Python, Pandas ; ValueError('window must be an integer',)
Solution 1
This is an error from Pandas. You are passing a string to df.rolling
, but it expects only integer values. You probably want to pass int(new)
instead.
Edit: as noted below, evidently the Pandas documentation is incomplete, and the real ultimate problem in this case is probably the lack of a time index, since creating a naive Dataframe and passing values like "10d"
definitely raises the indicated error:
In [2]: df = pd.DataFrame({'B': [0, 1, 2, 10, 4]})
In [3]: df.rolling('10d')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-2a9875316cd7> in <module>
----> 1 df.rolling('10d')
~/anaconda/lib/python3.7/site-packages/pandas/core/generic.py in rolling(self, window, min_periods, center, win_type, on, axis, closed)
8906 min_periods=min_periods,
8907 center=center, win_type=win_type,
-> 8908 on=on, axis=axis, closed=closed)
8909
8910 cls.rolling = rolling
~/anaconda/lib/python3.7/site-packages/pandas/core/window.py in rolling(obj, win_type, **kwds)
2467 return Window(obj, win_type=win_type, **kwds)
2468
-> 2469 return Rolling(obj, **kwds)
2470
2471
~/anaconda/lib/python3.7/site-packages/pandas/core/window.py in __init__(self, obj, window, min_periods, center, win_type, axis, on, closed, **kwargs)
78 self.win_freq = None
79 self.axis = obj._get_axis_number(axis) if axis is not None else None
---> 80 self.validate()
81
82 @property
~/anaconda/lib/python3.7/site-packages/pandas/core/window.py in validate(self)
1476
1477 elif not is_integer(self.window):
-> 1478 raise ValueError("window must be an integer")
1479 elif self.window < 0:
1480 raise ValueError("window must be non-negative")
ValueError: window must be an integer
Solution 2
As of today, the documentation states as follows:
window : int, or offset
Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.
If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. This is new in 0.19.0
It is not clear from me whether the time information is a column in your dataframe or part of a MultiIndex. For the first case, you can use .set_index('time')
.
For MultiIndex, currently, you cannot use offsets. See the related issue. If that works, you can use .reset_index()
to transform it into a single index dataframe (see here).
Update: you can also pass datetime columns for offset-based rolling metrics with the on
parameter (and, therefore, you do not have to have an index).
Solution 3
df.rolling
can also handle time periods. Make sure the date time is in pandas
format. If not, convert as such -
data['col'] = pd.to_datetime(data['col'])
Christophe Foyer
Updated on July 17, 2022Comments
-
Christophe Foyer almost 2 years
I seem to be having this issue with Pandas code inside a Bokeh callback.
Here's part of the output before the error. My dataframe seems normal and I'm not sure why it's upset
time temperature 0 2016-03-17 11:00:00 4.676 1 2016-03-17 11:30:00 4.633 2 2016-03-17 12:00:00 4.639 3 2016-03-17 12:30:00 4.603 4 2016-03-17 13:00:00 4.615 5 2016-03-17 13:30:00 4.650 6 2016-03-17 14:00:00 4.678 7 2016-03-17 14:30:00 4.698 8 2016-03-17 15:00:00 4.753 9 2016-03-17 15:30:00 4.847 ERROR:bokeh.server.protocol_handler:error handling message Message 'PATCH-DOC' ( revision 1): ValueError('window must be an integer',)
And here's the code I changed from the flask embed example (link here):
def callback(attr, old, new): df = pd.DataFrame.from_dict(source.data.copy()) print df[:10] if new == 0: data = df else: data = df.rolling('{0}D'.format(new)).mean() source.data = ColumnDataSource(data=data).data slider = Slider(start=0, end=30, value=0, step=1, title="Smoothing by N Days") slider.on_change('value', callback)
I can also include the full code if that help, but the main change I have is just a doc.add_periodic_callback() that fetches new data periodically.
-
Christophe Foyer almost 6 yearsthanks for the reply, I'm still not sure why pandas isn't happy since I didn't change anything from the source code but I'll try and figure it out. I didn't realize bokeh was just passing the error message
-
tillmo over 5 yearsThe answer is just wrong.
df.rolling
can also handle time periods like'10D'
(which will be the case here ifnew
is10
). So the error must have a different cause. -
tillmo over 5 yearsMaybe the problem is that the newly fetched data does not have a time index?
-
bigreddot over 5 yearsPerhaps you should make a helpful PR to update the Pandas documentation, since it currently clearly states that an integer is expected for
window
. -
pbaranski about 5 years
.set_index('time')
fixed my dataframe and problem gone then with mean() -
Oer about 3 yearsMake sure you convert to datetime type and then run "set_index" on that column. That fixed it for me.
-
Harald Thomson about 3 yearsThis happens if the index is not a pandas time index. See @madhurs answer