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import warnings
import pandas as pd
from geopandas import GeoDataFrame
from geopandas.array import _check_crs, _crs_mismatch_warn
def sjoin(
left_df, right_df, how="inner", op="intersects", lsuffix="left", rsuffix="right"
):
"""Spatial join of two GeoDataFrames.
Parameters
----------
left_df, right_df : GeoDataFrames
how : string, default 'inner'
The type of join:
* 'left': use keys from left_df; retain only left_df geometry column
* 'right': use keys from right_df; retain only right_df geometry column
* 'inner': use intersection of keys from both dfs; retain only
left_df geometry column
op : string, default 'intersects'
Binary predicate, one of {'intersects', 'contains', 'within'}.
See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.
lsuffix : string, default 'left'
Suffix to apply to overlapping column names (left GeoDataFrame).
rsuffix : string, default 'right'
Suffix to apply to overlapping column names (right GeoDataFrame).
"""
if not isinstance(left_df, GeoDataFrame):
raise ValueError(
"'left_df' should be GeoDataFrame, got {}".format(type(left_df))
)
if not isinstance(right_df, GeoDataFrame):
raise ValueError(
"'right_df' should be GeoDataFrame, got {}".format(type(right_df))
)
allowed_hows = ["left", "right", "inner"]
if how not in allowed_hows:
raise ValueError(
'`how` was "%s" but is expected to be in %s' % (how, allowed_hows)
)
allowed_ops = ["contains", "within", "intersects"]
if op not in allowed_ops:
raise ValueError(
'`op` was "%s" but is expected to be in %s' % (op, allowed_ops)
)
if not _check_crs(left_df, right_df):
_crs_mismatch_warn(left_df, right_df, stacklevel=3)
index_left = "index_%s" % lsuffix
index_right = "index_%s" % rsuffix
# due to GH 352
if any(left_df.columns.isin([index_left, index_right])) or any(
right_df.columns.isin([index_left, index_right])
):
raise ValueError(
"'{0}' and '{1}' cannot be names in the frames being"
" joined".format(index_left, index_right)
)
# query index
with warnings.catch_warnings():
# We don't need to show our own warning here
# TODO remove this once the deprecation has been enforced
warnings.filterwarnings(
"ignore", "Generated spatial index is empty", FutureWarning
)
if op == "within":
# within is implemented as the inverse of contains
# contains is a faster predicate
# see discussion at https://github.com/geopandas/geopandas/pull/1421
predicate = "contains"
sindex = left_df.sindex
input_geoms = right_df.geometry
else:
# all other predicates are symmetric
# keep them the same
predicate = op
sindex = right_df.sindex
input_geoms = left_df.geometry
if sindex:
l_idx, r_idx = sindex.query_bulk(input_geoms, predicate=predicate, sort=False)
result = pd.DataFrame({"_key_left": l_idx, "_key_right": r_idx})
else:
# when sindex is empty / has no valid geometries
result = pd.DataFrame(columns=["_key_left", "_key_right"], dtype=float)
if op == "within":
# within is implemented as the inverse of contains
# flip back the results
result = result.rename(
columns={"_key_left": "_key_right", "_key_right": "_key_left"}
)
# the spatial index only allows limited (numeric) index types, but an
# index in geopandas may be any arbitrary dtype. so reset both indices now
# and store references to the original indices, to be reaffixed later.
# GH 352
left_df = left_df.copy(deep=True)
try:
left_index_name = left_df.index.name
left_df.index = left_df.index.rename(index_left)
except TypeError:
index_left = [
"index_%s" % lsuffix + str(pos)
for pos, ix in enumerate(left_df.index.names)
]
left_index_name = left_df.index.names
left_df.index = left_df.index.rename(index_left)
left_df = left_df.reset_index()
right_df = right_df.copy(deep=True)
try:
right_index_name = right_df.index.name
right_df.index = right_df.index.rename(index_right)
except TypeError:
index_right = [
"index_%s" % rsuffix + str(pos)
for pos, ix in enumerate(right_df.index.names)
]
right_index_name = right_df.index.names
right_df.index = right_df.index.rename(index_right)
right_df = right_df.reset_index()
# perform join on the dataframes
if how == "inner":
result = result.set_index("_key_left")
joined = (
left_df.merge(result, left_index=True, right_index=True)
.merge(
right_df.drop(right_df.geometry.name, axis=1),
left_on="_key_right",
right_index=True,
suffixes=("_%s" % lsuffix, "_%s" % rsuffix),
)
.set_index(index_left)
.drop(["_key_right"], axis=1)
)
if isinstance(index_left, list):
joined.index.names = left_index_name
else:
joined.index.name = left_index_name
elif how == "left":
result = result.set_index("_key_left")
joined = (
left_df.merge(result, left_index=True, right_index=True, how="left")
.merge(
right_df.drop(right_df.geometry.name, axis=1),
how="left",
left_on="_key_right",
right_index=True,
suffixes=("_%s" % lsuffix, "_%s" % rsuffix),
)
.set_index(index_left)
.drop(["_key_right"], axis=1)
)
if isinstance(index_left, list):
joined.index.names = left_index_name
else:
joined.index.name = left_index_name
else: # how == 'right':
joined = (
left_df.drop(left_df.geometry.name, axis=1)
.merge(
result.merge(
right_df, left_on="_key_right", right_index=True, how="right"
),
left_index=True,
right_on="_key_left",
how="right",
)
.set_index(index_right)
.drop(["_key_left", "_key_right"], axis=1)
)
if isinstance(index_right, list):
joined.index.names = right_index_name
else:
joined.index.name = right_index_name
return joined
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