Find patterns in your data using Seaborn and Pandas
haridas n (~haridas) |
Data-science mainly involves understanding your data and identify suitable models based on the data. Mastering the standard tools like pandas and seaborn will be key to gain insights about ML problems.
Seaborn is a plotting library written on top of matploatlib to improve and augment the features of matplotlib, what there these features make the seaborn library a excellent pick over matplotlib ?
- Seaborn plots provides way more statistical information like SD, boundary line, etc from single plot with nicer aesthetics.
- Provides APIs to manage the theme and colours of the plot.
- Seaborn plots provide more information about the data sample by including the different statistical information on the same plot. eg; Avg, SD etc.
- Seaborn adds custom plots like regression plot, violin plot and more customisation for plot.
- Finally, Seaborn should be the default plotting library you should use if you are with python for data science works.
This tutorial coverers,
- Basics of pandas and seaborn
- Different plotting patterns using seaborn for your data.
- Plotting Single and bivariate distributions, categorical plots with distribution.
- Understand two variable behaviour using regression plots.
- How I decided to buy a petrol car instead of diesel car by analysing my fuel spending.
Lapatop with following packages installed.
pip install seaborn pands
Haridas is a Principal Engineer in Pramati Technologies, part of Labs team. He has 8+ years of experience in multiple domains like, Web development, SOA, ML, Devops. He has been working extensively in different ML use-cases and applying them in real scenarios.
- Twitter @haridas_n