GeoPandas - intro, plotting¶
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import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('bmh') # better for plotting geometries vs general plots.
from shapely.geometry import Point, Polygon, LineString
import pandas as pd
import geopandas as gpd
from geopandas import GeoSeries, GeoDataFrame
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('bmh') # better for plotting geometries vs general plots.
from shapely.geometry import Point, Polygon, LineString
import pandas as pd
import geopandas as gpd
from geopandas import GeoSeries, GeoDataFrame
Geometry types¶
Geopandas uses shapely.geometry geometry objects. Geopandas has 6 types of geometry objects
- Point
- Line (LineString)
- Polygon
- Multi-Point
- Multi-Line
- Multi-Polygon
Gotchas¶
- Geopandas is a growing project and its API could change over time
- Geopandas does not restrict or check for consistency in geometry type of its series.
Constructing GeoSeries from Shapely Points¶
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point_list = [Point(-120,45), Point(-121.2, 46), Point(-122.9, 47.5)]
gs = GeoSeries(point_list)
gs
point_list = [Point(-120,45), Point(-121.2, 46), Point(-122.9, 47.5)]
gs = GeoSeries(point_list)
gs
Out[2]:
0 POINT (-120 45) 1 POINT (-121.2 46) 2 POINT (-122.9 47.5) dtype: object
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type(gs)
type(gs)
Out[3]:
geopandas.geoseries.GeoSeries
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gs.geometry
gs.geometry
Out[4]:
0 POINT (-120 45) 1 POINT (-121.2 46) 2 POINT (-122.9 47.5) dtype: object
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gs.geom_type
gs.geom_type
Out[5]:
0 Point 1 Point 2 Point dtype: object
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gs.iloc[0]
gs.iloc[0]
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gs.crs = {'init':'epsg:4326'}
gs.crs = {'init':'epsg:4326'}
Plot the geometries¶
This internally uses descartes and matplotlib
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gs.plot()
gs.plot()
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a0e833080>
Constructing GeoSeries from a Pandas DataFrame¶
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data_dict = {'name':['a','b','c'],
'lat':[45,46,47.5],
'lon':[-120,-121.2,-123]}
df = pd.DataFrame(data_dict)
df.set_index(df['name'], inplace=True)
df
data_dict = {'name':['a','b','c'],
'lat':[45,46,47.5],
'lon':[-120,-121.2,-123]}
df = pd.DataFrame(data_dict)
df.set_index(df['name'], inplace=True)
df
Out[16]:
| lat | lon | name | |
|---|---|---|---|
| name | |||
| a | 45.0 | -120.0 | a |
| b | 46.0 | -121.2 | b |
| c | 47.5 | -123.0 | c |
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[xy for xy in zip(df['lon'], df['lat'])]
[xy for xy in zip(df['lon'], df['lat'])]
Out[17]:
[(-120.0, 45.0), (-121.2, 46.0), (-123.0, 47.5)]
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point_list = [Point(xy) for xy in zip(df['lon'], df['lat'])]
point_list
point_list = [Point(xy) for xy in zip(df['lon'], df['lat'])]
point_list
Out[18]:
[<shapely.geometry.point.Point at 0x1a0e8af5c0>, <shapely.geometry.point.Point at 0x1a19123400>, <shapely.geometry.point.Point at 0x1a191237f0>]
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gdf = GeoDataFrame(df, geometry=point_list)
gdf
gdf = GeoDataFrame(df, geometry=point_list)
gdf
Out[20]:
| lat | lon | name | geometry | |
|---|---|---|---|---|
| name | ||||
| a | 45.0 | -120.0 | a | POINT (-120 45) |
| b | 46.0 | -121.2 | b | POINT (-121.2 46) |
| c | 47.5 | -123.0 | c | POINT (-123 47.5) |
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gdf.plot()
gdf.plot()
Out[21]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a191bd9b0>
Reading a SHP file¶
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fire_stations = gpd.read_file('./data/FireStations.shp')
type(fire_stations)
fire_stations = gpd.read_file('./data/FireStations.shp')
type(fire_stations)
Out[22]:
geopandas.geodataframe.GeoDataFrame
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fire_stations.head()
fire_stations.head()
Out[23]:
| OBJECTID | post_id | Name | descriptio | cat1 | cat2 | cat3 | addrln1 | addrln2 | city | ... | source | ext_id | use_type | dis_status | latitude | longitude | date_updat | POINT_X | POINT_Y | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 40223266 | 2022.0 | Los Angeles County Fire Department - Battalion... | The Battalion provides fire and rescue service... | Public Safety | Fire Stations | None | 7643 W. Santa Monica Blvd. | None | Los Angeles | ... | 211 | None | publish | None | 34.090940 | -118.356388 | 2013-06-01 | 6.453766e+06 | 1.855666e+06 | POINT (6453766.230470523 1855666.492027521) |
| 1 | 40223440 | 2236.0 | Los Angeles County Fire Department - Battalion... | The Battalion provides fire and rescue service... | Public Safety | Fire Stations | Battalion HQ | 6031 Rickenbacker Rd. | None | Commerce | ... | 211 | None | publish | None | 33.988587 | -118.154162 | 2013-06-01 | 6.514937e+06 | 1.818263e+06 | POINT (6514937.276763365 1818262.767294437) |
| 2 | 40223442 | 2237.0 | Los Angeles County Fire Department - Battalion... | The Battalion provides fire and rescue service... | Public Safety | Fire Stations | None | 1059 N. White Ave. | None | Pomona | ... | 211 | None | publish | None | 34.067555 | -117.759366 | 2013-06-01 | 6.634548e+06 | 1.847052e+06 | POINT (6634547.880061358 1847052.198560596) |
| 3 | 40223459 | 2262.0 | Los Angeles County Fire Department - Battalion... | The Battalion provides fire and rescue service... | Public Safety | Fire Stations | None | 1260 Encinal Canyon Rd | None | Malibu | ... | 211 | None | publish | None | 34.084350 | -118.866037 | 2013-06-01 | 6.299441e+06 | 1.854207e+06 | POINT (6299441.115893021 1854206.637991846) |
| 4 | 40223463 | 2269.0 | Los Angeles County Fire Department - Battalion... | The Battalion provides fire and rescue service... | Public Safety | Fire Stations | Battalion HQ | 6301 S. Santa Fe Ave. | None | Huntington Park | ... | 211 | None | publish | None | 33.983196 | -118.230626 | 2013-06-01 | 6.491753e+06 | 1.816345e+06 | POINT (6491753.321819022 1816345.240289599) |
5 rows × 30 columns
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fire_stations.crs
fire_stations.crs
Out[24]:
{'proj': 'lcc',
'lat_1': 34.03333333333333,
'lat_2': 35.46666666666667,
'lat_0': 33.5,
'lon_0': -118,
'x_0': 2000000,
'y_0': 500000.0000000001,
'datum': 'NAD83',
'units': 'us-ft',
'no_defs': True}
Data exploration using pandas¶
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fire_stations.columns
fire_stations.columns
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Index(['OBJECTID', 'post_id', 'Name', 'descriptio', 'cat1', 'cat2', 'cat3',
'addrln1', 'addrln2', 'city', 'state', 'zip', 'hours', 'phones', 'url',
'email', 'info1', 'info2', 'link', 'org_name', 'source', 'ext_id',
'use_type', 'dis_status', 'latitude', 'longitude', 'date_updat',
'POINT_X', 'POINT_Y', 'geometry'],
dtype='object')
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fire_stations['city'].value_counts().head()
fire_stations['city'].value_counts().head()
Out[67]:
Los Angeles 65 Long Beach 25 Santa Clarita 10 Pasadena 10 Pomona 9 Name: city, dtype: int64
Plotting¶
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fire_stations.plot(figsize=(10,10))
fire_stations.plot(figsize=(10,10))
Out[39]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a19347048>
Overlay USA map¶
Read the bundled dataset and overlay it on top of this
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gpd.datasets.available
gpd.datasets.available
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['naturalearth_cities', 'naturalearth_lowres', 'nybb']
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world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.head()
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.head()
Out[42]:
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 0 | 28400000.0 | Asia | Afghanistan | AFG | 22270.0 | POLYGON ((61.21081709172574 35.65007233330923,... |
| 1 | 12799293.0 | Africa | Angola | AGO | 110300.0 | (POLYGON ((16.32652835456705 -5.87747039146621... |
| 2 | 3639453.0 | Europe | Albania | ALB | 21810.0 | POLYGON ((20.59024743010491 41.85540416113361,... |
| 3 | 4798491.0 | Asia | United Arab Emirates | ARE | 184300.0 | POLYGON ((51.57951867046327 24.24549713795111,... |
| 4 | 40913584.0 | South America | Argentina | ARG | 573900.0 | (POLYGON ((-65.50000000000003 -55.199999999999... |
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world.shape
world.shape
Out[45]:
(177, 6)
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world['name'].unique()
world['name'].unique()
Out[46]:
array(['Afghanistan', 'Angola', 'Albania', 'United Arab Emirates',
'Argentina', 'Armenia', 'Antarctica', 'Fr. S. Antarctic Lands',
'Australia', 'Austria', 'Azerbaijan', 'Burundi', 'Belgium',
'Benin', 'Burkina Faso', 'Bangladesh', 'Bulgaria', 'Bahamas',
'Bosnia and Herz.', 'Belarus', 'Belize', 'Bolivia', 'Brazil',
'Brunei', 'Bhutan', 'Botswana', 'Central African Rep.', 'Canada',
'Switzerland', 'Chile', 'China', "Côte d'Ivoire", 'Cameroon',
'Dem. Rep. Congo', 'Congo', 'Colombia', 'Costa Rica', 'Cuba',
'N. Cyprus', 'Cyprus', 'Czech Rep.', 'Germany', 'Djibouti',
'Denmark', 'Dominican Rep.', 'Algeria', 'Ecuador', 'Egypt',
'Eritrea', 'Spain', 'Estonia', 'Ethiopia', 'Finland', 'Fiji',
'Falkland Is.', 'France', 'Gabon', 'United Kingdom', 'Georgia',
'Ghana', 'Guinea', 'Gambia', 'Guinea-Bissau', 'Eq. Guinea',
'Greece', 'Greenland', 'Guatemala', 'Guyana', 'Honduras',
'Croatia', 'Haiti', 'Hungary', 'Indonesia', 'India', 'Ireland',
'Iran', 'Iraq', 'Iceland', 'Israel', 'Italy', 'Jamaica', 'Jordan',
'Japan', 'Kazakhstan', 'Kenya', 'Kyrgyzstan', 'Cambodia', 'Korea',
'Kosovo', 'Kuwait', 'Lao PDR', 'Lebanon', 'Liberia', 'Libya',
'Sri Lanka', 'Lesotho', 'Lithuania', 'Luxembourg', 'Latvia',
'Morocco', 'Moldova', 'Madagascar', 'Mexico', 'Macedonia', 'Mali',
'Myanmar', 'Montenegro', 'Mongolia', 'Mozambique', 'Mauritania',
'Malawi', 'Malaysia', 'Namibia', 'New Caledonia', 'Niger',
'Nigeria', 'Nicaragua', 'Netherlands', 'Norway', 'Nepal',
'New Zealand', 'Oman', 'Pakistan', 'Panama', 'Peru', 'Philippines',
'Papua New Guinea', 'Poland', 'Puerto Rico', 'Dem. Rep. Korea',
'Portugal', 'Paraguay', 'Palestine', 'Qatar', 'Romania', 'Russia',
'Rwanda', 'W. Sahara', 'Saudi Arabia', 'Sudan', 'S. Sudan',
'Senegal', 'Solomon Is.', 'Sierra Leone', 'El Salvador',
'Somaliland', 'Somalia', 'Serbia', 'Suriname', 'Slovakia',
'Slovenia', 'Sweden', 'Swaziland', 'Syria', 'Chad', 'Togo',
'Thailand', 'Tajikistan', 'Turkmenistan', 'Timor-Leste',
'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Taiwan', 'Tanzania',
'Uganda', 'Ukraine', 'Uruguay', 'United States', 'Uzbekistan',
'Venezuela', 'Vietnam', 'Vanuatu', 'Yemen', 'South Africa',
'Zambia', 'Zimbabwe'], dtype=object)
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usa = world[world['name']=='United States']
usa
usa = world[world['name']=='United States']
usa
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| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 168 | 313973000.0 | North America | United States | USA | 15094000.0 | (POLYGON ((-155.54211 19.08348000000001, -155.... |
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ax = fire_stations.plot(figsize=(10,10))
usa.plot(ax = ax)
ax = fire_stations.plot(figsize=(10,10))
usa.plot(ax = ax)
Out[49]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1939fc18>
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usa.plot()
usa.plot()
Out[50]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a193be9e8>
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usa.crs
usa.crs
Out[51]:
{'init': 'epsg:4326'}
Snap! They are in different projections and geopandas does not handle that. SO lets try to reproject the fire stations to match usa
Reprojection¶
Projecting the fire stations layer to match the world dataset from geopandas
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fire_stations_gcs = fire_stations.to_crs(epsg=4326)
fire_stations_gcs = fire_stations.to_crs(epsg=4326)
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fire_stations_gcs.crs
fire_stations_gcs.crs
Out[53]:
{'init': 'epsg:4326', 'no_defs': True}
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fire_stations_gcs.plot()
fire_stations_gcs.plot()
Out[54]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a193ec0f0>
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fire_stations_gcs.total_bounds
fire_stations_gcs.total_bounds
Out[60]:
array([-118.88357856, 33.32376846, -117.70773665, 34.75793147])
Overlay plot¶
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f, ax = plt.subplots(1, figsize=(12, 12))
ax.set_title('Fire stations in Southern California')
usa.plot(ax=ax, facecolor='lightgray', edgecolor='gray')
fire_stations_gcs.plot(ax=ax)
#this plots the whole US. To limit the scale to just SoCal, limit the axes
ax.set_xlim([-120, -118])
ax.set_ylim([33, 35])
f, ax = plt.subplots(1, figsize=(12, 12))
ax.set_title('Fire stations in Southern California')
usa.plot(ax=ax, facecolor='lightgray', edgecolor='gray')
fire_stations_gcs.plot(ax=ax)
#this plots the whole US. To limit the scale to just SoCal, limit the axes
ax.set_xlim([-120, -118])
ax.set_ylim([33, 35])
Out[66]:
(33, 35)
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damsel_gdf = gpd.read_file('./data/damselfish/DAMSELFISH_distributions.shp')
damsel_gdf.head(3)
damsel_gdf = gpd.read_file('./data/damselfish/DAMSELFISH_distributions.shp')
damsel_gdf.head(3)
Out[2]:
| ID_NO | BINOMIAL | ORIGIN | COMPILER | YEAR | CITATION | SOURCE | DIST_COMM | ISLAND | SUBSPECIES | ... | RL_UPDATE | KINGDOM_NA | PHYLUM_NAM | CLASS_NAME | ORDER_NAME | FAMILY_NAM | GENUS_NAME | SPECIES_NA | CATEGORY | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 183963.0 | Stegastes leucorus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | leucorus | VU | POLYGON ((-115.6437454219999 29.71392059300007... |
| 1 | 183963.0 | Stegastes leucorus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | leucorus | VU | POLYGON ((-105.589950704 21.89339825500002, -1... |
| 2 | 183963.0 | Stegastes leucorus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | leucorus | VU | POLYGON ((-111.159618439 19.01535626700007, -1... |
3 rows × 24 columns
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damsel_gdf.columns
damsel_gdf.columns
Out[3]:
Index(['ID_NO', 'BINOMIAL', 'ORIGIN', 'COMPILER', 'YEAR', 'CITATION', 'SOURCE',
'DIST_COMM', 'ISLAND', 'SUBSPECIES', 'SUBPOP', 'LEGEND', 'SEASONAL',
'TAX_COMM', 'RL_UPDATE', 'KINGDOM_NA', 'PHYLUM_NAM', 'CLASS_NAME',
'ORDER_NAME', 'FAMILY_NAM', 'GENUS_NAME', 'SPECIES_NA', 'CATEGORY',
'geometry'],
dtype='object')
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damsel_gdf.shape
damsel_gdf.shape
Out[4]:
(231, 24)
Plot a map by category¶
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damsel_gdf.plot(column='BINOMIAL', categorical=True,
figsize=(12, 4))
damsel_gdf.plot(column='BINOMIAL', categorical=True,
figsize=(12, 4))
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1d4a9eb8>
Groupby a column¶
The BINOMIAL column contains the full genus and species names of the fishes. We could split the shape file by each fish type
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damsel_gdf['BINOMIAL'].value_counts()
damsel_gdf['BINOMIAL'].value_counts()
Out[5]:
Amphiprion sandaracinos 51 Chromis cyanea 15 Chromis alpha 14 Chromis alta 11 Microspathodon bairdii 10 Abudefduf troschelii 10 Stegastes acapulcoensis 9 Chromis atrilobata 9 Microspathodon dorsalis 9 Stegastes arcifrons 8 Stegastes flavilatus 8 Stegastes beebei 8 Chromis limbaughi 7 Teixeirichthys jordani 7 Nexilosus latifrons 7 Abudefduf concolor 6 Chrysiptera flavipinnis 5 Azurina hirundo 5 Hypsypops rubicundus 4 Chromis pembae 4 Stegastes leucorus 3 Chromis intercrusma 3 Abudefduf declivifrons 3 Stegastes rectifraenum 3 Azurina eupalama 3 Chromis crusma 3 Chromis flavicauda 2 Stegastes redemptus 2 Stegastes baldwini 1 Chromis punctipinnis 1 Name: BINOMIAL, dtype: int64
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grouped = damsel_gdf.groupby(by='BINOMIAL')
grouped.head()
grouped = damsel_gdf.groupby(by='BINOMIAL')
grouped.head()
Out[6]:
| ID_NO | BINOMIAL | ORIGIN | COMPILER | YEAR | CITATION | SOURCE | DIST_COMM | ISLAND | SUBSPECIES | ... | RL_UPDATE | KINGDOM_NA | PHYLUM_NAM | CLASS_NAME | ORDER_NAME | FAMILY_NAM | GENUS_NAME | SPECIES_NA | CATEGORY | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 183963.0 | Stegastes leucorus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | leucorus | VU | POLYGON ((-115.6437454219999 29.71392059300007... |
| 1 | 183963.0 | Stegastes leucorus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | leucorus | VU | POLYGON ((-105.589950704 21.89339825500002, -1... |
| 2 | 183963.0 | Stegastes leucorus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | leucorus | VU | POLYGON ((-111.159618439 19.01535626700007, -1... |
| 3 | 183793.0 | Chromis intercrusma | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | intercrusma | LC | POLYGON ((-80.86500229899997 -0.77894492099994... |
| 4 | 183793.0 | Chromis intercrusma | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | intercrusma | LC | POLYGON ((-67.33922225599997 -55.6761029239999... |
| 5 | 183793.0 | Chromis intercrusma | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | intercrusma | LC | POLYGON ((-74.81822204599996 -51.4608230589999... |
| 6 | 183462.0 | Stegastes beebei | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | beebei | VU | POLYGON ((-86.13105102199995 5.598867493000057... |
| 7 | 183462.0 | Stegastes beebei | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | beebei | VU | POLYGON ((-80.68215272899994 4.037426486000072... |
| 8 | 183462.0 | Stegastes beebei | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | beebei | VU | POLYGON ((-90.22271068899994 -0.27334342499995... |
| 9 | 183462.0 | Stegastes beebei | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | beebei | VU | POLYGON ((-83.93571481699996 9.296388679000074... |
| 10 | 183462.0 | Stegastes beebei | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | beebei | VU | POLYGON ((-79.15961456299993 9.011326790000055... |
| 14 | 183586.0 | Stegastes rectifraenum | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | rectifraenum | LC | POLYGON ((-114.129066467 31.49156189000007, -1... |
| 15 | 183586.0 | Stegastes rectifraenum | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | rectifraenum | LC | POLYGON ((-106.7327677389999 23.60688894500004... |
| 16 | 183586.0 | Stegastes rectifraenum | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | rectifraenum | LC | POLYGON ((-111.159618439 19.01535626700007, -1... |
| 17 | 154831.0 | Chromis punctipinnis | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | punctipinnis | LC | POLYGON ((-121.799003601 36.75037002600004, -1... |
| 18 | 183240.0 | Chromis crusma | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | crusma | LC | POLYGON ((-80.82860578399999 -1.22984210599997... |
| 19 | 183240.0 | Chromis crusma | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | crusma | LC | POLYGON ((-67.33922225599997 -55.6761029239999... |
| 20 | 183240.0 | Chromis crusma | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | crusma | LC | POLYGON ((-74.81822204599996 -51.4608230589999... |
| 21 | 154856.0 | Chromis pembae | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | pembae | LC | POLYGON ((32.56497192400008 29.96966743400003,... |
| 22 | 154856.0 | Chromis pembae | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | pembae | LC | POLYGON ((55.90029544000004 -3.630027441999971... |
| 23 | 154856.0 | Chromis pembae | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | pembae | LC | POLYGON ((73.86925448500006 7.187452539000049,... |
| 24 | 154856.0 | Chromis pembae | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | pembae | LC | POLYGON ((39.89708328200004 16.32163810700007,... |
| 25 | 183567.0 | Stegastes redemptus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | redemptus | VU | POLYGON ((-112.128707413 25.07043759500004, -1... |
| 26 | 183567.0 | Stegastes redemptus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | redemptus | VU | POLYGON ((-111.159618439 19.01535626700007, -1... |
| 27 | 154915.0 | Teixeirichthys jordani | 1 | None | 2012 | Red List Index (Sampled Approach), Zoological ... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Teixeirichthys | jordani | LC | POLYGON ((121.6300326400001 33.04248618400004,... |
| 28 | 154915.0 | Teixeirichthys jordani | 1 | None | 2012 | Red List Index (Sampled Approach), Zoological ... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Teixeirichthys | jordani | LC | POLYGON ((32.56219482400007 29.97488975500005,... |
| 29 | 154915.0 | Teixeirichthys jordani | 1 | None | 2012 | Red List Index (Sampled Approach), Zoological ... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Teixeirichthys | jordani | LC | POLYGON ((130.9052090560001 34.02498196400006,... |
| 30 | 154915.0 | Teixeirichthys jordani | 1 | None | 2012 | Red List Index (Sampled Approach), Zoological ... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Teixeirichthys | jordani | LC | POLYGON ((56.32233070000007 -3.707270205999976... |
| 31 | 154915.0 | Teixeirichthys jordani | 1 | None | 2012 | Red List Index (Sampled Approach), Zoological ... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Teixeirichthys | jordani | LC | POLYGON ((40.64476131800006 -10.85502363999996... |
| 34 | 183452.0 | Chromis limbaughi | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | limbaughi | LC | POLYGON ((-112.417701721 29.37484550400006, -1... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 187 | 183483.0 | Abudefduf concolor | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | concolor | LC | POLYGON ((-91.09391784599995 -0.58096068999992... |
| 189 | 183397.0 | Abudefduf troschelii | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | troschelii | LC | POLYGON ((-90.09187344499998 13.72481517500006... |
| 190 | 183397.0 | Abudefduf troschelii | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | troschelii | LC | POLYGON ((-121.284812927 35.67465591400003, -1... |
| 191 | 183397.0 | Abudefduf troschelii | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | troschelii | LC | POLYGON ((-106.72576642 23.59774532600005, -10... |
| 192 | 183397.0 | Abudefduf troschelii | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | troschelii | LC | POLYGON ((-86.13105102199995 5.598867493000057... |
| 193 | 183397.0 | Abudefduf troschelii | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | troschelii | LC | POLYGON ((-111.159618439 19.01535626700007, -1... |
| 199 | 154920.0 | Chrysiptera flavipinnis | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chrysiptera | flavipinnis | LC | POLYGON ((144.147216637 -7.778888740999946, 14... |
| 200 | 154920.0 | Chrysiptera flavipinnis | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chrysiptera | flavipinnis | LC | POLYGON ((156.671737653 -6.481586890999949, 15... |
| 201 | 154920.0 | Chrysiptera flavipinnis | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chrysiptera | flavipinnis | LC | POLYGON ((164.5374754960001 -20.08774569199994... |
| 202 | 154920.0 | Chrysiptera flavipinnis | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chrysiptera | flavipinnis | LC | POLYGON ((151.2978177690001 -16.88691080299998... |
| 203 | 154920.0 | Chrysiptera flavipinnis | 1 | None | 2012 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.2 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chrysiptera | flavipinnis | LC | POLYGON ((159.58249191 -21.61306719099997, 159... |
| 204 | 183653.0 | Chromis atrilobata | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | atrilobata | LC | POLYGON ((-90.09187344499998 13.72481517500006... |
| 205 | 183653.0 | Chromis atrilobata | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | atrilobata | LC | POLYGON ((-113.422749196 30.12412492800007, -1... |
| 206 | 183653.0 | Chromis atrilobata | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | atrilobata | LC | POLYGON ((-106.72576642 23.59774532600005, -10... |
| 207 | 183653.0 | Chromis atrilobata | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | atrilobata | LC | POLYGON ((-86.13105102199995 5.598867493000057... |
| 208 | 183653.0 | Chromis atrilobata | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Chromis | atrilobata | LC | POLYGON ((-111.159618439 19.01535626700007, -1... |
| 213 | 183431.0 | Stegastes acapulcoensis | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | acapulcoensis | LC | POLYGON ((-90.09187344499998 13.72481517500006... |
| 214 | 183431.0 | Stegastes acapulcoensis | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | acapulcoensis | LC | POLYGON ((-106.726318976 23.59928014100007, -1... |
| 215 | 183431.0 | Stegastes acapulcoensis | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | acapulcoensis | LC | POLYGON ((-109.2706279609999 26.29294047400003... |
| 216 | 183431.0 | Stegastes acapulcoensis | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | acapulcoensis | LC | POLYGON ((-86.13105102199995 5.598867493000057... |
| 217 | 183431.0 | Stegastes acapulcoensis | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Stegastes | acapulcoensis | LC | POLYGON ((-111.170232026 19.01577707400003, -1... |
| 222 | 183367.0 | Hypsypops rubicundus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Hypsypops | rubicundus | LC | POLYGON ((-124.1523361 41.05706766400004, -124... |
| 223 | 183367.0 | Hypsypops rubicundus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Hypsypops | rubicundus | LC | POLYGON ((-117.4136322419999 29.18823463600006... |
| 224 | 183367.0 | Hypsypops rubicundus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Hypsypops | rubicundus | LC | POLYGON ((-117.330311632 21.41966897300006, -1... |
| 225 | 183367.0 | Hypsypops rubicundus | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Hypsypops | rubicundus | LC | POLYGON ((-110.714172363 25.30718421900008, -1... |
| 226 | 183774.0 | Azurina hirundo | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Azurina | hirundo | NT | POLYGON ((-120.118286133 34.47283172600004, -1... |
| 227 | 183774.0 | Azurina hirundo | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Azurina | hirundo | NT | POLYGON ((-117.4136322419999 29.18823463600006... |
| 228 | 183774.0 | Azurina hirundo | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Azurina | hirundo | NT | POLYGON ((-114.638387688 28.39007998000005, -1... |
| 229 | 183774.0 | Azurina hirundo | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Azurina | hirundo | NT | POLYGON ((-111.159618439 19.01535626700007, -1... |
| 230 | 183774.0 | Azurina hirundo | 1 | IUCN | 2010 | International Union for Conservation of Nature... | None | None | None | None | ... | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Azurina | hirundo | NT | POLYGON ((-114.244282766 22.71134688500007, -1... |
122 rows × 24 columns
In [9]:
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type(grouped), type(grouped['BINOMIAL'])
type(grouped), type(grouped['BINOMIAL'])
Out[9]:
(pandas.core.groupby.DataFrameGroupBy, pandas.core.groupby.SeriesGroupBy)
Split by column value and write to disk¶
Create output folder is not available
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!mkdir './data/fish_split'
!mkdir './data/fish_split'
We can iterate over the DataFrameGroupBy object and perform aggregations like std, mean, sum etc. However, here, we can split by column and write each individual fish species to its own shape file.
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import os
for key, value in grouped:
# create output name using key. Replace space with _
out_name = '{}.shp'.format(key.replace(" ","_"))
print("Writing: " + out_name)
#write to disk using geopandas
value.to_file(os.path.join("./data/fish_split", out_name))
import os
for key, value in grouped:
# create output name using key. Replace space with _
out_name = '{}.shp'.format(key.replace(" ","_"))
print("Writing: " + out_name)
#write to disk using geopandas
value.to_file(os.path.join("./data/fish_split", out_name))
Writing: Abudefduf_concolor.shp Writing: Abudefduf_declivifrons.shp Writing: Abudefduf_troschelii.shp Writing: Amphiprion_sandaracinos.shp Writing: Azurina_eupalama.shp Writing: Azurina_hirundo.shp Writing: Chromis_alpha.shp Writing: Chromis_alta.shp Writing: Chromis_atrilobata.shp Writing: Chromis_crusma.shp Writing: Chromis_cyanea.shp Writing: Chromis_flavicauda.shp Writing: Chromis_intercrusma.shp Writing: Chromis_limbaughi.shp Writing: Chromis_pembae.shp Writing: Chromis_punctipinnis.shp Writing: Chrysiptera_flavipinnis.shp Writing: Hypsypops_rubicundus.shp Writing: Microspathodon_bairdii.shp Writing: Microspathodon_dorsalis.shp Writing: Nexilosus_latifrons.shp Writing: Stegastes_acapulcoensis.shp Writing: Stegastes_arcifrons.shp Writing: Stegastes_baldwini.shp Writing: Stegastes_beebei.shp Writing: Stegastes_flavilatus.shp Writing: Stegastes_leucorus.shp Writing: Stegastes_rectifraenum.shp Writing: Stegastes_redemptus.shp Writing: Teixeirichthys_jordani.shp
Plot the last fish to see if its a smaller area than original:
In [14]:
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print(key)
value.plot()
print(key)
value.plot()
Teixeirichthys jordani
Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x110808eb8>
Dissolve polygons¶
An option is to dissolve each of the individual polygons into one for each fish. This way we can measure the area of each species. This is a proper geoprocessing implemented from Shapely lib.
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grouped.aggregate()
grouped.aggregate()
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damsel_gdf_dissolved = damsel_gdf.dissolve(by='BINOMIAL', aggfunc='first')
damsel_gdf_dissolved.shape
damsel_gdf_dissolved = damsel_gdf.dissolve(by='BINOMIAL', aggfunc='first')
damsel_gdf_dissolved.shape
Out[18]:
(30, 23)
This took a while, but we managed to shrink 231 features to 30 features. Let us plot and write it to disk for future processing.
In [20]:
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damsel_gdf_dissolved.head(3)
damsel_gdf_dissolved.head(3)
Out[20]:
| geometry | ID_NO | ORIGIN | COMPILER | YEAR | CITATION | SOURCE | DIST_COMM | ISLAND | SUBSPECIES | ... | TAX_COMM | RL_UPDATE | KINGDOM_NA | PHYLUM_NAM | CLASS_NAME | ORDER_NAME | FAMILY_NAM | GENUS_NAME | SPECIES_NA | CATEGORY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BINOMIAL | |||||||||||||||||||||
| Abudefduf concolor | (POLYGON ((-86.13105102199995 5.59886749300005... | 183483.0 | 1 | IUCN | 2010 | International Union for Conservation of Nature... | NaN | NaN | NaN | NaN | ... | NaN | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | concolor | LC |
| Abudefduf declivifrons | (POLYGON ((-90.09187344499998 13.7248151750000... | 183460.0 | 1 | IUCN | 2010 | International Union for Conservation of Nature... | NaN | NaN | NaN | NaN | ... | NaN | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | declivifrons | LC |
| Abudefduf troschelii | (POLYGON ((-91.24864959699994 -0.6798474789999... | 183397.0 | 1 | IUCN | 2010 | International Union for Conservation of Nature... | NaN | NaN | NaN | NaN | ... | NaN | 2012.1 | ANIMALIA | CHORDATA | ACTINOPTERYGII | PERCIFORMES | POMACENTRIDAE | Abudefduf | troschelii | LC |
3 rows × 23 columns
Make a choropleth map¶
Since the dataframe is reshaped by applying the categorical column as the index, I just create a simple choropleth map. Simply specify a cmap and that does the trick
In [24]:
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damsel_gdf_dissolved.plot(cmap='Paired', figsize=(12,4))
damsel_gdf_dissolved.plot(cmap='Paired', figsize=(12,4))
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1eb3feb8>
Export to disk¶
In [25]:
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damsel_gdf_dissolved.to_file('./data/damselfish/damselfish_dissolved.shp')
damsel_gdf_dissolved.to_file('./data/damselfish/damselfish_dissolved.shp')
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