Python - 将 CSV 列舍入到最接近的 30 分钟

Question

My CSV data is the following:

Columns:

• CRASH_MONTH (eg "1")
• CRASH_DAY (eg "1")
• TIMESTR (eg "8:40")

Wished result:

A new column named "CRASH_DATETIME" with a datetime Python object based with the corresponding date. Year doesn't matter, main goal is to track crashes by month, day and hour:minutes, which should be rounded to the nearest 30min.

Tried the following but failed:

from datetime import datetime, timedelta

def ceil_dt(month, day, hourWithMinutes, delta):
   hour,minutes = hourWithMinutes.split(':')
   int(month)
   int(day)
   int(hour)
   int(minutes)

   dt = datetime.datetime(month=month, day=day, hour=hour, minute=minutes)
   return dt + (datetime.min - dt) % delta

and

dataInitial['TIME'] = dataInitial.apply(lambda row: ceil_dt(row['CRASH_MONTH'], row['CRASH_DAY'], row['TIMESTR'], '30'))

But failed ( using Jupyter Notebook ):

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14010)()

TypeError: an integer is required

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-40-a9ef29fd7eb7> in <module>()
----> 1 dataInitial['TIME'] = dataInitial.apply(lambda row: ceil_dt(row['CRASH_MONTH'], row['CRASH_DAY'], row['TIMESTR'], '30'))

~/anaconda2/envs/tfdeeplearning/lib/python3.5/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, args, **kwds)
   4260                         f, axis,
   4261                         reduce=reduce,
-> 4262                         ignore_failures=ignore_failures)
   4263             else:
   4264                 return self._apply_broadcast(f, axis)

~/anaconda2/envs/tfdeeplearning/lib/python3.5/site-packages/pandas/core/frame.py in _apply_standard(self, func, axis, ignore_failures, reduce)
   4356             try:
   4357                 for i, v in enumerate(series_gen):
-> 4358                     results[i] = func(v)
   4359                     keys.append(v.name)
   4360             except Exception as e:

<ipython-input-40-a9ef29fd7eb7> in <lambda>(row)
----> 1 dataInitial['TIME'] = dataInitial.apply(lambda row: ceil_dt(row['CRASH_MONTH'], row['CRASH_DAY'], row['TIMESTR'], '30'))

~/anaconda2/envs/tfdeeplearning/lib/python3.5/site-packages/pandas/core/series.py in __getitem__(self, key)
    599         key = com._apply_if_callable(key, self)
    600         try:
--> 601             result = self.index.get_value(self, key)
    602 
    603             if not is_scalar(result):

~/anaconda2/envs/tfdeeplearning/lib/python3.5/site-packages/pandas/core/indexes/base.py in get_value(self, series, key)
   2475         try:
   2476             return self._engine.get_value(s, k,
-> 2477                                           tz=getattr(series.dtype, 'tz', None))
   2478         except KeyError as e1:
   2479             if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4404)()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4087)()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5210)()

KeyError: ('CRASH_MONTH', 'occurred at index CRASH_DATE')

Any ideas?

Answer 1

Your function has some minor problems regarding the conversions (not stored in the variable), the lack of the year and the timedelta. This version of the function works properly:

from datetime import datetime, timedelta

def ceil_dt(month, day, hourWithMinutes, delta):
    hour,minutes = hourWithMinutes.split(':')
    month = int(month)
    day = int(day)
    hour = int(hour)
    minutes = int(minutes)

    dt = datetime(year = 2019, month=month, day=day, hour=int(hour), minute=int(minutes))

    return dt + (datetime.min - dt) % timedelta(minutes=int(delta))