Returning lat and long of centroid point with GeoPandas

Question

I would like to return the lat, lon or x, y from the point object in the following series as I plan to link them with an API I built that links OSM and NetworkX. The centroids will be added as new nodes for network analysis.

gp.GeoSeries(zones.centroid).x, and gp.GeoSeries(zones.centroid).y as outlined in docs raise the following error:

AttributeError: 'GeoSeries' object has no attribute 'x'

Modifying things a bit and printing list(gp.GeoSeries(zones.centroid)) return thousands of shapely points of the following format:

[... <shapely.geometry.point.Point object at 0x0000000024035940>,
<shapely.geometry.point.Point object at 0x0000000024035978>, 
<shapely.geometry.point.Point object at 0x00000000240359B0>, 
<shapely.geometry.point.Point object at 0x00000000240359E8>, 
<shapely.geometry.point.Point object at 0x0000000024035A20>, 
<shapely.geometry.point.Point object at 0x0000000024035A58>, 
<shapely.geometry.point.Point object at 0x0000000024035A90>, 
<shapely.geometry.point.Point object at 0x0000000024035AC8>]

The code I'm using is the following:

import geopandas as gp
zones = gp.GeoDataFrame.from_file(shp_file)
for index, row in zones.iterrows():
    print index, gp.GeoSeries(zones.centroid)
result:
9700022.00    POINT (-122.8196050489696 54.00617624128658)
9700023.00    POINT (-122.7474362519174 53.99998921974029)
9700100.00    POINT (-121.4904983300892 53.98447191612864)
9700101.00    POINT (-122.5513619751679 53.73999791511078)
9700102.00    POINT (-123.0624037191615 53.62317549646422)
9700103.00    POINT (-123.0848175548173 54.05921695782788)

How can I return the x, y from the GeoPandas POINT object?

Answer 1

Ran into this problem myself. If you want the x and y as separate GeoDataFrame columns, then this works nicely:

gdf["x"] = gdf.centroid.map(lambda p: p.x)
gdf["y"] = gdf.centroid.map(lambda p: p.y)

Starting with GeoPandas 0.3.0, you can use the provided x and y properties instead:

gdf["x"] = gdf.centroid.x
gdf["y"] = gdf.centroid.y

Answer 2

Leaving the rest below, but the main thing was accessing the geometry properly. If iterating over rows, e.g. for index, row in zones.iterrows(): you can simply use row.geometry.centroid.x and row.geometry.centroid.y. Geometry is a special column included in a GeoDataFrame, so every row has a geometry attribute.
You are accessing that attribute, which contains a shapely object. That shapely object will have an attribute, centroid that, in turn contains a shapely.geometry.Point, which has attributes x and y, finally giving you the properties you want.

---

(This part was the original effort to get to x,y with map and shapely.geometry.Point.)
I am going to assume you want a list of (x, y) tuples? Create a quick accessor function for the x and y attributes on a Point and use map.

Edit: Okay, figured out that you may be accessing the geometry in the GeoDataFrame in an incorrect way. Geometry is a column in your GeoDataFrame, which by itself produces a series. Calling centroid on that column should give you a new GeoSeries of only those centroids. I suspect the way you were going about things was taking the centroid of every vertex in each polygon. Still cannot test this since I cannot install GeoPandas right now.

def getXY(pt):
    return (pt.x, pt.y)
centroidseries = zones['geometry'].centroid
centroidlist = map(getXY, centroidseries)

or if you want two separate lists of x and y coordinates

def getXY(pt):
    return (pt.x, pt.y)
centroidseries = zones['geometry'].centroid
x,y = [list(t) for t in zip(*map(getXY, centroidseries))]

Alternately, you should also be able to use zones.geometry.centroid instead of zones['geometry'].centroid. Either way, I think calling zones.centroid may be returning a GeoDataFrame instead of a GeoSeries, giving you the unexpected output when you wrap it in another GeoSeries.

Answer 3

This has been made easier as of GeoPandas 0.3.0.

You can now access x and y of shapely Points inside a geopandas GeoSeries using your_GeoDataFrame.geometry.x and your_GeoDataFrame.geometry.y

(Note : I'm using python 3.6.1, not sure about behavior in 2.7, sorry)

Source on github

Answer 4

The solution to extract the center point (latitude and longitude) from the polygon and multi-polygon.

import geopandas as gpd
df = gpd.read_file(path + 'df.geojson')
#Find the center point
df['Center_point'] = df['geometry'].centroid
#Extract lat and lon from the centerpoint
df["lat"] = df.Center_point.map(lambda p: p.x)
df["long"] = df.Center_point.map(lambda p: p.y)

Answer 5

if you just want a numpy array of centroids:

centroids = np.vstack([df.centroid.x, df.centroid.y]).T

or as a dataframe with extra columns (i.e name for example):

pd.DataFrame(np.vstack([df.name, df.centroid.x, df.centroid.y]).T, columns=['name', 'x', 'y'])

ex.

   name                                           geometry
0     1  POLYGON ((0.00000 1.00000, 0.00000 0.00000, 1....
1     2  POLYGON ((2.00000 1.00000, 2.00000 0.00000, 3....

# ==>
# np.vstack([df.centroid.x, df.centroid.y]).T
#    array([[0.5, 0.5],
#           [2.5, 0.5]])

Answer 6

I ran into a similar problem, only using polygon geometries and found this solution worked well for me, mind you it's using Python-3.6 so it may not work for Python-2.7.

import geopandas as gpd

zones = gpd.read_file('file_to_read.shp')

for i in range(0,len(zones)):
    zones.loc[i,'centroid_lon'] = zones.geometry.centroid.x.iloc[i]
    zones.loc[i,'centroid_lat'] = zones.geometry.centroid.y.iloc[i]

Answer 7

Here's a quick

df = pd.read_csv('data/hospitals.csv')
df.columns # 'Ownership', 'Lat', 'Long'
from shapely.geometry import Point
df['geometry'] = df.apply(lambda row: Point(row.Long,row.Lat),axis=1)

Then recreate the dataframe with geopandas

gdf = gpd.DataFrame(df) # creates geodataframe with Point geometry

Answer 8

This one worked for me:

import numpy as np
test = geopandas.read_file("test.geojson")`
centroids = np.column_stack((test.centroid.x, test.centroid.y)))