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ENH: Adding argmin, argmax to Series and DataFrame. #286

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ENH: Adding argmin, argmax to Series and DataFrame. #286

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50 changes: 49 additions & 1 deletion pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2488,6 +2488,30 @@ def min(self, axis=0, skipna=True):
np.putmask(values, -np.isfinite(values), np.inf)
return Series(values.min(axis), index=self._get_agg_axis(axis))

def argmin(self, axis=0, skipna=True):
"""
Return index of first occurence of minimum over requested axis.
NA/null values are excluded.

Parameters
----------
axis : {0, 1}
0 for row-wise, 1 for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA

Returns
-------
argmin : Series
"""
values = self.values.copy()
if skipna and not issubclass(values.dtype.type, np.integer):
np.putmask(values, -np.isfinite(values), np.inf)
argmin_index = self._get_agg_axis([1, 0][axis])
return Series([argmin_index[i] for i in values.argmin(axis)],
index=self._get_agg_axis(axis))

def max(self, axis=0, skipna=True):
"""
Return maximum over requested axis. NA/null values are excluded
Expand All @@ -2498,7 +2522,7 @@ def max(self, axis=0, skipna=True):
0 for row-wise, 1 for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
will be first index.

Returns
-------
Expand All @@ -2509,6 +2533,30 @@ def max(self, axis=0, skipna=True):
np.putmask(values, -np.isfinite(values), -np.inf)
return Series(values.max(axis), index=self._get_agg_axis(axis))

def argmax(self, axis=0, skipna=True):
"""
Return index of first occurence of maximum over requested axis.
NA/null values are excluded.

Parameters
----------
axis : {0, 1}
0 for row-wise, 1 for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be first index.

Returns
-------
max : Series
"""
values = self.values.copy()
if skipna and not issubclass(values.dtype.type, np.integer):
np.putmask(values, -np.isfinite(values), -np.inf)
argmax_index = self._get_agg_axis([1, 0][axis])
return Series([argmax_index[i] for i in values.argmax(axis)],
index=self._get_agg_axis(axis))

def prod(self, axis=0, skipna=True):
"""
Return product over requested axis. NA/null values are treated as 1
Expand Down
38 changes: 38 additions & 0 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -718,6 +718,25 @@ def min(self, axis=None, out=None, skipna=True):
np.putmask(arr, isnull(arr), np.inf)
return arr.min()

def argmin(self, axis=None, out=None, skipna=True):
"""
Index of first occurence of minimum of values.

Parameters
----------
skipna : boolean, default True
Exclude NA/null values

Returns
-------
Index of mimimum of values.
"""
arr = self.values.copy()
if skipna:
if not issubclass(arr.dtype.type, np.integer):
np.putmask(arr, isnull(arr), np.inf)
return self.index[arr.argmin()]

def max(self, axis=None, out=None, skipna=True):
"""
Maximum of values
Expand All @@ -737,6 +756,25 @@ def max(self, axis=None, out=None, skipna=True):
np.putmask(arr, isnull(arr), -np.inf)
return arr.max()

def argmax(self, axis=None, out=None, skipna=True):
"""
Index of first occurence of maximum of values.

Parameters
----------
skipna : boolean, default True
Exclude NA/null values

Returns
-------
Index of mimimum of values.
"""
arr = self.values.copy()
if skipna:
if not issubclass(arr.dtype.type, np.integer):
np.putmask(arr, isnull(arr), -np.inf)
return self.index[arr.argmax()]

def std(self, axis=None, dtype=None, out=None, ddof=1, skipna=True):
"""
Unbiased standard deviation of values
Expand Down
64 changes: 64 additions & 0 deletions pandas/tests/test_frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2729,10 +2729,74 @@ def test_min(self):
self._check_stat_op('min', np.min)
self._check_stat_op('min', np.min, frame=self.intframe)

def test_argmin(self):
def validate(f, s, axis, skipna):
def get_result(f, i, v, axis, skipna):
if axis == 0:
return (f[i][v], f[i].min(skipna=skipna))
else:
return (f[v][i], f.ix[i].min(skipna=skipna))
for i, v in s.iteritems():
(r1, r2) = get_result(f, i, v, axis, skipna)
if np.isnan(r1) or np.isinf(r1):
self.assert_(np.isnan(r2) or np.isinf(r2))
elif np.isnan(r2) or np.isinf(r2):
self.assert_(np.isnan(r1) or np.isinf(r1))
else:
self.assertEqual(r1, r2)

frame = self.frame
frame.ix[5:10] = np.nan
frame.ix[15:20, -2:] = np.nan
for skipna in [True, False]:
for axis in [0, 1]:
validate(frame,
frame.argmin(axis=axis, skipna=skipna),
axis,
skipna)
validate(self.intframe,
self.intframe.argmin(axis=axis, skipna=skipna),
axis,
skipna)

self.assertRaises(Exception, frame.argmin, axis=2)

def test_max(self):
self._check_stat_op('max', np.max)
self._check_stat_op('max', np.max, frame=self.intframe)

def test_argmax(self):
def validate(f, s, axis, skipna):
def get_result(f, i, v, axis, skipna):
if axis == 0:
return (f[i][v], f[i].max(skipna=skipna))
else:
return (f[v][i], f.ix[i].max(skipna=skipna))
for i, v in s.iteritems():
(r1, r2) = get_result(f, i, v, axis, skipna)
if np.isnan(r1) or np.isinf(r1):
self.assert_(np.isnan(r2) or np.isinf(r2))
elif np.isnan(r2) or np.isinf(r2):
self.assert_(np.isnan(r1) or np.isinf(r1))
else:
self.assertEqual(r1, r2)

frame = self.frame
frame.ix[5:10] = np.nan
frame.ix[15:20, -2:] = np.nan
for skipna in [True, False]:
for axis in [0, 1]:
validate(frame,
frame.argmax(axis=axis, skipna=skipna),
axis,
skipna)
validate(self.intframe,
self.intframe.argmax(axis=axis, skipna=skipna),
axis,
skipna)

self.assertRaises(Exception, frame.argmax, axis=2)

def test_mad(self):
f = lambda x: np.abs(x - x.mean()).mean()
self._check_stat_op('mad', f)
Expand Down
44 changes: 44 additions & 0 deletions pandas/tests/test_series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -492,9 +492,53 @@ def test_prod(self):
def test_min(self):
self._check_stat_op('min', np.min)

def test_argmin(self):
"""
test argmin
_check_stat_op approach can not be used here because of isnull check.
"""
# add some NaNs
self.series[5:15] = np.NaN

# skipna or no
self.assertEqual(self.series[self.series.argmin()], self.series.min())
self.assert_(isnull(self.series[self.series.argmin(skipna=False)]))

# no NaNs
nona = self.series.dropna()
self.assertEqual(nona[nona.argmin()], nona.min())
self.assertEqual(nona.index.values.tolist().index(nona.argmin()),
nona.values.argmin())

# all NaNs
allna = self.series * nan
self.assertEqual(allna.argmin(), allna.index[0])

def test_max(self):
self._check_stat_op('max', np.max)

def test_argmax(self):
"""
test argmax
_check_stat_op approach can not be used here because of isnull check.
"""
# add some NaNs
self.series[5:15] = np.NaN

# skipna or no
self.assertEqual(self.series[self.series.argmax()], self.series.max())
self.assert_(isnull(self.series[self.series.argmax(skipna=False)]))

# no NaNs
nona = self.series.dropna()
self.assertEqual(nona[nona.argmax()], nona.max())
self.assertEqual(nona.index.values.tolist().index(nona.argmax()),
nona.values.argmax())

# all NaNs
allna = self.series * nan
self.assertEqual(allna.argmax(), allna.index[0])

def test_std(self):
alt = lambda x: np.std(x, ddof=1)
self._check_stat_op('std', alt)
Expand Down