构建的相同 MultiIndex DataFrame 不聚合(均值)
Same MultiIndex DataFrame constructed doesn't aggregate (mean)
小问题:
我试图在以两种不同方式对多索引 Pandas DataFrame 进行分组后获取列(数据系列)的平均值。区别仅在于 DataFrame 的构造。一个给了我想要的结果,另一个给出了错误 DataError: No numeric types to aggregate
描述:
施工常用数据
import pandas as pd
import numpy as np
indexTuples = [('a', 1), ('b', 3), ('a', 2), ('c', 2), ('c', 3), ('b', 8)]
multiIndex = pd.MultiIndex.from_tuples(indexTuples, names = ['x', 'y'])
通过方法1构建DataFrame
columns = ['alpha', 'beta', 'gamma']
df = pd.DataFrame(index=multiIndex, columns=columns)
alpha = pd.Series(index=multiIndex)
beta = pd.Series(index=multiIndex)
gamma = pd.Series(index=multiIndex)
for tup in indexTuples:
alpha[tup[0], tup[1]] = np.random.randint(400)
beta[tup[0], tup[1]] = np.random.randint(400)
gamma[tup[0], tup[1]] = np.random.randint(400)
df.alpha = alpha
df.beta = beta
df.gamma = gamma
df.alpha['a'] = np.nan
df
给出如下所示的数据框
alpha beta gamma
x y
a 1 NaN 136.0 224.0
b 3 375.0 227.0 191.0
a 2 NaN 367.0 195.0
c 2 247.0 61.0 78.0
3 238.0 187.0 366.0
b 8 302.0 14.0 272.0
如果我执行以下操作,我会得到预期的结果
df.groupby(level='x').alpha.mean()
结果
x
a NaN
b 148.0
c 244.5
Name: alpha, dtype: float64
通过方法2构建DataFrame
columns = ['alpha', 'beta', 'gamma']
_df = pd.DataFrame(index=multiIndex, columns=columns)
for tup in indexTuples:
_df.alpha[tup[0], tup[1]] = np.random.randint(400)
_df.beta[tup[0], tup[1]] = np.random.randint(400)
_df.gamma[tup[0], tup[1]] = np.random.randint(400)
_df.alpha['a'] = np.nan
给出一个外观与 NaN
值相似的 DataFrame,如先前方法
所示
但是现在当我试图在按级别分组后求均值时
_df.groupby(level='x').alpha.mean()
我收到以下错误
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-192-ad2de6450fab> in <module>()
----> 1 _df.groupby(level='x').alpha.mean()
/film/tools/packages/pandas/0.18.0/CentOS-6.2_thru_7/python-2.7/lib/python2.7/site-packages/pandas-0.18.0-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in mean(self)
933 """
934 try:
--> 935 return self._cython_agg_general('mean')
936 except GroupByError:
937 raise
/film/tools/packages/pandas/0.18.0/CentOS-6.2_thru_7/python-2.7/lib/python2.7/site-packages/pandas-0.18.0-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
750
751 if len(output) == 0:
--> 752 raise DataError('No numeric types to aggregate')
753
754 return self._wrap_aggregated_output(output, names)
DataError: No numeric types to aggregate
为什么第一种情况有效而第二种情况无效?
当您构建 _df
时,dtype
变成了 object
。发生这种情况是因为在您定义 _df
时,您没有使用任何数据启动它并且默认为 object
。在构造 #1 中,您通过分配 series
独立构造的值和浮点类型来克服这个问题。在构造 #2 中,您显式地分配了 _df
个数据位置。这些位置已被视为 object
.
_df.dtypes
alpha object
beta object
gamma object
dtype: object
用这个来得到你的结果:
_df.astype(float).groupby(level='x').alpha.mean()
小问题:
我试图在以两种不同方式对多索引 Pandas DataFrame 进行分组后获取列(数据系列)的平均值。区别仅在于 DataFrame 的构造。一个给了我想要的结果,另一个给出了错误 DataError: No numeric types to aggregate
描述:
施工常用数据
import pandas as pd
import numpy as np
indexTuples = [('a', 1), ('b', 3), ('a', 2), ('c', 2), ('c', 3), ('b', 8)]
multiIndex = pd.MultiIndex.from_tuples(indexTuples, names = ['x', 'y'])
通过方法1构建DataFrame
columns = ['alpha', 'beta', 'gamma']
df = pd.DataFrame(index=multiIndex, columns=columns)
alpha = pd.Series(index=multiIndex)
beta = pd.Series(index=multiIndex)
gamma = pd.Series(index=multiIndex)
for tup in indexTuples:
alpha[tup[0], tup[1]] = np.random.randint(400)
beta[tup[0], tup[1]] = np.random.randint(400)
gamma[tup[0], tup[1]] = np.random.randint(400)
df.alpha = alpha
df.beta = beta
df.gamma = gamma
df.alpha['a'] = np.nan
df
给出如下所示的数据框
alpha beta gamma
x y
a 1 NaN 136.0 224.0
b 3 375.0 227.0 191.0
a 2 NaN 367.0 195.0
c 2 247.0 61.0 78.0
3 238.0 187.0 366.0
b 8 302.0 14.0 272.0
如果我执行以下操作,我会得到预期的结果
df.groupby(level='x').alpha.mean()
结果
x
a NaN
b 148.0
c 244.5
Name: alpha, dtype: float64
通过方法2构建DataFrame
columns = ['alpha', 'beta', 'gamma']
_df = pd.DataFrame(index=multiIndex, columns=columns)
for tup in indexTuples:
_df.alpha[tup[0], tup[1]] = np.random.randint(400)
_df.beta[tup[0], tup[1]] = np.random.randint(400)
_df.gamma[tup[0], tup[1]] = np.random.randint(400)
_df.alpha['a'] = np.nan
给出一个外观与 NaN
值相似的 DataFrame,如先前方法
但是现在当我试图在按级别分组后求均值时
_df.groupby(level='x').alpha.mean()
我收到以下错误
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-192-ad2de6450fab> in <module>()
----> 1 _df.groupby(level='x').alpha.mean()
/film/tools/packages/pandas/0.18.0/CentOS-6.2_thru_7/python-2.7/lib/python2.7/site-packages/pandas-0.18.0-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in mean(self)
933 """
934 try:
--> 935 return self._cython_agg_general('mean')
936 except GroupByError:
937 raise
/film/tools/packages/pandas/0.18.0/CentOS-6.2_thru_7/python-2.7/lib/python2.7/site-packages/pandas-0.18.0-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
750
751 if len(output) == 0:
--> 752 raise DataError('No numeric types to aggregate')
753
754 return self._wrap_aggregated_output(output, names)
DataError: No numeric types to aggregate
为什么第一种情况有效而第二种情况无效?
当您构建 _df
时,dtype
变成了 object
。发生这种情况是因为在您定义 _df
时,您没有使用任何数据启动它并且默认为 object
。在构造 #1 中,您通过分配 series
独立构造的值和浮点类型来克服这个问题。在构造 #2 中,您显式地分配了 _df
个数据位置。这些位置已被视为 object
.
_df.dtypes
alpha object
beta object
gamma object
dtype: object
用这个来得到你的结果:
_df.astype(float).groupby(level='x').alpha.mean()