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Jean Bredeche
99 lines
2.5 KiB
Python
99 lines
2.5 KiB
Python
"""
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Utilities for working with pandas objects.
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"""
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from itertools import product
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import operator as op
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import pandas as pd
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from distutils.version import StrictVersion
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pandas_version = StrictVersion(pd.__version__)
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def explode(df):
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"""
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Take a DataFrame and return a triple of
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(df.index, df.columns, df.values)
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"""
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return df.index, df.columns, df.values
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try:
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# This branch is hit in pandas 17
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sort_values = pd.DataFrame.sort_values
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except AttributeError:
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# This branch is hit in pandas 16
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sort_values = pd.DataFrame.sort
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if pandas_version >= StrictVersion('0.17.1'):
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july_5th_holiday_observance = lambda dtix: dtix[dtix.year != 2013]
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else:
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july_5th_holiday_observance = lambda dt: None if dt.year == 2013 else dt
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def _time_to_micros(time):
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"""Convert a time into microseconds since midnight.
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Parameters
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----------
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time : datetime.time
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The time to convert.
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Returns
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-------
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us : int
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The number of microseconds since midnight.
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Notes
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-----
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This does not account for leap seconds or daylight savings.
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"""
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seconds = time.hour * 60 * 60 + time.minute * 60 + time.second
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return 1000000 * seconds + time.microsecond
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_opmap = dict(zip(
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product((True, False), repeat=3),
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product((op.le, op.lt), (op.le, op.lt), (op.and_, op.or_)),
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))
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def mask_between_time(dts, start, end, include_start=True, include_end=True):
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"""Return a mask of all of the datetimes in ``dts`` that are between
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``start`` and ``end``.
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Parameters
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----------
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dts : pd.DatetimeIndex
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The index to mask.
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start : time
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Mask away times less than the start.
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end : time
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Mask away times greater than the end.
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include_start : bool, optional
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Inclusive on ``start``.
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include_end : bool, optional
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Inclusive on ``end``.
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Returns
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-------
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mask : np.ndarray[bool]
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A bool array masking ``dts``.
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See Also
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--------
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:meth:`pandas.DatetimeIndex.indexer_between_time`
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"""
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# This function is adapted from
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# `pandas.Datetime.Index.indexer_between_time` which was originally
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# written by Wes McKinney, Chang She, and Grant Roch.
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time_micros = dts._get_time_micros()
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start_micros = _time_to_micros(start)
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end_micros = _time_to_micros(end)
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left_op, right_op, join_op = _opmap[
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bool(include_start),
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bool(include_end),
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start_micros <= end_micros,
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]
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return join_op(
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left_op(start_micros, time_micros),
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right_op(time_micros, end_micros),
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)
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