Series.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last') [source] Sort by the values along either axis New in version 0.17.0. Parameters: axis : {0, ?index?}, default 0 Axis to direct sorting ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. inplace : bool, default False if True, perform operation in-place kind : {?qu

Expanding.quantile(quantile, **kwargs) [source] expanding quantile Parameters: quantile : float 0 <= quantile <= 1 Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

Panel.drop(labels, axis=0, level=None, inplace=False, errors='raise') [source] Return new object with labels in requested axis removed. Parameters: labels : single label or list-like axis : int or axis name level : int or level name, default None For MultiIndex inplace : bool, default False If True, do operation inplace and return None. errors : {?ignore?, ?raise?}, default ?raise? If ?ignore?, suppress error and existing labels are dropped. New in version 0.16.1. Returns: droppe

pandas.isnull(obj) [source] Detect missing values (NaN in numeric arrays, None/NaN in object arrays) Parameters: arr : ndarray or object value Object to check for null-ness Returns: isnulled : array-like of bool or bool Array or bool indicating whether an object is null or if an array is given which of the element is null. See also pandas.notnull boolean inverse of pandas.isnull

DataFrame.reindex_like(other, method=None, copy=True, limit=None, tolerance=None) [source] Return an object with matching indices to myself. Parameters: other : Object method : string or None copy : boolean, default True limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between labels of the other object and this object for inexact matches. New in version 0.17.0. Returns: reindexed : same as input Note

Series.dt.to_pydatetime() [source]

DataFrame.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

Series.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)} 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

CategoricalIndex.max(*args, **kwargs) [source] The maximum value of the object. Only ordered Categoricals have a maximum! Returns: max : the maximum of this Categorical Raises: TypeError If the Categorical is not ordered.

Series.str.isupper() [source] Check whether all characters in each string in the Series/Index are uppercase. Equivalent to str.isupper(). Returns: is : Series/array of boolean values