Panel.where(cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True) [source] Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. Parameters: cond : boolean NDFrame, array or callable If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn?t check it). New in version 0.18

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.values Numpy representation of NDFrame Notes The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type convention, mixing int64 and uint64 w

Panel.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False) [source] Modify Panel in place using non-NA values from passed Panel, or object coercible to Panel. Aligns on items Parameters: other : Panel, or object coercible to Panel join : How to join individual DataFrames {?left?, ?right?, ?outer?, ?inner?}, default ?left? overwrite : boolean, default True If True then overwrite values for common keys in the calling panel filter_func : callable(1d-array) -&

Panel.tz_localize(*args, **kwargs) [source] Localize tz-naive TimeSeries to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to localize level : int, str, default None If axis ia a MultiIndex, localize a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data ambiguous : ?infer?, bool-ndarray, ?NaT?, default ?raise? ?infer? will attempt to infer fall dst-transition hours based on order bool-ndarray wh

Panel.tz_convert(tz, axis=0, level=None, copy=True) [source] Convert tz-aware axis to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None If axis ia a MultiIndex, convert a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data Raises: TypeError If the axis is tz-naive.

Panel.tshift(periods=1, freq=None, axis='major') [source]

Panel.truncate(before=None, after=None, axis=None, copy=True) [source] Truncates a sorted NDFrame before and/or after some particular index value. If the axis contains only datetime values, before/after parameters are converted to datetime values. Parameters: before : date Truncate before index value after : date Truncate after index value axis : the truncation axis, defaults to the stat axis copy : boolean, default is True, return a copy of the truncated section Returns: truncated

Panel.transpose(*args, **kwargs) [source] Permute the dimensions of the Panel Parameters: args : three positional arguments: each oneof {0, 1, 2, ?items?, ?major_axis?, ?minor_axis?} copy : boolean, default False Make a copy of the underlying data. Mixed-dtype data will always result in a copy Returns: y : same as input Examples >>> p.transpose(2, 0, 1) >>> p.transpose(2, 0, 1, copy=True)

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