In a time as we find ourselves in, where data increases every 1.145 trillion MB per day (according to recent statistics) it is pertinent to know that getting to make headway as to understanding the data, discovering insights, and communicating these insights is tiring and time-consuming that’s where the game-changing tools in python come in. Their usefulness cannot be overemphasized as it helps to give a visual overview of the big chunk of messy data. In python, these game-changing tools are libraries like matplotlib and seaborn which makes it seamless and less tiring to get the work done.
#importing two major data visualization tools in python
import matplotlib.pyplot as plt
import seaborn as sns
In matplotlib for instance, we have various graphs and charts, we can use to make clear and easy comparisons between different variables in our data. To even make more beautiful visualizations, you can import the seaborn library to add additional colors and other beautiful features as it is a build-up of the matplotlib. For data visualization in python, there are a variety of charts and graphs in the
matplotlib and seaborn library from the histogram, to the scatterplot, boxplot, bar chart, pie chart, heatmap, violin plot, joint plot, kernel density estimation(KED) plot, and so on.
In my journey as a data scientist, I have come to understand that being able to visualize data has benefits beyond trying to make sense of data, it also helps in building regression models, as it helps us to have a visual understanding of features that are correlated to our target variables.
Data visualization is no doubt a game-changer as it not only brings to life the treasures that can be found in the data, it also gives you a map into discovering, understanding, and communicating insight in your data.
Written By: Feranmi Olusanya