/usr/local/lib64/python3.6/site-packages/pandas/io
Edit: /usr/local/lib64/python3.6/site-packages/pandas/io/date_converters.py (1739B)
"""This module is designed for community supported date conversion functions"""
import numpy as np
from pandas._libs.tslibs import parsing
def parse_date_time(date_col, time_col):
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):
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):
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):
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