大熊猫dataframes乘法具有或不具有广播
问题描述:
I have 2 dataframes:
>>> type(c)
Out[118]: pandas.core.frame.DataFrame
>>> type(N)
Out[119]: pandas.core.frame.DataFrame
>>> c
Out[114]:
t
2017-06-01 01:06:00 1.00
2017-06-01 01:13:00 1.00
2017-06-01 02:09:00 1.00
2017-06-26 22:47:00 1.00
>>> N
Out[115]:
0 1
2017-06-01 01:06:00 1.00 1.00
2017-06-01 01:13:00 1.00 1.00
2017-06-01 02:09:00 1.00 1.00
2017-06-26 22:47:00 1.00 1.00
我需要一起相乘这些得到一个4,2数据帧,其与C.Ñ的elementwise的每一列我尝试以下方法4没有运气的乘法:大熊猫dataframes乘法具有或不具有广播
>>> N.multiply(c, axis='index')
Out[116]:
0 1 t
2017-06-01 01:06:00 nan nan nan
2017-06-01 01:13:00 nan nan nan
2017-06-01 02:09:00 nan nan nan
2017-06-26 22:47:00 nan nan nan
>>> c[:]*N
Out[98]:
0 1 t
2017-06-01 01:06:00 nan nan nan
2017-06-01 01:13:00 nan nan nan
2017-06-01 02:09:00 nan nan nan
2017-06-26 22:47:00 nan nan nan
>>> c*N
Out[99]:
0 1 t
2017-06-01 01:06:00 nan nan nan
2017-06-01 01:13:00 nan nan nan
2017-06-01 02:09:00 nan nan nan
2017-06-26 22:47:00 nan nan nan
>>> c[:, None]*N
Traceback (most recent call last):
File "C:\...pandas\core\frame.py", line 1797, in __getitem__
return self._getitem_column(key)
File "C:\...core\frame.py", line 1804, in _getitem_column
return self._get_item_cache(key)
File "C:\...core\generic.py", line 1082, in _get_item_cache
res = cache.get(item)
TypeError: unhashable type
有没有办法,有没有广播这样做很容易?
答
问题是您传递一个DataFrame,所以它也尝试匹配列名称。如果你切列T,它会成为一个系列,它会适当地广播:
N.mul(c['t'], axis=0)
Out:
0 1
2017-06-01 01:06:00 1.0 1.0
2017-06-01 01:13:00 1.0 1.0
2017-06-01 02:09:00 1.0 1.0
2017-06-26 22:47:00 1.0 1.0
在numpy的阵列的情况下,你不需要任何指定。对于(4,2)和(4,1)形状,numpy将看到相同长度的轴并相应地进行广播。
考虑以下DataFrames:
N
Out:
0 1
2017-06-01 01:06:00 1.0 2.0
2017-06-01 01:13:00 6.0 5.0
2017-06-01 02:09:00 4.0 3.0
2017-06-26 22:47:00 4.0 7.0
c
Out:
t
2017-06-01 01:06:00 6.0
2017-06-01 01:13:00 2.0
2017-06-01 02:09:00 8.0
2017-06-26 22:47:00 2.0
您可以用.values
属性访问底层的数组,以便
N.values * c.values
Out:
array([[ 6., 12.],
[ 12., 10.],
[ 32., 24.],
[ 8., 14.]])
会给你同样的结果
N.mul(c['t'], axis=0)
Out:
0 1
2017-06-01 01:06:00 6.0 12.0
2017-06-01 01:13:00 12.0 10.0
2017-06-01 02:09:00 32.0 24.0
2017-06-26 22:47:00 8.0 14.0
但由于整个操作都很麻烦,你会失去标签。
注意:'c [:,无]'添加新轴的这种表示法适用于numpy数组 - 它不适用于DataFrames。如果你想先添加一个新的轴,你需要用'c.values [:,None]将它转换为一个numpy数组。' – ayhan