Welcome#

We type code in cells and then run them.#

2 + 4

6

2 ** 4

16

days_in_week = 7
hours_in_week = days_in_week * 24
hours_in_week

168

We use libraries other people wrote#

# Some code to set up our notebook for data science!

from datascience import *
from cs104 import *
import numpy as np
%matplotlib inline

We write code to manipulate and analyze data#

Hopkins Forest tree survey

hopkins_trees = Table.read_table("data/hopkins-trees.csv")
hopkins_trees.show(10)
plot genus species common name count
p00-1 Acer pensylvanicum Maple, striped 28
p00-1 Acer rubrum Maple, red 8
p00-1 Acer saccharum Maple, sugar 12
p00-1 Amelanchier canadensis Shadbush 17
p00-1 Betula alleghaniensis Birch, yellow 7
p00-1 Betula papyrifera Birch, paper 5
p00-1 Fagus grandifolia Beech, American 142
p00-1 Ostrya virginiana Hophornbeam 7
p00-1 Prunus serotina Cherry, black 11
p00-1 Quercus rubra Oak, red 6

... (3783 rows omitted)

hopkins_trees = hopkins_trees.drop("genus", "species")
hopkins_trees
plot common name count
p00-1 Maple, striped 28
p00-1 Maple, red 8
p00-1 Maple, sugar 12
p00-1 Shadbush 17
p00-1 Birch, yellow 7
p00-1 Birch, paper 5
p00-1 Beech, American 142
p00-1 Hophornbeam 7
p00-1 Cherry, black 11
p00-1 Oak, red 6

... (3783 rows omitted)

How many of each species?#

hopkins_trees.drop("plot").group("common name", sum).sort("count sum", descending=True)
common name count sum
Beech, American 42922
Maple, striped 8939
Maple, red 5564
Maple, sugar 5193
Ash, white 2523
Oak, red 2283
Birch, black 2144
Hophornbeam 1613
Honeysuckle, Morrow's 1608
Alder, speckled 1564

... (67 rows omitted)

tree_counts = hopkins_trees.drop("plot").group("common name", sum)
tree_counts = tree_counts.sort("count sum", descending=True)
tree_counts.take(np.arange(0,10)).barh("common name")
../_images/01-welcome-and-intro_13_0.png

Where are all the red maples?#

red_maples = hopkins_trees.where("common name", "Maple, red")
red_maples.sort("count", descending=True)
plot common name count
p0621 Maple, red 106
p1236 Maple, red 81
p0821 Maple, red 76
p1032 Maple, red 72
p0629 Maple, red 65
p1133 Maple, red 64
p1141 Maple, red 64
p0630 Maple, red 63
p0622 Maple, red 62
p0940 Maple, red 61

... (341 rows omitted)

But where are those plots?#

plot_info = Table.read_table("data/hopkins-plots.csv").select("plot", "latitude", "longitude")
plot_info
plot latitude longitude
p00-1 42.7472 -73.2759
p00-2 42.7472 -73.2772
p0000 42.7472 -73.2747
p0001 42.7472 -73.2735
p0002 42.7472 -73.2723
p0003 42.7472 -73.271
p0004 42.7472 -73.2698
p0005 42.7472 -73.2686
p0006 42.7472 -73.2673
p0007 42.7472 -73.2661

... (413 rows omitted)

red_maples.join("plot", plot_info)
plot common name count latitude longitude
p00-1 Maple, red 8 42.7472 -73.2759
p00-2 Maple, red 2 42.7472 -73.2772
p0000 Maple, red 13 42.7472 -73.2747
p0001 Maple, red 20 42.7472 -73.2735
p0002 Maple, red 12 42.7472 -73.2723
p0003 Maple, red 4 42.7472 -73.271
p0004 Maple, red 2 42.7472 -73.2698
p0005 Maple, red 5 42.7472 -73.2686
p0006 Maple, red 3 42.7472 -73.2673
p0007 Maple, red 6 42.7472 -73.2661

... (341 rows omitted)

Visualization!#

trees_with_lat_lon = hopkins_trees.join("plot", plot_info)

def population_map(tree_name):
    counts = trees_with_lat_lon.where("common name", tree_name)
    counts = counts.select("latitude", "longitude", "count")
    points = counts.with_columns("colors", "blue",
                                 "areas", 1.0 * counts.column("count")).drop("count")
    return Circle.map_table(points)

population_map("Maple, red")
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Exploration, Hypotheses, and Drawing Conclusions#

all_tree_names = np.unique(np.sort(trees_with_lat_lon.column("common name")))
interact(population_map, tree_name=Choice(all_tree_names))

Even More Visualization#

def population_choropleth(tree_name):
    counts = trees_with_lat_lon.where("common name", tree_name).select("plot", "count")
    return HopkinsForest.map_table(counts)

population_choropleth("Maple, red")
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