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# pandas.tseries.tools.to_datetime

`pandas.tseries.tools.``to_datetime`(arg, errors='ignore', dayfirst=False, utc=None, box=True, format=None, coerce=False, unit='ns', infer_datetime_format=False)

  Convert argument to datetime

  Parameters : | arg : string, datetime, array of strings (with possible NAs) errors : {‘ignore’, ‘raise’}, default ‘ignore’ Errors are ignored by default (values left untouched) dayfirst : boolean, default False If True parses dates with the day first, eg 20/01/2005 Warning: dayfirst=True is not strict, but will prefer to parse with day first (this is a known bug). utc : boolean, default None Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well) box : boolean, default True If True returns a DatetimeIndex, if False returns ndarray of values format : string, default None strftime to parse time, eg “%d/%m/%Y” coerce : force errors to NaT (False by default) unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch (e.g. a unix timestamp), which is an integer/float number infer_datetime_format: boolean, default False If no format is given, try to infer the format based on the first datetime string. Provides a large speed-up in many cases.
  Returns : | ret : datetime if parsing succeeded

  Examples

  Take separate series and convert to datetime

      >>> import pandas as pd
      >>> i = pd.date_range('20000101',periods=100)
      >>> df = pd.DataFrame(dict(year = i.year, month = i.month, day = i.day))
      >>> pd.to_datetime(df.year*10000 + df.month*100 + df.day, format='%Y%m%d')

  Or from strings

      >>> df = df.astype(str)
      >>> pd.to_datetime(df.day + df.month + df.year, format="%d%m%Y")

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