Lesson 25: Review of exercise 3

(c) 2018 Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.

This exercise was generated from a Jupyter notebook. You can download the notebook here.



In [1]:
import pandas as pd

Exercise 3.0: Complete practice exercises

Complete the practice exercises from lesson 24.


Exercise 3.1: Adding data to a DataFrame

In Lesson 23, we looked at a data set consisting of frog strikes. Recall that the header comments in the data file contained information about the frogs.

In [2]:
!head -20 data/frog_tongue_adhesion.csv

# These data are from the paper,
#   Kleinteich and Gorb, Sci. Rep., 4, 5225, 2014.
# It was featured in the New York Times.
#    http://www.nytimes.com/2014/08/25/science/a-frog-thats-a-living-breathing-pac-man.html
#
# The authors included the data in their supplemental information.
#
# Importantly, the ID refers to the identifites of the frogs they tested.
#   I:   adult, 63 mm snout-vent-length (SVL) and 63.1 g body weight,
#        Ceratophrys cranwelli crossed with Ceratophrys cornuta
#   II:  adult, 70 mm SVL and 72.7 g body weight,
#        Ceratophrys cranwelli crossed with Ceratophrys cornuta
#   III: juvenile, 28 mm SVL and 12.7 g body weight, Ceratophrys cranwelli
#   IV:  juvenile, 31 mm SVL and 12.7 g body weight, Ceratophrys cranwelli
date,ID,trial number,impact force (mN),impact time (ms),impact force / body weight,adhesive force (mN),time frog pulls on target (ms),adhesive force / body weight,adhesive impulse (N-s),total contact area (mm2),contact area without mucus (mm2),contact area with mucus / contact area without mucus,contact pressure (Pa),adhesive strength (Pa)
2013_02_26,I,3,1205,46,1.95,-785,884,1.27,-0.290,387,70,0.82,3117,-2030
2013_02_26,I,4,2527,44,4.08,-983,248,1.59,-0.181,101,94,0.07,24923,-9695
2013_03_01,I,1,1745,34,2.82,-850,211,1.37,-0.157,83,79,0.05,21020,-10239
2013_03_01,I,2,1556,41,2.51,-455,1025,0.74,-0.170,330,158,0.52,4718,-1381
2013_03_01,I,3,493,36,0.80,-974,499,1.57,-0.423,245,216,0.12,2012,-3975

So, each frog has associated with it an age (adult or juvenile), snout-vent-length (SVL), body weight, and species (either cross or cranwelli). For a tidy DataFrame, we should have a column for each of these values. Your task is to load in the data, and then add these columns to the DataFrame. For convenience, here is a DataFrame with data about each frog.

In [3]:
df_frog = pd.DataFrame(data={'ID': ['I', 'II', 'III', 'IV'],
                             'age': ['adult', 'adult', 'juvenile', 'juvenile'],
                             'SVL (mm)': [63, 70, 28, 31],
                             'weight (g)': [63.1, 72.7, 12.7, 12.7],
                             'species': ['cross', 'cross', 'cranwelli', 'cranwelli']})

Note: This is a good exercise in searching through Pandas documentation and other online resources, such as StackOverflow. Remember, much of your programming efforts are spent searching through documentation and the internet.

There are lots of ways to solve this problem. This is a good exercise in searching through the Pandas documentation and other online resources, such as Stack Overflow. Remember, much of your programming efforts are spent searching through documentation and the internet.

After you have added this information to the data frame, make a scatter plot of adhesive force versus impact force and color the points by whether the frog is a juvenile or adult.


Peter and Rosemary Grant have been working on the Galápagos island of Daphne Major for over forty years. During this time, they have collected lots and lots of data about physiological features of finches. In 2014, they published a book with a summary of some of their major results (Grant P. R., Grant B. R., 40 years of evolution. Darwin's finches on Daphne Major Island, Princeton University Press, 2014). They made their data from the book publicly available via the Dryad Digital Repository.

We will investigate their measurements of beak depth (the distance, top to bottom, of a closed beak) and beak length (base to tip on the top) of Darwin's finches. We will look at data from two species, Geospiza fortis and Geospiza scandens. The Grants provided data on the finches of Daphne for the years 1973, 1975, 1987, 1991, and 2012. I have included the data in the files grant_1973.csv, grant_1975.csv, grant_1987.csv, grant_1991.csv, and grant_2012.csv. They are in almost exactly the same format is in the Dryad repository; I have only deleted blank entries at the end of the files.

Note: If you want to skip the wrangling (which is very valuable experience), you can go directly to part (d). You can load in the DataFrame you generate in parts (a) through (c) from the file ~/git/bootcamp/data/grant_complete.csv.

a) Load each of the files into separate Pandas DataFrames. You might want to inspect the file first to make sure you know what character the comments start with and if there is a header row.

b) We would like to merge these all into one DataFrame. The problem is that they have different header names, and only the 1973 file has a year entry (called yearband). This is common with real data. It is often a bit messy and requires some wrangling.

  1. First, change the name of the yearband column of the 1973 data to year. Also, make sure the year format is four digits, not two!
  2. Next, add a year column to the other four DataFrames. You want tidy data, so each row in the DataFrame should have an entry for the year.

  3. Change the column names so that all the DataFrames have the same column names. I would choose column names

    ['band', 'species', 'beak length (mm)', 'beak depth (mm)', 'year']

  4. Concatenate the DataFrames into a single DataFrame. Be careful with indices! If you use pd.concat(), you will need to use the ignore_index=True kwarg. You might also need to use the axis kwarg.

c) The band field gives the number of the band on the bird's leg that was used to tag it. Are some birds counted twice? Are they counted twice in the same year? Do you think you should drop duplicate birds from the same year? How about different years? My opinion is that you should drop duplicate birds from the same year and keep the others, but I would be open to discussion on that. To practice your Pandas skills, though, let's delete only duplicate birds from the same year from the DataFrame. When you have made this DataFrame, save it as a CSV file.

Hint: The DataFrame methods duplicated() and drop_duplicates() will be useful.

After doing this work, it is worth saving your tidy DataFrame in a CSV document. To this using the to_csv() method of your DataFrame. Since the indices are uninformative, you should use the index=False kwarg. (I have already done this and saved it as ~/git/bootcamp/data/grant_complete.csv, which will help you do the rest of the exercise if you have problems with this part.)

d) It is informative to plot the measurement of each bird's beak as a point in the beak depth-beak length plane. For the 1987 data, plot beak depth vs. beak width for Geospiza fortis and for Geospiza scandens. Can you see the species demarcation?

e) Do part (d) again for all years. (Hint: Check out the row encoding, and/or read about faceting in the Altair docs). Describe what you see. Do you see the changes in the differences between species (presumably as a result of introgressive hybridization)? In your plots, make sure all plots have the same range on the axes.


3.3 The Anderson-Fisher iris data set

The Anderson-Fisher data set is a famous data set collected by Edgar Anderson and promoted by Ronald Fisher for use in his technique of linear discriminant analysis in taxonometric problems. The data set is now a classic data set that is used in data analysis. In this problem, you will explore this data set and ways of looking at it with Pandas/Altair. The data set is available in ~/data/fisher_iris.csv.

a) Generate a dash-dot plot of the petal width versus petal length. Why might this be a good way of visualizing this kind of data set?

b) Generate a matrix plot of this data set. What are the advantages of this kind of plot?

c) Explore for yourself! Come up with useful ways of plotting this multidimensional data set to help you explore it.