python
Data can be easily manipulated and transformed with Python through the use of the Pandas library. The Pandas library has various techniques for handling data; it can be used to drop missing values, merge datasets, fill up null values, etc.
The Pandas library can be imported using the import function, and it is imported as shown below:
import pandas as pd.
Displayed below are lines of code that display Pandas’s capabilities to manipulate data
Statistical Analysis with Python
Calculations and statistics have been made very easy with the use of Python libraries. The Python libraries used for statistics are NumPy and SciPy.
SciPy
The SciPy library can be used to solve complex and less complex calculations, such as linear equations, as shown below:
NumPy
The NumPy library can also be used to solve mathematical problems, as shown below. Calculations can also be done with arrays of numbers using the NumPy library.
Machine Learning with Python
Machine learning tasks such as classification, regression, clustering, etc. can be carried out using Python with the Scikit-learn library. The Scikit-learn library is made up of various tools that aid in the completion of machine-learning tasks. An example of how the Scikit-Learn library is used is displayed below:
In the example above, a regression model is created using the Scikit-learn library. The data is created first and split into test data, which would be used to test the data for accuracy and error and train data, which is used to train the model. After the model is created, it is later tested and scored using the Mean squared error, which is one of the numerous ways in which models are scored and checked for their accuracy in machine learning.
Conclusion
Python is a very powerful and versatile programming language that has various uses, including data analysis. Its large and vibrant community, coupled with its easy-to-use extensive libraries, makes it a perfect programming language for beginners to use and understand. Simple and complex visualisations that can be customised down to the tiniest details can be created using the Matplotlib, Seaborn, and Plotly libraries. Manipulation and transformation of data, which can be carried out using Pandas and mathematical operations, and computational analysis, which can be carried out using the NumPy and SciPy libraries. The Scikit-learn library, which contains various comprehensive tools that are used for various machine learning tasks, is also available to data analysts and scientists who use the Python programming language.