other_type_of_plots.rst
author Puneeth Chaganti <punchagan@fossee.in>
Thu, 07 Oct 2010 12:28:12 +0530
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permissions -rw-r--r--
Cleaned up script for getting started with files LO.
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.. 2.4 LO: other types of plots (3) [anoop] 
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.. -----------------------------------------
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.. * scatter 
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.. * pie chart 
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.. * bar chart 
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.. * log 
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.. * illustration of other plots, matplotlib help
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===================
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Other type of plots
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===================
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{{{ show the first slide }}}
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Hello and welcome to the tutorial other type of plots.
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{{{ show the outline slide }}}
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In this tutorial we will cover scatter plot, pie chart, bar chart and
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log plot. We will also see few other plots and also introduce you to
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the matplotlib help.
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Let us start with scatter plot. 
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{{{ switch to the next slide }}}
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In a scatter plot, the data is displayed as a collection of points,
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each having the value of one variable determining the position on the
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horizontal axis and the value of the other variable determining the
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position on the vertical axis. This kind of plot is also called a
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scatter chart, scatter diagram and scatter graph.
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Before we proceed further get your IPython interpreter running with
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the ``-pylab`` option. Start your IPython interpreter as
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::
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    ipython -pylab
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{{{ open the ipython interpreter in the terminal using the command
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ipython -pylab }}}
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{{{ switch to the next slide having the problem statement of first
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exercise }}}
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Now, let us plot a scatter plot showing the percentage profit of company A
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from the year 2000-2010. The data for the same is available in the
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file ``company-a-data.txt``. 
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{{{ open the file company-a-data.txt and show the content }}}
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The data file has two lines with a set of values in each line, the
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first line representing years and the second line representing the
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profit percentages.
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{{{ close the file and switch to the terminal }}}
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To product the scatter plot first we need to load the data from the
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file using ``loadtxt``. We learned in one of the previous sessions,
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and it can be done as ::
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    year,profit = loadtxt('/home/fossee/other-plot/company-a-data.txt',dtype=type(int()))
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Now in-order to generate the scatter graph we will use the function 
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``scatter()`` 
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::
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	scatter(year,profit)
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Notice that we passed two arguments to ``scatter()`` function, first
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one the values in x-coordinate, year, and the other the values in
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y-coordinate, the profit percentage.
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{{{ switch to the next slide which has the problem statement of
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problem to be tried out }}}
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Now here is a question for you to try out, plot the same data with red
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diamonds. 
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**Clue** - *try scatter? in your ipython interpreter* 
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.. scatter(year,profit,color='r',marker='d')
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Now let us move on to pie chart.
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{{{ switch to the slide which says about pie chart }}}
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A pie chart or a circle graph is a circular chart divided into
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sectors, illustrating proportion.
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{{{ switch to the slide showing the problem statement of second
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exercise question }}}
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Plot a pie chart representing the profit percentage of company A, with
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the same data from file ``company-a-data.txt``. So let us reuse the
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data we have loaded from the file previously.
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We can plot the pie chart using the function ``pie()``.
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::
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   pie(profit,labels=year)
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Notice that we passed two arguments to the function ``pie()``. The
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first one the values and the next one the set of labels to be used in
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the pie chart.
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{{{ switch to the next slide which has the problem statement of
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problem to be tried out }}}
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Now here is a question for you to try out, plot a pie chart with the
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same data with colors for each wedges as white, red, black, magenta,
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yellow, blue, green, cyan, yellow, magenta and blue respectively.
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**Clue** - *try pie? in your ipython interpreter* 
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.. pie(t,labels=s,colors=('w','r','k','m','y','b','g','c','y','m','b'))
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{{{ switch to the slide which says about bar chart }}}
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Now let us move on to bar chart. A bar chart or bar graph is a chart
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with rectangular bars with lengths proportional to the values that
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they represent.
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{{{ switch to the slide showing the problem statement of third
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exercise question }}}
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Plot a bar chart representing the profit percentage of company A, with
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the same data from file ``company-a-data.txt``. 
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So let us reuse the data we have loaded from the file previously.
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We can plot the bar chart using the function ``bar()``.
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::
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   bar(year,profit)
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Note that the function ``bar()`` needs at least two arguments one the
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values in x-coordinate and the other values in y-coordinate which is
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used to determine the height of the bars.
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{{{ switch to the next slide which has the problem statement of
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problem to be tried out }}}
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Now here is a question for you to try, plot a bar chart which is not
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filled and which is hatched with 45\ :sup:`o` slanting lines as shown
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in the image in the slide.
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**Clue** - *try bar? in your ipython interpreter* 
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.. bar(year,profit,fill=False,hatch='/')
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{{{ switch to the slide which says about bar chart }}}
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Now let us move on to log-log plot. A log-log graph or log-log plot is
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a two-dimensional graph of numerical data that uses logarithmic scales
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on both the horizontal and vertical axes. Because of the nonlinear
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scaling of the axes, a function of the form y = ax\ :sup:`b` will
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appear as a straight line on a log-log graph
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{{{ switch to the slide showing the problem statement of fourth
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exercise question }}}
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Plot a `log-log` chart of y=5*x\ :sup:`3` for x from 1-20.
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Before we actually plot let us calculate the points needed for
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that. And it could be done as,
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::
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    x = linspace(1,20,100)
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    y = 5*x**3
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Now we can plot the log-log chart using ``loglog()`` function,
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::
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    loglog(x,y)
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To understand the difference between a normal ``plot`` and a ``log-log
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plot`` let us create another plot using the function ``plot``.
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::
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    figure(2)
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    plot(x,y)
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{{{ show both the plots side by side }}}
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So that was ``log-log() plot``.
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{{{ switch to the next slide which says: "How to get help on
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matplotlib online"}}}
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Now we will see few more plots and also see how to access help of
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matplotlib over the internet.
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Help about matplotlib can be obtained from
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matplotlib.sourceforge.net/contents.html
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.. #[[Anoop: I am not so sure how to do the rest of it, so I guess we
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   can just browse through the side and tell them few. What is your
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   opinion??]]
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Now let us see few plots from
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matplotlib.sourceforge.net/users/screenshots.html
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{{{ browse through the site quickly }}}
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{{{ switch to recap slide }}}
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Now we have come to the end of this tutorial. We have covered scatter
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plot, pie chart, bar chart, log-log plot and also saw few other plots
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and covered how to access the matplotlib online help.
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{{{ switch to the thank you slide }}}
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Thank you!
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..  Author: Anoop Jacob Thomas <anoop@fossee.in>
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    Reviewer 1:
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    Reviewer 2:
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    External reviewer: