I have the polygon combination of lat-long1,lat2-long2 ..... and point like Lat - Long .
I have used GeoPandas library to get the result if there is any point is exist within polygon.
Sample Data of Polygon saved in csv file:
POLYGON((28.56056 77.36535,28.564635293716776
77.3675137204626,28.56871055311656 77.36967760850214,28.572785778190855 77.3718416641586,28.576860968931193 77.37400588747194,28.580936125329096 77.3761702784821,28.585011247376094 77.37833483722912,28.58908633506372 77.38049956375293,28.593161388383457 77.38266445809356,28.59723640732686 77.38482952029099,28.60131139188541 77.38699475038526,28.605386342050664 77.38916014841635,28.60946125781409 77.39132571442434,28.613536139167238 77.39349144844923,28.61761098610158 77.39565735053108,28.62168579860863 77.39782342070995,28.62576057667991 77.39998965902589,28.62983532030691 77.402156065519,28.633910029481108 77.40432264022931,28.637984704194054 77.40648938319696,28.642059344437207 77.408656294462,28.64068221074683 77.41187044231611,28.63920739580329 77.41502778244606,28.63763670052024 77.41812446187686,28.635972042808007 77.42115670220443,28.634215455216115 77.42412080422613,28.63236908243526 77.42701315247152,28.630435178662026 77.42983021962735,28.628416104829583 77.43256857085188,28.626314325707924 77.43522486797251,28.624132406877322 77.437795873562,28.621873011578572 77.44027845488824,28.619538897444272 77.4426695877325,28.617132913115164 77.44496636007166,28.614657994745563 77.44716597562005,28.612117162402576 77.44926575722634,28.609513516363293 77.45126315012166,28.606850233314923 77.45315572501488,28.604130562462267 77.45494118103147,28.60135782154758 77.45661734849246,28.598535392787774 77.45818219153013,28.595666718733966 77.45963381053753,28.592755298058414 77.46097044444889,28.589804681274302 77.46219047284835,28.586818466393503 77.46329241790465,28.583800294527727 77.46427494612952,28.58075384543836 77.46513686995802,28.57768283304089 77.46587714914885,28.574591000868892 77.4664948920035,28.571482117503592 77.46698935640259,28.568359971974488 77.46735995065883,28.565228369136484 77.46760623418534,28.56209112502966 77.4677279179792,28.558952062226695 77.4677248649196,28.55581500517431 77.46759708988064,28.552683775533943 77.46734475965891,28.552683775533943 77.46734475965891,28.553079397193876 77.4622453846313,28.553474828308865 77.45714597129259,28.55387006887434 77.4520465196603,28.554265118885752 77.44694702975198,28.554659978338513 77.4418475015852,28.555054647228083 77.43674793517746,28.555449125549913 77.43164833054634,28.555843413299442 77.42654868770937,28.55623751047213 77.42144900668411,28.556631417063407 77.41634928748812,28.55702513306874 77.41124953013893,28.55741865848359 77.40614973465412,28.557811993303396 77.40104990105122,28.55820513752363 77.39595002934782,28.558598091139757 77.39085011956145,28.558990854147225 77.38575017170969,28.559383426541523 77.3806501858101,28.559775808318093 77.37555016188024,28.560167999472434 77.37045009993768,28.56056 77.36535))
and second dataset is for LAT and LONG as 28.56282, 77.36824 respectively saved in csv file .
I have used below Python code to join both data set based on condition if point exist within polygon. like below
import pandas as pd
import shapely.geometry
from shapely.geometry import Point
import geopandas as gpd
site_df = pd.read_csv (r'lat_long_file.csv') # load lat and long file
site_df['geometry'] = pd.DataFrame(site_df).apply(lambda x: Point(x.LAT,x.LONG), axis='columns') # convert lat and long to point
gdf = gpd.GeoDataFrame(site_df, geometry = site_df.geometry,crs='EPSG:4326') #creating geo pandas data frame for point
from shapely import wkt
polygon_df = pd.read_csv (r'polygon_csv_file') #reading polygon sample raw string file
polygon_df['geometry'] = pd.DataFrame(polygon_df).apply(lambda row: shapely.wkt.loads(row.polygon), axis='columns') #converting string polygon to geometory
gd_polygon = gpd.GeoDataFrame(polygon_df, geometry = polygon_df.geometry,crs='EPSG:4326') #create geopandas dataframe
import shapely.speedups
shapely.speedups.enable() # this makes some spatial queries run faster
join_data = gpd.sjoin(gdf, gd_polygon, how="inner", op="within") //actual join condition
But that query does not retun anything . But point is exist within polygon. as we can see in below diagram
Green Location marker is point Lat and long which is exist within polygon.
I would check the axis order - WKT usually interpreted as longitude first, latitude second order, while the point you construct uses latitude:longitude order.
You can try removing the CRS identifier to see if it changes the result.
Also see
https://gis.stackexchange.com/questions/376751/shapely-flips-lat-long-coordinate
and
https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
your sample data is unusable as it's an image
have sourced a polygon - a county boundary in UK
constructed a geopandas data frame of a point that is within this county
have used plotly to demonstrate visually the data
have used your code fragment gpd.sjoin(gdf, gd_polygon, how="inner", op="within") to do spatial join and it correctly joins point to polygon
import requests, json
import geopandas as gpd
import plotly.express as px
import shapely.geometry
# fmt: off
# get a polygon and construct a point
res = requests.get("https://opendata.arcgis.com/datasets/69dc11c7386943b4ad8893c45648b1e1_0.geojson")
gd_polygon = gpd.GeoDataFrame.from_features(res.json()).loc[lambda d: d["LAD20NM"].str.contains("Hereford")]
gdf = gpd.GeoDataFrame(geometry=gd_polygon.loc[:,["LONG","LAT"]].apply(shapely.geometry.Point, axis=1)).reset_index(drop=True)
# fmt: on
# plot to show point is within polygon
px.scatter_mapbox(gd_polygon, lon="LONG", lat="LAT").update_traces(
name="gd_polygon"
).add_traces(
px.scatter_mapbox(gdf, lat=gdf2.geometry.y, lon=gdf2.geometry.x)
.update_traces(name="gdf", marker_color="red")
.data
).update_traces(
showlegend=True
).update_layout(
mapbox={
"style": "carto-positron",
"layers": [
{"source": json.loads(gd_polygon.geometry.to_json()), "type": "line"}
],
}
).show()
# spatial join, all good :-)
gpd.sjoin(gdf, gd_polygon, how="inner", op="within")
output
spatial join has worked, point is within polygon
geometry
index_right
OBJECTID
LAD20CD
LAD20NM
LAD20NMW
BNG_E
BNG_N
LONG
LAT
Shape__Area
Shape__Length
0
POINT (-2.73931 52.081539)
18
19
E06000019
Herefordshire, County of
349434
242834
-2.73931
52.0815
2.18054e+09
285427
I am pretty new with Python and I need some help.
I need to find the grid cells in the precipitation file (.nc) that matches the locations of water flow stations (excel file) and then extract time series for these grid cells.
I have a Exel file with 117 stations in Norway that contains columns with station name and their areal, latitude and longitude.
I also have a nc file with precipitation series for this stations.
I manage to run a python script (Jupyter notebook) for on station at a time, but want to run it for all stations.
How do i do this? I know I need to make a for loop some how.
This is my code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os
import xarray as xr
import cartopy.crs as ccrs
import cartopy as cy
metapath = "Minestasjoner.xlsx"
rrdatapath = "cropped_monsum_rr_ens_mean_0.25deg_reg_v20.0e.nc"
meta = pd.read_excel(metapath)
rrdata = xr.open_dataset(rrdatapath)
i=0
station = meta.iloc[i]["Regime"]*100000 + meta.iloc[i]["Main_nr"]
lon = meta.iloc[i]["Longitude"] #get longitude
lat = meta.iloc[i]["Latitude"] #get latitude
rr_at_obsloc = rrdata["rr"].sel(latitude=lat, longitude=lon, method='nearest')
df = rr_at_obsloc.to_dataframe()
print("Station %s with lon=%.2f and lat=%.2f have closest rr gridcell at lon=%.2f and lat=%.2f"%(station,lon,lat,df.longitude[0],df.latitude[0]))
df
I think the easiest way for you to do this is to make a python dictionary containing the station name and precipitation time-series for that station, and then to convert that dictionary to a pandas.DataFrame.
Here's how you do that in a simple loop:
"""
Everything you had previously...
"""
# Initialize empty dictionary to hold station names and time-series
station_name_and_data = {}
# Loop over all stations
for i in range(117):
# Get name of station 'i'
station = meta.iloc[i]["Regime"]*100000 + meta.iloc[i]["Main_nr"]
# Get lat/lon of station 'i'
lon = meta.iloc[i]["Longitude"]
lat = meta.iloc[i]["Latitude"]
# Extract precip time-series for this lat-lon
rr_at_obsloc = rrdata["rr"].sel(latitude=lat, longitude=lon, method='nearest')
# Put this station name and it's relevant time-series into a dictionary
station_name_and_data[station]=rr_at_obsloc
# Finally, convert this dictionary to a pandas dataframe
df = pd.DataFrame(data=station_name_and_data)
print(df)
I'm working with datasets where latitudes and longitudes are sometimes mislabeled and I need to flip the longitudes and the latitudes. The best solution I could come up with is to extract the x an y coordinates using df.geometry.x and df.geometry.y, create a new geometry column, and reconstruct the GeoDataFrame using the new geometry column. Or in code form:
import geopandas
from shapely.geometry import Point
gdf['coordinates'] = list(zip(gdf.geometry.y, gdf.geometry.x))
gdf['coordinates'] = gdf['coordinates'].apply(Point)
gdf= gpd.GeoDataFrame(point_data, geometry='coordinates', crs = 4326)
This is pretty ugly, requires creating a new column and isn't efficient for large datasets. Is there an easier way to flip the longitude and latitude coordinates of a GeoSeries/ GeoDataFrame?
You can create the geometry column directly:
df['geometry'] = df.apply(lambda row: Point(row['y'], row['x']), axis=1)
df = gpd.GeoDataFrame(df, crs=4326)
It works for Point and Polygon either:
gpd.GeoSeries(gdf['coordinates']).map(lambda polygon: shapely.ops.transform(lambda x, y: (y, x), polygon))
I would like to extract the values of the coordinate variables.
For example I create a DataArray as:
import xarray as xr
import numpy as np
import pandas as pd
years_arr=range(1982,1986)
time = pd.date_range('14/1/' + str(years_arr[0]) + ' 12:00:00', periods=len(years_arr), freq=pd.DateOffset(years=1))
lon = range(20,24)
lat = range(10,14)
arr1 = xr.DataArray(data, coords=[time, lat, lon], dims=['time', 'latitude', 'longitude'])
I now would like to output the lon values from arr1.
I'm asking as arr1 going into a function so I may not have the lon values available.
arr1.coords['lon'] gives you longitude as a xarray.DataArray, and arr1.coords['lon'].values gives you the values as a numpy array.
Another possible solution is:
time, lat, lon = arr1.indexes.values()
The result is a Float64Index for your lat/lon coordinates.
I have a dataframe with earthquake data called eq that has columns listing latitude and longitude. using geopandas I created a point column with the following:
from geopandas import GeoSeries, GeoDataFrame
from shapely.geometry import Point
s = GeoSeries([Point(x,y) for x, y in zip(df['longitude'], df['latitude'])])
eq['geometry'] = s
eq.crs = {'init': 'epsg:4326', 'no_defs': True}
eq
Now I have a geometry column with lat lon coordinates but I want to change the projection to UTM. Can anyone help with the transformation?
Latitude/longitude aren't really a projection, but sort of a default "unprojection". See this page for more details, but it probably means your data uses WGS84 or epsg:4326.
Let's build a dataset and, before we do any reprojection, we'll define the crs as epsg:4326
import geopandas as gpd
import pandas as pd
from shapely.geometry import Point
df = pd.DataFrame({'id': [1, 2, 3], 'population' : [2, 3, 10], 'longitude': [-80.2, -80.11, -81.0], 'latitude': [11.1, 11.1345, 11.2]})
s = gpd.GeoSeries([Point(x,y) for x, y in zip(df['longitude'], df['latitude'])])
geo_df = gpd.GeoDataFrame(df[['id', 'population']], geometry=s)
# Define crs for our geodataframe:
geo_df.crs = {'init': 'epsg:4326'}
I'm not sure what you mean by "UTM projection". From the wikipedia page I see there are 60 different UTM projections depending on the area of the world. You can find the appropriate epsg code online, but I'll just give you an example with a random epsgcode. This is the one for zone 33N for example
How do you do the reprojection? You can easily get this info from the geopandas docs on projection. It's just one line:
geo_df = geo_df.to_crs({'init': 'epsg:3395'})
and the geometry isn't coded as latitude/longitude anymore:
id population geometry
0 1 2 POINT (-8927823.161620541 1235228.11420853)
1 2 3 POINT (-8917804.407449147 1239116.84994171)
2 3 10 POINT (-9016878.754255159 1246501.097746004)