132 行
3.8 KiB
Python
132 行
3.8 KiB
Python
"""This module is designed for community supported date conversion functions"""
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from __future__ import annotations
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import warnings
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import numpy as np
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from pandas._libs.tslibs import parsing
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from pandas._typing import npt
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from pandas.util._exceptions import find_stack_level
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def parse_date_time(date_col, time_col) -> npt.NDArray[np.object_]:
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"""
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Parse columns with dates and times into a single datetime column.
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.. deprecated:: 1.2
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"""
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warnings.warn(
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"""
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Use pd.to_datetime(date_col + " " + time_col) instead to get a Pandas Series.
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Use pd.to_datetime(date_col + " " + time_col).to_pydatetime() instead to get a Numpy array.
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""", # noqa: E501
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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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) -> npt.NDArray[np.object_]:
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"""
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Parse columns with years, months and days into a single date column.
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.. deprecated:: 1.2
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"""
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warnings.warn(
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"""
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Use pd.to_datetime({"year": year_col, "month": month_col, "day": day_col}) instead to get a Pandas Series.
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Use ser = pd.to_datetime({"year": year_col, "month": month_col, "day": day_col}) and
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np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
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""", # noqa: E501
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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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(
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year_col, month_col, day_col, hour_col, minute_col, second_col
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) -> npt.NDArray[np.object_]:
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"""
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Parse columns with datetime information into a single datetime column.
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.. deprecated:: 1.2
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"""
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warnings.warn(
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"""
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Use pd.to_datetime({"year": year_col, "month": month_col, "day": day_col,
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"hour": hour_col, "minute": minute_col, second": second_col}) instead to get a Pandas Series.
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Use ser = pd.to_datetime({"year": year_col, "month": month_col, "day": day_col,
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"hour": hour_col, "minute": minute_col, second": second_col}) and
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np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
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""", # noqa: E501
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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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(
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year_col, month_col, day_col, hour_col, minute_col, second_col
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)
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def generic_parser(parse_func, *cols) -> np.ndarray:
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"""
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Use dateparser to parse columns with data information into a single datetime column.
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.. deprecated:: 1.2
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"""
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warnings.warn(
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"""
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Use pd.to_datetime instead.
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""",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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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: np.ndarray) -> np.ndarray:
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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) -> int:
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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(
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f"All columns must have the same length: {N}; column {i} has length {n}"
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)
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return N
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