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+ | ====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 | notebook 5 times; it should generate 5 different, moderately complex | ||
images. Each image should be 512 x 288. Have fun with it! | images. Each image should be 512 x 288. Have fun with it! | ||
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{{:cs401r_w2016:lab1.png?nolink|}} | {{: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 | Throughout this class, we will be using a combination of ipython | ||
notebooks and the anaconda python distribution. For this lab, you | notebooks and the anaconda python distribution. For this lab, you | ||
must install anaconda, and write a simple python program (using | 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: |
+ | 1) 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 | random lines, random curves, random text, etc. Each time the program | ||
is run, it should generate a different random image. Your image | is run, it should generate a different random image. Your image | ||
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[[http://cairographics.org/|cairo]] package as part of your image generator. | [[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://hatch.cs.byu.edu/courses/stat_ml/store_train.csv|Rossman store sales data]] | ||
+ | |||
+ | ---- | ||
+ | ====Installing anaconda:==== | ||
http://docs.continuum.io/anaconda/install | http://docs.continuum.io/anaconda/install | ||
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notebook. | 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> | <code python> | ||
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nbimage( data ) | nbimage( data ) | ||
</code> | </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:==== | ||
+ | |||
+ | 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> | ||
+ | |||
+ |