Plotting

Overview

Teaching: 15 min
Exercises: 15 min

Questions

• How can I plot my data?

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.

• Commonly use a sub-library called matplotlib.pyplot.
• The Jupyter Notebook will render plots inline if we ask it to using a “magic” command.

%matplotlib inline
import matplotlib.pyplot as plt

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

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.xlabel('Numbers')
plt.ylabel('Doubles')

Plot data directly from a Pandas dataframe.

• We can also plot Pandas dataframes.
• This implicitly uses matplotlib.pyplot.

import pandas

data = pandas.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')
data.ix['Australia'].plot()
plt.xticks(rotation=90)

Select and transform data, then plot it.

• 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')
plt.xticks(rotation=90)

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.xticks(rotation=90)
plt.ylabel('GDP per capita')

• Extract years from the last four characters of the columns’ names.
  • Store these in a list using the Accumulator pattern.
• Can also convert dataframe data to a list.

# Accumulator pattern to collect years (as character strings).
years = []
for col in data.columns:
    year = col[-4:]
    years.append(year)

# Australia data as list.
gdp_australia = data.ix['Australia'].tolist()

# Plot: 'b-' sets the line style.
plt.plot(years, gdp_australia, 'b-')

Scatter plot example

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

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')
data_europe.____.plot(label='min')
data_europe.____
plt.legend(loc='best')

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('gapminder_all.csv')
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.

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.

• Can plot many sets of data together.