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).

Series.count(level=None) [source] Return number of non-NA/null observations in the Series Parameters: level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series Returns: nobs : int or Series (if level specified)

Series.corr(other, method='pearson', min_periods=None) [source] Compute correlation with other Series, excluding missing values Parameters: other : Series method : {?pearson?, ?kendall?, ?spearman?} pearson : standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation min_periods : int, optional Minimum number of observations needed to have a valid result Returns: correlation : float

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.convert_objects(convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True) [source] Deprecated. Attempt to infer better dtype for object columns Parameters: convert_dates : boolean, default True If True, convert to date where possible. If ?coerce?, force conversion, with unconvertible values becoming NaT. convert_numeric : boolean, default False If True, attempt to coerce to numbers (including strings), with unconvertible values becoming NaN. convert_timedelt

Series.consolidate(inplace=False) [source] Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray). Mainly an internal API function, but available here to the savvy user Parameters: inplace : boolean, default False If False return new object, otherwise modify existing object Returns: consolidated : type of caller

Series.compress(condition, *args, **kwargs) [source] Return selected slices of an array along given axis as a Series See also numpy.ndarray.compress

Series.compound(axis=None, skipna=None, level=None) [source] Return the compound percentage of the values for the 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 level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar numeric_only : boolean, default None Include only float, int, boolean columns. I

Series.combine_first(other) [source] Combine Series values, choosing the calling Series?s values first. Result index will be the union of the two indexes Parameters: other : Series Returns: y : Series

Series.combine(other, func, fill_value=nan) [source] Perform elementwise binary operation on two Series using given function with optional fill value when an index is missing from one Series or the other Parameters: other : Series or scalar value func : function fill_value : scalar value Returns: result : Series