Plotly Library

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. Plotly.py is free and open source. The cornerstone of Data Science is data visualisation. The ability to plot our data gives us a rich understanding of the data and allows us to communicate our findings.
There is an exercise file that you should attempt to complete.

Python Data Visualisation Ecosystem: Python has many packages that allow us to visualise data. This brings an even richer understanding of the knowledge hidden in the data. The plotly library generates interactive plots, ideal for Data Science.

Bar Plots: Bar plots are visual representations of frequency and relative frequency. They are the preferred visual representation of categorical or discrete data.

Histograms: Histograms similarly visualise frequencies and relative frequencies. Unlike bar charts, they are used for continuous numerical data.

Box-Whisker Plots: Box-and-whisker charts give an indication of the distribution of numerical data values by incorporating the median and quartiles of the data.

Scatter Plots: Scatter plots compare pairs of numerical variables for each subject. They allow us to visualise correlation between numerical variables and can help in visualising linear models such as linear regression models.

Time Series Plots: Time series plots allow is to visualise change in a variable over time.  

Course Author: Juan Klopper
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