![]() The first step of any analysis is to load the dataset. Weekday: day of the week (Saturday, Sunday, Thursday and Friday) Smoker: Presence of smoker in a party? (No, Yes) Sex: Sex of person paying for the meal (Male, Female) ![]() The variables descriptions are as follows:īill: Total bill (cost of the meal), including tax, in US dollars The Tips data contains 244 observations and 7 variables (excluding the index). (1995), Practical Data Analysis: Case Studies in Business Statistics, Richard D. The dataset is also available through the Python package Seaborn.īryant, P. The data was reported in a collection of case studies for business statistics. In this blog, we primarily going to use the Tips dataset. Import seaborn as sns # Statistical plotting Import matplotlib.pyplot as plt # Plotting The first step of any analysis is to install and load the relevant libraries. I have aggregated different plots into the following categories. We will explore various types of plots and also tweak them a little bit to suit our need using Seaborn and Matplotlib library. The aim of the current article is to get familiar ourself with different types of plots. Plot saving and miscellaneous Aim of the article Seaborn plot modifications (legend, tick, and axis labels etc.) Facet, Pair and Joint plots using seaborn The Seaborn blog series will be comprised of the following five parts: The Seaborn library is built on the top of the Matplotlib library and also combined to the data structures from pandas. During learning, I have gone through these ups and downs. Especially, when you want it to be publication-ready. As per my experience, we could utilize seaborn (static plots) and Plotly (interactive plots) for the majority of exploratory analysis tasks with very few lines of codes and avoiding complexity.Īfter going through different plotting tools, especially in Python, I have observed that still there are challenges one would face while implementing plots using the Matplotlib and Seaborn library. So, I tried several libraries like Matplotlib, Seaborn, Bokeh and Plotly. So, I thought let’s see whether python visualization tools offer similar flexibility or not like what ggplot2 does. ![]() I have observed a significant improvement in python data analysis tools specifically, data manipulation, plotting and machine learning. Recently, I also started implementing the same using python due to recent advancements in this language libraries. When comes to visualization my all-time favourite is ggplot2 library (R’s plotting library: R is a statistical programming language) which is one of the popular plotting tools. In the data analysis part of the task, I have to often perform exploratory analysis. I work in the transportation domain, thus I’m fortunate that I get to work with lots of data. I’m a PhD student in the Department of Civil Engineering at IIT Guwahati. This helps us present the data in pictorial or graphical format. The visualization is an important part of any data analysis.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |