Teaching: 15 min
Exercises: 15 min
  • How can I plot my data?

  • How can I save my plot for publishing?

  • 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.

%matplotlib inline
import matplotlib.pyplot as plt
time = [0, 1, 2, 3]
position = [0, 100, 200, 300]

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

Simple Position-Time Plot

Plot data directly from a Pandas dataframe.

import pandas

data = pandas.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')

# Extract year from last 4 characters of each column name
years = data.columns.str.strip('gdpPercap_')
# Convert year values to integers, saving results back to dataframe
data.columns = years.astype(int)


GDP plot for Australia

Select and transform data, then plot it.

plt.ylabel('GDP per capita')

GDP plot for Australia and New Zealand

Many styles of plot are available.'ggplot')
plt.ylabel('GDP per capita')

GDP barplot for Australia

Data can also be plotted by calling the matplotlib plot function directly.

Get Australia data from dataframe

years = data.columns
gdp_australia = data.loc['Australia']

plt.plot(years, gdp_australia, 'g--')

GDP formatted plot for Australia

Can plot many sets of data together.

# Select two countries' worth of data.
gdp_australia = data.loc['Australia']
gdp_nz = data.loc['New Zealand']

# Plot with differently-colored markers.
plt.plot(years, gdp_australia, 'b-', label='Australia')
plt.plot(years, gdp_nz, 'g-', label='New Zealand')

# Create legend.
plt.legend(loc='upper left')
plt.ylabel('GDP per capita ($)')

GDP formatted plot for Australia and New Zealand

plt.scatter(gdp_australia, gdp_nz)

GDP correlation using plt.scatter

data.T.plot.scatter(x = 'Australia', y = 'New Zealand')

GDP correlation using data.T.plot.scatter

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.

data_europe = pandas.read_csv('data/gapminder_gdp_europe.csv', index_col='country')


data_europe = pandas.read_csv('data/gapminder_gdp_europe.csv', index_col='country')

Minima Maxima Solution


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)?

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


Correlations Solution 1

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.

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:

data_asia = pandas.read_csv('data/gapminder_gdp_asia.csv', index_col='country')


Correlations Solution 2

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 gdb nation has varied more notably.

More Correlations

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

data_all = pandas.read_csv('data/gapminder_all.csv', index_col='country')
data_all.plot(kind='scatter', x='gdpPercap_2007', y='lifeExp_2007',

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


More Correlations 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.

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


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 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, and plt.savefig seems not to be a possible approach. One possibility to save the figure to file is then to

  • save a reference to the current figure in a local variable (with plt.gcf)
  • call the savefig class method from that varible.
fig = plt.gcf() # get current figure

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.