@@ -1449,8 +1449,8 @@ def hist(
1449
1449
def kde (
1450
1450
self ,
1451
1451
bw_method : Literal ["scott" , "silverman" ] | float | Callable | None = None ,
1452
- weights : np .ndarray | None = None ,
1453
1452
ind : np .ndarray | int | None = None ,
1453
+ weights : np .ndarray | None = None ,
1454
1454
** kwargs ,
1455
1455
) -> PlotAccessor :
1456
1456
"""
@@ -1471,14 +1471,14 @@ def kde(
1471
1471
'scott', 'silverman', a scalar constant or a callable.
1472
1472
If None (default), 'scott' is used.
1473
1473
See :class:`scipy.stats.gaussian_kde` for more information.
1474
- weights : NumPy array, optional
1475
- Weights of datapoints. This must be the same shape as datapoints.
1476
- If None (default), the samples are assumed to be equally weighted.
1477
1474
ind : NumPy array or int, optional
1478
1475
Evaluation points for the estimated PDF. If None (default),
1479
1476
1000 equally spaced points are used. If `ind` is a NumPy array, the
1480
1477
KDE is evaluated at the points passed. If `ind` is an integer,
1481
1478
`ind` number of equally spaced points are used.
1479
+ weights : NumPy array, optional
1480
+ Weights of datapoints. This must be the same shape as datapoints.
1481
+ If None (default), the samples are assumed to be equally weighted.
1482
1482
**kwargs
1483
1483
Additional keyword arguments are documented in
1484
1484
:meth:`DataFrame.plot`.
@@ -1564,7 +1564,7 @@ def kde(
1564
1564
1565
1565
>>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
1566
1566
"""
1567
- return self (kind = "kde" , bw_method = bw_method , weights = weights , ind = ind , ** kwargs )
1567
+ return self (kind = "kde" , bw_method = bw_method , ind = ind , weights = weights , ** kwargs )
1568
1568
1569
1569
density = kde
1570
1570
0 commit comments