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Docstring: pd.to_datetime (issue #9107) #9189

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Jan 4, 2015
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Docstring: pd.to_datetime (issue #9107) #9189

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988397f
Docstring: pd.to_datetime (issue #9107)
Jan 2, 2015
72f4ccb
Docstring: pd.to_datetime - added output
Jan 4, 2015
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10 changes: 10 additions & 0 deletions pandas/tseries/tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,6 +200,8 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
If True, require an exact format match.
If False, allow the format to match anywhere in the target string.
coerce : force errors to NaT (False by default)
Timestamps outside the interval between Timestamp.min and Timestamp.max
(approximately 1677-09-22 to 2262-04-11) will be also forced to NaT.
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
Expand All @@ -212,6 +214,9 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or correspoding array/Series).

Examples
--------
Expand All @@ -226,6 +231,11 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,

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

Date that does not meet timestamp limitations:

>>> print pd.to_datetime('1300-01-01', format='%Y-%m-%d')
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I would omit print here (repr() is called implicitly in the REPL).

>>> print pd.to_datetime('1300-01-01', format='%Y-%m-%d', coerce=True)
"""
from pandas import Timestamp
from pandas.core.series import Series
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