{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Exercise 5.3: Adding data to a data frame\n", "\n", "<hr>"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import pandas as pd"]}, {"cell_type": "markdown", "metadata": {}, "source": ["<hr>\n", "\n", "We continue working with the frog tongue data. Recall that the header comments in the data file contained information about the frogs."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["# These data are from the paper,\n", "#   Kleinteich and Gorb, Sci. Rep., 4, 5225, 2014.\n", "# It was featured in the New York Times.\n", "#    http://www.nytimes.com/2014/08/25/science/a-frog-thats-a-living-breathing-pac-man.html\n", "#\n", "# The authors included the data in their supplemental information.\n", "#\n", "# Importantly, the ID refers to the identifites of the frogs they tested.\n", "#   I:   adult, 63 mm snout-vent-length (SVL) and 63.1 g body weight,\n", "#        Ceratophrys cranwelli crossed with Ceratophrys cornuta\n", "#   II:  adult, 70 mm SVL and 72.7 g body weight,\n", "#        Ceratophrys cranwelli crossed with Ceratophrys cornuta\n", "#   III: juvenile, 28 mm SVL and 12.7 g body weight, Ceratophrys cranwelli\n", "#   IV:  juvenile, 31 mm SVL and 12.7 g body weight, Ceratophrys cranwelli\n", "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)\n", "2013_02_26,I,3,1205,46,1.95,-785,884,1.27,-0.290,387,70,0.82,3117,-2030\n", "2013_02_26,I,4,2527,44,4.08,-983,248,1.59,-0.181,101,94,0.07,24923,-9695\n", "2013_03_01,I,1,1745,34,2.82,-850,211,1.37,-0.157,83,79,0.05,21020,-10239\n", "2013_03_01,I,2,1556,41,2.51,-455,1025,0.74,-0.170,330,158,0.52,4718,-1381\n", "2013_03_01,I,3,493,36,0.80,-974,499,1.57,-0.423,245,216,0.12,2012,-3975\n"]}], "source": ["!head -20 data/frog_tongue_adhesion.csv"]}, {"cell_type": "markdown", "metadata": {}, "source": ["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 data frame, 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 data frame. For convenience, here is a data frame with data about each frog."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["df_frog = pd.DataFrame(\n", "    data={\n", "        \"ID\": [\"I\", \"II\", \"III\", \"IV\"],\n", "        \"age\": [\"adult\", \"adult\", \"juvenile\", \"juvenile\"],\n", "        \"SVL (mm)\": [63, 70, 28, 31],\n", "        \"weight (g)\": [63.1, 72.7, 12.7, 12.7],\n", "        \"species\": [\"cross\", \"cross\", \"cranwelli\", \"cranwelli\"],\n", "    }\n", ")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Note: There are lots of ways to solve this problem. This is a good exercise in searching through the [Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/) and other online resources, such as [Stack Overflow](https://stackoverflow.com/questions). Remember, much of your programming efforts are spent searching through documentation and the internet.\n", "\n", "Finally, as a fun challenge, see if you can highlight the strike with the highest impact force for each frog in the data frame."]}, {"cell_type": "markdown", "metadata": {}, "source": ["<br />"]}], "metadata": {"anaconda-cloud": {}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10"}}, "nbformat": 4, "nbformat_minor": 4}