Series.str.endswith(pat, na=nan) [source] Return boolean Series indicating whether each string in the Series/Index ends with passed pattern. Equivalent to str.endswith(). Parameters: pat : string Character sequence na : bool, default NaN Returns: endswith : Series/array of boolean values

Series.dt.minute The minutes of the datetime

Series.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 whe

DataFrame.iloc Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4, 3, 0]. A slice object with ints, e.g. 1:7. A boolean array. A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above) .iloc will rai

TimedeltaIndex.is_lexsorted_for_tuple(tup) [source]

CategoricalIndex.nlevels

Index.is_integer() [source]

Series.hist(by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds) [source] Draw histogram of the input series using matplotlib Parameters: by : object, optional If passed, then used to form histograms for separate groups ax : matplotlib axis object If not passed, uses gca() grid : boolean, default True Whether to show axis grid lines xlabelsize : int, default None If specified changes the x-axis label size xrot : float,

Index.nbytes return the number of bytes in the underlying data

TimedeltaIndex.to_datetime(dayfirst=False) [source] DEPRECATED: use pandas.to_datetime() instead. For an Index containing strings or datetime.datetime objects, attempt conversion to DatetimeIndex