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1. Creating a Clean Chart

Begin by importing the packages we'll use.





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import pandas as pd
import matplotlib.pyplot as plt
import pylab as pyl

# This is an example of an iPython magic command.
# If we don't use this, then we can't see our matplotlib plots in our notebook
%matplotlib inline

Data looks better naked

What in the world does that mean?

  • Slide and data presentation often refers back to Edward Tufte and his book "The Visual Display of Quantitative Information."

  • Define naked data this way:

Data-ink is the non-erasable core of the graphic, the non-redundant ink arranged in response to variation in the numbers represented

  • If we remove all non-data-ink and redundant data-ink, within reason, we should be left with an informative graphic that reflects sound graphical design

“Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away”

– Antoine de Saint-Exupery

Build plots using simple data...

Load our data first

  • For this exercise, we'll use a simple dataset: letter frequency in the English language.




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dfLetterFrequency = pd.read_csv('../data/letter_frequency.csv', header=None, index_col=0, names=['Frequency'])

The simple pandas bar plot is sufficient for exploration...





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dfLetterFrequency.plot(kind='bar', figsize=(10,6))



    


















    
Out[29]:








<matplotlib.axes._subplots.AxesSubplot at 0x10ffcb8d0>

But if we're showing this to the world, we can do better.

Let's make this chart prettier...

We need a color palette

  • These are the "Tableau 20" colors as RGB. 




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tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),  
             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),  
             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),  
             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),  
             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
  • Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. 




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for i in range(len(tableau20)):  
    r, g, b = tableau20[i]  
    tableau20[i] = (r / 255., g / 255., b / 255.)



    











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N = 26
ind = np.arange(N)  # the x locations for the groups
width = 0.8       # the width of the bars

You typically want your plot to be ~1.33x wider than tall.

  • Common sizes: (10, 7.5) and (12, 9) 




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pyl.figure(figsize=(12, 9))

Remove the plot frame lines. They are unnecessary chartjunk.





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ax = pyl.subplot(111)  
ax.spines["top"].set_visible(False)  
ax.spines["bottom"].set_visible(False)  
ax.spines["right"].set_visible(False)  
ax.spines["left"].set_visible(False)

Ensure that the axis ticks only show up on the bottom and left of the plot.

  • Ticks on the right and top of the plot are generally unnecessary chartjunk. 




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ax.get_xaxis().tick_bottom()  
ax.get_yaxis().tick_left()

Set the labels





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ax.set_xticks(ind + 0.5 * width)
ax.set_xticklabels(dfLetterFrequency.index.values)

Limit the range of the plot to only where the data is.

  • Avoid unnecessary whitespace. 




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pyl.ylim(0, 14)
pyl.xlim(0,26)

Make sure your axis ticks are large enough to be easily read.

  • You don't want your viewers squinting to read your plot. 




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pyl.yticks(range(0, 14, 2), [str(x) + "%" for x in range(0, 14, 2)], fontsize=14)  
pyl.xticks(fontsize=14)

Provide tick lines across the plot to help your viewers trace along the axis ticks.

  • Make sure that the lines are light and small so they don't obscure the primary data lines. 




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for y in range(0, 14, 2):  
    plt.plot(range(0, 26), [y] * len(range(0, 26)), ":", lw=0.5, color="black", alpha=0.3)

Remove the tick marks.

  • They are unnecessary with the tick lines we just plotted. 




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plt.tick_params(axis="both", which="both", bottom="off", top="off",  
                labelbottom="on", left="off", right="off", labelleft="on")

Now that the plot is prepared, it's time to actually plot the data!





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plt.bar(ind, dfLetterFrequency.Frequency.values, width, color=tableau20[0], alpha=0.5)

matplotlib's title() call centers the title on the plot, but not the graph,

  • I used the text() call to customize where the title goes.

  • Make the title big enough so it spans the entire plot, but don't make it so big that it requires two lines to show.

  • Note that if the title is descriptive enough, it is unnecessary to include axis labels.

    • They are self-evident, in this plot's case. 




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pyl.text(6, 13.5, "Letter Frequency in English Writing", fontsize=17, ha="center")
  • And for your data sources, tell your viewers exactly where the data came from,
  • preferably with a direct link to the data.
  • Just telling your viewers that you used data from the "U.S. Census Bureau" is completely useless:
    • The U.S. Census Bureau provides all kinds of data.
    • How are your viewers supposed to know which data set you used? 




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pyl.text(0, -1, "Data source: Cryptological Mathematics, Robert Lewand.", fontsize=10)

Finally, save the figure as a PNG.

  • You can also save it as a PDF, JPEG, etc.
  • Just change the file extension in this call.
  • bbox_inches="tight" removes all the extra whitespace on the edges of your plot. 




In [28]:



    


savefig("../outputs/letter_frequency.png", bbox_inches="tight");
dataviz = plt.gcf()

Content source: dtherrick/dataviz_baseball

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