Since a picture is worth a thousand words, here is the Los Angeles Lakers' road to their NBA title in 2002. Wins are pictured with blue bars and losses with red bars. Note how easy it is to see the streaks for wins and losses.
The Lakers' 2004 season was their last with Shaq, when they reached the NBA finals and lost to Detroit (note the last 3 losses which sealed their fate in the finals).
Compare those days of glory with their abysmal 2005 performance, with only 2 wins in the last 21 games. Also note how the width of the last graphic is less than the previous 2, a consequence of the Lakers not making the playoffs this year.
The southern oscillation is defined as the barometric pressure difference between Tahiti and the Darwin Islands at sea level. The southern oscillation is a predictor of El Nino which in turn is thought to be a driver of world-wide weather. Specifically, repeated southern oscillation values less than -1 typically defines an El Nino.
Here is a sparkline for the southern oscillation from
1955to 1992 (456 sample data points obtained from NIST). The sparkline is plotted with a horizontal span drawn along the x axis covering data values between -1 and 0, so that values less than -1 can be more clearly seen.
Here is the per capita income in California from 1959 to 2003.
And here is the "real" per capita income (adjusted for inflation) in California, from 1959 to 2003.
Here is the monthly distribution of messages sent to comp.lang.py from 1994 to 2004, plotted per year. Minimum and maximum values are shown with blue dots and labeled in the graphics.
|Year ||Total |
|1994 ||3,018 |
|1995 ||4,026 |
|1996 ||8,378 |
|1997 ||12,910 |
|1998 ||19,533 |
|1999 ||24,725 |
|2000 ||42,961 |
|2001 ||55,271 |
|2002 ||56,750 |
|2003 ||64,548 |
There was an almost constant increase in the number of messages per year, from 1994 to 2004, the only exception being 2004, when there were fewer message than in 2002 and 2003.
Details on using sparkplot
1) Install the Numeric Python module (required by matplotlib)
2) Install matplotlib
3) Prepare data files: sparkplot simplistically assumes that its input data file contains just 1 column of numbers
4) Run sparkplot.py. Here are some command-line examples to get you going:
- given only the input file and no other option, sparkplot.py will generate a gray sparkline with the first and last data points plotted in red:
sparkplot.py -i CA_real_percapita_income.txt
The name of the output file is by default
The plotting of the first and last data points can be disabled with the --noplot_first and --noplot_last options.
- given the input file and the label_first, label_last, format=currency options, sparkplot.py will generate a gray sparkline with the first and last data points plotted in red and with the first and last data values displayed in a currency format:
sparkplot.py -i CA_real_percapita_income.txt --label_first --label_last --format=currency
The currency symbol is $ by default, but it can be changed with the --currency option.
sparkplot.py -i clpy_1997.txt --plot_min --plot_max --label_min --label_max --format=comma
- given the input file and the type=bars option, sparkplot.py will draw blue bars for the positive data values and red bars for the negative data values:
sparkplot.py -i lakers2005.txt --type=bars
As a side note, I think bar plots look better when the data file contains a relatively large number of data points, and the variation of the data is relatively small. This type of plots works especially well for sports-related graphics, where wins are represented as +1 and losses as -1.
- for other options, run sparkplot.py -h
I hope the sparkplot module will prove to be useful when you need to include sparkline graphics in your Web pages. All the caveats associated with alpha-level software apply :-) Let me know if you find it useful. I'm very much a beginner at using matplotlib, and as I become more acquainted with it I'll add more functionality to sparkplot.
Finally, kudos to John Hunter, the creator of matplotlib. I found this module extremely powerful and versatile. For a nice introduction to matplotlib, see also John's talk at PyCon05.
Note: the Blogger template system might have something to do with the fact that the graphics are shown with a border; when included in a "normal", white-background HTML page, there is no border and they integrate more seamlessly into the text.
Update 5/2/05: Thanks to Kragen Sitaker for pointing out a really simple solution to the "borders around images" problem -- just comment out the CSS definition for .post img in the Blogger template.