BUG: Display precision doesn't affect complex float numbers #25514 (#25745)

Author: sakarpanta

doc/source/whatsnew/v0.25.0.rst

@@ -293,6 +293,7 @@ Numeric
 
 - Bug in :meth:`to_numeric` in which large negative numbers were being improperly handled (:issue:`24910`)
 - Bug in :meth:`to_numeric` in which numbers were being coerced to float, even though ``errors`` was not ``coerce`` (:issue:`24910`)
+- Bug in :class:`format` in which floating point complex numbers were not being formatted to proper display precision and trimming (:issue:`25514`)
 - Bug in error messages in :meth:`DataFrame.corr` and :meth:`Series.corr`. Added the possibility of using a callable. (:issue:`25729`)
 - Bug in :meth:`Series.divmod` and :meth:`Series.rdivmod` which would raise an (incorrect) ``ValueError`` rather than return a pair of :class:`Series` objects as result (:issue:`25557`)
 - Raises a helpful exception when a non-numeric index is sent to :meth:`interpolate` with methods which require numeric index. (:issue:`21662`)

pandas/core/frame.py

@@ -2497,12 +2497,12 @@ def memory_usage(self, index=True, deep=False):
         ...              for t in dtypes])
         >>> df = pd.DataFrame(data)
         >>> df.head()
-           int64  float64  complex128 object  bool
-        0      1      1.0      (1+0j)      1  True
-        1      1      1.0      (1+0j)      1  True
-        2      1      1.0      (1+0j)      1  True
-        3      1      1.0      (1+0j)      1  True
-        4      1      1.0      (1+0j)      1  True
+           int64  float64  complex128  object  bool
+        0      1      1.0    1.0+0.0j       1  True
+        1      1      1.0    1.0+0.0j       1  True
+        2      1      1.0    1.0+0.0j       1  True
+        3      1      1.0    1.0+0.0j       1  True
+        4      1      1.0    1.0+0.0j       1  True
 
         >>> df.memory_usage()
         Index            80

pandas/io/formats/format.py

@@ -19,9 +19,9 @@
 from pandas.compat import lzip
 
 from pandas.core.dtypes.common import (
-    is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype,
-    is_extension_array_dtype, is_float, is_float_dtype, is_integer,
-    is_integer_dtype, is_list_like, is_numeric_dtype, is_scalar,
+    is_categorical_dtype, is_complex_dtype, is_datetime64_dtype,
+    is_datetime64tz_dtype, is_extension_array_dtype, is_float, is_float_dtype,
+    is_integer, is_integer_dtype, is_list_like, is_numeric_dtype, is_scalar,
     is_timedelta64_dtype)
 from pandas.core.dtypes.generic import (
     ABCIndexClass, ABCMultiIndex, ABCSeries, ABCSparseArray)
@@ -892,7 +892,7 @@ def format_array(values, formatter, float_format=None, na_rep='NaN',
         fmt_klass = Timedelta64Formatter
     elif is_extension_array_dtype(values.dtype):
         fmt_klass = ExtensionArrayFormatter
-    elif is_float_dtype(values.dtype):
+    elif is_float_dtype(values.dtype) or is_complex_dtype(values.dtype):
         fmt_klass = FloatArrayFormatter
     elif is_integer_dtype(values.dtype):
         fmt_klass = IntArrayFormatter
@@ -1084,6 +1084,7 @@ def format_values_with(float_format):
 
             # separate the wheat from the chaff
             values = self.values
+            is_complex = is_complex_dtype(values)
             mask = isna(values)
             if hasattr(values, 'to_dense'):  # sparse numpy ndarray
                 values = values.to_dense()
@@ -1094,7 +1095,10 @@ def format_values_with(float_format):
                                            for val in values.ravel()[imask]])
 
             if self.fixed_width:
-                return _trim_zeros(values, na_rep)
+                if is_complex:
+                    return _trim_zeros_complex(values, na_rep)
+                else:
+                    return _trim_zeros_float(values, na_rep)
 
             return values
 
@@ -1424,7 +1428,22 @@ def just(x):
     return result
 
 
-def _trim_zeros(str_floats, na_rep='NaN'):
+def _trim_zeros_complex(str_complexes, na_rep='NaN'):
+    """
+    Separates the real and imaginary parts from the complex number, and
+    executes the _trim_zeros_float method on each of those.
+    """
+    def separate_and_trim(str_complex, na_rep):
+        num_arr = str_complex.split('+')
+        return (_trim_zeros_float([num_arr[0]], na_rep) +
+                ['+'] +
+                _trim_zeros_float([num_arr[1][:-1]], na_rep) +
+                ['j'])
+
+    return [''.join(separate_and_trim(x, na_rep)) for x in str_complexes]
+
+
+def _trim_zeros_float(str_floats, na_rep='NaN'):
     """
     Trims zeros, leaving just one before the decimal points if need be.
     """

pandas/tests/io/formats/test_format.py

@@ -1370,6 +1370,19 @@ def test_to_string_float_index(self):
                     '5.0  4')
         assert result == expected
 
+    def test_to_string_complex_float_formatting(self):
+        # GH #25514
+        with pd.option_context('display.precision', 5):
+            df = DataFrame({'x': [
+                (0.4467846931321966 + 0.0715185102060818j),
+                (0.2739442392974528 + 0.23515228785438969j),
+                (0.26974928742135185 + 0.3250604054898979j)]})
+            result = df.to_string()
+            expected = ('                  x\n0  0.44678+0.07152j\n'
+                        '1  0.27394+0.23515j\n'
+                        '2  0.26975+0.32506j')
+            assert result == expected
+
     def test_to_string_ascii_error(self):
         data = [('0  ', '                        .gitignore ', '     5 ',
                  ' \xe2\x80\xa2\xe2\x80\xa2\xe2\x80'