问题描述
我有一个形状为(75,9)
的熊猫数据框.
I have a pandas dataframe of shape (75,9)
.
这些列中只有一列是numpy数组,每个数组的形状都是(100, 4, 3)
Only one of those columns is of numpy arrays, each of which is of shape (100, 4, 3)
我有一个奇怪的现象:
data = self.df[self.column_name].values[0]
形状为(100,4,3)
,但是
data = self.df[self.column_name].values
的形状为(75,),其中min
和max
不是数字对象"
is of shape (75,), with min
and max
are 'not a numeric object'
我希望data = self.df[self.column_name].values
的形状(75、100、4、3),并带有一些min
和max
.
I expected data = self.df[self.column_name].values
to be of shape (75, 100, 4, 3), with some min
and max
.
如何使一列numpy数组的行为类似于更高维度的numpy数组(长度=数据框中的行数)?
How can I make a column of numpy arrays behave like a numpy array of a higher dimension (with length=number of rows in the dataframe)?
复制:
some_df = pd.DataFrame(columns=['A'])
for i in range(10):
some_df.loc[i] = [np.random.rand(4, 6)]
print some_df['A'].values.shape
print some_df['A'].values[0].shape
打印(10L,)
,(4L,6L)
而不是所需的(10L, 4L, 6L)
,(4L,6L)
prints (10L,)
,(4L,6L)
instead of desired (10L, 4L, 6L)
,(4L,6L)
推荐答案
In [42]: some_df = pd.DataFrame(columns=['A'])
...: for i in range(4):
...: some_df.loc[i] = [np.random.randint(0,10,(1,3))]
...:
In [43]: some_df
Out[43]:
A
0 [[7, 0, 9]]
1 [[3, 6, 8]]
2 [[9, 7, 6]]
3 [[1, 6, 3]]
该列的numpy值是一个对象dtype数组,其中包含数组:
The numpy values of the column are an object dtype array, containing arrays:
In [44]: some_df['A'].to_numpy()
Out[44]:
array([array([[7, 0, 9]]), array([[3, 6, 8]]), array([[9, 7, 6]]),
array([[1, 6, 3]])], dtype=object)
如果这些数组都具有相同的形状,则stack
可以很好地将它们连接到新的维度:
If those arrays all have the same shape, stack
does a nice job of concatenating them on a new dimension:
In [45]: np.stack(some_df['A'].to_numpy())
Out[45]:
array([[[7, 0, 9]],
[[3, 6, 8]],
[[9, 7, 6]],
[[1, 6, 3]]])
In [46]: _.shape
Out[46]: (4, 1, 3)
这仅适用于一列.与所有concatenate
一样,stack
将输入参数视为可迭代的有效数组列表.
This only works with one column. stack
like all concatenate
treats the input argument as an iterable, effectively a list of arrays.
In [48]: some_df['A'].to_list()
Out[48]:
[array([[7, 0, 9]]),
array([[3, 6, 8]]),
array([[9, 7, 6]]),
array([[1, 6, 3]])]
In [50]: np.stack(some_df['A'].to_list()).shape
Out[50]: (4, 1, 3)
这篇关于将numpy数组的pandas列转换为高维的numpy数组的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!