63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
"""This module is designed for community supported date conversion functions"""
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from pandas.compat import range, map
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import numpy as np
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from pandas._libs.tslibs import parsing
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def parse_date_time(date_col, time_col):
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date_col = _maybe_cast(date_col)
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time_col = _maybe_cast(time_col)
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return parsing.try_parse_date_and_time(date_col, time_col)
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def parse_date_fields(year_col, month_col, day_col):
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year_col = _maybe_cast(year_col)
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month_col = _maybe_cast(month_col)
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day_col = _maybe_cast(day_col)
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return parsing.try_parse_year_month_day(year_col, month_col, day_col)
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def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col,
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second_col):
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year_col = _maybe_cast(year_col)
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month_col = _maybe_cast(month_col)
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day_col = _maybe_cast(day_col)
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hour_col = _maybe_cast(hour_col)
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minute_col = _maybe_cast(minute_col)
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second_col = _maybe_cast(second_col)
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return parsing.try_parse_datetime_components(year_col, month_col, day_col,
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hour_col, minute_col,
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second_col)
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def generic_parser(parse_func, *cols):
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N = _check_columns(cols)
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results = np.empty(N, dtype=object)
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for i in range(N):
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args = [c[i] for c in cols]
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results[i] = parse_func(*args)
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return results
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def _maybe_cast(arr):
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if not arr.dtype.type == np.object_:
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arr = np.array(arr, dtype=object)
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return arr
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def _check_columns(cols):
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if not len(cols):
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raise AssertionError("There must be at least 1 column")
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head, tail = cols[0], cols[1:]
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N = len(head)
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for i, n in enumerate(map(len, tail)):
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if n != N:
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raise AssertionError('All columns must have the same length: {0}; '
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'column {1} has length {2}'.format(N, i, n))
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return N
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