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.

import matplotlib.pyplot as plt

• If you’re using ipython and want to see plots appear as you create them you can use the ‘magic’ command:

%matplotlib tk

• Otherwise you’ll need to use plt.show() after creating each plot.

• 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-')

Can plot many sets of data together.

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

# Select two countries' worth of data.
gdp_australia = data.ix['Australia']
gdp_nz = data.ix['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 ($)')

• 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')

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

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('gapminder_gdp_asia.csv')
data_asia.describe().T.plot(kind='scatter', x='min', y='max')

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('gapminder_gdp_asia.csv')
data_asia.max().plot()
print(data_asia.idxmax())
print(data_asia.idxmin())

More Correlations

This short programs 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.