import
matplotlib.pyplot as plt
Simple plots are then (fairly) simple to create.
time = [0, 1, 2, 3]
position = [0, 100, 200, 300]
plt.plot(time, position)
plt.xlabel('Time (hr)')
plt.ylabel('Position (km)')
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:
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.
We can also plot Pandas dataframes.
This implicitly uses matplotlib.pyplot.
Before plotting, we convert the column headings from a string to integer data type, since they represent numerical values,
using str.replace() to remove the gpdPercap_
prefix and then astype(int)
to convert the series of string values (['1952', '1957', ..., '2007']) to a series of integers: [1925, 1957, ..., 2007].
import pandas as pd
data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')
# Extract year from last 4 characters of each column name
# The current column names are structured as 'gdpPercap_(year)',
# so we want to keep the (year) part only for clarity when plotting GDP vs. years
# To do this we use replace(), which removes from the string the characters stated in the argument
# This method works on strings, so we use replace() from Pandas Series.str vectorized string functions
years = data.columns.str.replace('gdpPercap_', '')
# Convert year values to integers, saving results back to dataframe
data.columns = years.astype(int)
data.loc['Australia'].plot()
By default, DataFrame.plot plots with the rows as the X axis.
We can transpose the data in order to plot multiple series.
data.T.plot()
plt.ylabel('GDP per capita')
Many styles of plot are available.
For example, do a bar plot using a fancier style.
plt.style.use('ggplot')
data.T.plot(kind='bar')
plt.ylabel('GDP per capita')
Data can also be plotted by calling the matplotlib plot function directly.
The command is plt.plot(x, y)
The color and format of markers can also be specified as an additional optional argument e.g., b- is a blue line, g-- is a green dashed line.
Get Australia data from dataframe
years = data.columns
gdp_australia = data.loc['Australia']
plt.plot(years, gdp_australia, 'g--')
# 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.xlabel('Year')
plt.ylabel('GDP per capita ($)')
Adding a Legend
Often when plotting multiple datasets on the same figure it is desirable to have
a legend describing the data.
This can be done in matplotlib in two stages:
Provide a label for each dataset in the figure:
plt.plot(years, gdp_australia, label='Australia')
plt.plot(years, gdp_nz, label='New Zealand')
Instruct matplotlib to create the legend.
plt.legend()
By default matplotlib will attempt to place the legend in a suitable position. If you
would rather specify a position this can be done with the loc= argument, e.g to place
the legend in the upper left corner of the plot, specify loc='upper left'
Plot a scatter plot correlating the GDP of Australia and New Zealand
Use either plt.scatter or DataFrame.plot.scatter
plt.scatter(gdp_australia, gdp_nz)
data.T.plot.scatter(x = 'Australia', y = 'New Zealand')
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 = 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)
Solution
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)
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)?
Solution
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.
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 = pd.read_csv('data/gapminder_gdp_asia.csv', index_col='country')
data_asia.max().plot()
print(data_asia.idxmax())
print(data_asia.idxmin())
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 gdb nation has varied
more notably.
This short program creates a plot showing
the correlation between GDP and life expectancy for 2007,
normalizing marker size by population:
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.
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
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,
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 variable.
data.plot(kind='bar')
fig = plt.gcf() # get current figure
fig.savefig('my_figure.png')
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.
