Series.flags

Panel4D.ndim Number of axes / array dimensions

pandas.merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches=True) [source] Perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys. For each row in the left DataFrame, we select the last row in the right DataFrame whose ?on? key is less than or equal to the left?s key. Both DataFrames must be s

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

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

TimedeltaIndex.ravel(order='C') [source] return an ndarray of the flattened values of the underlying data See also numpy.ndarray.ravel

StataReader.value_labels() [source] Returns a dict, associating each variable name a dict, associating each value its corresponding label

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.