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Visualization in Python with matplotlib
course content

Course Content

Visualization in Python with matplotlib

Visualization in Python with matplotlib

1. Basics: Line Charts
Creating an Empty PlotSimple Line ChartCreate a Simple Line ChartAdding One More LineAdd Data to the PlotAdding Legend to a PlotAdd LegendPlot Labels and TitleAdd Lables and TitlesChanging Line StyleCustomize Your Line ChartCreate a Complete Line Chart
2. Bar Charts
Simple Bar ChartCreate a Simple Bar ChartStacked BarsCreate a Stacked Bar ChartGrouped BarsCreate a Grouped Bar ChartHorizontal Bar ChartCreate a Horizontal Bar ChartCustomizing Bar ChartsCreate a Complete Bar Chart
3. Scatter Plots
The Simplest Scatter PlotCreate a Simple Scatter PlotSetting Plot ParametersSet Ticks and LimitsCustomizationCustomize Your Scatter PlotGrouping ObservationsGroup by RegionFurther GroupingGroup by Continuous VariableMaking Chart InformativeAdd Information to the Plot

bookCustomization

You may wonder why the plot from the previous chapter didn't look like the first one. That is not the bug, just without setting the limits matplotlib automatically set the limits such that there is a little gap between the first observations and axis. Like simple line plots, scatter plots can also be customized in several ways.

We can set the size and color of points. To set the points color use parameter c (possible values for this parameter are the same as for color parameter in .plot() function from the previous section), to set the size of the points use s parameter (either single integer/float number or array with the same size as the number of points). For example, we can relate the size of the points from the previous example to the values of 'population' column divided by 1000000 (to make these values smaller) and make all the points red.


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

# Reading the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/ed80401e-2684-4bc4-a077-99d13a386ac7/gapminder2017.csv', index_col = 0)

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

# Initialize a scatter plot
ax.scatter(data['gdp per capita'], data['internet users'], 
          s = data['population']/1000000, c = 'red')      

# Set some parameters
ax.set(ylim = (0, 50), xlim = (0, 20000), yticks = (0, 10, 20, 30, 40, 50), 
      title = 'Example Plot', xlabel = 'GDP per capita', 
      ylabel = '% of people with Internet access')

# Display the plot
plt.show()
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Section 3. Chapter 5
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