Series.filter(items=None, like=None, regex=None, axis=None) [source] Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters: items : list-like List of info axis to restrict to (must not all be present) like : string Keep info axis where ?arg in col == True? regex : string (regular expression) Keep info axis with re.search(regex, col)

Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) [source] Fill NA/NaN values using the specified method Parameters: value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list. method : {?backfi

Series.ffill(axis=None, inplace=False, limit=None, downcast=None) [source] Synonym for NDFrame.fillna(method=?ffill?)

Series.factorize(sort=False, na_sentinel=-1) [source] Encode the object as an enumerated type or categorical variable Parameters: sort : boolean, default False Sort by values na_sentinel: int, default -1 Value to mark ?not found? Returns: labels : the indexer to the original array uniques : the unique Index

Series.expanding(min_periods=1, freq=None, center=False, axis=0) [source] Provides expanding transformations. New in version 0.18.0. Parameters: min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). freq : string or DateOffset object, optional (default None) (DEPRECATED) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. center : boolean, default F

Series.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0) [source] Provides exponential weighted functions New in version 0.18.0. Parameters: com : float, optional Specify decay in terms of center of mass, \alpha = 1 / (1 + com),\text{ for } com \geq 0 span : float, optional Specify decay in terms of span, \alpha = 2 / (span + 1),\text{ for } span \geq 1 halflife : float, optional Specify decay in terms of half-life, \

Series.equals(other) [source] Determines if two NDFrame objects contain the same elements. NaNs in the same location are considered equal.

Series.eq(other, level=None, fill_value=None, axis=0) [source] Equal to of series and other, element-wise (binary operator eq). 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 level, matching I

Series.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 in

Series.duplicated(*args, **kwargs) [source] Return boolean Series denoting duplicate values Parameters: keep : {?first?, ?last?, False}, default ?first? first : Mark duplicates as True except for the first occurrence. last : Mark duplicates as True except for the last occurrence. False : Mark all duplicates as True. take_last : deprecated Returns: duplicated : Series