Reshape DataFrame to np.array

Question

Good afternoon, I have a dataframe with the dimension (963,1), how can I change something like this for a numpy format:

array ([244.1462534,212.68483386, 212.04058487, 236.39615555]

when I use, np.array (a), it appears to me, each element in a different row

[244.1462534,
244.1462534,
244.1462534,
244.1462534]

as there are many it is difficult to see it like this

Answer 1

Make a 1 column frame:

In [590]: df = pd.DataFrame(np.arange(5), columns=['x'])                                             
In [591]: df                                                                                         
Out[591]: 
   x
0  0
1  1
2  2
3  3
4  4

The array from that is (5,1) shaped:

In [592]: df.values                                                                                  
Out[592]: 
array([[0],
       [1],
       [2],
       [3],
       [4]])

One column is a Series, which is 1d:

In [594]: df['x']                                                                                    
Out[594]: 
0    0
1    1
2    2
3    3
4    4
Name: x, dtype: int64
In [595]: df['x'].values                                                                             
Out[595]: array([0, 1, 2, 3, 4])

But if you have the (5,1) shape array, there are lots of ways of reshaping it:

In [596]: df.values.ravel()                                                                          
Out[596]: array([0, 1, 2, 3, 4])

ravel , flatten , reshape , squeeze , even indexing. All these can be found in the basic numpy documentation.

Answer 2

Use values to convert dataframe to Numpy array. Check the Docs.

df.values

You can reshape if you want a flattened np array

df = pd.DataFrame(np.random.randn(10))
print (df.shape)
print (df.values.reshape(-1))

Output:

(10, 1)
[-1.43902815  0.72724325 -0.36741276 -1.96696158  0.5852711  -2.03214297
  0.11657485 -1.77276773  0.33315229 -1.37454383]