Lab 05 assignment (20 pts)

Contents

Lab 05 assignment (20 pts)#

UW Geospatial Data Analysis
CEE467/CEWA567
David Shean, Eric Gagliano, Quinn Brencher

Introduction#

Objectives#

  • Continue to explore GeoPandas functionality

  • Explore basic geometry operations (e.g., buffer, intersect, union)

  • Explore spatial filtering and spatial joins

  • Explore new visualization approaches (e.g., hexbin, interactive maps)

In order to cover some additional important vector concepts, let’s use a power plants dataset, and also explore some King County census data in tandem with some toxic release data.

Instructions#

  • For each question or task below, write some code in the empty cell and execute to preserve your output

  • If you are in the graduate section of the class, please complete the challenge questions

  • Work together, consult resources we’ve discussed, post on slack!

  • Remember, the lab assignment this week is broken into two notebooks!

Part 0: Prepare data#

!pip install -q censusdata

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point, Polygon
import xyzservices.providers as xyz
import os
import contextily as ctx
import censusdata
import seaborn as sns
import requests
import zipfile
import shutil

Load in the power plants data#

  • These data are a subset of a cool global power plant dataset:

Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, University of Groningen, World Resources Institute. 2018. Global Power Plant Database. Published on Resource Watch and Google Earth Engine; https://resourcewatch.org/ https://earthengine.google.com/

  • For details about this dataset, including a key to the column names, see https://developers.google.com/earth-engine/datasets/catalog/WRI_GPPD_power_plants#table-schema

  • Load the power plants geojson file into a GeoDataFrame and check the crs

# read in the data
pplant_fn = './data/conus_power_plants.geojson'
pplant_gdf = gpd.read_file(pplant_fn)
pplant_gdf.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

# take a quick look
pplant_gdf.head()
cap_year capacitymw comm_year country country_lg fuel1 fuel2 fuel3 fuel4 gppd_idnr ... gwh_2014 gwh_2015 gwh_2016 gwh_estimt latitude longitude name owner source geometry
0 0.0 149.0 1969.00000 USA United States of America Gas None None None USA0001233 ... 0.0 0.0 -0.209 613.166810 37.7328 -99.9497 Fort Dodge Sunflower Electric Power Corp U.S. Energy Information Administration POINT (-99.9497 37.7328)
1 0.0 730.2 2008.46946 USA United States of America Gas None None None USA0056502 ... 0.0 0.0 333.957 3004.928891 38.4459 -96.0652 Emporia Energy Center Westar Energy Inc U.S. Energy Information Administration POINT (-96.0652 38.4459)
2 0.0 11.0 1962.00000 USA United States of America Gas None None None USA0001332 ... 0.0 0.0 0.005 45.267348 37.2354 -97.0110 West 14th Street City of Winfield - (KS) U.S. Energy Information Administration POINT (-97.011 37.2354)
3 0.0 228.0 1957.50000 USA United States of America Gas None None None USA0001242 ... 0.0 0.0 75.912 938.268676 37.5967 -97.4136 Murray Gill Kansas Gas & Electric Co U.S. Energy Information Administration POINT (-97.4136 37.5967)
4 0.0 43.7 1982.00000 USA United States of America Gas None None None USA0050169 ... 0.0 0.0 0.000 179.834830 37.5813 -97.4241 Wichita Plant Occidental Chemical Corporation U.S. Energy Information Administration POINT (-97.4241 37.5813)

5 rows × 21 columns

Now we’ll read in the state polygons…#

states_url = 'http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_5m.json'
states_gdf = gpd.read_file(states_url)
states_gdf
GEO_ID STATE NAME LSAD CENSUSAREA geometry
0 0400000US01 01 Alabama 50645.326 MULTIPOLYGON (((-88.12466 30.28364, -88.08681 ...
1 0400000US02 02 Alaska 570640.950 MULTIPOLYGON (((-166.10574 53.98861, -166.0752...
2 0400000US04 04 Arizona 113594.084 POLYGON ((-112.53859 37.00067, -112.53454 37.0...
3 0400000US05 05 Arkansas 52035.477 POLYGON ((-94.04296 33.01922, -94.04304 33.079...
4 0400000US06 06 California 155779.220 MULTIPOLYGON (((-122.42144 37.86997, -122.4213...
5 0400000US08 08 Colorado 103641.888 POLYGON ((-106.19055 40.99761, -106.06118 40.9...
6 0400000US09 09 Connecticut 4842.355 POLYGON ((-71.79924 42.00806, -71.79792 41.935...
7 0400000US10 10 Delaware 1948.543 MULTIPOLYGON (((-75.56493 39.58325, -75.57627 ...
8 0400000US11 11 District of Columbia 61.048 POLYGON ((-77.0386 38.79151, -77.0389 38.80081...
9 0400000US12 12 Florida 53624.759 MULTIPOLYGON (((-82.82158 27.96444, -82.8298 2...
10 0400000US13 13 Georgia 57513.485 POLYGON ((-84.81048 34.98761, -84.80918 34.987...
11 0400000US15 15 Hawaii 6422.628 MULTIPOLYGON (((-155.77823 20.24574, -155.7727...
12 0400000US16 16 Idaho 82643.117 POLYGON ((-111.04416 43.02005, -111.04413 43.0...
13 0400000US17 17 Illinois 55518.930 POLYGON ((-89.36603 42.50027, -89.36156 42.500...
14 0400000US18 18 Indiana 35826.109 POLYGON ((-84.80248 40.52805, -84.80255 40.501...
15 0400000US19 19 Iowa 55857.130 POLYGON ((-91.21771 43.50055, -91.21827 43.497...
16 0400000US20 20 Kansas 81758.717 POLYGON ((-99.54112 36.99957, -99.55807 36.999...
17 0400000US21 21 Kentucky 39486.338 MULTIPOLYGON (((-89.48511 36.49769, -89.49254 ...
18 0400000US22 22 Louisiana 43203.905 MULTIPOLYGON (((-88.86507 29.75271, -88.88976 ...
19 0400000US23 23 Maine 30842.923 MULTIPOLYGON (((-70.15259 43.74679, -70.15196 ...
20 0400000US24 24 Maryland 9707.241 MULTIPOLYGON (((-76.04837 38.12055, -76.05681 ...
21 0400000US25 25 Massachusetts 7800.058 MULTIPOLYGON (((-70.8274 41.60207, -70.82374 4...
22 0400000US26 26 Michigan 56538.901 MULTIPOLYGON (((-85.82596 45.4043, -85.83352 4...
23 0400000US27 27 Minnesota 79626.743 POLYGON ((-92.20469 46.70404, -92.20569 46.702...
24 0400000US28 28 Mississippi 46923.274 MULTIPOLYGON (((-89.09562 30.23177, -89.07726 ...
25 0400000US29 29 Missouri 68741.522 POLYGON ((-89.54501 36.33681, -89.56044 36.337...
26 0400000US30 30 Montana 145545.801 POLYGON ((-105.0384 45.00034, -105.07661 45.00...
27 0400000US31 31 Nebraska 76824.171 POLYGON ((-104.05325 41.00141, -104.05316 41.0...
28 0400000US32 32 Nevada 109781.180 POLYGON ((-114.04214 40.99993, -114.04318 40.7...
29 0400000US33 33 New Hampshire 8952.651 POLYGON ((-72.4521 43.16141, -72.45187 43.1701...
30 0400000US34 34 New Jersey 7354.220 POLYGON ((-75.21088 39.86571, -75.21042 39.865...
31 0400000US35 35 New Mexico 121298.148 POLYGON ((-105.998 32.00233, -106.09976 32.002...
32 0400000US36 36 New York 47126.399 MULTIPOLYGON (((-74.04086 40.70012, -74.04002 ...
33 0400000US37 37 North Carolina 48617.905 MULTIPOLYGON (((-75.75376 35.19961, -75.74522 ...
34 0400000US38 38 North Dakota 69000.798 POLYGON ((-98.72438 45.93869, -98.90443 45.939...
35 0400000US39 39 Ohio 40860.694 MULTIPOLYGON (((-82.83512 41.70897, -82.82572 ...
36 0400000US40 40 Oklahoma 68594.921 POLYGON ((-96.94461 33.94922, -96.95231 33.944...
37 0400000US41 41 Oregon 95988.013 POLYGON ((-121.92224 45.64908, -121.90827 45.6...
38 0400000US42 42 Pennsylvania 44742.703 POLYGON ((-79.91617 39.72089, -80.07595 39.721...
39 0400000US44 44 Rhode Island 1033.814 MULTIPOLYGON (((-71.38359 41.46478, -71.38928 ...
40 0400000US45 45 South Carolina 30060.696 POLYGON ((-79.29075 33.11005, -79.29159 33.109...
41 0400000US46 46 South Dakota 75811.000 POLYGON ((-104.05449 44.18038, -104.05539 44.2...
42 0400000US47 47 Tennessee 41234.896 POLYGON ((-83.47211 36.59728, -83.3749 36.5973...
43 0400000US48 48 Texas 261231.711 MULTIPOLYGON (((-97.13436 27.89633, -97.1336 2...
44 0400000US49 49 Utah 82169.620 POLYGON ((-111.04655 41.25172, -111.04672 40.9...
45 0400000US50 50 Vermont 9216.657 POLYGON ((-72.4338 43.233, -72.43447 43.23043,...
46 0400000US51 51 Virginia 39490.086 MULTIPOLYGON (((-75.97361 37.83582, -75.9717 3...
47 0400000US53 53 Washington 66455.521 MULTIPOLYGON (((-122.51954 48.28831, -122.5227...
48 0400000US54 54 West Virginia 24038.210 POLYGON ((-80.07595 39.72135, -79.91617 39.720...
49 0400000US55 55 Wisconsin 54157.805 MULTIPOLYGON (((-90.40331 47.02669, -90.40332 ...
50 0400000US56 56 Wyoming 97093.141 POLYGON ((-110.04848 40.99756, -110.12164 40.9...
51 0400000US72 72 Puerto Rico 3423.775 MULTIPOLYGON (((-65.3277 18.29584, -65.33745 1...

Limit states_gdf to the lower 48#

  • We want to filter out Alaska, Puerto Rico, and Hawaii from this dataset

    • One possible approach would be to use boolean indexing on states_gdf, creating an index where states_gdf[‘NAME’] contains Alaska, Puero Rico, and Hawaii

    • Then you could index back into states_gdf where your index is False

Hint: Check out the .isin() function

# STUDENT CODE HERE

Create a quick plot of power plant capacity overlayed on top of the state polygons to verify everything looks good#

  • Can re-use plotting code near the end of Lab04

  • If plotting a variable that is highly skewed to one side, consider passing norm=colors.LogNorm() to your plot() function to create a logarithmic colorbar.

  • Note that here we’re plotting in WGS84, not a projected coordinate system. This will result in some distortions!

# STUDENT CODE HERE
../../_images/capacity_wgs84.png

Extract the MultiPolygon geometry object for Washington from your reprojected states GeoDataFrame#

  • Use the state ‘NAME’ value to isolate the approprate GeoDataFrame record for Washington to create wa_gdf

# STUDENT CODE HERE

Part 1: Geometric Operations for power plant points (4 pts)#

Clip the power plant points to Washington state polygon#

  • You could do this in two steps:

    • Identify records from the power plants GeoDataFrame that intersects the WA state geometry

    • Extract those records and store in a new GeoDataFrame

# STUDENT CODE HERE

Define an azimuthal equidistant projection for Washington State#

  • We’re going to do calculations of distance in this section, so we need to use a projection that preserves distance

  • Let’s quickly define a custom Azimuthal Equidistant projection for our power plant points and polgyons

ae_proj_str = f'+proj=aeqd +lat_0={wa_gdf.iloc[0].geometry.centroid.y:.2f} +lon_0={wa_gdf.iloc[0].geometry.centroid.x:.2f} +datum=WGS84 +units=m +no_defs'

pplant_gdf_wa = pplant_gdf_wa.to_crs(ae_proj_str)
wa_gdf = wa_gdf.to_crs(ae_proj_str)

Compute some statistics for the WA state power plants#

  • How many power plants are in WA state?

  • What is median capacitymw capacity of WA state power plants in megawatts?

# STUDENT CODE HERE

There are 138 power plants in Washington state.

# STUDENT CODE HERE

Plot the resulting points#

  • Add the WA polygon outline

    • Note that you don’t need/want to use the Polygon Geometry object here. Remember, you have a GeoDataFrame containing just the WA geometry. And you know how to plot GeoDataFrames.

  • Add the WA state centroid you calculated earlier using a distinct marker style (e.g., '*' or 'x')

    • Can use simple matplotlib plot with the coordinates, or can use wa_gdf.centroid.plot()

  • As above, consider passing norm=colors.LogNorm() to your plot() function to create a logarithmic colorbar.

# STUDENT CODE HERE
../../_images/capacity_wa_aeqd.png

Find the distance from the power plants to the WA state centroid#

  • See the GeoDataFrame distance function

  • Create a plot showing this distance for each power plant point.

c = wa_gdf.iloc[0].geometry.centroid

# STUDENT CODE HERE

# STUDENT CODE HERE
../../_images/capacity_wa_distfromcentroid.png

What is the distance between this point (point closest to the centroid) and the centroid in km?#

# STUDENT CODE HERE

What is the capacitymw value of this point?#

  • For this, we need to isolate the original GeoDataFrame record corresponding to the minimum distance

  • We talked about argmin or argmax for NumPy arrays. For a DataFrame, we want to use the idxmin() method on the column or DataSeries containing the distance values, which should give us the index value:

    • https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmin.html

    • Can then use the index value to locate the corresponding record in the GeoDataFrame with standard Pandas indexing: https://pandas.pydata.org/docs/user_guide/indexing.html

# STUDENT CODE HERE

# STUDENT CODE HERE

# STUDENT CODE HERE

The closest power plant to the centroid of Washington state has a capacity of 1299.6 MW.

Challenge Question (GS required/UG +0.5): Which power plant is farthest from the WA state perimeter?#

  • Note that this is different than the above calcuations for point to point distance, you’re looking for point to polyline distance

  • What is the distance in km?

  • This should be pretty straightforward, combining elements from the above questions/demo

  • This kind of calculation can be useful to consider distance from the coast and climatology (temperature, precipitation and other environmental variables)

# STUDENT CODE HERE

# STUDENT CODE HERE

# STUDENT CODE HERE
../../_images/capacity_wa_distfromborder.png

Part 2: Aggregate power plants for state polygons and compute capacitymw statistics (3 pts)#

OK, we computed some stats for power plants within WA state. Now let’s find the power plants that intersect each state polygon.

Objectives:

  1. Compute the total number of power plants and total capacitymw energy generation capacity for each state

  2. Generate choropleth maps for both

One approach would be to loop through each state polygon, and do an intersection operation like we did for WA state above. But this is inefficient, and doesn’t scale well. It is much more efficient to do a spatial join between the power plant points and the polygons with the intersection operator, then groupby and aggregate to compute the relevant statistics for each state.

You may have learned how to perform a join or spatial join in a GIS class, perhaps using ArcMap or QGIS here. Luckily, GeoPandas has you covered.

First, let’s define an Albers Equal Area projection for our data#

  • We’re going to be doing area-based calculations in this section, so we need to use a projection that preserves area

  • We need to project both the power plant points and the state polygons

# get extent and center coordinates
pplant_extent = pplant_gdf.total_bounds
pplant_center_x = (pplant_extent[0] + pplant_extent[2])/2
pplant_center_y = (pplant_extent[3] + pplant_extent[1])/2

# define proj string
aea_proj_str = f'+proj=aea +lat_1={pplant_extent[1]:.2f} +lat_2={pplant_extent[3]:.2f} +lat_0={pplant_center_y:.2f} +lon_0={pplant_center_x:.2f}'

# reproject 
pplant_gdf_aea = pplant_gdf.to_crs(aea_proj_str)
states_gdf_aea = states_gdf.to_crs(aea_proj_str)

Now we can get rolling with analysis!

Start with a spatial join between the power plant points and state polygons#

Let’s extract the columns that matter to us#

  • Since we only care about capacitymw values here let’s create some smaller GeoDataFrames:

    • Extract the ['capacitymw', 'geometry'] columns from the power plant GeoDataFrame

    • Extract the ['NAME', 'geometry'] columns from the states GeoDataFrame

pplant_gdf_aea_lite = pplant_gdf_aea[['capacitymw', 'geometry']]
states_gdf_aea_lite = states_gdf_aea[['NAME','geometry']]

Now try a spatial join between these two#

  • Use the power plant points as the “left” GeoDataFrame and the States as the “right” GeoDataFrame

  • Start by using default options (op='intersects', how='inner')

  • Note the output geometry type and columns

# STUDENT CODE HERE
capacitymw geometry index_right NAME
0 149.0 POINT (-373798.752 116463.932) 16 Kansas
1 730.2 POINT (-38844.692 189244.381) 16 Kansas
2 11.0 POINT (-121518.346 52569.772) 16 Kansas
3 228.0 POINT (-155679.348 94157.393) 16 Kansas
4 43.7 POINT (-156617.964 92425.8) 16 Kansas
... ... ... ... ...
7863 1.3 POINT (1763173.316 1010517.952) 45 Vermont
7864 1.8 POINT (1828872.636 991070.108) 45 Vermont
7865 1.8 POINT (1783973.835 1092752.901) 45 Vermont
7866 1.8 POINT (1787741.825 1101022.7) 45 Vermont
7867 1.8 POINT (1812915.624 1010086.014) 45 Vermont

7868 rows × 4 columns

Now reverse the order of the two input GeoDataFrames#

  • Use default options again

# STUDENT CODE HERE

Which one makes more sense?#

  • Remember, we’re trying to add a State name to each power plant

  • You’ll need to decide which one to use moving forward

# STUDENT CODE HERE

Confidence check#

  • Run a head on our new GeoDataFrame

  • For each power plant point, there should be a new column with the corresponding state NAME

# STUDENT CODE HERE
capacitymw geometry index_right NAME
0 149.0 POINT (-373798.752 116463.932) 16 Kansas
1 730.2 POINT (-38844.692 189244.381) 16 Kansas
2 11.0 POINT (-121518.346 52569.772) 16 Kansas
3 228.0 POINT (-155679.348 94157.393) 16 Kansas
4 43.7 POINT (-156617.964 92425.8) 16 Kansas

Now aggregate by state NAME#

  • Note: the GeoPandas doc example uses a dissolve operation here, which is a wrapper around the groupby and agg Pandas functions

    • https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html

  • But look at the geometry column for the points in your GeoDataFrame. The dissolve is going to compute the union for the points in each state and return a MultiPoint Geometry for each state, which we don’t really want/need. It will also take longer to run.

  • Instead, let’s try a groupby operation for the NAME column, and then agg with a list of functions we care about: ['count', 'sum'] (statistics for point count and mean point elevation for each state)

    • We did this in Lab02!

# STUDENT CODE HERE
count sum
NAME
Alabama 71 31451.9
Arizona 121 32991.9
Arkansas 56 16341.8
California 1273 82100.4
Colorado 163 17838.5
Connecticut 94 7489.1
Delaware 25 3628.3
District of Columbia 2 24.9
Florida 148 66705.4
Georgia 157 39580.0
Idaho 135 5672.2
Illinois 204 50908.7
Indiana 141 28438.7
Iowa 228 18334.7
Kansas 131 16526.4
Kentucky 47 24066.4
Louisiana 83 28351.8
Maine 101 5211.7
Maryland 93 13710.0
Massachusetts 299 14769.4
Michigan 222 29868.9
Minnesota 304 17829.1
Mississippi 41 18073.9
Missouri 119 23758.3
Montana 51 6446.7
Nebraska 101 9186.8
Nevada 77 12213.3
New Hampshire 65 4699.7
New Jersey 257 20737.8
New Mexico 95 9264.8
New York 384 42593.2
North Carolina 552 35402.8
North Dakota 54 8334.6
Ohio 156 31533.0
Oklahoma 100 28318.4
Oregon 151 11397.9
Pennsylvania 212 48174.7
Rhode Island 27 2063.9
South Carolina 97 24738.1
South Dakota 34 4390.3
Tennessee 64 24029.0
Texas 404 128609.5
Utah 100 9798.3
Vermont 85 647.0
Virginia 135 28348.7
Washington 138 36879.8
West Virginia 35 16041.4
Wisconsin 173 17793.4
Wyoming 63 9480.2

Confidence check#

  • That groupby and agg should be pretty fast (~1-2 seconds)

  • Check the type() of the output

  • Since groupby and agg are Pandas operation, you should now have a new Pandas DataFrame (not a GeoPandas GeoDataFrame) with index of state names and columns for count and mean values in each state

# STUDENT CODE HERE

Part 3: Plots! (2 pts)#

  • How are we going to visualize these data?

  • We have count power plant points and sum of power plant capacity for each state! Objective #1 complete!

  • We now want to visualize these values, but we lost our state Polygon/MultiPolygon Geometry along the way

  • No problem, we can combine our DataFrame with the original State GeoDataFrame

    • Can use merge to do an attribute join on the shared state 'NAME' index for the two

      • Take a moment to review the my_agg_df.merge? options

      • In this case, we have a common NAME field, so we can use the on='NAME' here for attribute join

    • Careful about the order of the two inputs here (state GeoDataFrame and the DataFrame with your stats) - you want to preserve the index and geometry of the GeoDataFrame, adding the count and mean columns.

      • If you get it right, you should be able to plot() the GeoPandas GeoDataFrame and get a map. If you got it backwards, the output of plot() will be a line plot for the regular Pandas DataFrame

Combine states_gdf_aea_lite and pplant_gdf_aea_states_agg#

# STUDENT CODE HERE
NAME geometry count sum
0 Alabama MULTIPOLYGON (((708732.822 -707811.735, 712514... 71 31451.9
1 Arizona POLYGON ((-1465661.328 152092.001, -1465314.16... 121 32991.9
2 Arkansas POLYGON ((143568.999 -425106.846, 143453.305 -... 56 16341.8
3 California MULTIPOLYGON (((-2277167.082 437548.205, -2277... 1273 82100.4
4 Colorado POLYGON ((-869898.203 525860.898, -859309.622 ... 163 17838.5
5 Connecticut POLYGON ((1914586.875 827355.12, 1916674.15 81... 94 7489.1
6 Delaware MULTIPOLYGON (((1672374.733 490114.235, 167132... 25 3628.3
7 District of Columbia POLYGON ((1567915.217 377568.875, 1567691.059 ... 2 24.9
8 Florida MULTIPOLYGON (((1242485.092 -913944.857, 12416... 148 66705.4
9 Georgia POLYGON ((962929.008 -150182.712, 963044.303 -... 157 39580.0
10 Idaho POLYGON ((-1230181.552 805125.905, -1230203.4 ... 135 5672.2
11 Illinois POLYGON ((503155.34 665167.903, 503516.948 665... 204 50908.7
12 Indiana POLYGON ((894352.421 474951.802, 894675.136 47... 141 28438.7
13 Iowa POLYGON ((348987.571 770124.612, 348959.631 76... 228 18334.7
14 Kansas POLYGON ((-341967.525 31793.248, -343441.586 3... 131 16526.4
15 Kentucky MULTIPOLYGON (((536161.774 -15266.298, 535511.... 47 24066.4
16 Louisiana MULTIPOLYGON (((642856.893 -772479.666, 640773... 83 28351.8
17 Maine MULTIPOLYGON (((1993500.901 1050797.037, 19934... 101 5211.7
18 Maryland MULTIPOLYGON (((1665553.015 319169.587, 166474... 93 13710.0
19 Massachusetts MULTIPOLYGON (((2002613.155 802194.458, 200300... 299 14769.4
20 Michigan MULTIPOLYGON (((754921.168 1014852.429, 754632... 222 29868.9
21 Minnesota POLYGON ((258053.196 1127649.709, 257983.22 11... 304 17829.1
22 Mississippi MULTIPOLYGON (((617333.202 -720210.395, 619071... 41 18073.9
23 Missouri POLYGON ((532055.49 -33817.124, 530696.779 -33... 119 23758.3
24 Montana POLYGON ((-731942.509 966869.411, -734898.94 9... 51 6446.7
25 Nebraska POLYGON ((-694630.265 509210.448, -694472.302 ... 101 9186.8
26 Nevada POLYGON ((-1509379.882 621585.357, -1514309.94... 77 12213.3
27 New Hampshire POLYGON ((1832486.548 941701.831, 1832272.334 ... 65 4699.7
28 New Jersey POLYGON ((1694855.025 527587.343, 1694887.162 ... 257 20737.8
29 New Mexico POLYGON ((-962156.637 -490075.176, -971543.292... 95 9264.8
30 New York MULTIPOLYGON (((1769835.568 640821.672, 176988... 384 42593.2
31 North Carolina MULTIPOLYGON (((1757076.83 -505.517, 1757744.4... 552 35402.8
32 North Dakota POLYGON ((-238747.793 1041014.554, -252542.498... 54 8334.6
33 Ohio MULTIPOLYGON (((1038876.036 627347.386, 103943... 156 31533.0
34 Oklahoma POLYGON ((-120841.278 -320183.875, -121545.802... 100 28318.4
35 Oregon POLYGON ((-2001881.083 1275307.447, -2000685.2... 151 11397.9
36 Pennsylvania POLYGON ((1310406.813 439185.218, 1297170.347 ... 212 48174.7
37 Rhode Island MULTIPOLYGON (((1962413.703 775852.024, 196207... 27 2063.9
38 South Carolina POLYGON ((1486583.586 -291788.318, 1486513.265... 97 24738.1
39 South Dakota POLYGON ((-663728.581 867745.595, -663122.263 ... 34 4390.3
40 Tennessee POLYGON ((1059101.891 45001.574, 1067538.033 4... 64 24029.0
41 Texas MULTIPOLYGON (((-148636.721 -1001679.649, -148... 404 128609.5
42 Utah POLYGON ((-1261827.854 607316.873, -1266361.16... 100 9798.3
43 Vermont POLYGON ((1832008.719 949915.85, 1832024.845 9... 85 647.0
44 Virginia MULTIPOLYGON (((1678275.509 288759.333, 167854... 135 28348.7
45 Washington MULTIPOLYGON (((-1965917.764 1572283.877, -196... 138 36879.8
46 West Virginia POLYGON ((1297170.347 437108.531, 1310406.813 ... 35 16041.4
47 Wisconsin MULTIPOLYGON (((392532.25 1169772.73, 392531.7... 173 17793.4
48 Wyoming POLYGON ((-1185091.513 566352.116, -1191060.30... 63 9480.2

Create a choropleth map to visualize the count of power plants in each state#

  • https://geopandas.readthedocs.io/en/latest/mapping.html#choropleth-maps

# STUDENT CODE HERE
../../_images/count_states.png

Create a choropleth map to visualize the total power plant capacity in megawatts in each state#

# STUDENT CODE HERE
../../_images/capacity_states.png

Part 4: Point density visualization: hexbin plots (2 pts)#

OK, now we have statistics and plots for our existing polygons. But what if we want to compute similar statistics for a regularly spaced grid of cells? For our count, this will give us point density, allowing for better visualization of the spatial distribution.

We could define a set of adjacent rectangular polygons, and repeat our sjoin, groupby, agg sequence above. Or we can use some existing matplotlib functionality, and create a hexbin plot!

Hexagonal cells are preferable over a standard square/rectangular grid for various reasons, especially since projection distortion impacts spatial binning.

Here are some resources on generating hexbins using Python and matplotlib:

  • https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hexbin.html

  • http://darribas.org/gds15/content/labs/lab_09.html

Note: an equal-area projection is always good idea for a hexbin plot. Fortunately, we started with our AEA projection, so we’re all set!

Create a hexbin that shows the number of power plants in each cell#

  • Play around with the gridsize option to set the number of bins in each dimension (or specify the dimensions of your bins)

  • Use the mincnt option to avoid plotting cells with 0 count

  • Overlay the state polygons to help visualize

  • Set your plot xlim and ylim to the power plant point bounds

  • Can use linear color ramp with vmin and vmax options, or try a logarithmic color ramp, since we have a broad range of counts

# STUDENT CODE HERE
../../_images/count_hexbin.png

Create a second hexbin plot that shows the total energy generation capacity in each cell#

  • See documentation for the C and reduce_C_function options

# STUDENT CODE HERE
../../_images/capacity_hexbin.png

Challenge question (GS required/UG +0.5): Generate a Kernel Density Estimator (KDE) plot using seaborn#

  • This is another nice approach to estimate the point density on a continuous grid

  • See motivation here for an explanation: https://en.wikipedia.org/wiki/Multivariate_kernel_density_estimation#Motivation

  • Seaborn has a nice implementation for 2D data

    • https://seaborn.pydata.org/generated/seaborn.kdeplot.html#seaborn.kdeplot

    • https://seaborn.pydata.org/tutorial/distributions.html

# STUDENT CODE HERE
../../_images/kdeplot.png

Static plots with tiled basemaps using contextily#

  • Our state outlines provide some context for our plots, but what if we want a raster map background

    • Freely available tiled web maps are great: https://en.wikipedia.org/wiki/Tiled_web_map

  • Can use the convenient contextily package for this: https://github.com/geopandas/contextily

Plot the WA points using our custom projected coordinate system#

  • Make sure to pass in the appropriate CRS object to add_basemap

  • For the particular basemap, try ctx.providers.Esri.WorldImagery, and then at least one additional different ctx.provider

# STUDENT CODE HERE
../../_images/capacity_conus_basemap.png

Repeat for WA state#

Thought question: Do you note anything about the relationship between power plant locations and rivers in Washington?

# STUDENT CODE HERE
../../_images/capacity_wa_basemap.png

Challenge Question (GS required/UG +0.5): Make a figure with choropleth maps showing total energy generated in 2016 from each different source in each state.#

  • Use a loop for this

    • Could use a structure like for fuel,ax in zip(pplant_gdf_aea_states.fuel1.unique(), axs.flatten()):

  • Use the fuel1 and gwh_2016 columns

  • Set a common vmin and vmax to enable comparison between maps

  • Use string formatting to label each plot appropriately

# STUDENT CODE HERE
../../_images/all_production_2016_subplots.png

Part 5: Interactive plots (2 pts)#

  • See standalone notebook

  • Please submit this interactive notebook with all plots commented out (completion grade!)

Part 6: Exploring King County Census data (3 pts)#

Below are two functions for getting US Census data and Census tract geometries#

  • check out the censusdata package: https://github.com/jtleider/censusdata

def get_census_data(tables, state, county, year=2019):
    '''Download census data for a given state and county fips code.'''

    # Download the data
    data = censusdata.download('acs5', year,  # Use 2019 ACS 5-year estimates
                               censusdata.censusgeo([('state', state), ('county', county), ('tract', '*')]),
                               list(tables.keys()))

    # Rename the column
    data.rename(columns=tables, inplace=True)

    # Extract information from the first column
    data['Name'] = data.index.to_series().apply(lambda x: x.name)
    data['SummaryLevel'] = data.index.to_series().apply(lambda x: x.sumlevel())
    data['State'] = data.index.to_series().apply(lambda x: x.geo[0][1])
    data['County'] = data.index.to_series().apply(lambda x: x.geo[1][1])
    data['Tract'] = data.index.to_series().apply(lambda x: x.geo[2][1])
    data.reset_index(drop=True, inplace=True)
    data = data[['Tract','Name']+list(tables.values())].set_index('Tract')
    
    return data

def get_census_tract_geom(state_fips, county_fips):
    '''Download census tract geometries for a given state and county fips code, storing in /tmp and cleaning up after.'''

    temp_dir = "/tmp/census_tracts"
    zip_path = os.path.join(temp_dir, f'tl_2019_{state_fips}_tract.zip')

    # Ensure temp directory exists
    os.makedirs(temp_dir, exist_ok=True)

    # Download the file
    url = f'https://www2.census.gov/geo/tiger/TIGER2019/TRACT/tl_2019_{state_fips}_tract.zip'
    response = requests.get(url, stream=True)
    if response.status_code != 200:
        raise Exception(f"Failed to download file: {url}")

    # Save ZIP file to temp directory
    with open(zip_path, "wb") as file:
        file.write(response.content)

    # Extract the ZIP file
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        zip_ref.extractall(temp_dir)

    # Find the shapefile in extracted contents
    for file in os.listdir(temp_dir):
        if file.endswith(".shp"):
            shapefile_path = os.path.join(temp_dir, file)
            break

    # Read the shapefile into a GeoDataFrame
    tracts = gpd.read_file(shapefile_path)

    # Filter by county and set index
    tracts = tracts[tracts['COUNTYFP'] == county_fips]
    tracts = tracts.rename(columns={'TRACTCE': 'Tract'}).set_index('Tract')

    # Cleanup: Remove extracted files and ZIP file
    shutil.rmtree(temp_dir)

    return tracts[['geometry']]

Define our areas of interest#

  • Both functions will require Federal Information Processing System (FIPS) codes that identify geographic areas

  • I’ve started you with the correct state and county fips codes, but you can find more here: https://www.census.gov/geographies/reference-files/2017/demo/popest/2017-fips.html

# Define the state and county for Seattle
state_fips = '53'  # FIPS code for Washington
county_fips = '033'  # FIPS code for King County

Define our variables of interest#

  • I’ve started you with some of the most common variables, feel free to add your own and explore

    • Check out additional table IDs: https://www.census.gov/programs-surveys/acs/data/data-tables/table-ids-explained.html

  • Some variables we will have to calculate on our own :)

tables = {
'B19013_001E': 'MedianIncome',
'B01003_001E': 'TotalPopulation',
'B01002_001E': 'MedianAge',
'B17001_002E': 'PopulationBelowPovertyLevel',
'B02001_002E': 'PopulationWhiteAlone',
'B02001_003E': 'PopulationBlackAlone',
'B02001_004E': 'PopulationAmericanIndianAlaskaNativeAlone',
'B02001_005E': 'PopulationAsianAlone',
'B02001_006E': 'PopulationNativeHawaiianPacificIslanderAlone',
'B02001_007E': 'PopulationSomeOtherRaceAlone',
'B02001_008E': 'PopulationTwoOrMoreRaces',
'B03002_003E': 'PopulationNotHispanicWhiteAlone',
'B03003_003E': 'PopulationHispanic',
'B25064_001E': 'MedianGrossRent',
'B25077_001E': 'MedianHomeValue',
'B25035_001E': 'MedianYearStructureBuilt',
'B25001_001E': 'TotalHousingUnits',
'B25004_001E': 'TotalVacantHousingUnits',
'B25003_002E': 'OccupiedHousingUnitsOwnerOccupied',
'B25003_003E': 'OccupiedHousingUnitsRenterOccupied',
'B27001_005E': 'PopulationNoHealthInsuranceCoverage',
}

Pull in the census data#

census_df = get_census_data(tables, state_fips, county_fips)
census_df.head()
Name MedianIncome TotalPopulation MedianAge PopulationBelowPovertyLevel PopulationWhiteAlone PopulationBlackAlone PopulationAmericanIndianAlaskaNativeAlone PopulationAsianAlone PopulationNativeHawaiianPacificIslanderAlone ... PopulationNotHispanicWhiteAlone PopulationHispanic MedianGrossRent MedianHomeValue MedianYearStructureBuilt TotalHousingUnits TotalVacantHousingUnits OccupiedHousingUnitsOwnerOccupied OccupiedHousingUnitsRenterOccupied PopulationNoHealthInsuranceCoverage
Tract
020700 Census Tract 207, King County, Washington 82367 4064 39.4 406 2555 434 10 671 20 ... 2109 602 1617 489000 1974 1581 0 777 804 0
020800 Census Tract 208, King County, Washington 116563 4279 48.9 217 3618 64 19 423 0 ... 3420 207 1328 706900 1963 1787 39 1348 400 0
020900 Census Tract 209, King County, Washington 103690 3420 43.6 271 2378 342 5 439 0 ... 2366 118 1243 590100 1973 1511 181 955 375 0
021000 Census Tract 210, King County, Washington 98083 6024 41.4 369 3587 455 87 1254 67 ... 3339 564 1752 477600 1962 2363 193 1618 552 0
022001 Census Tract 220.01, King County, Washington 102143 5729 43.8 236 4107 35 14 473 56 ... 3963 687 1407 636600 1989 2473 93 1397 983 0

5 rows × 22 columns

Let’s calculate our own variables#

  • Create new columns in your census_data_gdf for…

    • RatioMedianGrossRentToIncome

    • PercentRented

    • PercentOwned

    • PopulationNonWhite (TotalPopulation - PopulationNotHispanicWhiteAlone)

# STUDENT CODE HERE

Now let’s use this convert_to_percentages() function to convert our raw population numbers to percentages#

def convert_to_percentages(df, total_population_column='TotalPopulation'):
    '''Converts population counts to percentages'''
    for column in df.columns:
        if column.startswith('Population'):
            new_column_name = 'Percent' + column
            df[new_column_name] = (df[column] / df[total_population_column]) * 100
            df.drop(column, axis=1, inplace=True)
    return df

census_df = convert_to_percentages(census_df)
census_df.head()
Name MedianIncome TotalPopulation MedianAge MedianGrossRent MedianHomeValue MedianYearStructureBuilt TotalHousingUnits TotalVacantHousingUnits OccupiedHousingUnitsOwnerOccupied ... PercentPopulationBlackAlone PercentPopulationAmericanIndianAlaskaNativeAlone PercentPopulationAsianAlone PercentPopulationNativeHawaiianPacificIslanderAlone PercentPopulationSomeOtherRaceAlone PercentPopulationTwoOrMoreRaces PercentPopulationNotHispanicWhiteAlone PercentPopulationHispanic PercentPopulationNoHealthInsuranceCoverage PercentPopulationNonWhite
Tract
020700 Census Tract 207, King County, Washington 82367 4064 39.4 1617 489000 1974 1581 0 777 ... 10.679134 0.246063 16.510827 0.492126 3.764764 5.437992 51.894685 14.812992 0.0 48.105315
020800 Census Tract 208, King County, Washington 116563 4279 48.9 1328 706900 1963 1787 39 1348 ... 1.495677 0.444029 9.885487 0.000000 0.210330 3.412012 79.925216 4.837579 0.0 20.074784
020900 Census Tract 209, King County, Washington 103690 3420 43.6 1243 590100 1973 1511 181 955 ... 10.000000 0.146199 12.836257 0.000000 2.573099 4.912281 69.181287 3.450292 0.0 30.818713
021000 Census Tract 210, King County, Washington 98083 6024 41.4 1752 477600 1962 2363 193 1618 ... 7.553121 1.444223 20.816733 1.112218 1.377822 8.150730 55.428287 9.362550 0.0 44.571713
022001 Census Tract 220.01, King County, Washington 102143 5729 43.8 1407 636600 1989 2473 93 1397 ... 0.610927 0.244371 8.256240 0.977483 9.128993 9.094083 69.174376 11.991622 0.0 30.825624

5 rows × 26 columns

Let’s add geometries for the census tracts and Seattle#

  • Pull in the census geometries using the provided get_census_tract_geom() using the same FIPS codes

  • Geojson of Seattle available here: https://github.com/seattleflu/seattle-geojson

tract_geom_gdf = get_census_tract_geom(state_fips, county_fips)
seattle_gdf = gpd.read_file('https://raw.githubusercontent.com/seattleflu/seattle-geojson/master/seattle_geojsons/2016_seattle_city.geojson', engine='pyogrio')

f,ax=plt.subplots(1,2,figsize=(10,4))

tract_geom_gdf.plot(ax=ax[0])
seattle_gdf.plot(ax=ax[1])

ax[0].set_title('King County Census Tracts')
ax[1].set_title('Seattle geometry')

Text(0.5, 1.0, 'Seattle geometry')
../../_images/d9f9bd2bfae96425379dd95914a08e4a4b787cb22f929b2009fff58d657727fe.png

Combine the census data and census tract geometry and plot#

  • You can try .join() to create a combined dataframe

    • what should you put for the “how” argument?

  • If it is not already, convert the dataframe into a geodataframe census_kc_gdf

    • What crs is the new geodataframe in? reproject it to EPSG:32610!

  • Filter your new census_data_gdf by ‘TotalPopulation’ > 0 to get rid of empty tracts

  • Make sure your Seattle geodataframe is also in EPSG:32610, then use the .clip() operation to create census_sea_gdf

  • Create a figure with two total population choropleth plots, one for King County, and one for Seattle

# STUDENT CODE HERE

# STUDENT CODE HERE

# STUDENT CODE HERE

# STUDENT CODE HERE

# STUDENT CODE HERE
../../_images/population_tracts.png

Written response: Explore the map#

  • Use geopandas .explore() on census_kc_gdf to explore the map

  • Pass in a variable of interest to column

  • Be mindful of your vmin and vmax settings!

  • At submission time, please comment out this cell to reduce notebook size at turn in :)

  • Tell us something you saw!

# STUDENT CODE HERE

STUDENT WRITTEN RESPONSE HERE

Get a rough idea of variable correlation#

  • Use the pandas built-in .corr() functionality and store the correlation matrix in census_corr

  • Pass in numeric_only=True to avoid errors

  • Pass in census_corr to sns.heatmap() to visualize the correlations

# STUDENT CODE HERE

# STUDENT CODE HERE
../../_images/census_variable_correlation.png

Written response: Try the variable correlation above for both census_kc_gdf and also census_sea_gdf What do you see?#

STUDENT WRITTEN RESPONSE HERE

Explore one of these relationships with a scatterplot#

  • Try your own, don’t copy the example!

  • Besides plotting some variable on x and y, try setting the color/size of your points to another variable as well

# STUDENT CODE HERE
../../_images/percent_non_white_vs_rented.png

Written response: Describe an insight from the exploration of your plot!#

STUDENT WRITTEN RESPONSE HERE

Part 7: Distribution of toxic releases in King County (4 pts)#

  • Let’s explore how toxic releases are distributed across our census tracts. For this, let’s use the Toxic Release Inventory (TRI)

  • From the TRI website: “TRI tracks the management of certain toxic chemicals that may pose a threat to human health and the environment. U.S. facilities in different industry sectors must report annually how much of each chemical is released to the environment and/or managed through recycling, energy recovery and treatment. (A “release” of a chemical means that it is emitted to the air or water, or placed in some type of land disposal.)”

  • TRI has all sorts of information for the US, but we are going to be focused on releases in King County

  • Read more about the TRI here: https://www.epa.gov/toxics-release-inventory-tri-program/what-toxics-release-inventory

Let’s bring in the Toxic release data as a geodataframe in the correct crs#

  • For the sake of time, we’ve done done this for you and saved a geojson to the data folder.

  • The code to do this is preserved below

# def get_tri_data(year=2019):
#     '''Get the Toxic Release Inventory data for a given year'''
#     url = f'https://data.epa.gov/efservice/downloads/tri/mv_tri_basic_download/{year}_US/csv'
#     columns_of_interest = ['2. TRIFD','4. FACILITY NAME','8. ST','23. INDUSTRY SECTOR','37. CHEMICAL','65. ON-SITE RELEASE TOTAL', '12. LATITUDE','13. LONGITUDE']
#     tri_data = pd.read_csv(url,usecols=columns_of_interest)
#     tri_data = pd.read_csv(url)
#     tri_data = tri_data[columns_of_interest]
#     tri_data.columns = tri_data.columns.str.replace('^[0-9. ]*', '', regex=True)
#     return tri_data

# tri_df = pd.DataFrame()

# for year in range(1987,2022):
#     tri_df = pd.concat([tri_df,get_tri_data(year)])
# tri_df

# tri_wa_df = tri_df[tri_df['ST'] == 'WA']
# tri_wa_df = tri_wa_df.reset_index(drop=True)
# tri_wa_gdf = gpd.GeoDataFrame(tri_wa_df, geometry=gpd.points_from_xy(tri_wa_df.LONGITUDE, tri_wa_df.LATITUDE)).set_crs('EPSG:4326').to_crs('EPSG:32610')
# tri_kc_gdf = gpd.sjoin(tri_wa_gdf, tract_geom_gdf.to_crs(32610), predicate="intersects")
# tri_kc_gdf = tri_kc_gdf.reset_index(drop=True)
# tri_kc_gdf = tri_kc_gdf.drop(columns='Tract')
# tri_kc_gdf.to_file("data/tri_kc.geojson", driver="GeoJSON")

# read in data
tri_kc_gdf = gpd.read_file('data/tri_kc.geojson')

Clip locations to the convex hull of our census data#

  • Remember to do the unary_union before using convex hull!

  • Hint: use intersects :)

# STUDENT CODE HERE

Sort the geodataframe by ON-SITE RELEASE TOTAL#

# STUDENT CODE HERE
TRIFD FACILITY NAME ST INDUSTRY SECTOR CHEMICAL ON-SITE RELEASE TOTAL LATITUDE LONGITUDE geometry
662 98106STTLS2414S SEATTLE STEEL INC WA Primary Metals Aluminum oxide (fibrous forms) 2138500.0 47.569331 -122.367332 POINT (547585.548 5268628.876)
683 98108THBNG7500E BOEING COMMERCIAL AIRPLANE GROUP NORTH BOEING ... WA Transportation Equipment Trichloroethylene 682000.0 47.531169 -122.310812 POINT (551874.276 5264423.885)
774 98108RHNPL9229E RHONE-POULENC INC WA Chemicals Toluene 636166.0 47.520258 -122.303946 POINT (552401.934 5263215.89)
565 98108RHNPL9229E RHONE-POULENC INC WA Chemicals Toluene 570000.0 47.520258 -122.303946 POINT (552401.934 5263215.89)
1853 98002BNGCM70015 BOEING COMMERCIAL AIRPLANES - AUBURN WA Transportation Equipment 1,1,1-Trichloroethane 570000.0 47.283625 -122.242770 POINT (557263.362 5236961.139)
... ... ... ... ... ... ... ... ... ...
5598 98032BRLNG20245 BURLINGTON ENVIRONMENTAL LLC WA Hazardous Waste Nitrate compounds (water dissociable; reportab... 0.0 47.418259 -122.238184 POINT (557463.831 5251926.719)
7190 98032VNWTR8201S UNIVAR SOLUTIONS KENT WA Chemical Wholesalers Toluene 0.0 47.411106 -122.230700 POINT (558036.206 5251137.334)
7191 98032XTCMT5411S EXOTIC METALS FORMING CO WA Transportation Equipment Nickel compounds 0.0 47.399148 -122.264995 POINT (555461.544 5249783.393)
2320 98032MRCNN1220N REXAM BEVERAGE CAN CO RE: KENT WA FACILITY WA Fabricated Metals Nitric acid 0.0 47.404217 -122.238960 POINT (557420.559 5250365.608)
3636 98108TMPRS701SO TRIM SYSTEMS WA Plastics and Rubber Ethylene glycol 0.0 47.538461 -122.325001 POINT (550799.249 5265224.911)

9147 rows × 9 columns

Written response: What do you notice? What are the lowest values, and why do you think those are the lowest values?#

STUDENT WRITTEN RESPONSE HERE

Make a figure with two plots. On the left, the ON-SITE RELEASE TOTAL as points, and on the right, a hexbin plot of release_count.#

  • Make the colorscale logarithmic

# STUDENT CODE HERE
../../_images/onsight_releases_locations.png

Plot the release sites on top of the median income census data#

  • Now focus on Seattle: create tri_sea_gdf by clipping tri_kc_gdf

  • Plot tri_sea_gdf on top of ‘MedianIncome’ from census_sea_gdf

# STUDENT CODE HERE

# STUDENT CODE HERE
../../_images/release_sites_income.png

Written response: What do you notice about the distribution of toxic releases as it relates to median income?#

STUDENT WRITTEN RESPONSE HERE

Do a spatial join to get the census tract for each facility in Seattle#

  • First buffer each facility by 100m

  • Then join the facilities with the census tracts

  • Find the total releases in pounds per tract (groupby tract, then sum)

  • Find the total number of discrete release events per tract (groupby tract, then count)

tri_sea_gdf.loc[:,'geometry'] = tri_sea_gdf.buffer(100)

# STUDENT CODE HERE

census_sea_gdf['TRI_RELEASE_SUM'] = tri_sea_join_census_gdf.groupby('Tract')['ON-SITE RELEASE TOTAL'].sum().sort_index()
census_sea_gdf['TRI_RELEASE_SUM'] = census_sea_gdf['TRI_RELEASE_SUM'].fillna(0)

# STUDENT CODE HERE

Create choropleth maps of Seattle: one for total on-site release (pounds), and one for number of discrete on-site release events#

# STUDENT CODE HERE
../../_images/tri_by_tract.png

Written response: Take a moment to reflect. What do you see?#

STUDENT WRITTEN RESPONSE HERE

Submit your work#

  1. Save this notebook with all code and output (Make sure when you save the notebook, all cells show their outputs).

  2. Use the terminal to stage, commit, and push your notebook to your GitHub repository. It should look something like this…

  • git add 01_lab.ipynb

  • git commit -m “Completed Lab 01 exercises”

  • git push

  1. Verify that your notebook appears in your GitHub repository. Double check to make sure all the ouputs are visible!

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