Series.values Return Series as ndarray or ndarray-like depending on the dtype Returns: arr : numpy.ndarray or ndarray-like Examples >>> pd.Series([1, 2, 3]).values array([1, 2, 3]) >>> pd.Series(list('aabc')).values array(['a', 'a', 'b', 'c'], dtype=object) >>> pd.Series(list('aabc')).astype('category').values [a, a, b, c] Categories (3, object): [a, b, c] Timezone aware datetime data is converted to UTC: >>> pd.Series(pd.date_range('20130101', period

Series.valid(inplace=False, **kwargs) [source]

Series.update(other) [source] Modify Series in place using non-NA values from passed Series. Aligns on index Parameters: other : Series

Series.unstack(level=-1, fill_value=None) [source] Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame. The level involved will automatically get sorted. Parameters: level : int, string, or list of these, default last level Level(s) to unstack, can pass level name fill_value : replace NaN with this value if the unstack produces missing values Returns: unstacked : DataFrame Examples >>> s one a 1. one b 2. two a 3. two b 4. >>> s.unstack(

Series.unique() [source] Return np.ndarray of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : np.ndarray

Series.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 w

Series.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.

Series.tshift(periods=1, freq=None, axis=0) [source] Shift the time index, using the index?s frequency if available. Parameters: periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, default None Increment to use from the tseries module or time rule (e.g. ?EOM?) axis : int or basestring Corresponds to the axis that contains the Index Returns: shifted : NDFrame Notes If freq is not specified then tries to use the fre

Series.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: truncate

Series.truediv(other, level=None, fill_value=None, axis=0) [source] Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a