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Visualization in Python with matplotlib
course content

Conteúdo do Curso

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

bookThe Simplest Scatter Plot

Welcome to the third section of the course! Let's consider one more plot type widely used in analytics - scatter plots. This type of plot is quite easy to understand - it's just a collection of points with specific coordinates. Often this type of plot is used in experiments or discovering if there is relation between factors.

To build a scatter plot we need to do the same as for a simple line plot. But this time we use .scatter() applied to Axes object (instead of .plot() in the previous section). The first parameter of this function will be the x-axis, and the second - y-axis. Throughout this section, you will use the 'gapminder' dataset (as of 2017), which contains different economical, demographical, social indicators. Before we start, let's explore the data you will work with.


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

# 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)

# Describing the data
print(data.columns)
print(data.info())
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For instance, let's visualize on scatter plot the gdp per capita and share of population with access to the Internet.


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

# 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'])

# Display the plot
plt.show()
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For instance, if you run the code above, you will see that there is a relation between economical wellness and internet availability for people.

Disclaimer: FREE DATA FROM WORLD BANK VIA GAPMINDER.ORG, CC-BY LICENSE.

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Seção 3. Capítulo 1
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