DatetimeIndex.is_object() [source]

SeriesGroupBy.unique() Return np.ndarray of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : np.ndarray

pandas has proven very successful as a tool for working with time series data, especially in the financial data analysis space. Using the NumPy datetime64 and timedelta64 dtypes, we have consolidated a large number of features from other Python libraries like scikits.timeseries as well as created a tremendous amount of new functionality for manipulating time series data. In working with time series data, we will frequently seek to: generate sequences of fixed-frequency dates and time spans co

DatetimeIndex.time Returns numpy array of datetime.time. The time part of the Timestamps.

Panel.as_matrix() [source]

Series.to_dense() [source] Return dense representation of NDFrame (as opposed to sparse)

SeriesGroupBy.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True) [source]

Series.iat Fast integer location scalar accessor. Similarly to iloc, iat provides integer based lookups. You can also set using these indexers.

DataFrame.rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0) [source] Provides rolling window calculcations. New in version 0.18.0. Parameters: window : int, or offset Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size. If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-

DatetimeIndex.sort(*args, **kwargs) [source]