英文:
How to change type of Pandas Series of vectors from str to numerical?
问题
我有一个由固定大小的向量组成的Series，但它是以str形式存在的。如何将此系列的类型更改为数值向量？
这是此系列的预览：
附言：类似问题的提供的答案没有帮助。
英文:
I do have a Series that consists of fixed-sized vectors, but as str. How can I change this series' type to a numerical vector?
Here is the preview of this serie:
p.s. The provided answers in a similar question did not help.
答案1
得分: 1
你可以使用pd.eval来处理nan值：
out = gen_vec.apply(pd.eval, local_dict={'nan': np.nan})
使用ast模块中的literal_eval：
import ast
out = gen_vec.apply(ast.literal_eval)
输出：
>>> out
0 [[0.6304918890918207, -0.5886238157645294, -0....
1 [[-0.6302182776914216, 0.9368165801475401, 0.7...
2 [[0.6153572001094536, -0.07547153598238743, -0...
3 [[0.1583211249108949, -0.07501481771633367, -0...
4 [[0.9793698091130785, 0.6140448218764745, -0.9...
dtype: object
>>> out.loc[0]
[[0.6304918890918207, -0.5886238157645294, -0.3194771085022785],
[-0.7222439829639373, 0.682891259912199, -0.9084527274979692],
[0.9372246370318329, -0.8042811128682565, -0.39435908071826065]]
>>> type(out.loc[0])
list
输入示例：
data = ['[[0.6304918890918207, -0.5886238157645294, -0.3194771085022785], [-0.7222439829639373, 0.682891259912199, -0.9084527274979692], [0.9372246370318329, -0.8042811128682565, -0.39435908071826065]]',
'[[ -0.6302182776914216, 0.9368165801475401, 0.7293141762489015], [-0.10363402231002539, 0.22356716941880794, 0.6796536411142267], [0.739412959837795, 0.3434906849876964, 0.6840523183724572]]',
'[[0.6153572001094536, -0.07547153598238743, -0.3147739134079086], [-0.4517142976978141, -0.7661353319665889, -0.08218569081022897], [0.21828238409073308, -0.8458822924041092, -0.8100486062713181]]',
'[[0.1583211249108949, -0.07501481771633367, -0.8430782622316249], [0.11189737816973255, -0.890710343331605, 0.2881597201674384], [-0.8188156405874802, -0.16829948165814113, -0.9222470203602522]]',
'[[0.9793698091130785, 0.6140448218764745, -0.9485282042022696], [0.7188762127494397, 0.042247790689530884, -0.5645509356734524], [-0.26842956038325627, -0.993030492245303, -0.8585439320376391]]']
gen_vec = pd.Series(data)
英文:
As you have nan values, you can use pd.eval:
out = gen_vec.apply(pd.eval, local_dict={'nan': np.nan})
Use literal_eval from ast module:
import ast:
out = gen_vec.apply(ast.literal_eval)
Output:
>>> out
0 [[0.6304918890918207, -0.5886238157645294, -0....
1 [[-0.6302182776914216, 0.9368165801475401, 0.7...
2 [[0.6153572001094536, -0.07547153598238743, -0...
3 [[0.1583211249108949, -0.07501481771633367, -0...
4 [[0.9793698091130785, 0.6140448218764745, -0.9...
dtype: object
>>> out.loc[0]
[[0.6304918890918207, -0.5886238157645294, -0.3194771085022785],
[-0.7222439829639373, 0.682891259912199, -0.9084527274979692],
[0.9372246370318329, -0.8042811128682565, -0.39435908071826065]]
>>> type(out.loc[0])
list
Input example:
data = ['[[0.6304918890918207, -0.5886238157645294, -0.3194771085022785], [-0.7222439829639373, 0.682891259912199, -0.9084527274979692], [0.9372246370318329, -0.8042811128682565, -0.39435908071826065]]',
'[[-0.6302182776914216, 0.9368165801475401, 0.7293141762489015], [-0.10363402231002539, 0.22356716941880794, 0.6796536411142267], [0.739412959837795, 0.3434906849876964, 0.6840523183724572]]',
'[[0.6153572001094536, -0.07547153598238743, -0.3147739134079086], [-0.4517142976978141, -0.7661353319665889, -0.08218569081022897], [0.21828238409073308, -0.8458822924041092, -0.8100486062713181]]',
'[[0.1583211249108949, -0.07501481771633367, -0.8430782622316249], [0.11189737816973255, -0.890710343331605, 0.2881597201674384], [-0.8188156405874802, -0.16829948165814113, -0.9222470203602522]]',
'[[0.9793698091130785, 0.6140448218764745, -0.9485282042022696], [0.7188762127494397, 0.042247790689530884, -0.5645509356734524], [-0.26842956038325627, -0.993030492245303, -0.8585439320376391]]']
gen_vec = pd.Series(data)
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