dadams/california-equity-git
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

venv

Browse Source
This commit is contained in:
David Adams
2025-01-26 19:24:23 -08:00
parent 32cd60e92b
commit d1dde0dbc6
4155 changed files with 29170 additions and 216373 deletions
Download Patch File Download Diff File Expand all files Collapse all files

591
.venv/lib/python3.12/site-packages/geopandas/tools/sjoin.py
View File

@@ -1,11 +1,12 @@
from typing import Optional
import warnings
from functools import partial
from typing import Optional
import numpy as np
import pandas as pd
from geopandas import GeoDataFrame
from geopandas import _compat as compat
from geopandas._compat import PANDAS_GE_30
from geopandas.array import _check_crs, _crs_mismatch_warn
@@ -16,6 +17,8 @@ def sjoin(
predicate="intersects",
lsuffix="left",
rsuffix="right",
distance=None,
on_attribute=None,
**kwargs,
):
"""Spatial join of two GeoDataFrames.
@@ -43,6 +46,16 @@ def sjoin(
Suffix to apply to overlapping column names (left GeoDataFrame).
rsuffix : string, default 'right'
Suffix to apply to overlapping column names (right GeoDataFrame).
distance : number or array_like, optional
Distance(s) around each input geometry within which to query the tree
for the 'dwithin' predicate. If array_like, must be
one-dimesional with length equal to length of left GeoDataFrame.
Required if ``predicate='dwithin'``.
on_attribute : string, list or tuple
Column name(s) to join on as an additional join restriction on top
of the spatial predicate. These must be found in both DataFrames.
If set, observations are joined only if the predicate applies
and values in specified columns match.
Examples
--------
@@ -74,12 +87,12 @@ def sjoin(
>>> groceries_w_communities = geopandas.sjoin(groceries, chicago)
>>> groceries_w_communities.head() # doctest: +SKIP
OBJECTID Ycoord Xcoord ... GonorrF GonorrM Tuberc
0 16 41.973266 -87.657073 ... 170.8 468.7 13.6
87 365 41.961707 -87.654058 ... 170.8 468.7 13.6
90 373 41.963131 -87.656352 ... 170.8 468.7 13.6
140 582 41.969131 -87.674882 ... 170.8 468.7 13.6
1 18 41.696367 -87.681315 ... 800.5 741.1 2.6
OBJECTID community geometry
0 16 UPTOWN MULTIPOINT ((-87.65661 41.97321))
1 18 MORGAN PARK MULTIPOINT ((-87.68136 41.69713))
2 22 NEAR WEST SIDE MULTIPOINT ((-87.63918 41.86847))
3 23 NEAR WEST SIDE MULTIPOINT ((-87.65495 41.87783))
4 27 CHATHAM MULTIPOINT ((-87.62715 41.73623))
[5 rows x 95 columns]
See also
@@ -92,40 +105,42 @@ def sjoin(
Every operation in GeoPandas is planar, i.e. the potential third
dimension is not taken into account.
"""
if "op" in kwargs:
op = kwargs.pop("op")
deprecation_message = (
"The `op` parameter is deprecated and will be removed"
" in a future release. Please use the `predicate` parameter"
" instead."
)
if predicate != "intersects" and op != predicate:
override_message = (
"A non-default value for `predicate` was passed"
f' (got `predicate="{predicate}"`'
f' in combination with `op="{op}"`).'
" The value of `predicate` will be overridden by the value of `op`,"
" , which may result in unexpected behavior."
f"\n{deprecation_message}"
)
warnings.warn(override_message, UserWarning, stacklevel=4)
else:
warnings.warn(deprecation_message, FutureWarning, stacklevel=4)
predicate = op
if kwargs:
first = next(iter(kwargs.keys()))
raise TypeError(f"sjoin() got an unexpected keyword argument '{first}'")
_basic_checks(left_df, right_df, how, lsuffix, rsuffix)
on_attribute = _maybe_make_list(on_attribute)
indices = _geom_predicate_query(left_df, right_df, predicate)
_basic_checks(left_df, right_df, how, lsuffix, rsuffix, on_attribute=on_attribute),
joined = _frame_join(indices, left_df, right_df, how, lsuffix, rsuffix)
indices = _geom_predicate_query(
left_df, right_df, predicate, distance, on_attribute=on_attribute
)
joined, _ = _frame_join(
left_df,
right_df,
indices,
None,
how,
lsuffix,
rsuffix,
predicate,
on_attribute=on_attribute,
)
return joined
def _basic_checks(left_df, right_df, how, lsuffix, rsuffix):
def _maybe_make_list(obj):
if isinstance(obj, tuple):
return list(obj)
if obj is not None and not isinstance(obj, list):
return [obj]
return obj
def _basic_checks(left_df, right_df, how, lsuffix, rsuffix, on_attribute=None):
"""Checks the validity of join input parameters.
`how` must be one of the valid options.
@@ -142,6 +157,8 @@ def _basic_checks(left_df, right_df, how, lsuffix, rsuffix):
left index suffix
rsuffix : str
right index suffix
on_attribute : list, default None
list of column names to merge on along with geometry
"""
if not isinstance(left_df, GeoDataFrame):
raise ValueError(
@@ -162,20 +179,28 @@ def _basic_checks(left_df, right_df, how, lsuffix, rsuffix):
if not _check_crs(left_df, right_df):
_crs_mismatch_warn(left_df, right_df, stacklevel=4)
index_left = "index_{}".format(lsuffix)
index_right = "index_{}".format(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)
)
if on_attribute:
for attr in on_attribute:
if (attr not in left_df) and (attr not in right_df):
raise ValueError(
f"Expected column {attr} is missing from both of the dataframes."
)
if attr not in left_df:
raise ValueError(
f"Expected column {attr} is missing from the left dataframe."
)
if attr not in right_df:
raise ValueError(
f"Expected column {attr} is missing from the right dataframe."
)
if attr in (left_df.geometry.name, right_df.geometry.name):
raise ValueError(
"Active geometry column cannot be used as an input "
"for on_attribute parameter."
)
def _geom_predicate_query(left_df, right_df, predicate):
def _geom_predicate_query(left_df, right_df, predicate, distance, on_attribute=None):
"""Compute geometric comparisons and get matching indices.
Parameters
@@ -184,6 +209,9 @@ def _geom_predicate_query(left_df, right_df, predicate):
right_df : GeoDataFrame
predicate : string
Binary predicate to query.
on_attribute: list, default None
list of column names to merge on along with geometry
Returns
-------
@@ -191,163 +219,292 @@ def _geom_predicate_query(left_df, right_df, predicate):
DataFrame with matching indices in
columns named `_key_left` and `_key_right`.
"""
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
)
original_predicate = predicate
original_predicate = predicate
if predicate == "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
sindex = right_df.sindex
input_geoms = left_df.geometry
if predicate == "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
sindex = right_df.sindex
input_geoms = left_df.geometry
if sindex:
l_idx, r_idx = sindex.query(input_geoms, predicate=predicate, sort=False)
indices = pd.DataFrame({"_key_left": l_idx, "_key_right": r_idx})
l_idx, r_idx = sindex.query(
input_geoms, predicate=predicate, sort=False, distance=distance
)
else:
# when sindex is empty / has no valid geometries
indices = pd.DataFrame(columns=["_key_left", "_key_right"], dtype=float)
l_idx, r_idx = np.array([], dtype=np.intp), np.array([], dtype=np.intp)
if original_predicate == "within":
# within is implemented as the inverse of contains
# flip back the results
indices = indices.rename(
columns={"_key_left": "_key_right", "_key_right": "_key_left"}
r_idx, l_idx = l_idx, r_idx
indexer = np.lexsort((r_idx, l_idx))
l_idx = l_idx[indexer]
r_idx = r_idx[indexer]
if on_attribute:
for attr in on_attribute:
(l_idx, r_idx), _ = _filter_shared_attribute(
left_df, right_df, l_idx, r_idx, attr
)
return l_idx, r_idx
def _reset_index_with_suffix(df, suffix, other):
"""
Equivalent of df.reset_index(), but with adding 'suffix' to auto-generated
column names.
"""
index_original = df.index.names
if PANDAS_GE_30:
df_reset = df.reset_index()
else:
# we already made a copy of the dataframe in _frame_join before getting here
df_reset = df
df_reset.reset_index(inplace=True)
column_names = df_reset.columns.to_numpy(copy=True)
for i, label in enumerate(index_original):
# if the original label was None, add suffix to auto-generated name
if label is None:
new_label = column_names[i]
if "level" in new_label:
# reset_index of MultiIndex gives "level_i" names, preserve the "i"
lev = new_label.split("_")[1]
new_label = f"index_{suffix}{lev}"
else:
new_label = f"index_{suffix}"
# check new label will not be in other dataframe
if new_label in df.columns or new_label in other.columns:
raise ValueError(
"'{0}' cannot be a column name in the frames being"
" joined".format(new_label)
)
column_names[i] = new_label
return df_reset, pd.Index(column_names)
def _process_column_names_with_suffix(
left: pd.Index, right: pd.Index, suffixes, left_df, right_df
):
"""
Add suffixes to overlapping labels (ignoring the geometry column).
This is based on pandas' merge logic at https://github.com/pandas-dev/pandas/blob/
a0779adb183345a8eb4be58b3ad00c223da58768/pandas/core/reshape/merge.py#L2300-L2370
"""
to_rename = left.intersection(right)
if len(to_rename) == 0:
return left, right
lsuffix, rsuffix = suffixes
if not lsuffix and not rsuffix:
raise ValueError(f"columns overlap but no suffix specified: {to_rename}")
def renamer(x, suffix, geometry):
if x in to_rename and x != geometry and suffix is not None:
return f"{x}_{suffix}"
return x
lrenamer = partial(
renamer,
suffix=lsuffix,
geometry=getattr(left_df, "_geometry_column_name", None),
)
rrenamer = partial(
renamer,
suffix=rsuffix,
geometry=getattr(right_df, "_geometry_column_name", None),
)
# TODO retain index name?
left_renamed = pd.Index([lrenamer(lab) for lab in left])
right_renamed = pd.Index([rrenamer(lab) for lab in right])
dups = []
if not left_renamed.is_unique:
# Only warn when duplicates are caused because of suffixes, already duplicated
# columns in origin should not warn
dups = left_renamed[(left_renamed.duplicated()) & (~left.duplicated())].tolist()
if not right_renamed.is_unique:
dups.extend(
right_renamed[(right_renamed.duplicated()) & (~right.duplicated())].tolist()
)
# TODO turn this into an error (pandas has done so as well)
if dups:
warnings.warn(
f"Passing 'suffixes' which cause duplicate columns {set(dups)} in the "
f"result is deprecated and will raise a MergeError in a future version.",
FutureWarning,
stacklevel=4,
)
return indices
return left_renamed, right_renamed
def _frame_join(join_df, left_df, right_df, how, lsuffix, rsuffix):
def _restore_index(joined, index_names, index_names_original):
"""
Set back the the original index columns, and restoring their name as `None`
if they didn't have a name originally.
"""
if PANDAS_GE_30:
joined = joined.set_index(list(index_names))
else:
joined.set_index(list(index_names), inplace=True)
# restore the fact that the index didn't have a name
joined_index_names = list(joined.index.names)
for i, label in enumerate(index_names_original):
if label is None:
joined_index_names[i] = None
joined.index.names = joined_index_names
return joined
def _adjust_indexers(indices, distances, original_length, how, predicate):
"""
The left/right indexers from the query represents an inner join.
For a left or right join, we need to adjust them to include the rows
that would not be present in an inner join.
"""
# the indices represent an inner join, no adjustment needed
if how == "inner":
return indices, distances
l_idx, r_idx = indices
if how == "right":
# re-sort so it is sorted by the right indexer
indexer = np.lexsort((l_idx, r_idx))
l_idx, r_idx = l_idx[indexer], r_idx[indexer]
if distances is not None:
distances = distances[indexer]
# switch order
r_idx, l_idx = l_idx, r_idx
# determine which indices are missing and where they would need to be inserted
idx = np.arange(original_length)
l_idx_missing = idx[~np.isin(idx, l_idx)]
insert_idx = np.searchsorted(l_idx, l_idx_missing)
# for the left indexer, insert those missing indices
l_idx = np.insert(l_idx, insert_idx, l_idx_missing)
# for the right indexer, insert -1 -> to get missing values in pandas' reindexing
r_idx = np.insert(r_idx, insert_idx, -1)
# for the indices, already insert those missing values manually
if distances is not None:
distances = np.insert(distances, insert_idx, np.nan)
if how == "right":
# switch back
l_idx, r_idx = r_idx, l_idx
return (l_idx, r_idx), distances
def _frame_join(
left_df,
right_df,
indices,
distances,
how,
lsuffix,
rsuffix,
predicate,
on_attribute=None,
):
"""Join the GeoDataFrames at the DataFrame level.
Parameters
----------
join_df : DataFrame
Indices and join data returned by the geometric join.
Must have columns `_key_left` and `_key_right`
with integer indices representing the matches
from `left_df` and `right_df` respectively.
Additional columns may be included and will be copied to
the resultant GeoDataFrame.
left_df : GeoDataFrame
right_df : GeoDataFrame
indices : tuple of ndarray
Indices returned by the geometric join. Tuple with with integer
indices representing the matches from `left_df` and `right_df`
respectively.
distances : ndarray, optional
Passed trough and adapted based on the indices, if needed.
how : string
The type of join to use on the DataFrame level.
lsuffix : string
Suffix to apply to overlapping column names (left GeoDataFrame).
rsuffix : string
Suffix to apply to overlapping column names (right GeoDataFrame).
how : string
The type of join to use on the DataFrame level.
on_attribute: list, default None
list of column names to merge on along with geometry
Returns
-------
GeoDataFrame
Joined GeoDataFrame.
"""
# 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
index_left = "index_{}".format(lsuffix)
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_{}".format(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()
if on_attribute: # avoid renaming or duplicating shared column
right_df = right_df.drop(on_attribute, axis=1)
index_right = "index_{}".format(rsuffix)
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_{}".format(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()
if how in ("inner", "left"):
right_df = right_df.drop(right_df.geometry.name, axis=1)
else: # how == 'right':
left_df = left_df.drop(left_df.geometry.name, axis=1)
left_df = left_df.copy(deep=False)
left_nlevels = left_df.index.nlevels
left_index_original = left_df.index.names
left_df, left_column_names = _reset_index_with_suffix(left_df, lsuffix, right_df)
right_df = right_df.copy(deep=False)
right_nlevels = right_df.index.nlevels
right_index_original = right_df.index.names
right_df, right_column_names = _reset_index_with_suffix(right_df, rsuffix, left_df)
# if conflicting names in left and right, add suffix
left_column_names, right_column_names = _process_column_names_with_suffix(
left_column_names,
right_column_names,
(lsuffix, rsuffix),
left_df,
right_df,
)
left_df.columns = left_column_names
right_df.columns = right_column_names
left_index = left_df.columns[:left_nlevels]
right_index = right_df.columns[:right_nlevels]
# perform join on the dataframes
if how == "inner":
join_df = join_df.set_index("_key_left")
joined = (
left_df.merge(join_df, left_index=True, right_index=True)
.merge(
right_df.drop(right_df.geometry.name, axis=1),
left_on="_key_right",
right_index=True,
suffixes=("_{}".format(lsuffix), "_{}".format(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":
join_df = join_df.set_index("_key_left")
joined = (
left_df.merge(join_df, 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=("_{}".format(lsuffix), "_{}".format(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
original_length = len(right_df) if how == "right" else len(left_df)
(l_idx, r_idx), distances = _adjust_indexers(
indices, distances, original_length, how, predicate
)
# the `take` method doesn't allow introducing NaNs with -1 indices
# left = left_df.take(l_idx)
# therefore we are using the private _reindex_with_indexers as workaround
new_index = pd.RangeIndex(len(l_idx))
left = left_df._reindex_with_indexers({0: (new_index, l_idx)})
right = right_df._reindex_with_indexers({0: (new_index, r_idx)})
if PANDAS_GE_30:
kwargs = {}
else:
kwargs = dict(copy=False)
joined = pd.concat([left, right], axis=1, **kwargs)
if how in ("inner", "left"):
joined = _restore_index(joined, left_index, left_index_original)
else: # how == 'right':
joined = (
left_df.drop(left_df.geometry.name, axis=1)
.merge(
join_df.merge(
right_df, left_on="_key_right", right_index=True, how="right"
),
left_index=True,
right_on="_key_left",
how="right",
suffixes=("_{}".format(lsuffix), "_{}".format(rsuffix)),
)
.set_index(index_right)
.drop(["_key_left", "_key_right"], axis=1)
.set_geometry(right_df.geometry.name)
)
if isinstance(index_right, list):
joined.index.names = right_index_name
else:
joined.index.name = right_index_name
joined = joined.set_geometry(right_df.geometry.name)
joined = _restore_index(joined, right_index, right_index_original)
return joined
return joined, distances
def _nearest_query(
@@ -357,13 +514,8 @@ def _nearest_query(
how: str,
return_distance: bool,
exclusive: bool,
on_attribute: Optional[list] = None,
):
if not (compat.USE_SHAPELY_20 or (compat.USE_PYGEOS and compat.PYGEOS_GE_010)):
raise NotImplementedError(
"Currently, only PyGEOS >= 0.10.0 or Shapely >= 2.0 supports "
"`nearest_all`. " + compat.INSTALL_PYGEOS_ERROR
)
# use the opposite of the join direction for the index
use_left_as_sindex = how == "right"
if use_left_as_sindex:
@@ -393,15 +545,37 @@ def _nearest_query(
distances = distances[sort_order]
else:
l_idx, r_idx = input_idx, tree_idx
join_df = pd.DataFrame(
{"_key_left": l_idx, "_key_right": r_idx, "distances": distances}
)
else:
# when sindex is empty / has no valid geometries
join_df = pd.DataFrame(
columns=["_key_left", "_key_right", "distances"], dtype=float
)
return join_df
l_idx, r_idx = np.array([], dtype=np.intp), np.array([], dtype=np.intp)
if return_distance:
distances = np.array([], dtype=np.float64)
else:
distances = None
if on_attribute:
for attr in on_attribute:
(l_idx, r_idx), shared_attribute_rows = _filter_shared_attribute(
left_df, right_df, l_idx, r_idx, attr
)
distances = distances[shared_attribute_rows]
return (l_idx, r_idx), distances
def _filter_shared_attribute(left_df, right_df, l_idx, r_idx, attribute):
"""
Returns the indices for the left and right dataframe that share the same entry
in the attribute column. Also returns a Boolean `shared_attribute_rows` for rows
with the same entry.
"""
shared_attribute_rows = (
left_df[attribute].iloc[l_idx].values == right_df[attribute].iloc[r_idx].values
)
l_idx = l_idx[shared_attribute_rows]
r_idx = r_idx[shared_attribute_rows]
return (l_idx, r_idx), shared_attribute_rows
def sjoin_nearest(
@@ -453,7 +627,6 @@ def sjoin_nearest(
exclusive : bool, default False
If True, the nearest geometries that are equal to the input geometry
will not be returned, default False.
Requires Shapely >= 2.0.
Examples
--------
@@ -466,7 +639,7 @@ def sjoin_nearest(
... ).to_crs(groceries.crs)
>>> chicago.head() # doctest: +SKIP
ComAreaID ... geometry
ComAreaID ... geometry
0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844...
1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816...
2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801...
@@ -475,19 +648,19 @@ def sjoin_nearest(
[5 rows x 87 columns]
>>> groceries.head() # doctest: +SKIP
OBJECTID Ycoord ... Category geometry
0 16 41.973266 ... NaN MULTIPOINT (-87.65661 41.97321)
1 18 41.696367 ... NaN MULTIPOINT (-87.68136 41.69713)
2 22 41.868634 ... NaN MULTIPOINT (-87.63918 41.86847)
3 23 41.877590 ... new MULTIPOINT (-87.65495 41.87783)
4 27 41.737696 ... NaN MULTIPOINT (-87.62715 41.73623)
OBJECTID Ycoord ... Category geometry
0 16 41.973266 ... NaN MULTIPOINT ((-87.65661 41.97321))
1 18 41.696367 ... NaN MULTIPOINT ((-87.68136 41.69713))
2 22 41.868634 ... NaN MULTIPOINT ((-87.63918 41.86847))
3 23 41.877590 ... new MULTIPOINT ((-87.65495 41.87783))
4 27 41.737696 ... NaN MULTIPOINT ((-87.62715 41.73623))
[5 rows x 8 columns]
>>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago)
>>> groceries_w_communities[["Chain", "community", "geometry"]].head(2)
Chain community geometry
0 VIET HOA PLAZA UPTOWN MULTIPOINT (1168268.672 1933554.350)
87 JEWEL OSCO UPTOWN MULTIPOINT (1168837.980 1929246.962)
Chain community geometry
0 VIET HOA PLAZA UPTOWN MULTIPOINT ((1168268.672 1933554.35))
1 COUNTY FAIR FOODS MORGAN PARK MULTIPOINT ((1162302.618 1832900.224))
To include the distances:
@@ -495,10 +668,10 @@ def sjoin_nearest(
>>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago, \
distance_col="distances")
>>> groceries_w_communities[["Chain", "community", \
"distances"]].head(2) # doctest: +SKIP
Chain community distances
0 VIET HOA PLAZA UPTOWN 0.0
87 JEWEL OSCO UPTOWN 0.0
"distances"]].head(2)
Chain community distances
0 VIET HOA PLAZA UPTOWN 0.0
1 COUNTY FAIR FOODS MORGAN PARK 0.0
In the following example, we get multiple groceries for Uptown because all
results are equidistant (in this case zero because they intersect).
@@ -508,7 +681,7 @@ distance_col="distances")
distance_col="distances", how="right")
>>> uptown_results = \
chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"]
>>> uptown_results[["Chain", "community"]] # doctest: +SKIP
>>> uptown_results[["Chain", "community"]]
Chain community
30 VIET HOA PLAZA UPTOWN
30 JEWEL OSCO UPTOWN
@@ -528,6 +701,7 @@ chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"]
Every operation in GeoPandas is planar, i.e. the potential third
dimension is not taken into account.
"""
_basic_checks(left_df, right_df, how, lsuffix, rsuffix)
left_df.geometry.values.check_geographic_crs(stacklevel=1)
@@ -535,19 +709,26 @@ chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"]
return_distance = distance_col is not None
join_df = _nearest_query(
left_df, right_df, max_distance, how, return_distance, exclusive
indices, distances = _nearest_query(
left_df,
right_df,
max_distance,
how,
return_distance,
exclusive,
)
joined, distances = _frame_join(
left_df,
right_df,
indices,
distances,
how,
lsuffix,
rsuffix,
None,
)
if return_distance:
join_df = join_df.rename(columns={"distances": distance_col})
else:
join_df.pop("distances")
joined = _frame_join(join_df, left_df, right_df, how, lsuffix, rsuffix)
if return_distance:
columns = [c for c in joined.columns if c != distance_col] + [distance_col]
joined = joined[columns]
joined[distance_col] = distances
return joined