Plotting

Last updated on 2024-06-20 | Edit this page

Overview

Questions

  • Can I use Python to create plots?
  • How can I customize plots generated in Python?

Objectives

  • Create plot objects
  • Change the aesthetics of a plot such as colour
  • Edit the axis labels
  • Create customized plot styles to meet their needs
Disclaimer

Plotting with Matplotlib

Matplotlib is a Python library that can be used to visualize data. Pandas has powerful built-in plotting capabilities based off of matplotlib, but more flexibility is available to us if access matplotlib directly. The toolbox matplotlib.pyplot is a collection of functions that make matplotlib work like MATLAB. In most cases, this is all that you will need to use, but there are many other useful tools in matplotlib that you should explore.

We will cover a few basic commands for formatting plots in this lesson. A great resource for help styling your figures is the matplotlib gallery (http://matplotlib.org/gallery.html), which includes plots in many different styles and the source code that creates them.

The simplest of plots is the 2 dimensional line plot. These next few examples walk through the basic commands for making line plots using pyplots.

Using pyplot:

First, import the pyplot toolbox:

PYTHON 

    import matplotlib.pyplot as plt

We can start by plotting the values of a list of numbers (matplotlib can handle many types of numeric data, including numpy arrays and pandas DataFrames - we are just using a list as an example!):

PYTHON 

    list_numbers = [2, 4, 6, 8]
    plt.plot(list_numbers)
    plt.show()

The command plt.show() prompts Python to display the figure. Without it, it creates an object in memory but doesn’t produce a visible plot.

If you provide the plot() function with only one list of numbers, it assumes that it is a sequence of y-values and plots them against their index (the first value in the list is plotted at x=0, the second at x=1, etc). If the function plot() receives two lists, it assumes the first one is the x-values and the second the y-values. The line connecting the points will follow the list in order:

PYTHON 

    plt.plot(list_numbers,[200, 400, 800, 1600])
    plt.show()

A third, optional argument in plot() is a string of characters that indicates the line type and color for the plot. The default value is a continuous blue line. For example, we can make the line red ('r'), with circles at every data point ('o'), and a dot-dash pattern ('-.'). Look through the matplotlib gallery for more examples.

PYTHON 

    plt.plot(list_numbers,[200, 400, 800, 1600], 'ro-.')
    plt.axis([0,10,0,2000])
    plt.show()

The command plt.axis() sets the limits of the axes from a list of [xmin, xmax, ymin, ymax] values (the square brackets are needed because the argument for the function axis() is one list of values, not four separate numbers!).

A single figure can include multiple lines. We can include a legend by adding the optional keyword argument label='' in plot() and then call plt.legend(). The functions xlabel() and ylabel() will label the axes, and title() will write a title above the figure:

PYTHON 

# convert acquistion to datetime data type
works_df.acquisition_date = pd.to_datetime(works_df['acquisition_date'])

# subset on flag
dei = works_df[works_df['is_dei'] == True]
non_dei = works_df[works_df['is_dei'] == False]

# Group by acquisition year (or another suitable period)
acquisition_dei = dei.resample('Y', on='acquisition_date').size()
acquisition_non_dei = non_dei.resample('Y', on='acquisition_date').size()
works = works_df.resample('Y', on='acquisition_date').size()

# Plotting
plt.plot(acquisition_dei.index, acquisition_dei.values,"r--",label="DEI")
plt.plot(acquisition_non_dei.index, acquisition_non_dei.values,"g^",label="non-DEI")
plt.plot(works.index, works.values,"bo-",label="Combined",alpha=.25)
plt.title('Number of Acquisitions Over Time')
plt.xlabel('Year')
plt.ylabel('Number of Acquisitions')
plt.grid(True)
plt.legend()
plt.show()

Like MATLAB, pyplot is stateful; it keeps track of the current figure and plotting area, and any plotting functions are directed to those axes. To make more than one figure, we use the command plt.figure() with an increasing figure number inside the parentheses:

PYTHON 

    # this is the first figure
    plt.figure(1)
    plt.plot(acquisition_dei.index, acquisition_dei.values,"r--",label="DEI")

    plt.legend(loc='upper left', shadow=True, fontsize='x-large')
    plt.title('This is figure 1')

    # this is a second figure
    plt.figure(2)
    plt.plot(acquisition_non_dei.index, acquisition_non_dei.values,"g^",label="non-DEI")

    plt.legend(loc='lower right', shadow=True, fontsize='x-large')
    plt.title('This is figure 2')

    plt.show()

A single figure can also include multiple plots in a grid pattern. The subplot() command specifies the number of rows, the number of columns, and the number of the space in the grid that particular plot is occupying:

PYTHON 

    plt.figure(1)

    plt.subplot(2,2,1) # two row, two columns, position 1
    plt.plot(works.index, works.values,"bo-",label="Combined",alpha=.25)

    plt.subplot(2,2,2) # two row, two columns, position 2
    plt.plot(acquisition_dei.index, acquisition_dei.values,"r--",label="DEI")

    plt.subplot(2,2,3) # two row, two columns, position 3
    plt.plot(acquisition_non_dei.index, acquisition_non_dei.values,"g^",label="non-DEI")

    plt.show()

What can we and can’t we ask?

  • What other DEI related questions could you ask of the data as is?
  • What questions could you answer by reformulating the data currently in the dataset.
  • What is a question you have that cannot be answered with the current data?
    • How would you go about getting the data needed.
    • How much would that data cost in time, effort, dollars?

Make other types of plots:

  1. Create a histogram of checkouts.

  2. Run the following code:

PYTHON 

ethnicity_df = pd.read_csv('ethnicity.csv')
ethnicity_df.loc[ethnicity_df.intl.isnull(),['intl']]= ethnicity_df.intl.min()
ethnicity_df.intl = ethnicity_df.intl.astype('int')

plot_df = ethnicity_df[ethnicity_df.term == 'Fall']
plot_df.set_index('year',inplace=True)
plot_df = plot_df.sort_index()
plot_df
plot_df[['amind', 'black', 'hispanic', 'asian','pacific_islander', 'multi', 'white', 'unknown', 'intl']].plot()

Use a for loop to recreate that plot using matplotlib. Make sure you have a title, legend, and label your axis. Hint: You can reuse plot_df.

  1. Matplotlib can make many other types of plots in much the same way that it makes 2 dimensional line plots. Look through the examples in http://matplotlib.org/users/screenshots.html and try a few of them (click on the “Source code” link and copy and paste into a new cell in ipython notebook or save as a text file with a .py extension and run in the command line).

PYTHON 

plt.figure(figsize=(10, 6))
plt.hist(works_df['checkouts'], bins=len(works_df['checkouts'].unique()), log=True, edgecolor='black')
plt.title('Histogram of Checkouts (Logarithmic Y-axis)')
plt.xlabel('Checkouts')
plt.ylabel('Frequency (log scale)')
plt.grid(True)
plt.show()

PYTHON 

cols = ['amind', 'black', 'hispanic', 'asian',
       'pacific_islander', 'multi', 'white', 'unknown', 'intl']
styles = ['r-', 'b--', '-.', ':', 'r--', 'b-', 'g-', 'g--', 'r-.']
for ethnicity in enumerate(cols):
    plt.plot(plot_df.index,plot_df[ethnicity[1]],styles[ethnicity[0]],label = ethnicity[1],alpha=.5)

plt.legend()
plt.ylabel('Number of Students')
plt.xlabel('Year')
plt.title('Students by Ethnicity')

Plotting with other libraries.

matplotlib is not your only option. There are many other libraries that can simplify the steps required or add additional functionality with seaborn and bokeh being just two examples. There is no way to cover them all here, but be aware that you have options and that you should not let differences in syntax dissuade you from exploring to finding the right fit for your needs.