EDA in Python
Contents
EDA in Python¶
Note that only the training dataset should be used in data exploratory analysis. This way, we emulate a real situation where predictions are performed on unknown targets, and we don’t want our analysis and decisions to be biased by our knowledge of the test data.
Matplotlib¶
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
Seaborn¶
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Resources
Plotly¶
Plotly’s Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
Dash¶
Dash is a Python framework provided by plotly for building web applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It’s particularly suited for anyone who works with data in Python.