Your Everyday Cheatsheet for Python’s Matplotlib

Your Everyday Cheatsheet for Python’s Matplotlib

A Complete Visualization Course. Matplotlib is the most widely used visualization tools in python. It is well supported in a wide range of environments such as web application servers, graphical user interface toolkits, Jupiter notebook and iPython notebook, iPython shell

Matplotlib is the most widely used visualization tools in python. It is well supported in a wide range of environments such as web application servers, graphical user interface toolkits, Jupiter notebook and iPython notebook, iPython shell.

Matplolib Architecture

Matplotlib has three main layers: the backend layer, the artist layer, and the scripting layer. The backend layer has three interface classes: figure canvas that defines the area of the plot, renderer that knows how to draw on figure canvas, and event that handles the user inputs such as clicks. The Artist layer knows how to use the Renderer and draw on the canvas. Everything on a Matplotlib plot is an instance of an artist layer. The ticks, title, labels the plot itself everything is an individual artist. The scripting layer is a lighter interface and very useful for everyday purposes. In this article, I will demonstrate all the examples using the scripting layer and I used a Jupyter Notebook environment.

I suggest that you run every piece of code yourself if you are reading this article to learn.

Data Preparation

Data preparation is a common task before any data visualization or data analysis project. Because data never comes in the way you want. I am using a dataset that contains Canadian Immigration information. Import the necessary packages and the dataset first.

import numpy as np  
import pandas as pd
df = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',
                       sheet_name='Canada by Citizenship',
                       skiprows=range(20),
                       skipfooter=2)
df.head()

I am skipping the first 20 rows and the last 2 rows because they are just text not tabulated data. The dataset is too big. So I cannot show a screenshot of the data. But to get the idea about the dataset, see the column names:

data-visualization data-science towards-data-science matplotlib python

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