问题
I am unable to find the entry on the method dot()
in the official documentation. However the method is there and I can use it. Why is this?
On this topic, is there a way compute an element-wise multiplication of every row in a data frame with another vector? (and obtain a dataframe
back?), i.e. similar to dot()
but rather than computing the dot product, one computes the element-wise product.
回答1:
Here is an example of how to multiply a DataFrame by a vector:
In [60]: df = pd.DataFrame({'A': [1., 1., 1., 2., 2., 2.], 'B': np.arange(1., 7.)})
In [61]: vector = np.array([2,2,2,3,3,3])
In [62]: df.mul(vector, axis=0)
Out[62]:
A B
0 2 2
1 2 4
2 2 6
3 6 12
4 6 15
5 6 18
回答2:
mul
is doing essentially an outer-product, while dot
is an inner product. Let me expand on the accepted answer:
In [13]: df = pd.DataFrame({'A': [1., 1., 1., 2., 2., 2.], 'B': np.arange(1., 7.)})
In [14]: v1 = np.array([2,2,2,3,3,3])
In [15]: v2 = np.array([2,3])
In [16]: df.shape
Out[16]: (6, 2)
In [17]: v1.shape
Out[17]: (6,)
In [18]: v2.shape
Out[18]: (2,)
In [24]: df.mul(v2)
Out[24]:
A B
0 2 3
1 2 6
2 2 9
3 4 12
4 4 15
5 4 18
In [26]: df.dot(v2)
Out[26]:
0 5
1 8
2 11
3 16
4 19
5 22
dtype: float64
So:
df.mul
takes matrix of shape (6,2) and vector (6, 1) and returns matrix shape (6,2)
While:
df.dot
takes matrix of shape (6,2) and vector (2,1) and returns (6,1).
These are not the same operation, they are outer and inner products, respectively.
回答3:
It's quite hard to say with a degree of accuracy.
Often, a method exists and is undocumented because it's considered internal by the vendor, and may be subject to change.
It could, of course, be a simple oversight by the folks who put together the documentation.
Regarding your second question; I don't really know about that - but it might be better to make a new S/O question for it. Just scanning the the API, could you do something with the DataFrame's .applymap(function) feature ?
来源:https://stackoverflow.com/questions/15753916/get-dot-product-of-dataframe-with-vector-and-return-dataframe-in-pandas