TimedeltaIndex.is_integer() [source]

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

Series.dt.second The seconds of the datetime

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

DataFrame.to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True) [source] Write DataFrame to a excel sheet Parameters: excel_writer : string or ExcelWriter object File path or existing ExcelWriter sheet_name : string, default ?Sheet1? Name of sheet which will contain DataFrame na_rep : string, default ?? Missing

classmethod DataFrame.from_csv(path, header=0, sep=', ', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False) [source] Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a DataFrame of time series data. This method only differs from the p