DataFrameGroupBy.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) Fill NA/NaN values using the specified method Parameters: value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list. method : {?backf

DataFrameGroupBy.hist(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds) Draw histogram of the DataFrame?s series using matplotlib / pylab. Parameters: data : DataFrame column : string or sequence If passed, will be used to limit data to a subset of columns by : object, optional If passed, then used to form histograms for separate groups grid : boolean, default T

DataFrameGroupBy.diff(periods=1, axis=0) 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

DataFrameGroupBy.ffill(limit=None) [source] Forward fill the values Parameters: limit : integer, optional limit of how many values to fill See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.cumprod(axis=0, *args, **kwargs) [source] Cumulative product for each group See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.cumsum(axis=0, *args, **kwargs) [source] Cumulative sum for each group See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.describe(percentiles=None, include=None, exclude=None) 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

DataFrameGroupBy.cummin(axis=None, skipna=True, *args, **kwargs) 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

DataFrameGroupBy.cummax(axis=None, skipna=True, *args, **kwargs) 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

DataFrameGroupBy.cov(min_periods=None) 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).