Fixed column widths to conform to PEP8.

geopandas · Apr 11, 2015 · 2af7022 · 2af7022
1 parent d43d5e3
commit 2af7022
Showing 1 changed file with 51 additions and 10 deletions.
diff --git a/geopandas/tools/sjoin.py b/geopandas/tools/sjoin.py
@@ -4,14 +4,16 @@
 from shapely import prepared
 import geopandas as gpd
 
-def sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right', **kwargs):
+def sjoin(left_df, right_df, how='inner', op='intersects',
+          lsuffix='left', rsuffix='right', **kwargs):
     """Spatial join of two GeoDataFrames.
 
     left_df, right_df are GeoDataFrames
     how: 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
+        inner -> use intersection of keys from both dfs;
+                 retain only left_df geometry column
     op: binary predicate {'intersects', 'contains', 'within'}
         see http://toblerity.org/shapely/manual.html#binary-predicates
     lsuffix: suffix to apply to overlapping column names (left GeoDataFrame)
@@ -48,7 +50,8 @@ def sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuff
         tree_idx.insert(i, right_df_bounds[i])
 
     # FIND INTERSECTION OF SPATIAL INDEX
-    idxmatch = left_df['geometry'].apply(lambda x: x.bounds).apply(lambda x: list(tree_idx.intersection(x)))
+    idxmatch = (left_df['geometry'].apply(lambda x: x.bounds)
+                .apply(lambda x: list(tree_idx.intersection(x))))
     idxmatch = idxmatch[idxmatch.str.len() > 0]
 
     r_idx = np.concatenate(idxmatch.values)
@@ -61,27 +64,65 @@ def find_intersects(a1, a2):
     def find_contains(a1, a2):
         return a1.contains(a2)
 
-    predicate_d = {'intersects': find_intersects, 'contains': find_contains, 'within': find_contains}
+    predicate_d = {'intersects': find_intersects,
+                   'contains': find_contains,
+                   'within': find_contains}
 
     check_predicates = np.vectorize(predicate_d[op])
 
     # CHECK PREDICATES
-    result = pd.DataFrame(np.column_stack([l_idx, r_idx, check_predicates(left_df['geometry'].apply(lambda x: prepared.prep(x)).values[l_idx], right_df['geometry'].values[r_idx])]))
+    result = (
+              pd.DataFrame(
+                  np.column_stack(
+                      [l_idx,
+                       r_idx,
+                       check_predicates(
+                           left_df['geometry']
+                           .apply(lambda x: prepared.prep(x)).values[l_idx],
+                           right_df['geometry'].values[r_idx])
+                       ]))
+               )
+
     result.columns = ['index_%s' % lsuffix, 'index_%s' % rsuffix, 'match_bool']
-    result = pd.DataFrame(result[result['match_bool']==1]).drop('match_bool', axis=1)
+    result = (
+              pd.DataFrame(result[result['match_bool']==1])
+              .drop('match_bool', axis=1)
+              )
 
     # IF 'WITHIN', SWAP NAMES AGAIN
     if op == "within":
         # within implemented as the inverse of contains; swap names
         left_df, right_df = right_df, left_df
-        result = result.rename(columns={'index_%s' % (lsuffix): 'index_%s' % (rsuffix), 'index_%s' % (rsuffix): 'index_%s' % (lsuffix)})
+        result = result.rename(columns={
+                    'index_%s' % (lsuffix): 'index_%s' % (rsuffix),
+                    'index_%s' % (rsuffix): 'index_%s' % (lsuffix)})
 
     # APPLY JOIN
     if how == 'inner':
         result = result.set_index('index_%s' % lsuffix)
-        return left_df.merge(result, left_index=True, right_index=True).merge(right_df.drop('geometry', axis=1), left_on='index_%s' % rsuffix, right_index=True, suffixes=('_%s' % lsuffix, '_%s' % rsuffix))
+        return (
+                left_df
+                .merge(result, left_index=True, right_index=True)
+                .merge(right_df.drop('geometry', axis=1),
+                    left_on='index_%s' % rsuffix, right_index=True,
+                    suffixes=('_%s' % lsuffix, '_%s' % rsuffix))
+                )
     elif how == 'left':
         result = result.set_index('index_%s' % lsuffix)
-        return left_df.merge(result, left_index=True, right_index=True, how='left').merge(right_df.drop('geometry', axis=1), how='left', left_on='index_%s' % rsuffix, right_index=True, suffixes=('_%s' % lsuffix, '_%s' % rsuffix))
+        return (
+                left_df
+                .merge(result, left_index=True, right_index=True, how='left')
+                .merge(right_df.drop('geometry', axis=1),
+                    how='left', left_on='index_%s' % rsuffix, right_index=True,
+                    suffixes=('_%s' % lsuffix, '_%s' % rsuffix))
+                )
     elif how == 'right':
-        return left_df.drop('geometry', axis=1).merge(result.merge(right_df, left_on='index_%s' % rsuffix, right_index=True, how='right'), left_index=True, right_on='index_%s' % lsuffix, how='right').set_index('index_%s' % rsuffix)
+        return (
+                left_df
+                .drop('geometry', axis=1)
+                .merge(result.merge(right_df,
+                    left_on='index_%s' % rsuffix, right_index=True,
+                    how='right'), left_index=True,
+                    right_on='index_%s' % lsuffix, how='right')
+                .set_index('index_%s' % rsuffix)
+                )