Series.str.contains(pat, case=True, flags=0, na=nan, regex=True) [source] Return boolean Series/array whether given pattern/regex is contained in each string in the Series/Index. Parameters: pat : string Character sequence or regular expression case : boolean, default True If True, case sensitive flags : int, default 0 (no flags) re module flags, e.g. re.IGNORECASE na : default NaN, fill value for missing values. regex : bool, default True If True use re.search, otherwise use Python

MultiIndex.get_level_values(level) [source] Return vector of label values for requested level, equal to the length of the index Parameters: level : int or level name Returns: values : ndarray

Series.dt.week The week ordinal of the year

classmethod MultiIndex.from_product(iterables, sortorder=None, names=None) [source] Make a MultiIndex from the cartesian product of multiple iterables Parameters: iterables : list / sequence of iterables Each iterable has unique labels for each level of the index. sortorder : int or None Level of sortedness (must be lexicographically sorted by that level). names : list / sequence of strings or None Names for the levels in the index. Returns: index : MultiIndex See also MultiInd

Panel4D.empty True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0. See also pandas.Series.dropna, pandas.DataFrame.dropna Notes If NDFrame contains only NaNs, it is still not considered empty. See the example below. Examples An example of an actual empty DataFrame. Notice the index is empty: >>> df_empty = pd.DataFrame({'A' : []}) >>> df_empty Empty DataFrame Columns: [A] Index: [] >>> df_empty.empty True If we only have NaNs i

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

class pandas.Grouper(key=None, level=None, freq=None, axis=0, sort=False) [source] A Grouper allows the user to specify a groupby instruction for a target object This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object. These are local specifications and will override ?global? settings, that is the parameters axis and level which are passed to the groupby itself. Parameters: key : string, defa

MultiIndex.swaplevel(i=-2, j=-1) [source] Swap level i with level j. Do not change the ordering of anything Parameters: i, j : int, string (can be mixed) Level of index to be swapped. Can pass level name as string. Returns: swapped : MultiIndex Changed in version 0.18.1: The indexes i and j are now optional, and default to the two innermost levels of the index.

Panel.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return sample standard deviation 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 pa

MultiIndex.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 NA,