ENH: support Akima 1D interpolation

doc/source/whatsnew/v0.17.1.txt

@@ -25,6 +25,7 @@ Enhancements
   objects for the ``filepath_or_buffer`` argument. (:issue:`11033`)
 - ``DataFrame`` now uses the fields of a ``namedtuple`` as columns, if columns are not supplied (:issue:`11181`)
 - Improve the error message displayed in :func:`pandas.io.gbq.to_gbq` when the DataFrame does not match the schema of the destination table (:issue:`11359`)
+- Akima 1D interpolation is now supported (:issue:`7588`)
 
 .. _whatsnew_0171.api:
 
@@ -118,3 +119,7 @@ Bug Fixes
 - Bug in ``to_excel`` with openpyxl 2.2+ and merging (:issue:`11408`)
 
 - Bug in ``DataFrame.to_dict()`` produces a ``np.datetime64`` object instead of ``Timestamp`` when only datetime is present in data (:issue:`11327`)
+
+- Bug in ``Panel.fillna()`` does not fill across axis 0 (:issue:`8251`)
+
+- Bug in ``Panel.fillna()`` loses index names (:issue:`3570`)

pandas/core/categorical.py

@@ -10,7 +10,7 @@
 from pandas.core.algorithms import factorize
 from pandas.core.base import PandasObject, PandasDelegate
 import pandas.core.common as com
-from pandas.core.missing import interpolate_2d
+from pandas.core.missing import pad
 from pandas.util.decorators import cache_readonly, deprecate_kwarg
 
 from pandas.core.common import (ABCSeries, ABCIndexClass, ABCPeriodIndex, ABCCategoricalIndex,
@@ -1313,8 +1313,7 @@ def fillna(self, value=None, method=None, limit=None):
         if method is not None:
 
             values = self.to_dense().reshape(-1, len(self))
-            values = interpolate_2d(
-                values, method, 0, None, value).astype(self.categories.dtype)[0]
+            values = pad(values, method, 0, None, value).astype(self.categories.dtype)[0]
             values = _get_codes_for_values(values, self.categories)
 
         else:

pandas/core/generic.py

@@ -2774,39 +2774,27 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
         # set the default here, so functions examining the signaure
         # can detect if something was set (e.g. in groupby) (GH9221)
         if axis is None:
-            axis = 0
+            axis = self._stat_axis_name
         axis = self._get_axis_number(axis)
         method = mis._clean_fill_method(method)
 
         from pandas import DataFrame
         if value is None:
             if method is None:
                 raise ValueError('must specify a fill method or value')
-            if self._is_mixed_type and axis == 1:
+            if self._is_mixed_type:
+                if (self.ndim > 2) and (axis == 0):
+                    raise NotImplementedError('cannot fill across axis 0 for mixed dtypes')
                 if inplace:
-                    raise NotImplementedError()
-                result = self.T.fillna(method=method, limit=limit).T
-
-                # need to downcast here because of all of the transposes
-                result._data = result._data.downcast()
-
-                return result
-
-            # > 3d
-            if self.ndim > 3:
-                raise NotImplementedError(
-                    'Cannot fillna with a method for > 3dims'
-                )
+                    raise NotImplementedError('cannot fill inplace for mixed dtypes')
+                elif (self.ndim == 2) and (axis == 1):
+                    result = self.T.fillna(method=method, limit=limit).T
 
-            # 3d
-            elif self.ndim == 3:
+                    # need to downcast here because of all of the transposes
+                    result._data = result._data.downcast()
 
-                # fill in 2d chunks
-                result = dict([(col, s.fillna(method=method, value=value))
-                               for col, s in compat.iteritems(self)])
-                return self._constructor.from_dict(result).__finalize__(self)
+                    return result
 
-            # 2d or less
             method = mis._clean_fill_method(method)
             new_data = self._data.interpolate(method=method,
                                               axis=axis,

pandas/core/internals.py

@@ -910,12 +910,12 @@ def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
         values = self.values if inplace else self.values.copy()
         values, _, fill_value, _ = self._try_coerce_args(values, fill_value)
         values = self._try_operate(values)
-        values = mis.interpolate_2d(values,
-                                    method=method,
-                                    axis=axis,
-                                    limit=limit,
-                                    fill_value=fill_value,
-                                    dtype=self.dtype)
+        values = mis.pad(values,
+                         method=method,
+                         axis=axis,
+                         limit=limit,
+                         fill_value=fill_value,
+                         dtype=self.dtype)
         values = self._try_coerce_result(values)
 
         blocks = [self.make_block(values,
@@ -950,8 +950,8 @@ def func(x):
 
             # process a 1-d slice, returning it
             # should the axis argument be handled below in apply_along_axis?
-            # i.e. not an arg to mis.interpolate_1d
-            return mis.interpolate_1d(index, x, method=method, limit=limit,
+            # i.e. not an arg to mis.interpolate
+            return mis.interpolate(index, x, method=method, limit=limit,
                                       limit_direction=limit_direction,
                                       fill_value=fill_value,
                                       bounds_error=False, **kwargs)
@@ -2358,7 +2358,7 @@ def make_block_same_class(self, values, placement,
     def interpolate(self, method='pad', axis=0, inplace=False,
                     limit=None, fill_value=None, **kwargs):
 
-        values = mis.interpolate_2d(
+        values = mis.pad(
             self.values.to_dense(), method, axis, limit, fill_value)
         return self.make_block_same_class(values=values,
                                           placement=self.mgr_locs)
@@ -3774,7 +3774,7 @@ def reindex(self, new_axis, indexer=None, method=None, fill_value=None,
 
         # fill if needed
         if method is not None or limit is not None:
-            new_values = mis.interpolate_2d(new_values, method=method,
+            new_values = mis.pad(new_values, method=method,
                                             limit=limit, fill_value=fill_value)
 
         if self._block.is_sparse:

pandas/core/missing.py

@@ -39,7 +39,7 @@ def _clean_interp_method(method, **kwargs):
     valid = ['linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear',
              'quadratic', 'cubic', 'barycentric', 'polynomial',
              'krogh', 'piecewise_polynomial',
-             'pchip', 'spline']
+             'pchip', 'spline', 'akima']
     if method in ('spline', 'polynomial') and order is None:
         raise ValueError("You must specify the order of the spline or "
                          "polynomial.")
@@ -49,9 +49,9 @@ def _clean_interp_method(method, **kwargs):
     return method
 
 
-def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
-                   limit_direction='forward',
-                   fill_value=None, bounds_error=False, order=None, **kwargs):
+def interpolate(xvalues, yvalues, method='linear', limit=None,
+                limit_direction='forward',
+                fill_value=None, bounds_error=False, order=None, **kwargs):
     """
     Logic for the 1-d interpolation.  The result should be 1-d, inputs
     xvalues and yvalues will each be 1-d arrays of the same length.
@@ -144,7 +144,7 @@ def _interp_limit(invalid, fw_limit, bw_limit):
 
     sp_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic',
                   'barycentric', 'krogh', 'spline', 'polynomial',
-                  'piecewise_polynomial', 'pchip']
+                  'piecewise_polynomial', 'pchip', 'akima']
     if method in sp_methods:
         inds = np.asarray(xvalues)
         # hack for DatetimeIndex, #1646
@@ -156,6 +156,8 @@ def _interp_limit(invalid, fw_limit, bw_limit):
             bounds_error=bounds_error, order=order, **kwargs)
         result[violate_limit] = np.nan
         return result
+    else: 
+        raise ValueError('interpolation method not found')
 
 
 def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
@@ -214,20 +216,51 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
             y = y.copy()
         if not new_x.flags.writeable:
             new_x = new_x.copy()
-        method = alt_methods[method]
-        new_y = method(x, y, new_x, **kwargs)
+        if method == 'akima':
+            try:
+                interpolator = interpolate.Akima1DInterpolator(x, y)
+            except AttributeError:
+                raise ImportError("Your version of scipy does not support "
+                                  "Akima interpolation" )
+            new_y = interpolator(new_x)
+        else:
+            method = alt_methods[method]
+            new_y = method(x, y, new_x, **kwargs)
     return new_y
 
 
-def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None):
-    """ perform an actual interpolation of values, values will be make 2-d if
-    needed fills inplace, returns the result
+def pad(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None):
+    """ 
+    Perform an actual interpolation of values. 1-d values will be made 2-d temporarily. 
+    Returns the result
     """
 
+    ndim = values.ndim
+    shape = values.shape
+
+    func = partial(pad, method=method, limit=limit, fill_value=fill_value, dtype=dtype)
+
+    if ndim > 2:
+        if ndim == 3:
+            if axis == 0:
+                for n in range(shape[1]):
+                    values[:,n] = func(values[:,n], axis=1)
+            else:
+                for n in range(shape[0]):
+                    values[n] = func(values[n], axis=(1 if axis == 1 else 0))
+        else:
+            if axis == 0:
+                for n in range(shape[1]):
+                    values[:,n] = func(values[:,n], axis=0)
+            else:
+                for n in range(shape[0]):
+                    values[n] = func(values[n], axis=axis-1)
+
+        return values
+
     transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
 
     # reshape a 1 dim if needed
-    ndim = values.ndim
     if values.ndim == 1:
         if axis != 0:  # pragma: no cover
             raise AssertionError("cannot interpolate on a ndim == 1 with "

pandas/tests/test_generic.py

@@ -1167,6 +1167,15 @@ def test_interp_alt_scipy(self):
         expected.ix[5,'A'] = 6.125
         assert_frame_equal(result, expected)
 
+        try:
+            from scipy.interpolate import Akima1DInterpolator
+        except ImportError:
+            raise nose.SkipTest('scipy.interpolate.Akima1DInterpolator missing')
+        result = df.interpolate(method='akima')
+        expected.ix[2,'A'] = 3
+        expected.ix[5,'A'] = 6
+        assert_frame_equal(result, expected)
+
     def test_interp_rowwise(self):
         df = DataFrame({0: [1, 2, np.nan, 4],
                         1: [2, 3, 4, np.nan],

pandas/tests/test_panel.py

@@ -1454,20 +1454,95 @@ def test_fillna(self):
         assert_frame_equal(filled['ItemA'],
                            panel['ItemA'].fillna(method='backfill'))
 
+        # Fill forward.
+        filled = self.panel.fillna(method='ffill')
+        assert_frame_equal(filled['ItemA'],
+                           self.panel['ItemA'].fillna(method='ffill'))
+
+        # With limit.
+        filled = self.panel.fillna(method='backfill', limit=1)
+        assert_frame_equal(filled['ItemA'],
+                           self.panel['ItemA'].fillna(method='backfill', limit=1))
+
+        # With downcast.
+        rounded = self.panel.apply(lambda x: x.apply(np.round))
+        filled = rounded.fillna(method='backfill', downcast='infer')
+        assert_frame_equal(filled['ItemA'],
+                           rounded['ItemA'].fillna(method='backfill', downcast='infer'))
+
+        # Now explicitly request axis 1.
+        filled = self.panel.fillna(method='backfill', axis=1)
+        assert_frame_equal(filled['ItemA'],
+                           self.panel['ItemA'].fillna(method='backfill', axis=0))
+
+        # Fill along axis 2, equivalent to filling along axis 1 of each
+        # DataFrame.
+        filled = self.panel.fillna(method='backfill', axis=2)
+        assert_frame_equal(filled['ItemA'],
+                           self.panel['ItemA'].fillna(method='backfill', axis=1))
+
+        # Fill an empty panel.
         empty = self.panel.reindex(items=[])
         filled = empty.fillna(0)
         assert_panel_equal(filled, empty)
 
+        # either method or value must be specified
         self.assertRaises(ValueError, self.panel.fillna)
+        # method and value can not both be specified
         self.assertRaises(ValueError, self.panel.fillna, 5, method='ffill')
 
+        # can't pass list or tuple, only scalar
         self.assertRaises(TypeError, self.panel.fillna, [1, 2])
         self.assertRaises(TypeError, self.panel.fillna, (1, 2))
 
         # limit not implemented when only value is specified
         p = Panel(np.random.randn(3,4,5))
         p.iloc[0:2,0:2,0:2] = np.nan
-        self.assertRaises(NotImplementedError, lambda : p.fillna(999,limit=1))
+        self.assertRaises(NotImplementedError, lambda : p.fillna(999, limit=1))
+
+    def test_fillna_axis_0(self):
+        # GH 8395
+
+        # Forward fill along axis 0, interpolating values across DataFrames.
+        filled = self.panel.fillna(method='ffill', axis=0)
+        nan_indexes = self.panel['ItemB']['C'].index[
+            self.panel['ItemB']['C'].apply(np.isnan)]
+
+        # Values from ItemA are filled into ItemB.
+        assert_series_equal(filled['ItemB']['C'][nan_indexes],
+                            self.panel['ItemA']['C'][nan_indexes])
+
+        # Backfill along axis 0.
+        filled = self.panel.fillna(method='backfill', axis=0)
+
+        # The test data lacks values that can be backfilled on axis 0.
+        assert_panel_equal(filled, self.panel)
+
+        # Reverse the panel and backfill along axis 0, to properly test
+        # backfill.
+        reverse_panel = self.panel.reindex_axis(reversed(self.panel.axes[0]))
+        filled = reverse_panel.fillna(method='bfill', axis=0)
+        nan_indexes = reverse_panel['ItemB']['C'].index[
+            reverse_panel['ItemB']['C'].apply(np.isnan)]
+        assert_series_equal(filled['ItemB']['C'][nan_indexes],
+                            reverse_panel['ItemA']['C'][nan_indexes])
+
+        # Fill along axis 0 with limit.
+        filled = self.panel.fillna(method='ffill', axis=0, limit=1)
+        a_nan = self.panel['ItemA']['C'].index[
+            self.panel['ItemA']['C'].apply(np.isnan)]
+        b_nan = self.panel['ItemB']['C'].index[
+            self.panel['ItemB']['C'].apply(np.isnan)]
+
+        # Cells that are nan in ItemB but not in ItemA remain unfilled in
+        # ItemC.
+        self.assertTrue(
+            filled['ItemC']['C'][b_nan.diff(a_nan)].apply(np.isnan).all())
+
+        # limit not implemented when only value is specified
+        panel = self.panel.copy()
+        panel['str'] = 'foo'
+        self.assertRaises(NotImplementedError, lambda : panel.fillna(method='ffill', axis=0))        
 
     def test_ffill_bfill(self):
         assert_panel_equal(self.panel.ffill(),