DataFrame.asof(where, subset=None) [source] The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) New in version 0.19.0: For DataFrame If there is no good value, NaN is returned. Parameters: where : date or array of dates subset : string or list of strings, default None if not None use these columns for NaN propagation Returns: where is scalar value or NaN if input is Series Series if input is DataFrame

Panel.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] This method returns the minimum of the values in the object. If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin. Parameters: axis : {items (0), major_axis (1), minor_axis (2)} 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 MultiInde

Series.cummax(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative max 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: cummax : scalar

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

SeriesGroupBy.nsmallest(*args, **kwargs) [source] Return the smallest n elements. Parameters: n : int Return this many ascending sorted values keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. take_last : deprecated Returns: bottom_n : Series The n smallest values in the Series, in sorted order See also Series.nlargest Notes Faster than .sort_values().head(n) for small n rela

Rolling.min(*args, **kwargs) [source] rolling minimum Parameters: how : string, default ?min? (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

GroupBy.max() [source] Compute max of group values See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DatetimeIndex.astype(dtype, copy=True) [source] Create an Index with values cast to dtypes. The class of a new Index is determined by dtype. When conversion is impossible, a ValueError exception is raised. Parameters: dtype : numpy dtype or pandas type copy : bool, default True By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is returne

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

DataFrame.mod(other, axis='columns', level=None, fill_value=None) [source] Modulo of dataframe and other, element-wise (binary operator mod). 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 value. If both Da