BUG: Fix FloatingArray output formatting

pandas/core/frame.py

@@ -2656,7 +2656,7 @@ def memory_usage(self, index=True, deep=False) -> Series:
         Examples
         --------
         >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
-        >>> data = dict([(t, np.ones(shape=5000).astype(t))
+        >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
         ...              for t in dtypes])
         >>> df = pd.DataFrame(data)
         >>> df.head()
@@ -2691,7 +2691,7 @@ def memory_usage(self, index=True, deep=False) -> Series:
         int64          40000
         float64        40000
         complex128     80000
-        object        160000
+        object        180000
         bool            5000
         dtype: int64
 
@@ -2790,7 +2790,7 @@ def transpose(self, *args, copy: bool = False) -> DataFrame:
         >>> df2_transposed
                       0     1
         name      Alice   Bob
-        score       9.5     8
+        score       9.5   8.0
         employed  False  True
         kids          0     0
 

pandas/io/formats/format.py

@@ -1311,7 +1311,7 @@ def _format_strings(self) -> List[str]:
             float_format = get_option("display.float_format")
             if float_format is None:
                 precision = get_option("display.precision")
-                float_format = lambda x: f"{x: .{precision:d}g}"
+                float_format = lambda x: f"{x: .{precision:d}f}"
         else:
             float_format = self.float_format
 
@@ -1372,6 +1372,8 @@ def _format(x):
                     tpl = " {v}"
                 fmt_values.append(tpl.format(v=_format(v)))
 
+        fmt_values = _trim_zeros_float(str_floats=fmt_values, decimal=".")
+
         return fmt_values
 
 
@@ -1891,27 +1893,31 @@ def _trim_zeros_float(
     Trims zeros, leaving just one before the decimal points if need be.
     """
     trimmed = str_floats
-    number_regex = re.compile(fr"\s*[\+-]?[0-9]+(\{decimal}[0-9]*)?")
+    number_regex = re.compile(fr"^\s*[\+-]?[0-9]+\{decimal}[0-9]*$")
 
-    def _is_number(x):
+    def is_number_with_decimal(x):
         return re.match(number_regex, x) is not None
 
-    def _cond(values):
-        finite = [x for x in values if _is_number(x)]
-        has_decimal = [decimal in x for x in finite]
+    def should_trim(values: Union[np.ndarray, List[str]]) -> bool:
+        """
+        Determine if an array of strings should be trimmed.
 
-        return (
-            len(finite) > 0
-            and all(has_decimal)
-            and all(x.endswith("0") for x in finite)
-            and not (any(("e" in x) or ("E" in x) for x in finite))
-        )
+        Returns True if all numbers containing decimals (defined by the
+        above regular expression) within the array end in a zero, otherwise
+        returns False.
+        """
+        numbers = [x for x in values if is_number_with_decimal(x)]
+        return len(numbers) > 0 and all(x.endswith("0") for x in numbers)
 
-    while _cond(trimmed):
-        trimmed = [x[:-1] if _is_number(x) else x for x in trimmed]
+    while should_trim(trimmed):
+        trimmed = [x[:-1] if is_number_with_decimal(x) else x for x in trimmed]
 
     # leave one 0 after the decimal points if need be.
-    return [x + "0" if x.endswith(decimal) and _is_number(x) else x for x in trimmed]
+    result = [
+        x + "0" if is_number_with_decimal(x) and x.endswith(decimal) else x
+        for x in trimmed
+    ]
+    return result
 
 
 def _has_names(index: Index) -> bool:

pandas/tests/io/formats/test_format.py

@@ -3439,3 +3439,25 @@ def test_to_string_complex_number_trims_zeros():
     result = s.to_string()
     expected = "0    1.00+1.00j\n1    1.00+1.00j\n2    1.05+1.00j"
     assert result == expected
+
+
+def test_nullable_float_to_string(float_ea_dtype):
+    # https://github.com/pandas-dev/pandas/issues/36775
+    dtype = float_ea_dtype
+    s = pd.Series([0.0, 1.0, None], dtype=dtype)
+    result = s.to_string()
+    expected = """0     0.0
+1     1.0
+2    <NA>"""
+    assert result == expected
+
+
+def test_nullable_int_to_string(any_nullable_int_dtype):
+    # https://github.com/pandas-dev/pandas/issues/36775
+    dtype = any_nullable_int_dtype
+    s = pd.Series([0, 1, None], dtype=dtype)
+    result = s.to_string()
+    expected = """0       0
+1       1
+2    <NA>"""
+    assert result == expected