The 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.

# 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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# 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())

For instance, let's visualize on scatter plot the gdp per capita and share of population with access to the Internet.

# 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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# 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()

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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Abschnitt 3. Kapitel 1