DOC: document internal methods in types/common.py

Partially addresses gh-15895.

pandas/types/common.py

@@ -31,6 +31,20 @@
 
 
 def _ensure_float(arr):
+    """
+    Ensure that an array object has a float dtype if possible.
+
+    Parameters
+    ----------
+    arr : ndarray, Series
+        The array whose data type we want to enforce as float.
+
+    Returns
+    -------
+    float_arr : The original array cast to the float dtype if
+                possible. Otherwise, the original array is returned.
+    """
+
     if issubclass(arr.dtype.type, (np.integer, np.bool_)):
         arr = arr.astype(float)
     return arr
@@ -46,6 +60,20 @@ def _ensure_float(arr):
 
 
 def _ensure_categorical(arr):
+    """
+    Ensure that an array-like object is a Categorical (if not already).
+
+    Parameters
+    ----------
+    arr : array-like
+        The array that we want to convert into a Categorical.
+
+    Returns
+    -------
+    cat_arr : The original array cast as a Categorical. If it already
+              is a Categorical, we return as is.
+    """
+
     if not is_categorical(arr):
         from pandas import Categorical
         arr = Categorical(arr)
@@ -220,10 +248,22 @@ def is_datetime_or_timedelta_dtype(arr_or_dtype):
 
 def _is_unorderable_exception(e):
     """
-    return a boolean if we an unorderable exception error message
+    Check if the exception raised is an unorderable exception.
+
+    The error message differs for 3 <= PY <= 3.5 and PY >= 3.6, so
+    we need to condition based on Python version.
+
+    Parameters
+    ----------
+    e : Exception or sub-class
+        The exception object to check.
 
-    These are different error message for PY>=3<=3.5 and PY>=3.6
+    Returns
+    -------
+    is_orderable_exc : Whether or not the exception raised is an
+                       unorderable exception.
     """
+
     if PY36:
         return "'>' not supported between instances of" in str(e)
 
@@ -346,7 +386,22 @@ def is_complex_dtype(arr_or_dtype):
 
 
 def _coerce_to_dtype(dtype):
-    """ coerce a string / np.dtype to a dtype """
+    """
+    Coerce a string or np.dtype to a pandas or numpy
+    dtype if possible.
+
+    If we cannot convert to a pandas dtype initially,
+    we convert to a numpy dtype.
+
+    Parameters
+    ----------
+    dtype : The dtype that we want to coerce.
+
+    Returns
+    -------
+    pd_or_np_dtype : The coerced dtype.
+    """
+
     if is_categorical_dtype(dtype):
         dtype = CategoricalDtype()
     elif is_datetime64tz_dtype(dtype):
@@ -359,8 +414,27 @@ def _coerce_to_dtype(dtype):
 
 
 def _get_dtype(arr_or_dtype):
+    """
+    Get the dtype instance associated with an array
+    or dtype object.
+
+    Parameters
+    ----------
+    arr_or_dtype : ndarray, Series, dtype, type
+        The array-like or dtype object whose dtype we want to extract.
+
+    Returns
+    -------
+    obj_dtype : The extract dtype instance from the
+                passed in array or dtype object.
+
+    Raises
+    ------
+    TypeError : The passed in object is None.
+    """
+
     if arr_or_dtype is None:
-        raise TypeError
+        raise TypeError("Cannot deduce dtype from null object")
     if isinstance(arr_or_dtype, np.dtype):
         return arr_or_dtype
     elif isinstance(arr_or_dtype, type):
@@ -385,6 +459,21 @@ def _get_dtype(arr_or_dtype):
 
 
 def _get_dtype_type(arr_or_dtype):
+    """
+    Get the type (NOT dtype) instance associated with
+    an array or dtype object.
+
+    Parameters
+    ----------
+    arr_or_dtype : ndarray, Series, dtype, type
+        The array-like or dtype object whose type we want to extract.
+
+    Returns
+    -------
+    obj_type : The extract type instance from the
+               passed in array or dtype object.
+    """
+
     if isinstance(arr_or_dtype, np.dtype):
         return arr_or_dtype.type
     elif isinstance(arr_or_dtype, type):
@@ -410,16 +499,27 @@ def _get_dtype_type(arr_or_dtype):
 
 
 def _get_dtype_from_object(dtype):
-    """Get a numpy dtype.type-style object. This handles the datetime64[ns]
-    and datetime64[ns, TZ] compat
+    """
+    Get a numpy dtype.type-style object for a dtype object.
+
+    This methods also includes handling of the datetime64[ns] and
+    datetime64[ns, TZ] objects.
+
+    If no dtype can be found, we return ``object``.
+
+    Parameters
+    ----------
+    dtype : dtype, type
+        The dtype object whose numpy dtype.type-style
+        object we want to extract.
 
-    Notes
-    -----
-    If nothing can be found, returns ``object``.
+    Returns
+    -------
+    dtype_object : The extracted numpy dtype.type-style object.
     """
 
-    # type object from a dtype
     if isinstance(dtype, type) and issubclass(dtype, np.generic):
+        # Type object from a dtype
         return dtype
     elif is_categorical(dtype):
         return CategoricalDtype().type
@@ -429,7 +529,7 @@ def _get_dtype_from_object(dtype):
         try:
             _validate_date_like_dtype(dtype)
         except TypeError:
-            # should still pass if we don't have a datelike
+            # Should still pass if we don't have a date-like
             pass
         return dtype.type
     elif isinstance(dtype, string_types):
@@ -444,17 +544,33 @@ def _get_dtype_from_object(dtype):
         try:
             return _get_dtype_from_object(getattr(np, dtype))
         except (AttributeError, TypeError):
-            # handles cases like _get_dtype(int)
-            # i.e., python objects that are valid dtypes (unlike user-defined
-            # types, in general)
-            # TypeError handles the float16 typecode of 'e'
+            # Handles cases like _get_dtype(int) i.e.,
+            # Python objects that are valid dtypes
+            # (unlike user-defined types, in general)
+            #
+            # TypeError handles the float16 type code of 'e'
             # further handle internal types
             pass
 
     return _get_dtype_from_object(np.dtype(dtype))
 
 
 def _validate_date_like_dtype(dtype):
+    """
+    Check whether the dtype is a date-like dtype. Raises an error if invalid.
+
+    Parameters
+    ----------
+    dtype : dtype, type
+        The dtype to check.
+
+    Raises
+    ------
+    TypeError : The dtype could not be casted to a date-like dtype.
+    ValueError : The dtype is an illegal date-like dtype (e.g. the
+                 the frequency provided is too specific)
+    """
+
     try:
         typ = np.datetime_data(dtype)[0]
     except ValueError as e: