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ENH: Added the level arguments to the series and frame math operations #313

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2 pandas/__init__.py
@@ -9,7 +9,7 @@
try:
import pandas._tseries as lib
except Exception, e: # pragma: no cover
- if 'No module named' in e.message:
+ if 'No module named' in str(e):
raise ImportError('C extensions not built: if you installed already '
'verify that you are not importing from the source '
'directory')
View
85 pandas/core/frame.py
@@ -2425,7 +2425,7 @@ def _count_level(self, level, axis=0, numeric_only=False):
return DataFrame(result, index=index, columns=columns)
- def sum(self, axis=0, numeric_only=False, skipna=True):
+ def sum(self, axis=0, numeric_only=False, skipna=True, level=None):
"""
Return sum over requested axis
@@ -2438,6 +2438,8 @@ def sum(self, axis=0, numeric_only=False, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Examples
--------
@@ -2456,6 +2458,10 @@ def sum(self, axis=0, numeric_only=False, skipna=True):
-------
sum : Series
"""
+ if not level is None:
@wesm

wesm Nov 6, 2011

Owner

You can write level is not None instead of not level is None

@adamklein

adamklein Nov 7, 2011

Contributor

Is this the preferred syntax? I know they're the same.

@wesm

wesm Nov 7, 2011

Owner

I see x is not None more than not x is None, but they're indeed equivalent
+ sumfunc = lambda x: x.sum(skipna=skipna)
+ return self.groupby(level=level).aggregate(sumfunc)
+
y, axis_labels = self._get_agg_data(axis, numeric_only=numeric_only)
if len(axis_labels) == 0:
@@ -2479,7 +2485,7 @@ def sum(self, axis=0, numeric_only=False, skipna=True):
return Series(the_sum, index=axis_labels)
- def min(self, axis=0, skipna=True):
+ def min(self, axis=0, skipna=True, level=None):
"""
Return minimum over requested axis. NA/null values are excluded
@@ -2490,6 +2496,8 @@ def min(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
@@ -2498,9 +2506,14 @@ def min(self, axis=0, skipna=True):
values = self.values.copy()
if skipna and not issubclass(values.dtype.type, np.integer):
np.putmask(values, -np.isfinite(values), np.inf)
+
+ if not level is None:
+ minfunc = lambda x: x.min(skipna=skipna)
+ return self.groupby(level=level).aggregate(minfunc)
+
return Series(values.min(axis), index=self._get_agg_axis(axis))
- def max(self, axis=0, skipna=True):
+ def max(self, axis=0, skipna=True, level=None):
"""
Return maximum over requested axis. NA/null values are excluded
@@ -2511,6 +2524,8 @@ def max(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
@@ -2519,9 +2534,14 @@ def max(self, axis=0, skipna=True):
values = self.values.copy()
if skipna and not issubclass(values.dtype.type, np.integer):
np.putmask(values, -np.isfinite(values), -np.inf)
+
+ if not level is None:
+ maxfunc = lambda x: x.max(skipna=skipna)
+ return self.groupby(level=level).aggregate(maxfunc)
+
return Series(values.max(axis), index=self._get_agg_axis(axis))
- def prod(self, axis=0, skipna=True):
+ def prod(self, axis=0, skipna=True, level=None):
"""
Return product over requested axis. NA/null values are treated as 1
@@ -2532,23 +2552,28 @@ def prod(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
product : Series
"""
+ if not level is None:
+ prodfunc = lambda x: x.prod(skipna=skipna)
+ return self.groupby(level=level).aggregate(prodfunc)
+
y = np.array(self.values, subok=True)
if skipna:
if not issubclass(y.dtype.type, np.integer):
y[np.isnan(y)] = 1
result = y.prod(axis)
count = self.count(axis)
result[count == 0] = nan
- return Series(result, index=self._get_agg_axis(axis))
- product = prod
+ return Series(result, index=self._get_agg_axis(axis))
- def mean(self, axis=0, skipna=True):
+ def mean(self, axis=0, skipna=True, level=None):
"""
Return mean over requested axis. NA/null values are excluded
@@ -2559,11 +2584,17 @@ def mean(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
mean : Series
"""
+ if not level is None:
+ meanfunc = lambda x: x.mean(skipna=skipna)
+ return self.groupby(level=level).aggregate(meanfunc)
+
summed = self.sum(axis, numeric_only=True, skipna=skipna)
count = self.count(axis, numeric_only=True).astype(float)
return summed / count
@@ -2599,7 +2630,7 @@ def f(arr):
return self.apply(f, axis=axis)
- def median(self, axis=0, skipna=True):
+ def median(self, axis=0, skipna=True, level=None):
"""
Return median over requested axis, NA/null are exluded
@@ -2610,11 +2641,17 @@ def median(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
Series or TimeSeries
"""
+ if not level is None:
+ medianfunc = lambda x: x.median(skipna=skipna)
+ return self.groupby(level=level).aggregate(medianfunc)
+
if axis == 0:
med = [self[col].median(skipna=skipna) for col in self.columns]
return Series(med, index=self.columns)
@@ -2624,7 +2661,7 @@ def median(self, axis=0, skipna=True):
else:
raise Exception('Must have 0<= axis <= 1')
- def mad(self, axis=0, skipna=True):
+ def mad(self, axis=0, skipna=True, level=None):
"""
Return mean absolute deviation over requested axis
@@ -2635,18 +2672,24 @@ def mad(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
mad : Series
"""
+ if not level is None:
+ madfunc = lambda x: x.mad(skipna=skipna)
+ return self.groupby(level=level).aggregate(madfunc)
+
if axis == 0:
demeaned = self - self.mean(axis=0)
else:
demeaned = self.sub(self.mean(axis=1), axis=0)
return np.abs(demeaned).mean(axis=axis, skipna=skipna)
- def var(self, axis=0, skipna=True):
+ def var(self, axis=0, skipna=True, level=None):
"""
Return unbiased variance over requested axis
@@ -2657,11 +2700,17 @@ def var(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
var : Series
"""
+ if not level is None:
+ varfunc = lambda x: x.var(skipna=skipna)
+ return self.groupby(level=level).aggregate(varfunc)
+
y, axis_labels = self._get_agg_data(axis, numeric_only=True)
mask = np.isnan(y)
@@ -2677,7 +2726,7 @@ def var(self, axis=0, skipna=True):
return Series(theVar, index=axis_labels)
- def std(self, axis=0, skipna=True):
+ def std(self, axis=0, skipna=True, level=None):
"""
Return unbiased std deviation over requested axis
@@ -2688,14 +2737,20 @@ def std(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
std : Series
"""
+ if not level is None:
+ stdfunc = lambda x: x.std(skipna=skipna)
+ return self.groupby(level=level).aggregate(stdfunc)
+
return np.sqrt(self.var(axis=axis, skipna=skipna))
- def skew(self, axis=0, skipna=True):
+ def skew(self, axis=0, skipna=True, level=None):
"""
Return unbiased skewness over requested axis
@@ -2706,11 +2761,17 @@ def skew(self, axis=0, skipna=True):
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
+ level : integer, default None
+ Choose a level to groupby before applying operation
Returns
-------
skew : Series
"""
+ if not level is None:
+ skewfunc = lambda x: x.skew(skipna=skipna)
+ return self.groupby(level=level).aggregate(skewfunc)
+
y, axis_labels = self._get_agg_data(axis, numeric_only=True)
mask = np.isnan(y)
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