Index.fillna(value=None, downcast=None) [source] Fill NA/NaN values with the specified value Parameters: value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string ?infer? which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) Returns: filled : %(klass)s

Series.copy(deep=True) [source] Make a copy of this objects data. Parameters: deep : boolean or string, default True Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices or the data are copied. Note that when deep=True data is copied, actual python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data. Returns: copy : type

Series.subtract(other, level=None, fill_value=None, axis=0) [source] Subtraction of series and other, element-wise (binary operator sub). 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, m

CategoricalIndex.add_categories(*args, **kwargs) [source] Add new categories. new_categories will be included at the last/highest place in the categories and will be unused directly after this call. Parameters: new_categories : category or list-like of category The new categories to be included. inplace : boolean (default: False) Whether or not to add the categories inplace or return a copy of this categorical with added categories. Returns: cat : Categorical with new categories adde

Series.pow(other, level=None, fill_value=None, axis=0) [source] Exponential power of series and other, element-wise (binary operator pow). 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,

TimedeltaIndex.shift(n, freq=None) [source] Specialized shift which produces a DatetimeIndex Parameters: n : int Periods to shift by freq : DateOffset or timedelta-like, optional Returns: shifted : DatetimeIndex

DataFrame.squeeze(**kwargs) [source] Squeeze length 1 dimensions.

pandas.infer_freq(index, warn=True) [source] Infer the most likely frequency given the input index. If the frequency is uncertain, a warning will be printed. Parameters: index : DatetimeIndex or TimedeltaIndex if passed a Series will use the values of the series (NOT THE INDEX) warn : boolean, default True Returns: freq : string or None None if no discernible frequency TypeError if the index is not datetime-like ValueError if there are less than three values.

Series.kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1 Parameters: axis : {index (0)} 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 sc

Series.str.get_dummies(sep='|') [source] Split each string in the Series by sep and return a frame of dummy/indicator variables. Parameters: sep : string, default ?|? String to split on. Returns: dummies : DataFrame See also pandas.get_dummies Examples >>> Series(['a|b', 'a', 'a|c']).str.get_dummies() a b c 0 1 1 0 1 1 0 0 2 1 0 1 >>> Series(['a|b', np.nan, 'a|c']).str.get_dummies() a b c 0 1 1 0 1 0 0 0 2 1 0 1