Data Visualization Projects Page
So I decided that I wanted to learn about data visualization in Python, and so here are a few visualizations that I created as a personal project. Basically, data visualization involves displaying data in the form of graphs and charts, and is an incredibly important part of becoming a data analyst. There are softwares such as Microsoft Power BI, Tableau and even Python which are popular visualization tools. Specifically speaking about Python, it offers many libraries which allow you to visualize data, such as Matplotlib, Seaborn, Plotly and Plotly Express.
- Matplotlib and Seaborn can help you create static plots to display data in the forms of bar charts, pie charts, histograms, violin plots, scatter plots and more! Seaborn offers relational plots and categorical plots as its subcategories.
- Plotly and Plotly Express can help you create dynamic plots, which display information on hovering your mouse over the charts. They support bar charts, scatter plots, box plots, line charts, and more!
Here are the links to my visualizations:
A brief introduction about the libraries used:
1. NumPy:
NumPy is an open source Python library used for numerical manipulation of arrays. Python does utilise arrays however, they are slow to process. Numpy provides 50 times faster efficiency as compared to Python lists. This library is very commonly used in the field of data science.
2. Pandas:
Pandas is a Python libraries which is primarly used in data analysis and can be used to create database tables for the same. It has built-in functions for cleaning, manipulating, exploring and analyzing datasets. Pandas dataframes can be used in exploratory data analysis and subsequently to produce a variety of plot diagrams.
3. Matplotlib:
Matplotlib is an open source tool, used to create primitive visualization plots. Certain kinds of plots such as histograms, bar graphs, pie charts, line graphs and more… can be created via this library.
4. Seaborn:
Seaborn is a Python library which with the help of Matplotlib, is used to plot graphs of a distributed variance. It contains two subcategories: relational plots and categorical plots. Relational plots can be used in bivariate analysis or to plot frequency distribution. Categorical plots plot frequency distribution with respect to particular categories, whose respective data is to be analyzed.
5. Plotly:
Plotly is a JavaScript-based visualization library, which contains 3D graphics’ charts. Dynamic plots such as horizontal bar plots, pie plots, line plots, scatter plots etc. can be created using this library.
Here’s where you can find the datasets I’d used:
Rainfall Analysis of India Dataset
Student Test Score Analysis Dataset