How to use the geopandas.tools.sjoin function in geopandas

def test_sjoin_values(self):
        # GH190
        self.polydf.index = [1, 3, 4, 5, 6]
        df = sjoin(self.pointdf, self.polydf, how='left')
        self.assertEquals(df.shape, (21,8))
        df = sjoin(self.polydf, self.pointdf, how='left')
        self.assertEquals(df.shape, (12,8))

def test_sjoin_duplicate_column_name(self):
        pointdf2 = self.pointdf.rename(columns={'pointattr1': 'Shape_Area'})
        df = sjoin(pointdf2, self.polydf, how="left")
        self.assertTrue('Shape_Area_left' in df.columns)
        self.assertTrue('Shape_Area_right' in df.columns)

def test_sjoin_left(self):
        df = sjoin(self.pointdf, self.polydf, how='left')
        self.assertEquals(df.shape, (21,8))
        for i, row in df.iterrows():
            self.assertEquals(row.geometry.type, 'Point')
        self.assertTrue('pointattr1' in df.columns)
        self.assertTrue('BoroCode' in df.columns)

def test_sjoin_right(self):
        # the inverse of left
        df = sjoin(self.pointdf, self.polydf, how="right")
        df2 = sjoin(self.polydf, self.pointdf, how="left")
        self.assertEquals(df.shape, (12, 8))
        self.assertEquals(df.shape, df2.shape)
        for i, row in df.iterrows():
            self.assertEquals(row.geometry.type, 'MultiPolygon')
        for i, row in df2.iterrows():
            self.assertEquals(row.geometry.type, 'MultiPolygon')

def test_sjoin_right(self):
        # the inverse of left
        df = sjoin(self.pointdf, self.polydf, how="right")
        df2 = sjoin(self.polydf, self.pointdf, how="left")
        self.assertEquals(df.shape, (12, 8))
        self.assertEquals(df.shape, df2.shape)
        for i, row in df.iterrows():
            self.assertEquals(row.geometry.type, 'MultiPolygon')
        for i, row in df2.iterrows():
            self.assertEquals(row.geometry.type, 'MultiPolygon')

def test_sjoin_values(self):
        # GH190
        self.polydf.index = [1, 3, 4, 5, 6]
        df = sjoin(self.pointdf, self.polydf, how='left')
        self.assertEquals(df.shape, (21,8))
        df = sjoin(self.polydf, self.pointdf, how='left')
        self.assertEquals(df.shape, (12,8))

try:
        point_poly_join = sjoin(points2, polys, how="left", op="within") # point_poly_join.loc[point_poly_join.FEATUREID > 1]
        fld = 'GRIDCODE'  #next(str(unicode(x)) for x in polys.columns if x != 'geometry')
    except:
        polys['link'] = np.nan
        point_poly_join = polys #gpd.GeoDataFrame( pd.concat( [points2, polys], ignore_index=True) ) 
        fld = 'link'
    # Create group of all points in catchment
    grouped = point_poly_join.groupby('FEATUREID')
    point_poly_count = grouped[fld].count() # point_poly_count.head() next((x for x in points2.columns if x != 'geometry'),None)
    # Join Count column on to NHDCatchments table and keep only 'COMID','CatAreaSqKm','CatCount'
    final = polys.join(point_poly_count, on='FEATUREID', lsuffix='_', how='left')
    final = final[['FEATUREID', 'AreaSqKM', fld]].fillna(0)       
    cols = ['COMID', 'CatAreaSqKm%s' % appendMetric, 'CatCount%s' % appendMetric]
    if not summaryfield == None: # Summarize fields in list with gpd table including duplicates
        point_poly_dups = sjoin(points, polys, how="left", op="within")
        grouped2 = point_poly_dups.groupby('FEATUREID')
        for x in summaryfield: # Sum the field in summary field list for each catchment             
            point_poly_stats = grouped2[x].sum()
            point_poly_stats.name = x
            final = final.join(point_poly_stats, on='FEATUREID', how='left').fillna(0)
            cols.append('Cat' + x + appendMetric)
    final.columns = cols
    # Merge final table with Pct_Full table based on COMID and fill NA's with 0
    final = pd.merge(final, pct_full, on='COMID', how='left')
    if len(mask_dir) > 0:
        if not summaryfield == None:
            final.columns = ['COMID','CatAreaSqKmRp100','CatCountRp100']+ ['Cat'  + y + appendMetric for y in summaryfield] + ['CatPctFullRp100']
        else:
            final.columns = ['COMID','CatAreaSqKmRp100','CatCountRp100','CatPctFullRp100']
    final['CatPctFull%s' % appendMetric] = final['CatPctFull%s' % appendMetric].fillna(100) # final.head() final.loc[final.CatCount == 0]
    #print "elapsed time " + str(dt.now()-startTime)

* 'inner': use intersection of keys from both dfs;
                retain only left_df geometry column
    op      : string, default 'intersection'
              One of {'intersects', 'contains', 'within'}.
              See http://toblerity.org/shapely/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 namei
              (right GeoDataFrame).
    """
    import geopandas as gpd
    gdf1 = to_gdf(df1, geom_col=left_geom_col)
    gdf2 = to_gdf(df2, geom_col=right_geom_col)
    out = gpd.tools.sjoin(gdf1, gdf2, **kwargs)
    return to_df(out)