pandas 熊猫系列到二维数组
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Pandas series to 2d array
提问by crayxt
So, I used the answer from Put a 2d Array into a Pandas Seriesto put 2D numpy array to pandas series. In short, it is
因此，我使用Put a 2d Array into a Pandas Series 中的答案将2D numpy 数组放入Pandas 系列。简而言之，就是
a = np.zeros((5,2))
s = pd.Series(list(a))
Now, what is the cheapest way to convert that pandas Series back to 2D array?
If I try s.values, I get array of arrays with objectdtype.
现在，将Pandas系列转换回二维数组的最便宜的方法是什么？如果我尝试s.values，我会得到带有objectdtype的数组数组。
So far I tried np.vstack(s.values)but it copies the data, of course.
到目前为止，我尝试过，np.vstack(s.values)但它当然会复制数据。
回答by jezrael
I believe you need:
我相信你需要：
a = np.array(s.values.tolist())
print (a)
[[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]]
a = np.zeros((50000,2))
s = pd.Series(list(a))
In [131]: %timeit (np.vstack(s.values))
10 loops, best of 3: 107 ms per loop
In [132]: %timeit (np.array(s.values.tolist()))
10 loops, best of 3: 19.7 ms per loop
In [133]: %timeit (np.array(s.tolist()))
100 loops, best of 3: 19.6 ms per loop
But if transpose difference is small (but caching):
但如果转置差异很小（但缓存）：
a = np.zeros((2,50000))
s = pd.Series(list(a))
#print (s)
In [159]: %timeit (np.vstack(s.values))
The slowest run took 23.31 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 55.7 μs per loop
In [160]: %timeit (np.array(s.values.tolist()))
The slowest run took 7.20 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 49.8 μs per loop
In [161]: %timeit (np.array(s.tolist()))
The slowest run took 7.31 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 62.6 μs per loop
