Panel.at_time(time, asof=False) [source] Select values at particular time of day (e.g. 9:30AM). Parameters: time : datetime.time or string Returns: values_at_time : type of caller

Panel.at Fast label-based scalar accessor Similarly to loc, at provides label based scalar lookups. You can also set using these indexers.

Panel.axes Return index label(s) of the internal NDFrame

Panel.astype(dtype, copy=True, raise_on_error=True, **kwargs) [source] Cast object to input numpy.dtype Return a copy when copy = True (be really careful with this!) Parameters: dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame?s columns to column-specific typ

Panel.as_matrix() [source]

Panel.as_blocks(copy=True) [source] Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) Parameters: copy : boolean, default True Returns: values : a dict of dtype -> Constructor Types

Panel.asof(where, subset=None) [source] The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) New in version 0.19.0: For DataFrame If there is no good value, NaN is returned. Parameters: where : date or array of dates subset : string or list of strings, default None if not None use these columns for NaN propagation Returns: where is scalar value or NaN if input is Series Series if input is DataFrame wher

Panel.asfreq(freq, method=None, how=None, normalize=False) [source] Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Parameters: freq : DateOffset object, or string method : {?backfill?/?bfill?, ?pad?/?ffill?}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): ?pad? / ?ffill?: propagate last valid observation forward to next valid ?backfill? / ?bfill?: use N

Panel.any(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether any element is True over requested axis 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 level, collapsing into a DataFrame bool_only : boolean, default None Incl

Panel.apply(func, axis='major', **kwargs) [source] Applies function along axis (or axes) of the Panel Parameters: func : function Function to apply to each combination of ?other? axes e.g. if axis = ?items?, the combination of major_axis/minor_axis will each be passed as a Series; if axis = (?items?, ?major?), DataFrames of items & major axis will be passed axis : {?items?, ?minor?, ?major?}, or {0, 1, 2}, or a tuple with two axes Additional keyword arguments will be passed as keyw