如何根据条件将 dataframe 列拆分为单独的列

Question

I am trying to split the following dataframe into separate columns. I want all the text in one column and the numbers to be split on white space.

df[0].head(10)

0                                                   []
1               [Andaman and Nicobar, 194, 52, 142, 0]
2        [Andhra Pradesh, 40,646, 19,814, 20,298, 534]
3                [Arunachal Pradesh, 609, 431, 175, 3]
4                   [Assam, 20,646, 6,490, 14,105, 51]
5                  [Bihar, 23,589, 8,767, 14,621, 201]
6                      [Chandigarh, 660, 169, 480, 11]
7              [Chhattisgarh, 4,964, 1,429, 3,512, 23]
8    [Dadra and Nagar Haveli and Daman, 585, 182, 4...
9                          [Daman and Diu, 0, 0, 0, 0]
Name: 0, dtype: object

If I split just on white space and expand, though numbers are getting split correctly, the text is getting split into multiple columns. Since the text for different observations span different number of columns, I cannot concat them again. Obviously, the solution is writing the right 'regex' and splitting on it. I am unable to figure out the regex required, hence request inputs.

df1 = df[0].str.split(' ', expand= True)
df1.head(10)
    0   1   2   3   4   5   6   7   8   9
0   []  None    None    None    None    None    None    None    None    None
1   [Andaman    and     Nicobar,    194,    52,     142,    0]  None    None    None
2   [Andhra     Pradesh,    40,646,     19,814,     20,298,     534]    None    None    None    None
3   [Arunachal  Pradesh,    609,    431,    175,    3]  None    None    None    None
4   [Assam,     20,646,     6,490,  14,105,     51]     None    None    None    None    None
5   [Bihar,     23,589,     8,767,  14,621,     201]    None    None    None    None    None
6   [Chandigarh,    660,    169,    480,    11]     None    None    None    None    None
7   [Chhattisgarh,  4,964,  1,429,  3,512,  23]     None    None    None    None    None
8   [Dadra  and     Nagar   Haveli  and     Daman,  585,    182,    401,    2]
9   [Daman  and     Diu,    0,  0,  0,  0]  None    None    None

The result I am expecting shall be like this:

        0                                   1       2       3       4       5       6       7       8       9
    0   []                                  None    None    None    None    None    None    None    None    None
    1   [Andaman and Nicobar,               194,    52,     142,    0]      None    None    None    None    None
    2   [Andhra Pradesh,                    40,646, 19,814, 20,298, 534]    None    None    None    None    None
    3   [Arunachal Pradesh,                 609,    431,    175,    3]      None    None    None    None    None
    4   [Assam,                             20,646, 6,490,  14,105, 51]     None    None    None    None    None
    5   [Bihar,                             23,589, 8,767,  14,621, 201]    None    None    None    None    None
    6   [Chandigarh,                        660,    169,    480,    11]     None    None    None    None    None
    7   [Chhattisgarh,                      4,964,  1,429,  3,512,  23]     None    None    None    None    None
    8   [Dadra and Nagar Haveli and Daman,  585,    182,    401,    2]      None    None    None    None    None
    9   [Daman and Diu,                     0,      0,      0,      0]      None    None    None    None    None

Answer 1

you could use str.replace and str.extract to re-shape your dataframe.

names = df[0].str.extract('(\D+)').replace('\[|,','',regex=True).rename(columns={0 : 'names'})


df_new = names.join(df[0].str.replace('\D+,','').str.strip(']').str.split(' ',expand=True))

---

print(df_new)

                                  names 0        1        2        3     4
0                   Andaman and Nicobar       194,      52,     142,     0
1                        Andhra Pradesh    40,646,  19,814,  20,298,   534
2                     Arunachal Pradesh       609,     431,     175,     3
3                                 Assam    20,646,   6,490,  14,105,    51
4                                 Bihar    23,589,   8,767,  14,621,   201
5                            Chandigarh       660,     169,     480,    11
6                          Chhattisgarh     4,964,   1,429,   3,512,    23
7      Dadra and Nagar Haveli and Daman       585,     182,     4...  None
8                         Daman and Diu         0,       0,       0,     0