Series.dot(other) [source] Matrix multiplication with DataFrame or inner-product with Series objects Parameters: other : Series or DataFrame Returns: dot_product : scalar or Series

Series.div(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 lev

Series.divide(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

Series.describe(percentiles=None, include=None, exclude=None) [source] Generate various summary statistics, excluding NaN values. Parameters: percentiles : array-like, optional The percentiles to include in the output. Should all be in the interval [0, 1]. By default percentiles is [.25, .5, .75], returning the 25th, 50th, and 75th percentiles. include, exclude : list-like, ?all?, or None (default) Specify the form of the returned result. Either: None to both (default). The result will

Series.diff(periods=1) [source] 1st discrete difference of object Parameters: periods : int, default 1 Periods to shift for forming difference Returns: diffed : Series

Series.data return the data pointer of the underlying data

Series.cumsum(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative sum over requested axis. Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cumsum : scalar

Series.cummin(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative minimum over requested axis. Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cummin : scalar

Series.cumprod(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative product over requested axis. Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cumprod : scalar

Series.cov(other, min_periods=None) [source] Compute covariance with Series, excluding missing values Parameters: other : Series min_periods : int, optional Minimum number of observations needed to have a valid result Returns: covariance : float Normalized by N-1 (unbiased estimator).