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--- cs401r_w2016:lab1 [2016/01/02 23:55]
admin
+++ cs401r_w2016:lab1 [2021/06/30 23:42] (current)
@@ Line 16: / Line 16: @@
 {{:cs401r_w2016:lab1.png?nolink|}}
-**Part 2:** Your notebook should use the pandas library to read in some CSV data and plot some of it.
+**Part 2:** Your notebook should use the pandas library to read in the Rossman store sales data (a CSV dataset) and plot the sales of store #1.  Your plot should look something like this:
-Your plot should look something like this:
 {{:cs401r_w2016:lab1_storesales.png?direct&700|}}
+Done correctly, this should only take a few lines of code.
+----
+====Grading standards:====
+Your notebook will be graded on the following:
+  * 20% Successfully turned in a notebook with working code
+  * 20% Random image with 50 random elements
+  * 20% Correctly used pandas to load store sales data
+  * 30% Some sort of plot of sales data (only for store #1!)
+  * 10% Tidy and legible figures, including labeled axes where appropriate
 ----
@@ Line 27: / Line 39: @@
 notebooks and the anaconda python distribution.  For this lab, you
 must install anaconda, and write a simple python program (using
-ipython notebooks) and use it to generate simple random images.
+ipython notebooks).  As described above, the notebook should do two things:
+) generate simple random images, and 2) plot some data using pandas.
-You can generate any sort of random image that you want -- consider
+For part 1, you can generate any sort of random image that you want -- consider
 random lines, random curves, random text, etc.  Each time the program
 is run, it should generate a different random image.  Your image
@@ Line 39: / Line 52: @@
 In preparation for future labs, we strongly encourage you to use the
 [[http://cairographics.org/|cairo]] package as part of your image generator.
+For part 2, the data you should use is downloadable here:
+[[http://liftothers.org/courses/stat_ml/store_train.csv|Rossman store sales data]]
 ----
@@ Line 60: / Line 77: @@
 should see "Notebook" and "Python 2".  This will create a new
 notebook.
+**Note:** When you turn in your notebook, you should turn in the ''.ipynb'' file.  Do not take a screen shot, or turn in an HTML page.
 Here's some starter code to help you generate an image.  The ''nbimage'' function will display the image inline in the notebook:
@@ Line 97: / Line 116: @@
 nbimage( data )
 </code>
+----
+====Using Pandas:====
+For the second part of this lab, you will need to understand the ''pandas'' python package, just a little bit.  For this lab, you only need to know how to select some data from a CSV file.
+You should read through this tutorial and play with it.
+[[http://synesthesiam.com/posts/an-introduction-to-pandas.html|Tutorial on using Pandas]]
+For this lab, you need select the data for store #1 and plot it.
+An important part of generating visualizations is conveying information cleanly and accurately.  You should therefore label all axes, and in particular, the x-axis should be labeled using dates (See the example image).  This involves a bit of python trickery, but check out some helpful functions in the hints below.
+----
+====Hints:====
+When using an ipython notebook, it's nice to make your plots show up inline.  To do this, add the following lines to the first cell of your notebook:
+<code python>
+# this tells seaborn and matplotlib to generate plots inline in the notebook
+%matplotlib inline
+# these two lines allow you to control the figure size
+%pylab inline
+pylab.rcParams['figure.figsize'] = (16.0, 8.0)
+</code>
+The following python functions might be helpful:
+<code python>
+import matplotlib.pyplot as plt
+plt.plot_date
+pandas.to_datetime
+plt.legend
+plt.xlabel
+plt.ylabel
+plt.tight_layout
+</code>

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