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--- cs401r_w2016:lab1 [2015/12/23 20:47]
admin
+++ cs401r_w2016:lab1 [2021/06/30 23:42] (current)
@@ Line 1: / Line 1: @@
+====Objective:====
-===Objective:===
+Get started with anaconda, python, ipython notebooks, and pandas.  Begin producing simple visualizations of data and images.
-Get started with python, ipython notebooks and anaconda.
+----
+====Deliverable:====
-===Deliverable:===
+For this lab, you will submit an ipython notebook.  This notebook will have two parts:
-An ipython notebook that generates a random image.  We will run this
+**Part 1:**  Your notebook should generate a random image.  We will run this
 notebook 5 times; it should generate 5 different, moderately complex
 images.  Each image should be 512 x 288.  Have fun with it!
@@ Line 14: / Line 16: @@
 {{:cs401r_w2016:lab1.png?nolink|}}
-===Description:===
+**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:
+{{: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
+----
+====Description:====
 Throughout this class, we will be using a combination of ipython
 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 32: / Line 53: @@
 [[http://cairographics.org/|cairo]] package as part of your image generator.
-===Installing anaconda:===
+For part 2, the data you should use is downloadable here:
+[[http://liftothers.org/courses/stat_ml/store_train.csv|Rossman store sales data]]
+----
+====Installing anaconda:====
 http://docs.continuum.io/anaconda/install
@@ Line 52: / Line 78: @@
 notebook.
-Here's some starter code to help you generate an image:
+**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:
 <code python>
@@ Line 88: / 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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