# Plotting

## Overview

### Questions

• How can I plot my data?
• How can I save my plot for publishing?

### 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.

### PYTHON

import matplotlib.pyplot as plt
• Simple plots are then (fairly) simple to create.

### PYTHON

time = [0, 1, 2, 3]
position = [0, 100, 200, 300]

plt.plot(time, position)
plt.xlabel('Time (hr)')
plt.ylabel('Position (km)')

### Display All Open Figures

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:

### PYTHON

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.

## Plot data directly from a Pandas dataframe.

• We can also plot Pandas dataframes.
• 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].

### PYTHON

import pandas as pd

# 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()

## Select and transform data, then plot it.

### PYTHON

data.T.plot()
plt.ylabel('GDP per capita')

## Many styles of plot are available.

• For example, do a bar plot using a fancier style.

### PYTHON

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

## Data can also be plotted by calling the matplotlibplot 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

### PYTHON

years = data.columns
gdp_australia = data.loc['Australia']

plt.plot(years, gdp_australia, 'g--')

## Can plot many sets of data together.

### PYTHON

# 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 (\$)')

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:

### PYTHON

plt.plot(years, gdp_australia, label='Australia')
plt.plot(years, gdp_nz, label='New Zealand')
• Instruct matplotlib to create the legend.

### PYTHON

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

### PYTHON

plt.scatter(gdp_australia, gdp_nz)

### PYTHON

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.

### PYTHON

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)

### PYTHON

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)

### 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)?

### PYTHON

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.

### Correlations (continued)

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:

### PYTHON

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())

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 program creates a plot showing the correlation between GDP and life expectancy for 2007, normalizing marker size by population:

### PYTHON

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.

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

### PYTHON

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. In addition to using plt.savefig, we can save a reference to the current figure in a local variable (with plt.gcf) and call the savefig class method from that variable to save the figure to file.

### PYTHON

data.plot(kind='bar')
fig = plt.gcf() # get current figure
fig.savefig('my_figure.png')

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.

### Keypoints

• 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: see the Python Graph Gallery for more options.
• Can plot many sets of data together.