1. -- Shrub Volume 2 --

    This problem is related to Shrub Volume, but using the Pandas library.

    Dr. Granger is interested in studying the factors controlling the size and carbon storage of shrubs. This research is part of a larger area of research trying to understand carbon storage by plants. She has conducted a small preliminary experiment looking at the effect of three different treatments on shrub volume at four different locations. She wants to conduct a preliminary analysis of these data to include in a grant proposal and she would like you to conduct the analysis for her (she might be a world renowned expert in carbon storage in plants, but she sure doesn’t know much about computers). She has placed a data file on the web for you to download.

    1. Import the data using Pandas and print out the first few rows of the data using the .head() method.
    2. Select the data from the length column and print it out.
    3. Select the data from the site and experiment columns and print it out.
    4. Multiple the length, width, and height columns together to get a volume column and print it out.
    5. Calculate the shrub carbon for all of the shrubs using the equation 1.8 + 2 * log(volume) where volume is the volume of the shrub. You’ll need to use the numpy version of the log() function.
    6. Select the height data for all of the plants with heights greater than 5 and print out the result.
    7. The following code calculates the average height of a plant at each site:

      data_means = data.groupby('site').mean()

      Modify the code to calculate the average height of a plant in each experiment type.

    8. Calculate the maximum height of a plant in each site and print it out.
    [click here for output]
  2. -- Mammal Body Size 1 --

    There were a relatively large number of extinctions of mammalian species roughly 10,000 years ago. To help understand why these extinctions happened scientists are interested in understanding whether there were differences in the body size of those species that went extinct and those that did not. Since we’re starting to get pretty good at this whole programming thing let’s stop messing around with made up datasets and do some serious analysis.

    Download the largest dataset on mammalian body size in the world. Fortunately this dataset has data on the mass of recently extinct mammals as well as extant mammals (i.e., those that are still alive today). Take a look at the metadata to understand the structure of the data. One key thing to remember is that species can occur on more than one continent, and if they do then they will occur more than once in this dataset. Also let’s ignore species that went extinct in the very recent past (designated by the word ‘historical’ in the ‘status’ column).

    Import the data into Python. If you’ve looked at a lot of data you’ll realize that this dataset is tab delimited. The special character to indicate tab in Python is \t.

    To start let’s explore the data a little and then start looking at the major question.

    1. The following code will determine how many genera (plural of genus) there are in the dataset: len(data.groupby(['genus'])). Modify this code to determine the number of species. Remember that a species is uniquely defined by the combination of its genus name and its species name. Print the result to the screen. The number should be between 4000 and 5000.
    2. Find out how many of the species are extinct and how many are extant, print the result to the screen. Hint: first separate the data into the extinct and extant components and then count the number of species.
    3. Find out how many families are present in the dataset.
    4. Now print the genus name, the species name, and the mass of the largest and smallest species (note, it is not possible for a mammal to have negative mass)
    5. Now let’s get to work. Calculate the average (i.e., mean) mass of an extinct species and the average mass of an extant species. The function mean() should help you here. It is available as both a numpy function and a Pandas DataFrame method. Don’t worry about species that occur more than once. We’ll consider the values on different continents to represent independent data points. Print out the the average mass of extant species and the average mass
      of extinct species.
    [click here for output]
  3. -- Mammal Body Size 2 --

    This is a follow up to Mammal Body Size 1.

    Looking at the average mass of extinct and extant species overall is useful, but there are lots of different processes that could cause size-biased extinctions so it’s not as informative as we might like. However, if we see the exact same pattern on each of the different continents that might really tell us something. Repeat the analysis in Mammal Body Size 1, but this time compare the mean masses within each of the different continents. Export your results to a csv file where the first entry on each line is the continent, the second entry is the average mass of the extant species on that continent, the third entry is the average mass of the extinct species on that continent, and the forth entry is the difference between the average extant and average extinct masses. Call the file continent_mass_differences.csv. If you notice anything strange think about what’s going on and present the final data in the way that makes the most sense to you.

    [click here for output]