126 lines
3.5 KiB
Python
126 lines
3.5 KiB
Python
"""This module is designed for community supported date conversion functions"""
|
|
import warnings
|
|
|
|
import numpy as np
|
|
|
|
from pandas._libs.tslibs import parsing
|
|
|
|
|
|
def parse_date_time(date_col, time_col):
|
|
"""
|
|
Parse columns with dates and times into a single datetime column.
|
|
|
|
.. deprecated:: 1.2
|
|
"""
|
|
warnings.warn(
|
|
"""
|
|
Use pd.to_datetime(date_col + " " + time_col) instead to get a Pandas Series.
|
|
Use pd.to_datetime(date_col + " " + time_col).to_pydatetime() instead to get a Numpy array.
|
|
""", # noqa: E501
|
|
FutureWarning,
|
|
stacklevel=2,
|
|
)
|
|
date_col = _maybe_cast(date_col)
|
|
time_col = _maybe_cast(time_col)
|
|
return parsing.try_parse_date_and_time(date_col, time_col)
|
|
|
|
|
|
def parse_date_fields(year_col, month_col, day_col):
|
|
"""
|
|
Parse columns with years, months and days into a single date column.
|
|
|
|
.. deprecated:: 1.2
|
|
"""
|
|
warnings.warn(
|
|
"""
|
|
Use pd.to_datetime({"year": year_col, "month": month_col, "day": day_col}) instead to get a Pandas Series.
|
|
Use ser = pd.to_datetime({"year": year_col, "month": month_col, "day": day_col}) and
|
|
np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
|
|
""", # noqa: E501
|
|
FutureWarning,
|
|
stacklevel=2,
|
|
)
|
|
|
|
year_col = _maybe_cast(year_col)
|
|
month_col = _maybe_cast(month_col)
|
|
day_col = _maybe_cast(day_col)
|
|
return parsing.try_parse_year_month_day(year_col, month_col, day_col)
|
|
|
|
|
|
def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col, second_col):
|
|
"""
|
|
Parse columns with datetime information into a single datetime column.
|
|
|
|
.. deprecated:: 1.2
|
|
"""
|
|
|
|
warnings.warn(
|
|
"""
|
|
Use pd.to_datetime({"year": year_col, "month": month_col, "day": day_col,
|
|
"hour": hour_col, "minute": minute_col, second": second_col}) instead to get a Pandas Series.
|
|
Use ser = pd.to_datetime({"year": year_col, "month": month_col, "day": day_col,
|
|
"hour": hour_col, "minute": minute_col, second": second_col}) and
|
|
np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
|
|
""", # noqa: E501
|
|
FutureWarning,
|
|
stacklevel=2,
|
|
)
|
|
|
|
year_col = _maybe_cast(year_col)
|
|
month_col = _maybe_cast(month_col)
|
|
day_col = _maybe_cast(day_col)
|
|
hour_col = _maybe_cast(hour_col)
|
|
minute_col = _maybe_cast(minute_col)
|
|
second_col = _maybe_cast(second_col)
|
|
return parsing.try_parse_datetime_components(
|
|
year_col, month_col, day_col, hour_col, minute_col, second_col
|
|
)
|
|
|
|
|
|
def generic_parser(parse_func, *cols):
|
|
"""
|
|
Use dateparser to parse columns with data information into a single datetime column.
|
|
|
|
.. deprecated:: 1.2
|
|
"""
|
|
|
|
warnings.warn(
|
|
"""
|
|
Use pd.to_datetime instead.
|
|
""",
|
|
FutureWarning,
|
|
stacklevel=2,
|
|
)
|
|
|
|
N = _check_columns(cols)
|
|
results = np.empty(N, dtype=object)
|
|
|
|
for i in range(N):
|
|
args = [c[i] for c in cols]
|
|
results[i] = parse_func(*args)
|
|
|
|
return results
|
|
|
|
|
|
def _maybe_cast(arr):
|
|
if not arr.dtype.type == np.object_:
|
|
arr = np.array(arr, dtype=object)
|
|
return arr
|
|
|
|
|
|
def _check_columns(cols):
|
|
if not len(cols):
|
|
raise AssertionError("There must be at least 1 column")
|
|
|
|
head, tail = cols[0], cols[1:]
|
|
|
|
N = len(head)
|
|
|
|
for i, n in enumerate(map(len, tail)):
|
|
if n != N:
|
|
raise AssertionError(
|
|
f"All columns must have the same length: {N}; column {i} has length {n}"
|
|
)
|
|
|
|
return N
|