https://matplotlib.org

https://github.com/matplotlib/matplotlib

https://matplotlib.org/gallery.html

tutorials
https://matplotlib.org/tutorials/index.html
https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python

cheatsheet
https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf
https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb

axes
https://matplotlib.org/api/axes_api.html
https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html
https://www.labri.fr/perso/nrougier/teaching/matplotlib/

xtick
https://matplotlib.org/examples/ticks_and_spines/ticklabels_demo_rotation.html
https://stackoverflow.com/questions/26358200/xticks-by-pandas-plot-rename-with-the-string
https://jakevdp.github.io/PythonDataScienceHandbook/04.10-customizing-ticks.html

ticker
https://matplotlib.org/api/ticker_api.html

formatter
https://matplotlib.org/gallery/api/date_index_formatter.html
https://matplotlib.org/gallery/ticks_and_spines/date_index_formatter.html

plotting categorical
https://matplotlib.org/gallery/lines_bars_and_markers/categorical_variables.html

import matplotlib.pyplot as plt

data = {'apples': 10, 'lemons': 5, 'limes': 20, 'oranges': 15}
names = list(data.keys())
values = list(data.values())

%mathplotlib inline
plt.plot(names, values)

subplots
https://pandas.pydata.org/pandas-docs/stable/visualization.html#subplots

defaults
import matplotlib.pyplot as plt
print(plt.rcParams) # to examine all values
print(plt.rcParams.get('figure.figsize')