Series.mod(other, level=None, fill_value=None, axis=0) [source] Modulo of series and other, element-wise (binary operator mod). 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 In

Series.clip(lower=None, upper=None, axis=None, *args, **kwargs) [source] Trim values at input threshold(s). Parameters: lower : float or array_like, default None upper : float or array_like, default None axis : int or string axis name, optional Align object with lower and upper along the given axis. Returns: clipped : Series Examples >>> df 0 1 0 0.335232 -1.256177 1 -1.367855 0.746646 2 0.027753 -1.176076 3 0.230930 -0.679613 4 1.261967 0.570967 >>>

Resampler.__iter__() [source] Groupby iterator Returns: Generator yielding sequence of (name, subsetted object) for each group

DataFrame.cov(min_periods=None) [source] Compute pairwise covariance of columns, excluding NA/null values Parameters: min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Returns: y : DataFrame Notes y contains the covariance matrix of the DataFrame?s time series. The covariance is normalized by N-1 (unbiased estimator).

CategoricalIndex.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.str.split(pat=None, n=-1, expand=False) [source] Split each string (a la re.split) in the Series/Index by given pattern, propagating NA values. Equivalent to str.split(). Parameters: pat : string, default None String or regular expression to split on. If None, splits on whitespace n : int, default -1 (all) None, 0 and -1 will be interpreted as return all splits expand : bool, default False If True, return DataFrame/MultiIndex expanding dimensionality. If False, return Series/In

Rolling.apply(func, args=(), kwargs={}) [source] rolling function apply Parameters: func : function Must produce a single value from an ndarray input *args and **kwargs are passed to the function Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

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,

Styler.bar(subset=None, axis=0, color='#d65f5f', width=100) [source] Color the background color proptional to the values in each column. Excludes non-numeric data by default. New in version 0.17.1. Parameters: subset: IndexSlice, default None a valid slice for data to limit the style application to axis: int color: str width: float A number between 0 or 100. The largest value will cover width percent of the cell?s width Returns: self : Styler

MultiIndex.inferred_type = None