REF: de-duplicate Block.__init__

doc/source/user_guide/io.rst

@@ -4735,6 +4735,7 @@ Write to a feather file.
 Read from a feather file.
 
 .. ipython:: python
+   :okwarning:
 
    result = pd.read_feather("example.feather")
    result
@@ -4818,6 +4819,7 @@ Write to a parquet file.
 Read from a parquet file.
 
 .. ipython:: python
+   :okwarning:
 
    result = pd.read_parquet("example_fp.parquet", engine="fastparquet")
    result = pd.read_parquet("example_pa.parquet", engine="pyarrow")
@@ -4827,6 +4829,7 @@ Read from a parquet file.
 Read only certain columns of a parquet file.
 
 .. ipython:: python
+   :okwarning:
 
    result = pd.read_parquet(
        "example_fp.parquet",
@@ -4895,6 +4898,7 @@ Partitioning Parquet files
 Parquet supports partitioning of data based on the values of one or more columns.
 
 .. ipython:: python
+   :okwarning:
 
     df = pd.DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]})
     df.to_parquet(path="test", engine="pyarrow", partition_cols=["a"], compression=None)

doc/source/user_guide/scale.rst

@@ -71,6 +71,7 @@ To load the columns we want, we have two options.
 Option 1 loads in all the data and then filters to what we need.
 
 .. ipython:: python
+   :okwarning:
 
    columns = ["id_0", "name_0", "x_0", "y_0"]
 
@@ -79,6 +80,7 @@ Option 1 loads in all the data and then filters to what we need.
 Option 2 only loads the columns we request.
 
 .. ipython:: python
+   :okwarning:
 
    pd.read_parquet("timeseries_wide.parquet", columns=columns)
 
@@ -98,6 +100,7 @@ referred to as "low-cardinality" data). By using more efficient data types, you
 can store larger datasets in memory.
 
 .. ipython:: python
+   :okwarning:
 
    ts = pd.read_parquet("timeseries.parquet")
    ts
@@ -206,6 +209,7 @@ counts up to this point. As long as each individual file fits in memory, this wi
 work for arbitrary-sized datasets.
 
 .. ipython:: python
+   :okwarning:
 
    %%time
    files = pathlib.Path("data/timeseries/").glob("ts*.parquet")
@@ -289,6 +293,7 @@ returns a Dask Series with the same dtype and the same name.
 To get the actual result you can call ``.compute()``.
 
 .. ipython:: python
+   :okwarning:
 
    %time ddf["name"].value_counts().compute()
 
@@ -322,6 +327,7 @@ Dask implements the most used parts of the pandas API. For example, we can do
 a familiar groupby aggregation.
 
 .. ipython:: python
+   :okwarning:
 
    %time ddf.groupby("name")[["x", "y"]].mean().compute().head()
 
@@ -345,6 +351,7 @@ we need to supply the divisions manually.
 Now we can do things like fast random access with ``.loc``.
 
 .. ipython:: python
+   :okwarning:
 
    ddf.loc["2002-01-01 12:01":"2002-01-01 12:05"].compute()
 

pandas/core/internals/blocks.py

@@ -134,16 +134,20 @@ def __init__(self, values, placement, ndim: int):
             1 for SingleBlockManager/Series, 2 for BlockManager/DataFrame
         """
         # TODO(EA2D): ndim will be unnecessary with 2D EAs
-        self.ndim = self._check_ndim(values, ndim)
         self.mgr_locs = placement
         self.values = self._maybe_coerce_values(values)
+        self.ndim = self._check_ndim(values, ndim)
 
         if self._validate_ndim and self.ndim and len(self.mgr_locs) != len(self.values):
             raise ValueError(
                 f"Wrong number of items passed {len(self.values)}, "
                 f"placement implies {len(self.mgr_locs)}"
             )
 
+        if self.is_extension and self.ndim == 2 and len(self.mgr_locs) != 1:
+            # TODO(EA2D): check unnecessary with 2D EAs
+            raise AssertionError("block.size != values.size")
+
     def _maybe_coerce_values(self, values):
         """
         Ensure we have correctly-typed values.
@@ -180,7 +184,19 @@ def _check_ndim(self, values, ndim):
         ValueError : the number of dimensions do not match
         """
         if ndim is None:
-            ndim = values.ndim
+            warnings.warn(
+                "Accepting ndim=None in the Block constructor is deprecated, "
+                "this will raise in a future version.",
+                FutureWarning,
+                stacklevel=3,
+            )
+            if self.is_extension:
+                if len(self.mgr_locs) != 1:
+                    ndim = 1
+                else:
+                    ndim = 2
+            else:
+                ndim = values.ndim
 
         if self._validate_ndim and values.ndim != ndim:
             raise ValueError(
@@ -1667,33 +1683,6 @@ class ExtensionBlock(Block):
 
     values: ExtensionArray
 
-    def __init__(self, values, placement, ndim: int):
-        """
-        Initialize a non-consolidatable block.
-
-        'ndim' may be inferred from 'placement'.
-
-        This will call continue to call __init__ for the other base
-        classes mixed in with this Mixin.
-        """
-
-        # Placement must be converted to BlockPlacement so that we can check
-        # its length
-        if not isinstance(placement, libinternals.BlockPlacement):
-            placement = libinternals.BlockPlacement(placement)
-
-        # Maybe infer ndim from placement
-        if ndim is None:
-            if len(placement) != 1:
-                ndim = 1
-            else:
-                ndim = 2
-        super().__init__(values, placement, ndim=ndim)
-
-        if self.ndim == 2 and len(self.mgr_locs) != 1:
-            # TODO(EA2D): check unnecessary with 2D EAs
-            raise AssertionError("block.size != values.size")
-
     @property
     def shape(self):
         # TODO(EA2D): override unnecessary with 2D EAs