DataFrame.div(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator truediv). Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this va

DataFrame.diff(periods=1, axis=0) [source] 1st discrete difference of object Parameters: periods : int, default 1 Periods to shift for forming difference axis : {0 or ?index?, 1 or ?columns?}, default 0 Take difference over rows (0) or columns (1). Returns: diffed : DataFrame

DataFrame.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 wi

DataFrame.cumprod(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative product over requested axis. Parameters: axis : {index (0), columns (1)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cumprod : Series

DataFrame.cummin(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative minimum over requested axis. Parameters: axis : {index (0), columns (1)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cummin : Series

DataFrame.cummax(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative max over requested axis. Parameters: axis : {index (0), columns (1)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cummax : Series

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).

DataFrame.count(axis=0, level=None, numeric_only=False) [source] Return Series with number of non-NA/null observations over requested axis. Works with non-floating point data as well (detects NaN and None) Parameters: axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? for row-wise, 1 or ?columns? for column-wise level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame numeric_only : boolea

DataFrame.corrwith(other, axis=0, drop=False) [source] Compute pairwise correlation between rows or columns of two DataFrame objects. Parameters: other : DataFrame axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? to compute column-wise, 1 or ?columns? for row-wise drop : boolean, default False Drop missing indices from result, default returns union of all Returns: correls : Series

DataFrame.corr(method='pearson', min_periods=1) [source] Compute pairwise correlation of columns, excluding NA/null values Parameters: 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 required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation Returns: y