classmethod DataFrame.from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None) [source] Convert structured or record ndarray to DataFrame Parameters: data : ndarray (structured dtype), list of tuples, dict, or DataFrame index : string, list of fields, array-like Field of array to use as the index, alternately a specific set of input labels to use exclude : sequence, default None Columns or fields to exclude columns : sequence, default None Column name

DataFrame.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument 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 level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level,

Panel.cumprod(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative product over requested axis. 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 Returns: cumprod : DataFrame

MultiIndex.map(mapper) [source] Apply mapper function to its values. Parameters: mapper : callable Function to be applied. Returns: applied : array

Panel4D.between_time(start_time, end_time, include_start=True, include_end=True) [source] Select values between particular times of the day (e.g., 9:00-9:30 AM). Parameters: start_time : datetime.time or string end_time : datetime.time or string include_start : boolean, default True include_end : boolean, default True Returns: values_between_time : type of caller

classmethod MultiIndex.from_arrays(arrays, sortorder=None, names=None) [source] Convert arrays to MultiIndex Parameters: arrays : list / sequence of array-likes Each array-like gives one level?s value for each data point. len(arrays) is the number of levels. sortorder : int or None Level of sortedness (must be lexicographically sorted by that level) Returns: index : MultiIndex See also MultiIndex.from_tuples Convert list of tuples to MultiIndex MultiIndex.from_product Make a

Window.sum(*args, **kwargs) [source] window sum Parameters: how : string, default None (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.window, pandas.DataFrame.window

Series.argsort(axis=0, kind='quicksort', order=None) [source] Overrides ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values Parameters: axis : int (can only be zero) kind : {?mergesort?, ?quicksort?, ?heapsort?}, default ?quicksort? Choice of sorting algorithm. See np.sort for more information. ?mergesort? is the only stable algorithm order : ignored Returns: argsorted : Series, with -1 indicated where nan value

MultiIndex.rename(names, level=None, inplace=False) [source] Set new names on index. Defaults to returning new index. Parameters: names : str or sequence name(s) to set level : int, level name, or sequence of int/level names (default None) If the index is a MultiIndex (hierarchical), level(s) to set (None for all levels). Otherwise level must be None inplace : bool if True, mutates in place Returns: new index (of same type and class...etc) [if inplace, returns None] Examples >&

pandas.infer_freq(index, warn=True) [source] Infer the most likely frequency given the input index. If the frequency is uncertain, a warning will be printed. Parameters: index : DatetimeIndex or TimedeltaIndex if passed a Series will use the values of the series (NOT THE INDEX) warn : boolean, default True Returns: freq : string or None None if no discernible frequency TypeError if the index is not datetime-like ValueError if there are less than three values.