Interactive online version:
Plotting
Import the LArray library:
[1]:
from larray import *
Import the test array population from the demography_eurostat dataset:
[2]:
demography_eurostat = load_example_data('demography_eurostat')
population = demography_eurostat.population / 1_000_000
# show the 'population' array
population
[2]:
country gender\time 2013 2014 2015 2016 2017
Belgium Male 5.472856 5.493792 5.524068 5.569264 5.589272
Belgium Female 5.665118 5.687048 5.713206 5.741853 5.762455
France Male 31.772665 32.045129 32.174258 32.247386 32.318973
France Female 33.827685 34.120851 34.283895 34.391005 34.485148
Germany Male 39.380976 39.556923 39.835457 40.514123 40.697118
Germany Female 41.14277 41.21054 41.36208 41.661561 41.824535
Inline matplotlib (required in notebooks):
[3]:
%matplotlib inline
Create and show a simple plot (last axis define the different curves to draw):
[4]:
population['Belgium'].plot()
[4]:
<Axes: xlabel='time'>
Create a Line plot with grid, user-defined x ticks, y label and title.
Save the plot as a png file.
and show it
[5]:
population['Belgium'].plot(grid=True,
xticks=[2013, 2014, 2016, 2017],
ylabel='population (millions)',
title='Belgium population by gender',
# saves figure in a file
filepath='Belgium_population.png',
# by default, when the plot is saved to a file, it is *not* shown
show=True)
[5]:
<Axes: title={'center': 'Belgium population by gender'}, xlabel='time', ylabel='population (millions)'>
Specify line styles and width:
[6]:
# line styles: '-' for solid line, '--' for dashed line, '-.' for dash-dotted line and ':' for dotted line
population['Male'].plot(style=['-', '--', '-.'],
linewidth=2,
title='Male population by country')
[6]:
<Axes: title={'center': 'Male population by country'}, xlabel='time'>
Configuring the legend can be done by passing a dict to the legend argument. For example, to put the legend in a specific position inside the graph, one would use legend={'loc': <position>}.
Where <position> can be: 'best' (default), 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center' or 'center'.
[7]:
population['Belgium'].plot(title='Belgium population by gender',
legend={'loc': 'lower right'})
[7]:
<Axes: title={'center': 'Belgium population by gender'}, xlabel='time'>
There are many other ways to customize the legend, see the “Other parameters” section of matplotlib’s legend documentation. For example, to put the legend outside the plot:
[8]:
population['Belgium'].plot(title='Belgium population by gender',
legend={'bbox_to_anchor': (1.25, 0.6)})
[8]:
<Axes: title={'center': 'Belgium population by gender'}, xlabel='time'>
Create a Bar plot:
[9]:
population['Belgium'].plot.bar()
[9]:
<Axes: xlabel='time'>
Specify bounds for the y axis:
[10]:
population['Belgium'].plot.bar(ylim=[5.3, 5.8])
[10]:
<Axes: xlabel='time'>
Create a stacked bar plot:
[11]:
population['Belgium'].plot.bar(stack='gender')
[11]:
<Axes: xlabel='time'>
Create a multiplot figure (using subplots=axes):
[12]:
population.plot(subplots=('country', 'gender'),
sharex=True,
ylabel='population (millions)',
figsize=(8, 8))
[12]:
array([[ <Axes: title={'center': 'Belgium Male'}, xlabel='time', ylabel='population (millions)'>,
<Axes: title={'center': 'Belgium Female'}, xlabel='time', ylabel='population (millions)'>],
[ <Axes: title={'center': 'France Male'}, xlabel='time', ylabel='population (millions)'>,
<Axes: title={'center': 'France Female'}, xlabel='time', ylabel='population (millions)'>],
[ <Axes: title={'center': 'Germany Male'}, xlabel='time', ylabel='population (millions)'>,
<Axes: title={'center': 'Germany Female'}, xlabel='time', ylabel='population (millions)'>]], dtype=object)
Let us now demonstrate heatmaps using some random data (because the population array does not lend itself well to heatmaps)
[13]:
from larray.random import randint
[14]:
random_data = randint(0, 100, axes='a=a0..a29;b=b0..b29')
[15]:
random_data.plot.heatmap(title='Some random data')
[15]:
<Axes: title={'center': 'Some random data'}, xlabel='b', ylabel='a'>
For full control over plots, one can use Matplotlib API directly:
[16]:
import matplotlib.pyplot as plt
# create a matplotlib figure with 4 subplots in a 2 rows/2 columns grid
fig, ax = plt.subplots(2, 2, figsize=(10, 8), tight_layout=True)
# line plot with 2 curves (Males and Females) in the top left corner (ax=ax[0, 0])
# we do not want to see the plot yet, so we could specify show=False but this is not
# necassary because show is False by default if the ax argument is used.
population['Belgium'].plot(ax=ax[0, 0],
title='line plot')
# bar plot in the top right corner (0, 1)
population[2017].percent('gender').plot.bar(ax=ax[0, 1],
ylabel='% of population',
title='bar plot')
# pie chart in the bottom left corner (1, 0)
population['Belgium', 2017].plot.pie(ax=ax[1, 0],
title='pie chart')
# scatter plot in the bottom right corner (1, 1)
population['Belgium'].plot.scatter(ax=ax[1, 1],
x='Male', y='Female',
# using the year as color index
c=population.time,
# use a specific color map (otherwise we get a gray gradient)
colormap='viridis',
title='scatter plot',
# since this is the last command to create our plot, we want to display it
show=True)
[16]:
<Axes: title={'center': 'scatter plot'}, xlabel='Male', ylabel='Female'>
See plot for more details and examples.
See pyplot tutorial for a short introduction to matplotlib.pyplot.