Avoid float16

Author: rhshadrach

doc/source/user_guide/basics.rst

@@ -2063,14 +2063,13 @@ or a passed ``Series``), then it will be preserved in DataFrame operations. Furt
 different numeric dtypes will **NOT** be combined. The following example will give you a taste.
 
 .. ipython:: python
-   :okwarning:
 
-   df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32")
+   df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float64")
    df1
    df1.dtypes
    df2 = pd.DataFrame(
        {
-           "A": pd.Series(np.random.randn(8), dtype="float16"),
+           "A": pd.Series(np.random.randn(8), dtype="float32"),
            "B": pd.Series(np.random.randn(8)),
            "C": pd.Series(np.random.randint(0, 255, size=8), dtype="uint8"),  # [0,255] (range of uint8)
        }

doc/source/whatsnew/v0.11.0.rst

@@ -73,12 +73,11 @@ Dtypes
 Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste.
 
 .. ipython:: python
-   :okwarning:
 
-   df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
+   df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float64')
    df1
    df1.dtypes
-   df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
+   df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float32'),
                        'B': pd.Series(np.random.randn(8)),
                        'C': pd.Series(range(8), dtype='uint8')})
    df2

pandas/core/arrays/datetimelike.py

@@ -2520,6 +2520,10 @@ def _validate_inferred_freq(
     freq : DateOffset or None
     """
     if inferred_freq is not None:
+        offset1 = to_offset(freq)
+        offset2 = to_offset(inferred_freq)
+        print(offset1, offset2)
+        print(type(offset1), type(offset2))
         if freq is not None and freq != inferred_freq:
             raise ValueError(
                 f"Inferred frequency {inferred_freq} from passed "