Panel.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return unbiased variance over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument Parameters: axis : {items (0), major_axis (1), minor_axis (2)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular

Panel.multiply(other, axis=0) [source] Multiplication of series and other, element-wise (binary operator mul). Equivalent to panel * other. Parameters: other : DataFrame or Panel axis : {items, major_axis, minor_axis} Axis to broadcast over Returns: Panel See also Panel.rmul

Series.cat.remove_categories(*args, **kwargs) [source] Removes the specified categories. removals must be included in the old categories. Values which were in the removed categories will be set to NaN Parameters: removals : category or list of categories The categories which should be removed. inplace : boolean (default: False) Whether or not to remove the categories inplace or return a copy of this categorical with removed categories. Returns: cat : Categorical with removed categori

DatetimeIndex.take(indices, axis=0, allow_fill=True, fill_value=None, **kwargs) [source] return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. Parameters: indices : list Indices to be taken axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True fill_value : bool, default None If allow_fill=True and fill_value is not None, indices specified by -1 is regarded as NA. If Index doesn?t hold N

Panel.to_xarray() [source] Return an xarray object from the pandas object. Returns: a DataArray for a Series a Dataset for a DataFrame a DataArray for higher dims Notes See the xarray docs Examples >>> df = pd.DataFrame({'A' : [1, 1, 2], 'B' : ['foo', 'bar', 'foo'], 'C' : np.arange(4.,7)}) >>> df A B C 0 1 foo 4.0 1 1 bar 5.0 2 2 foo 6.0 >>> df.to_xarray() <xarray.Dataset> Dimensions: (index

Resampler.median(_method='median', *args, **kwargs) [source] Compute median of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

Resampler.var(ddof=1, *args, **kwargs) [source] Compute variance of groups, excluding missing values Parameters: ddof : integer, default 1 degrees of freedom

Series.to_timestamp(freq=None, how='start', copy=True) [source] Cast to datetimeindex of timestamps, at beginning of period Parameters: freq : string, default frequency of PeriodIndex Desired frequency how : {?s?, ?e?, ?start?, ?end?} Convention for converting period to timestamp; start of period vs. end Returns: ts : Series with DatetimeIndex

DataFrameGroupBy.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear') Return values at the given quantile over requested axis, a la numpy.percentile. Parameters: q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute axis : {0, 1, ?index?, ?columns?} (default 0) 0 or ?index? for row-wise, 1 or ?columns? for column-wise interpolation : {?linear?, ?lower?, ?higher?, ?midpoint?, ?nearest?} New in version 0.18.0. This optional par

Panel4D.last(offset) [source] Convenience method for subsetting final periods of time series data based on a date offset. Parameters: offset : string, DateOffset, dateutil.relativedelta Returns: subset : type of caller Examples ts.last(?5M?) -> Last 5 months