Plotting

Estimated time: 30 minutes

Overview

Questions

• How can I plot my data?
• How can I save my plot for publishing?

Objectives

• Create a time series plot showing a single data set.
• Create a scatter plot showing relationship between two data sets.

matplotlib is the most widely used scientific plotting library in Python.

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• Commonly use a sub-library called matplotlib.pyplot.
• The Jupyter Notebook will render plots inline by default.

PYTHON

import matplotlib.pyplot as plt

• Simple plots are then (fairly) simple to create.

PYTHON

time = [0, 1, 2, 3]
position = [0, 100, 200, 300]

plt.plot(time, position)
plt.xlabel('Time (hr)')
plt.ylabel('Position (km)')

Callout

Display All Open Figures

In our Jupyter Notebook example, running the cell should generate the figure directly below the code. The figure is also included in the Notebook document for future viewing. However, other Python environments like an interactive Python session started from a terminal or a Python script executed via the command line require an additional command to display the figure.

Instruct matplotlib to show a figure:

PYTHON

plt.show()

This command can also be used within a Notebook - for instance, to display multiple figures if several are created by a single cell.

Plot data directly from a Pandas dataframe.

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• You can also plot data directly from a DataFrame.
• Let’s start with the gdp_americas CSV file.

PYTHON

import pandas as pd
gdp_americas = pd.read_csv("data/gapminder_gdp_americas.csv")

• To plot a single, numeric variable you can use a histogram.
• We can plot the distribution of GDP in 1957 by using .loc to select the column followed by .hist().

PYTHON

gdp_americas.loc[:, "gdpPercap_1957"].hist()

• To plot two or more variables, you can use the built-in Pandas bar chart feature.

• For example, to plot the GDP of each country in gdp_americas in 1952 as a bar chart, assign “country” to the x-axis and “gdpPerCap_195” to the y-axis.

PYTHON

gdp_americas.plot.bar(x="country", y="gdpPercap_1952")

Pandas is great for making quick charts, but matplotlib will give you more control and customization options.

Challenge

Minima and Maxima

Fill in the blanks below to plot the minimum GDP per capita over time for all the countries in Europe. Modify it again to plot the maximum GDP per capita over time for Europe.

PYTHON

data_europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
data_europe.____.plot(label='min')
data_europe.____
plt.legend(loc='best')
plt.xticks(rotation=90)

Show me the solution

PYTHON

data_europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
data_europe.min().plot(label='min')
data_europe.max().plot(label='max')
plt.legend(loc='best')
plt.xticks(rotation=90)

Challenge

Correlations

Modify the example in the notes to create a scatter plot showing the relationship between the minimum and maximum GDP per capita among the countries in Asia for each year in the data set. What relationship do you see (if any)?

Show me the solution

PYTHON

data_asia = pd.read_csv('data/gapminder_gdp_asia.csv', index_col='country')
data_asia.describe().T.plot(kind='scatter', x='min', y='max')

No particular correlations can be seen between the minimum and maximum GDP values year on year. It seems the fortunes of asian countries do not rise and fall together.

Challenge

Correlations (continued)

You might note that the variability in the maximum is much higher than that of the minimum. Take a look at the maximum and the max indexes:

PYTHON

data_asia = pd.read_csv('data/gapminder_gdp_asia.csv', index_col='country')
data_asia.max().plot()
print(data_asia.idxmax())
print(data_asia.idxmin())

Show me the solution

Seems the variability in this value is due to a sharp drop after 1972. Some geopolitics at play perhaps? Given the dominance of oil producing countries, maybe the Brent crude index would make an interesting comparison? Whilst Myanmar consistently has the lowest GDP, the highest GDP nation has varied more notably.

Challenge

More Correlations

This short program creates a plot showing the correlation between GDP and life expectancy for 2007, normalizing marker size by population:

PYTHON

data_all = pd.read_csv('data/gapminder_all.csv', index_col='country')
data_all.plot(kind='scatter', x='gdpPercap_2007', y='lifeExp_2007',
              s=data_all['pop_2007']/1e6)

Using online help and other resources, explain what each argument to plot does.

Show me the solution

A good place to look is the documentation for the plot function - help(data_all.plot).

kind - As seen already this determines the kind of plot to be drawn.

x and y - A column name or index that determines what data will be placed on the x and y axes of the plot

s - Details for this can be found in the documentation of plt.scatter. A single number or one value for each data point. Determines the size of the plotted points.

Callout

Saving your plot to a file

If you are satisfied with the plot you see you may want to save it to a file, perhaps to include it in a publication. There is a function in the matplotlib.pyplot module that accomplishes this: savefig. Calling this function, e.g. with

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plt.savefig('my_figure.png')

will save the current figure to the file my_figure.png. The file format will automatically be deduced from the file name extension (other formats are pdf, ps, eps and svg).

Note that functions in plt refer to a global figure variable and after a figure has been displayed to the screen (e.g. with plt.show) matplotlib will make this variable refer to a new empty figure. Therefore, make sure you call plt.savefig before the plot is displayed to the screen, otherwise you may find a file with an empty plot.

When using dataframes, data is often generated and plotted to screen in one line. In addition to using plt.savefig, we can save a reference to the current figure in a local variable (with plt.gcf) and call the savefig class method from that variable to save the figure to file.

PYTHON

data.plot(kind='bar')
fig = plt.gcf() # get current figure
fig.savefig('my_figure.png')

Callout

Making your plots accessible

Whenever you are generating plots to go into a paper or a presentation, there are a few things you can do to make sure that everyone can understand your plots.

• Always make sure your text is large enough to read. Use the fontsize parameter in xlabel, ylabel, title, and legend, and tick_params with labelsize to increase the text size of the numbers on your axes.
• Similarly, you should make your graph elements easy to see. Use s to increase the size of your scatterplot markers and linewidth to increase the sizes of your plot lines.
• Using color (and nothing else) to distinguish between different plot elements will make your plots unreadable to anyone who is colorblind, or who happens to have a black-and-white office printer. For lines, the linestyle parameter lets you use different types of lines. For scatterplots, marker lets you change the shape of your points. If you’re unsure about your colors, you can use Coblis or Color Oracle to simulate what your plots would look like to those with colorblindness.

Key Points

• matplotlib is the most widely used scientific plotting library in Python.
• Plot data directly from a Pandas dataframe.
• Select and transform data, then plot it.
• Many styles of plot are available: see the Python Graph Gallery for more options.
• Can plot many sets of data together.