A basic Matplotlib figure is made up of multiple components common to different plot types. It will be useful to familiarize ourselves with the terminologies, as we will be using them frequently in plotting. To get you up to speed, we have prepared a glossary of these basic objects. For clearer illustration, here is a plot adapted from Matplotlib's official website that nicely highlights the anatomy of a typical Matplotlib figure:

• Figure: A figure is the whole plotting area that contains all plot elements. Multiple subplots may be tiled in grid within one figure.
• Subplot: A subplot is a subregion in a figure that contains all of the relevant...

Many elements in a Matplotlib figure can have their colors specified. There are several ways to do so. You will come across the color parameter as a keyword argument for style settings very often in different functions. The alternate abbreviated keyword c can often be used. We will first briefly introduce the general rule here.

There is a list of common colors we can quickly call with single letters:

• b: Blue
• g: Green
• r: Red
• c: Cyan
• m: Magenta
• y: Yellow
• k: Black
• w: White

For an informative figure, we typically have a number of text elements, including the title, labels of axes and ticks, legend, and any additional annotations. We can adjust the font size and font family in the default rc settings. These settings are set in a dictionary-like variable, matplotlib.rcParams, so you can do import matplotlib and define a parameter like this:

matplotlib.rcParams['font.size'] = 18 

Matplotlib also provides functions to alter the settings. The matplotlib.rc() changes the parameters one by one, whereas matplotlib.rcParams.update() accepts a dictionary input to change multiple settings simultaneously. Let's say we would like to change the font size to 20 and font family to serif, then use. We can do so in two ways:

matplotlib.rc('font', size=18)
matplotlib.rc('font&apos...

Lines and markers are key components found among various plots. Many times, we may want to customize their appearance to better distinguish different datasets or for better or more consistent styling. Whereas markers are mainly used to show data, such as line plots and scatter plots, lines are involved in various components, such as grids, axes, and box outlines. Like text properties, we can easily apply similar settings for different line or marker objects with the same method.

Most lines in Matplotlib are drawn with the lines  class, including the ones that display the data and those setting area boundaries. Their style can be adjusted by altering parameters in ...

Lines of grids, ticks, and axes help us to visually locate and measure the data values. Their distribution and style determine whether they make good visual aids for the plot or clutter the figure. We will demonstrate the basic methods here.

Sometimes it may not be easy to tell the coordinates of any point in the plot. Grid lines extend from axis tick marks and help us estimate the value at a certain position.

Grids can be added by calling pyplot.grid(). By default, grid lines will be added at major tick marks...

We have learned to set the style details step by step so far. The matplotlib.style module provides a handy way to apply a predefined global style to the whole figure. There are a number of built-in style sheets coming along the matplotlib package. You can call matplotlib.style.available to check them out:

The function returns a list of built-in style sheets, including classic, seaborn, and ggplot. Classic refers to the Matplotlib style before version 2.0. Seaborn is a popular package built on top of Matplotlib that offers some special plotting APIs and themes for generating aesthetically attractive statistical figures. In Matplotlib 2.0, we can easily work on the styling natively for simple plot types, without importing an extra module. Multiple style sheets for different purposes are available. The ...

Title and legend are pieces of text that facilitate quick comprehension of the data context. Although a title is not required or recommended, sometimes, such as in inline figures of many scientific publications, adding a title for your plot often helps make the message clear, especially when your figure is not accompanied by explanatory text. For plots with multiple datasets, it is a good practice to keep a data legend with a distinct color or pattern code labeled with the corresponding identities.

The title of a figure can be set by pyplot.title() or axes.set_title(). Text properties can be supplied as keyword arguments.

Now that we have gone through each style setting one by one, it's your showtime to combine all the techniques!

import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np 
import matplotlib as mpl

mpl.style.use('seaborn-darkgrid')

# 2001-2015 per genome sequencing cost in USD
# Adapted from NIH National Human Genome Research Institute figures
# genome.gov/sequencingcosts
# Data were quoted from builtwith.com on May 8th 2017
# Seasonal data were averaged by year for simplicity

# Prepare the data
years = list(range(2001,2016))
y_pos = np.arange(len(years)) 
seqcost = [95263071.92,70175437.42,61448421.50,53751684.08,
40157554.23,28780376.21,\
20442576.14,19934345.74,18519312.16,17534969.56,16159699.44,
16180224.10,\
13801124.19,12585658.90,11732534.52,11455315.22,10474556.36,
9408738.91,\
9047002.97,8927342.14,7147571.39,3063819.99,1352982.23,752079...

In this chapter, you learned how to set the styles, including colors, sizes, and shapes, of various elements in a Matplotlib plot. Now you are also aware of some stylistic considerations for a refined figure.

In the next chapter, we will continue to discuss the appearance of plots, moving our focus to the layout and annotation.