我知道我可以通过执行以下操作来应用numpy方法：

dataList是DataFrames的列表(相同的列/行).

testDF = (concat(dataList, axis=1, keys=range(len(dataList)))

.swaplevel(0, 1, axis=1)

.sortlevel(axis=1)

.groupby(level=0, axis=1))

testDF.aggregate(numpy.mean)

testDF.aggregate(numpy.var)

等等.但是,如果我想计算均值(sem)的标准误差怎么办？

我试过了：

testDF.aggregate(scipy.stats.sem)

但它给出了一个令人困惑的错误.有人知道怎么做吗？ scipy.stats方法有何不同之处？

这是一些为我重现错误的代码：

from scipy import stats as st

import pandas

import numpy as np

df_list = []

for ii in range(30):

df_list.append(pandas.DataFrame(np.random.rand(600, 10),

columns = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']))

testDF = (pandas.concat(df_list, axis=1, keys=range(len(df_list)))

.swaplevel(0, 1, axis=1)

.sortlevel(axis=1)

.groupby(level=0, axis=1))

testDF.aggregate(st.sem)

这是错误消息：

---------------------------------------------------------------------------

AssertionError Traceback (most recent call last)

in ()

12 .groupby(level=0, axis=1))

13

---> 14 testDF.aggregate(st.sem)

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)

1177 return self._python_agg_general(arg, *args, **kwargs)

1178 else:

-> 1179 result = self._aggregate_generic(arg, *args, **kwargs)

1180

1181 if not self.as_index:

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/groupby.py in _aggregate_generic(self, func, *args, **kwargs)

1248 else:

1249 result = DataFrame(result, index=obj.index,

-> 1250 columns=result_index)

1251 else:

1252 result = DataFrame(result)

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)

300 mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)

301 elif isinstance(data, dict):

--> 302 mgr = self._init_dict(data, index, columns, dtype=dtype)

303 elif isinstance(data, ma.MaskedArray):

304 mask = ma.getmaskarray(data)

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/frame.py in _init_dict(self, data, index, columns, dtype)

389

390 # consolidate for now

--> 391 mgr = BlockManager(blocks, axes)

392 return mgr.consolidate()

393

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/internals.py in __init__(self, blocks, axes, do_integrity_check)

329

330 if do_integrity_check:

--> 331 self._verify_integrity()

332

333 def __nonzero__(self):

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/internals.py in _verify_integrity(self)

404 mgr_shape = self.shape

405 for block in self.blocks:

--> 406 assert(block.values.shape[1:] == mgr_shape[1:])

407 tot_items = sum(len(x.items) for x in self.blocks)

408 assert(len(self.items) == tot_items)

AssertionError:

解决方法:

更新的答案：

看来我可以使用各种库的工作版本来复制它.稍后,我将检查我的家庭版本,以查看这些功能的文档是否有所不同.

在此期间,以下内容使用了您的确切编辑版本对我有用：

In [35]: testDF.aggregate(lambda x: st.sem(x, axis=None))

Out[35]:

Int64Index: 600 entries, 0 to 599

Data columns:

A 600 non-null values

B 600 non-null values

C 600 non-null values

D 600 non-null values

E 600 non-null values

F 600 non-null values

G 600 non-null values

H 600 non-null values

I 600 non-null values

J 600 non-null values

dtypes: float64(10)

这使我怀疑它与sem()轴约定有关.它的默认值为0,最终映射到的Pandas对象可能具有第0个怪异的轴或其他东西.当我使用选项axis = None时,它使应用了该对象的对象变得杂乱无章,这使它起作用.

就像进行健全性检查一样,我也这样做,它也起作用：

In [37]: testDF.aggregate(lambda x: st.sem(x, axis=1))

Out[37]:

Int64Index: 600 entries, 0 to 599

Data columns:

A 600 non-null values

B 600 non-null values

C 600 non-null values

D 600 non-null values

E 600 non-null values

F 600 non-null values

G 600 non-null values

H 600 non-null values

I 600 non-null values

J 600 non-null values

dtypes: float64(10)

但是您应该检查以确保这实际上是您想要的SEM值,可能是在一些较小的示例数据上.

较旧的答案：

这可能与scipy.stats的模块问题有关吗？当我使用该模块时,我必须从scipy import stats中将其称为st或类似名称. import scipy.stats不起作用,并调用import scipy; scipy.stats.sem给出错误,指出不存在名为“ stats”的模块.

熊猫似乎根本没有找到这种功能.我认为错误消息应该得到改善,因为这并不明显.

>>> from scipy import stats as st

>>> import pandas

>>> import numpy as np

>>> df_list = []

>>> for ii in range(10):

... df_list.append(pandas.DataFrame(np.random.rand(10,3),

... columns = ['A', 'B', 'C']))

...

>>> df_list

# Suppressed the output cause it was big.

>>> testDF = (pandas.concat(df_list, axis=1, keys=range(len(df_list)))

... .swaplevel(0, 1, axis=1)

... .sortlevel(axis=1)

... .groupby(level=0, axis=1))

>>> testDF

>>> testDF.aggregate(np.mean)

key_0 A B C

0 0.660324 0.408377 0.374681

1 0.459768 0.345093 0.432542

2 0.498985 0.443794 0.524327

3 0.605572 0.563768 0.558702

4 0.561849 0.488395 0.592399

5 0.466505 0.433560 0.408804

6 0.561591 0.630218 0.543970

7 0.423443 0.413819 0.486188

8 0.514279 0.479214 0.534309

9 0.479820 0.506666 0.449543

>>> testDF.aggregate(np.var)

key_0 A B C

0 0.093908 0.095746 0.055405

1 0.075834 0.077010 0.053406

2 0.094680 0.092272 0.095552

3 0.105740 0.126101 0.099316

4 0.087073 0.087461 0.111522

5 0.105696 0.110915 0.096959

6 0.082860 0.026521 0.075242

7 0.100512 0.051899 0.060778

8 0.105198 0.100027 0.097651

9 0.082184 0.060460 0.121344

>>> testDF.aggregate(st.sem)

A B C

0 0.089278 0.087590 0.095891

1 0.088552 0.081365 0.098071

2 0.087968 0.116361 0.076837

3 0.110369 0.087563 0.096460

4 0.101328 0.111676 0.046567

5 0.085044 0.099631 0.091284

6 0.113337 0.076880 0.097620

7 0.087243 0.087664 0.118925

8 0.080569 0.068447 0.106481

9 0.110658 0.071082 0.084928

似乎为我工作.

标签：pandas,scipy,statistics,python,numpy

来源： https://codeday.me/bug/20191201/2078362.html