Improved docstring and return type hints for to_datetime (pandas-dev#42494)

Author: smarie

pandas/core/tools/datetimes.py

@@ -691,111 +691,192 @@ def to_datetime(
     """
     Convert argument to datetime.
 
+    This function converts a scalar, array-like, :class:`Series` or
+    :class:`DataFrame`/dict-like to a pandas datetime object.
+
     Parameters
     ----------
     arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
-        The object to convert to a datetime. If the DataFrame is provided, the method
-        expects minimally the following columns: "year", "month", "day".
+        The object to convert to a datetime. If a :class:`DataFrame` is provided, the
+        method expects minimally the following columns: :const:`"year"`,
+        :const:`"month"`, :const:`"day"`.
     errors : {'ignore', 'raise', 'coerce'}, default 'raise'
-        - If 'raise', then invalid parsing will raise an exception.
-        - If 'coerce', then invalid parsing will be set as NaT.
-        - If 'ignore', then invalid parsing will return the input.
+        - If :const:`'raise'`, then invalid parsing will raise an exception.
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`.
+        - If :const:`'ignore'`, then invalid parsing will return the input.
     dayfirst : bool, default False
-        Specify a date parse order if `arg` is str or its list-likes.
-        If True, parses dates with the day first, eg 10/11/12 is parsed as
-        2012-11-10.
+        Specify a date parse order if `arg` is str or is list-like.
+        If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"`
+        is parsed as :const:`2012-11-10`.
 
         .. warning::
 
-            dayfirst=True is not strict, but will prefer to parse
+            ``dayfirst=True`` is not strict, but will prefer to parse
             with day first. If a delimited date string cannot be parsed in
             accordance with the given `dayfirst` option, e.g.
             ``to_datetime(['31-12-2021'])``, then a warning will be shown.
 
     yearfirst : bool, default False
-        Specify a date parse order if `arg` is str or its list-likes.
+        Specify a date parse order if `arg` is str or is list-like.
 
-        - If True parses dates with the year first, eg 10/11/12 is parsed as
-          2010-11-12.
-        - If both dayfirst and yearfirst are True, yearfirst is preceded (same
-          as dateutil).
+        - If :const:`True` parses dates with the year first, e.g.
+          :const:`"10/11/12"` is parsed as :const:`2010-11-12`.
+        - If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is
+          preceded (same as :mod:`dateutil`).
 
         .. warning::
 
-            yearfirst=True is not strict, but will prefer to parse
+            ``yearfirst=True`` is not strict, but will prefer to parse
             with year first.
 
     utc : bool, default None
-        Return UTC DatetimeIndex if True (converting any tz-aware
-        datetime.datetime objects as well).
+        Control timezone-related parsing, localization and conversion.
+
+        - If :const:`True`, the function *always* returns a timezone-aware
+          UTC-localized :class:`Timestamp`, :class:`Series` or
+          :class:`DatetimeIndex`. To do this, timezone-naive inputs are
+          *localized* as UTC, while timezone-aware inputs are *converted* to UTC.
+
+        - If :const:`False` (default), inputs will not be coerced to UTC.
+          Timezone-naive inputs will remain naive, while timezone-aware ones
+          will keep their time offsets. Limitations exist for mixed
+          offsets (typically, daylight savings), see :ref:`Examples
+          <to_datetime_tz_examples>` section for details.
+
+        See also: pandas general documentation about `timezone conversion and
+        localization
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
+        #time-zone-handling>`_.
+
     format : str, default None
-        The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
-        all the way up to nanoseconds.
-        See strftime documentation for more information on choices:
-        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
-    exact : bool, True by default
-        Behaves as:
-        - If True, require an exact format match.
-        - If False, allow the format to match anywhere in the target string.
+        The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. Note that
+        :const:`"%f"` will parse all the way up to nanoseconds. See
+        `strftime documentation
+        <https://docs.python.org/3/library/datetime.html
+        #strftime-and-strptime-behavior>`_ for more information on choices.
+    exact : bool, default True
+        Control how `format` is used:
+
+        - If :const:`True`, require an exact `format` match.
+        - If :const:`False`, allow the `format` to match anywhere in the target
+          string.
 
     unit : str, default 'ns'
         The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
         integer or float number. This will be based off the origin.
-        Example, with unit='ms' and origin='unix' (the default), this
+        Example, with ``unit='ms'`` and ``origin='unix'`` (the default), this
         would calculate the number of milliseconds to the unix epoch start.
     infer_datetime_format : bool, default False
-        If True and no `format` is given, attempt to infer the format of the
-        datetime strings based on the first non-NaN element,
+        If :const:`True` and no `format` is given, attempt to infer the format
+        of the datetime strings based on the first non-NaN element,
         and if it can be inferred, switch to a faster method of parsing them.
         In some cases this can increase the parsing speed by ~5-10x.
     origin : scalar, default 'unix'
         Define the reference date. The numeric values would be parsed as number
         of units (defined by `unit`) since this reference date.
 
-        - If 'unix' (or POSIX) time; origin is set to 1970-01-01.
-        - If 'julian', unit must be 'D', and origin is set to beginning of
-          Julian Calendar. Julian day number 0 is assigned to the day starting
-          at noon on January 1, 4713 BC.
+        - If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01.
+        - If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to
+          beginning of Julian Calendar. Julian day number :const:`0` is assigned
+          to the day starting at noon on January 1, 4713 BC.
         - If Timestamp convertible, origin is set to Timestamp identified by
           origin.
     cache : bool, default True
-        If True, use a cache of unique, converted dates to apply the datetime
-        conversion. May produce significant speed-up when parsing duplicate
-        date strings, especially ones with timezone offsets. The cache is only
-        used when there are at least 50 values. The presence of out-of-bounds
-        values will render the cache unusable and may slow down parsing.
+        If :const:`True`, use a cache of unique, converted dates to apply the
+        datetime conversion. May produce significant speed-up when parsing
+        duplicate date strings, especially ones with timezone offsets. The cache
+        is only used when there are at least 50 values. The presence of
+        out-of-bounds values will render the cache unusable and may slow down
+        parsing.
 
         .. versionchanged:: 0.25.0
-            - changed default value from False to True.
+            changed default value from :const:`False` to :const:`True`.
 
     Returns
     -------
     datetime
         If parsing succeeded.
-        Return type depends on input:
-
-        - list-like:
-            - DatetimeIndex, if timezone naive or aware with the same timezone
-            - Index of object dtype, if timezone aware with mixed time offsets
-        - Series: Series of datetime64 dtype
-        - DataFrame: 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 corresponding
-        array/Series).
+        Return type depends on input (types in parenthesis correspond to
+        fallback in case of unsuccessful timezone or out-of-range timestamp
+        parsing):
+
+        - scalar: :class:`Timestamp` (or :class:`datetime.datetime`)
+        - array-like: :class:`DatetimeIndex` (or :class:`Series` with
+          :class:`object` dtype containing :class:`datetime.datetime`)
+        - Series: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+        - DataFrame: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+
+    Raises
+    ------
+    ParserError
+        When parsing a date from string fails.
+    ValueError
+        When another datetime conversion error happens. For example when one
+        of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or
+        when a Timezone-aware :class:`datetime.datetime` is found in an array-like
+        of mixed time offsets, and ``utc=False``.
 
     See Also
     --------
     DataFrame.astype : Cast argument to a specified dtype.
     to_timedelta : Convert argument to timedelta.
     convert_dtypes : Convert dtypes.
 
+    Notes
+    -----
+
+    Many input types are supported, and lead to different output types:
+
+    - **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime`
+      module or :mod:`numpy`). They are converted to :class:`Timestamp` when
+      possible, otherwise they are converted to :class:`datetime.datetime`.
+      None/NaN/null scalars are converted to :const:`NaT`.
+
+    - **array-like** can contain int, float, str, datetime objects. They are
+      converted to :class:`DatetimeIndex` when possible, otherwise they are
+      converted to :class:`Index` with :class:`object` dtype, containing
+      :class:`datetime.datetime`. None/NaN/null entries are converted to
+      :const:`NaT` in both cases.
+
+    - **Series** are converted to :class:`Series` with :class:`datetime64`
+      dtype when possible, otherwise they are converted to :class:`Series` with
+      :class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null
+      entries are converted to :const:`NaT` in both cases.
+
+    - **DataFrame/dict-like** are converted to :class:`Series` with
+      :class:`datetime64` dtype. For each row a datetime is created from assembling
+      the various dataframe columns. Column keys can be common abbreviations
+      like [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) or
+      plurals of the same.
+
+    The following causes are responsible for :class:`datetime.datetime` objects
+    being returned (possibly inside an :class:`Index` or a :class:`Series` with
+    :class:`object` dtype) instead of a proper pandas designated type
+    (:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series`
+    with :class:`datetime64` dtype):
+
+    - when any input element is before :const:`Timestamp.min` or after
+      :const:`Timestamp.max`, see `timestamp limitations
+      <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
+      #timeseries-timestamp-limits>`_.
+
+    - when ``utc=False`` (default) and the input is an array-like or
+      :class:`Series` containing mixed naive/aware datetime, or aware with mixed
+      time offsets. Note that this happens in the (quite frequent) situation when
+      the timezone has a daylight savings policy. In that case you may wish to
+      use ``utc=True``.
+
     Examples
     --------
-    Assembling a datetime from multiple columns of a DataFrame. The keys can be
-    common abbreviations like ['year', 'month', 'day', 'minute', 'second',
+
+    **Handling various input formats**
+
+    Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys
+    can be common abbreviations like ['year', 'month', 'day', 'minute', 'second',
     'ms', 'us', 'ns']) or plurals of the same
 
     >>> df = pd.DataFrame({'year': [2015, 2016],
@@ -806,20 +887,7 @@ def to_datetime(
     1   2016-03-05
     dtype: datetime64[ns]
 
-    If a date does not meet the `timestamp limitations
-    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
-    #timeseries-timestamp-limits>`_, passing errors='ignore'
-    will return the original input instead of raising any exception.
-
-    Passing errors='coerce' will force an out-of-bounds date to NaT,
-    in addition to forcing non-dates (or non-parseable dates) to NaT.
-
-    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
-    datetime.datetime(1300, 1, 1, 0, 0)
-    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
-    NaT
-
-    Passing infer_datetime_format=True can often-times speedup a parsing
+    Passing ``infer_datetime_format=True`` can often-times speedup a parsing
     if its not an ISO8601 format exactly, but in a regular format.
 
     >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
@@ -854,16 +922,99 @@ def to_datetime(
     DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
                   dtype='datetime64[ns]', freq=None)
 
-    In case input is list-like and the elements of input are of mixed
-    timezones, return will have object type Index if utc=False.
+    **Non-convertible date/times**
+
+    If a date does not meet the `timestamp limitations
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
+    #timeseries-timestamp-limits>`_, passing ``errors='ignore'``
+    will return the original input instead of raising any exception.
+
+    Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`,
+    in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`.
+
+    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
+    datetime.datetime(1300, 1, 1, 0, 0)
+    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
+    NaT
+
+    .. _to_datetime_tz_examples:
+
+    **Timezones and time offsets**
+
+    The default behaviour (``utc=False``) is as follows:
+
+    - Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00:15'])
+    DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
+                  dtype='datetime64[ns]', freq=None)
+
+    - Timezone-aware inputs *with constant time offset* are converted to
+      timezone-aware :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500'])
+    DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
+                  dtype='datetime64[ns, pytz.FixedOffset(-300)]', freq=None)
+
+    - However, timezone-aware inputs *with mixed time offsets* (for example
+      issued from a timezone with daylight savings, such as Europe/Paris)
+      are **not successfully converted** to a :class:`DatetimeIndex`. Instead a
+      simple :class:`Index` containing :class:`datetime.datetime` objects is
+      returned:
 
-    >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'])
-    Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object')
+    >>> pd.to_datetime(['2020-10-25 02:00 +0200', '2020-10-25 04:00 +0100'])
+    Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00],
+          dtype='object')
+
+    - A mix of timezone-aware and timezone-naive inputs is converted to
+      a timezone-aware :class:`DatetimeIndex` if the offsets of the timezone-aware
+      are constant:
+
+    >>> from datetime import datetime
+    >>> pd.to_datetime(["2020-01-01 01:00 -01:00", datetime(2020, 1, 1, 3, 0)])
+    DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'],
+                  dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None)
+
+    - Finally, mixing timezone-aware strings and :class:`datetime.datetime` always
+      raises an error, even if the elements all have the same time offset.
+
+    >>> from datetime import datetime, timezone, timedelta
+    >>> d = datetime(2020, 1, 1, 18, tzinfo=timezone(-timedelta(hours=1)))
+    >>> pd.to_datetime(["2020-01-01 17:00 -0100", d])
+    Traceback (most recent call last):
+        ...
+    ValueError: Tz-aware datetime.datetime cannot be converted to datetime64
+                unless utc=True
+
+    |
+
+    Setting ``utc=True`` solves most of the above issues:
+
+    - Timezone-naive inputs are *localized* as UTC
+
+    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Timezone-aware inputs are *converted* to UTC (the output represents the
+      exact same datetime, but viewed from the UTC time offset `+00:00`).
 
     >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
     ...                utc=True)
     DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
                   dtype='datetime64[ns, UTC]', freq=None)
+
+    - Inputs can contain both naive and aware, string or datetime, the above
+      rules still apply
+
+    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 12:00 -0530',
+    ...                datetime(2020, 1, 1, 18),
+    ...                datetime(2020, 1, 1, 18,
+    ...                tzinfo=timezone(-timedelta(hours=1)))],
+    ...                utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 17:30:00+00:00',
+                   '2020-01-01 18:00:00+00:00', '2020-01-01 19:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
     """
     if arg is None:
         return None
@@ -968,9 +1119,9 @@ def _assemble_from_unit_mappings(arg, errors, tz):
     arg : DataFrame
     errors : {'ignore', 'raise', 'coerce'}, default 'raise'
 
-        - If 'raise', then invalid parsing will raise an exception
-        - If 'coerce', then invalid parsing will be set as NaT
-        - If 'ignore', then invalid parsing will return the input
+        - If :const:`'raise'`, then invalid parsing will raise an exception
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`
+        - If :const:`'ignore'`, then invalid parsing will return the input
     tz : None or 'utc'
 
     Returns