Plotting Data using Matplotlib

The Matplotlib library is used for creating static, animated, and interactive 2D plots or figures in Python.

The major components of a graph or plot are title, legend, ticks, x-label, and y-label.

The function used to save the plot is `savefig()`.

Pyplot provides many customisation options to make a chart more meaningful and readable. We can add a title using `title()`, set axis labels using `xlabel()` and `ylabel()`, show or hide grid lines using `grid()`, and display a legend using `legend()`. We can also change the appearance of the plot by using markers, colours, line styles, and line widths. For example, `marker='*'` adds star markers, `color='green'` changes the line colour, `linestyle='--'` makes a dashed line, and `linewidth=2` makes the line thicker. For bar charts and histograms, we can also adjust properties like edgecolor, fill, hatch, and in pie charts we can use explode and autopct.

A legend is used to identify the plotted data and show which line, bar, or symbol belongs to which dataset.

Pandas visualisation means using the built-in `.plot()` method of Series and DataFrame objects to plot graphs directly from Pandas data. It is a wrapper around Matplotlib’s pyplot plotting functions.

Open data is data that is open to the public for access and use. The chapter names Open Government Data (OGD) Platform India at data.gov.in as one source. Another website can also be used from which open data is available, but among the chapter’s content, data.gov.in is explicitly given.

A scatter plot can be used for comparison where we want to show the relationship between two variables. From the chapter, an example is discount offered versus sales made.