Visualization in Python with matplotlib

Visualization in Python with matplotlib

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1. Basics: Line Charts

In this section, we will be introduced to the very basics of matplotlib - creating empty Figure and Axes, how to build a line plot, display multiple lines on one plot, add a legend, customize line type, and so on...

Creating an Empty Plot

Simple Line Chart

Create a Simple Line Chart

Adding One More Line

Add Data to the Plot

Adding Legend to a Plot

Plot Labels and Title

Add Lables and Titles

Changing Line Style

Customize Your Line Chart

Create a Complete Line Chart

2. Bar Charts

In this section, we will learn about other chart types, such as bar plots, histograms, box plots, how to set necessary parameters and customize them.

Simple Bar Chart

Create a Simple Bar Chart

Create a Stacked Bar Chart

Create a Grouped Bar Chart

Horizontal Bar Chart

Create a Horizontal Bar Chart

Customizing Bar Charts

Create a Complete Bar Chart

3. Scatter Plots

In this section, we will learn the scatter plots - one of the easiest ways to discover the relationships in your data.

The Simplest Scatter Plot

Create a Simple Scatter Plot

Setting Plot Parameters

Set Ticks and Limits

Customize Your Scatter Plot

Grouping Observations

Group by Region

Further Grouping

Group by Continuous Variable

Making Chart Informative

Add Information to the Plot

Зміст курсу

Visualization in Python with matplotlib

Adding One More Line

Quite good! That's a good visualization! Let's expand our chart with new data.

We may want to compare the emission levels by displaying several lines on a single chart. It can be easily done by calling the .plot() method of Axes object multiple times. For instance, if you call ax.plot() two times, you will get two lines on one chart. By default, the first line will be blue, the second one will be orange.


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# Import the libraries
import matplotlib.pyplot as plt
import pandas as pd

# Load the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/ed80401e-2684-4bc4-a077-99d13a386ac7/co2.csv', index_col = 0)

# Filter the data
ita = data.loc['Italy'] 
swe = data.loc['Sweden']

# Create Figure and Axes objects
fig, ax = plt.subplots()

# Initialize lines
ax.plot(ita.index.astype(int), ita.values) 
ax.plot(swe.index.astype(int), swe.values)

# Display the plot
plt.show()

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