使用列表中的标签将 numpy 数组转换为 pandas dataframe

Question

please advice how to perform the following premutations:

array = [1, 3, 2] (numpy.ndarray)

l1 = ['foo_qwe1_ert1', 'bar_qwe2_ert2', 'baz_qwe3_ert3'] (list)

I need to get the following pandas dataframe:

Column1	Column2	Column3
foo	qwe1	ert1
baz	qwe3	ert3
bar	qwe2	ert2

the problem is the list contains text labels from 0 to 30(format: XXX_YYY_ZZZ) and numpy.array has shape (3536,) and contains numbers from 0 to 30. I need to assign label for each number in array and save it as pandas dataframe

Answer 1

You can just use:

df = pd.DataFrame(data={'list':['foo_qwe1_ert1', 'bar_qwe2_ert2', 'baz_qwe3_ert3']})
df[['Column1', 'Column2', 'Column3']] = df['list'].str.split('_', expand=True)
df.drop(columns=['list'], inplace=True)

OUTPUT:

  Column1 Column2 Column3
0     foo    qwe1    ert1
1     bar    qwe2    ert2
2     baz    qwe3    ert3

Answer 2

First use:

df = pd.DataFrame([x.split('_') for x in l1], columns=['Column1', 'Column2', 'Column3'])
print (df)
  Column1 Column2 Column3
0     foo    qwe1    ert1
1     bar    qwe2    ert2
2     baz    qwe3    ert3

Answer 3

You can use str.split and then reindex :

df = pd.Series(l1).str.split('_', expand=True)
df.index = [1,2,3]
df = df.reindex(array).reset_index(drop=True).rename(columns={i:'Column'+str(i+1) for i in df.columns})

Output:

  Column1 Column2 Column3
0     foo    qwe1    ert1
1     baz    qwe3    ert3
2     bar    qwe2    ert2