TimedeltaIndex.groupby(values) [source] Group the index labels by a given array of values. Parameters: values : array Values used to determine the groups. Returns: groups : dict {group name -> group labels}

DatetimeIndex.base return the base object if the memory of the underlying data is shared

pandas.read_table(filepath_or_buffer, sep='\t', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False,

TimedeltaIndex.is_monotonic alias for is_monotonic_increasing (deprecated)

Index.isin(values, level=None) [source] Compute boolean array of whether each index value is found in the passed set of values. Parameters: values : set or list-like Sought values. New in version 0.18.1. Support for values as a set level : str or int, optional Name or position of the index level to use (if the index is a MultiIndex). Returns: is_contained : ndarray (boolean dtype) Notes If level is specified: if it is the name of one and only one index level, use that level; other

DatetimeIndex.nanosecond The nanoseconds of the datetime

MultiIndex.memory_usage(deep=False) [source] Memory usage of my values Parameters: deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: bytes used See also numpy.ndarray.nbytes Notes Memory usage does not include memory consumed by elements that are not components of the array if deep=False

Series.dt.weekofyear The week ordinal of the year

MultiIndex.symmetric_difference(other, result_name=None) [source] Compute the symmetric difference of two Index objects. It?s sorted if sorting is possible. Parameters: other : Index or array-like result_name : str Returns: symmetric_difference : Index Notes symmetric_difference contains elements that appear in either idx1 or idx2 but not both. Equivalent to the Index created by idx1.difference(idx2) | idx2.difference(idx1) with duplicates dropped. Examples >>> idx1 = Index([1,

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