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.

%matplotlib inline
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

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

Numbers-doubles plot

Plot data directly from a Pandas dataframe.

import pandas

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

GDP plot for Australia

Select and transform data, then plot it.

data.T.plot()
plt.ylabel('GDP per capita')
plt.xticks(rotation=90)

GDP plot for Australia and New Zealand

Many styles of plot are available.

plt.style.use('ggplot')
data.T.plot(kind='bar')
plt.xticks(rotation=90)
plt.ylabel('GDP per capita')

GDP barplot for Australia

# 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.loc['Australia'].tolist()

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

GDP formatted plot for Australia

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.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 ($)')

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.____.plot(label='min')
data_europe.____
plt.legend(loc='best')
plt.xticks(rotation=90)

Solution

data_europe = pandas.read_csv('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)

Minima Maxima Solution

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

Solution

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('gapminder_gdp_asia.csv', index_col='country')
data_asia.max().plot()
print(data_asia.idxmax())
print(data_asia.idxmin())

Solution

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 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', 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

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.

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.