DEPR: nested parse_dates in read_csv

asv_bench/benchmarks/io/csv.py

@@ -445,16 +445,6 @@ def setup(self, engine):
         data = data.format(*two_cols)
         self.StringIO_input = StringIO(data)
 
-    def time_multiple_date(self, engine):
-        read_csv(
-            self.data(self.StringIO_input),
-            engine=engine,
-            sep=",",
-            header=None,
-            names=list(string.digits[:9]),
-            parse_dates=[[1, 2], [1, 3]],
-        )
-
     def time_baseline(self, engine):
         read_csv(
             self.data(self.StringIO_input),

doc/source/user_guide/io.rst

@@ -267,13 +267,10 @@ skip_blank_lines : boolean, default ``True``
 Datetime handling
 +++++++++++++++++
 
-parse_dates : boolean or list of ints or names or list of lists or dict, default ``False``.
+parse_dates : boolean or list of ints or names, default ``False``.
   * If ``True`` -> try parsing the index.
   * If ``[1, 2, 3]`` ->  try parsing columns 1, 2, 3 each as a separate date
     column.
-  * If ``[[1, 3]]`` -> combine columns 1 and 3 and parse as a single date
-    column.
-  * If ``{'foo': [1, 3]}`` -> parse columns 1, 3 as date and call result 'foo'.
 
   .. note::
      A fast-path exists for iso8601-formatted dates.
@@ -828,74 +825,6 @@ The simplest case is to just pass in ``parse_dates=True``:
    # These are Python datetime objects
    df.index
 
-It is often the case that we may want to store date and time data separately,
-or store various date fields separately. the ``parse_dates`` keyword can be
-used to specify a combination of columns to parse the dates and/or times from.
-
-You can specify a list of column lists to ``parse_dates``, the resulting date
-columns will be prepended to the output (so as to not affect the existing column
-order) and the new column names will be the concatenation of the component
-column names:
-
-.. ipython:: python
-   :okwarning:
-
-   data = (
-       "KORD,19990127, 19:00:00, 18:56:00, 0.8100\n"
-       "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n"
-       "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n"
-       "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n"
-       "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n"
-       "KORD,19990127, 23:00:00, 22:56:00, -0.5900"
-   )
-
-   with open("tmp.csv", "w") as fh:
-       fh.write(data)
-
-   df = pd.read_csv("tmp.csv", header=None, parse_dates=[[1, 2], [1, 3]])
-   df
-
-By default the parser removes the component date columns, but you can choose
-to retain them via the ``keep_date_col`` keyword:
-
-.. ipython:: python
-   :okwarning:
-
-   df = pd.read_csv(
-       "tmp.csv", header=None, parse_dates=[[1, 2], [1, 3]], keep_date_col=True
-   )
-   df
-
-Note that if you wish to combine multiple columns into a single date column, a
-nested list must be used. In other words, ``parse_dates=[1, 2]`` indicates that
-the second and third columns should each be parsed as separate date columns
-while ``parse_dates=[[1, 2]]`` means the two columns should be parsed into a
-single column.
-
-You can also use a dict to specify custom name columns:
-
-.. ipython:: python
-   :okwarning:
-
-   date_spec = {"nominal": [1, 2], "actual": [1, 3]}
-   df = pd.read_csv("tmp.csv", header=None, parse_dates=date_spec)
-   df
-
-It is important to remember that if multiple text columns are to be parsed into
-a single date column, then a new column is prepended to the data. The ``index_col``
-specification is based off of this new set of columns rather than the original
-data columns:
-
-
-.. ipython:: python
-   :okwarning:
-
-   date_spec = {"nominal": [1, 2], "actual": [1, 3]}
-   df = pd.read_csv(
-       "tmp.csv", header=None, parse_dates=date_spec, index_col=0
-   )  # index is the nominal column
-   df
-
 .. note::
    If a column or index contains an unparsable date, the entire column or
    index will be returned unaltered as an object data type. For non-standard
@@ -908,10 +837,6 @@ data columns:
    for your data to store datetimes in this format, load times will be
    significantly faster, ~20x has been observed.
 
-.. deprecated:: 2.2.0
-   Combining date columns inside read_csv is deprecated. Use ``pd.to_datetime``
-   on the relevant result columns instead.
-
 
 Date parsing functions
 ++++++++++++++++++++++

doc/source/whatsnew/v3.0.0.rst

@@ -225,6 +225,7 @@ Removal of prior version deprecations/changes
 - Disallow calling :meth:`Series.replace` or :meth:`DataFrame.replace` without a ``value`` and with non-dict-like ``to_replace`` (:issue:`33302`)
 - Disallow constructing a :class:`arrays.SparseArray` with scalar data (:issue:`53039`)
 - Disallow indexing an :class:`Index` with a boolean indexer of length zero, it now raises ``ValueError`` (:issue:`55820`)
+- Disallow nested sequences for 'parse_dates' in :func:`read_csv`, combine the desired columns using :func:`to_datetime` after parsing instead (:issue:`55569`)
 - Disallow non-standard (``np.ndarray``, :class:`Index`, :class:`ExtensionArray`, or :class:`Series`) to :func:`isin`, :func:`unique`, :func:`factorize` (:issue:`52986`)
 - Disallow passing a pandas type to :meth:`Index.view` (:issue:`55709`)
 - Disallow units other than "s", "ms", "us", "ns" for datetime64 and timedelta64 dtypes in :func:`array` (:issue:`53817`)

pandas/io/parsers/readers.py

@@ -814,12 +814,11 @@ def read_csv(
         ):
             depr = True
         if depr:
-            warnings.warn(
-                "Support for nested sequences for 'parse_dates' in pd.read_csv "
-                "is deprecated. Combine the desired columns with pd.to_datetime "
-                "after parsing instead.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
+            raise ValueError(
+                # GH#55569
+                "Nested sequences for 'parse_dates' is no longer supported. "
+                "Combine the desired columns with pd.to_datetime after parsing "
+                "instead."
             )
 
     if infer_datetime_format is not lib.no_default: