通过使用 Pandas Python 合并列来填充空白区域

Question

I am a Python noob. I have an unstructured text file that I'm trying to capture to a dataframe and export to excel. I need to merge 38 to 36, 45 to 43, and 79 to 78 filling in the empty space with the data on the merging column.

Dummy Dataset

	0	5	36	38	43	45	78	79
1	A	01JUN2022		1.2		B		1.2
2	C	01JUN2022		1.4		D		1.4
3	E	01JUN2022	1.5		F		1.6
4	G	01JUN2022	1.7		H		1.7
5	I	01JUN2022	1.4		J		1.8
6	K	01JUN2022	1.7		L		1.3
1	A	01JUN2022		1.2		B		1.2
2	C	01JUN2022		1.4	D		1.4
3	E	01JUN2022		1.5	F		1.6
4	G	01JUN2022	1.7		H		1.7
5	I	01JUN2022	1.4			J		1.8
6	K	01JUN2022	1.7		L			1.3

Required output

	0	5	36	43	79
1	A	01JUN2022	1.2	B	1.2
2	C	01JUN2022	1.4	D	1.4
3	E	01JUN2022	1.5	F	1.6
4	G	01JUN2022	1.7	H	1.7
5	I	01JUN2022	1.4	J	1.8
6	K	01JUN2022	1.7	L	1.3
1	A	01JUN2022	1.2	B	1.2
2	C	01JUN2022	1.4	D	1.4
3	E	01JUN2022	1.5	F	1.6
4	G	01JUN2022	1.7	H	1.7
5	I	01JUN2022	1.4	J	1.8
6	K	01JUN2022	1.7	L	1.3

Answer 1

Would start by converting '' to NaN as follows

df = df.replace(r'^\s*$', np.nan, regex=True)

Then one can use pandas.Series.combine_first

df['36'] = df['36'].combine_first(df['38'])
df['43'] = df['43'].combine_first(df['45'])
df['79'] = df['79'].combine_first(df['78'])

[Out]:
    id  0          5   36   38 43   45   78   79
0    1  A  01JUN2022  1.2  1.2  B    B  NaN  1.2
1    2  C  01JUN2022  1.4  1.4  D    D  NaN  1.4
2    3  E  01JUN2022  1.5  NaN  F  NaN  1.6  1.6
3    4  G  01JUN2022  1.7  NaN  H  NaN  1.7  1.7
4    5  I  01JUN2022  1.4  NaN  J  NaN  1.8  1.8
5    6  K  01JUN2022  1.7  NaN  L  NaN  1.3  1.3
6    1  A  01JUN2022  1.2  1.2  B    B  NaN  1.2
7    2  C  01JUN2022  1.4  1.4  D  NaN  1.4  1.4
8    3  E  01JUN2022  1.5  1.5  F  NaN  1.6  1.6
9    4  G  01JUN2022  1.7  NaN  H  NaN  1.7  1.7
10   5  I  01JUN2022  1.4  NaN  J    J  NaN  1.8
11   6  K  01JUN2022  1.7  NaN  L  NaN  NaN  1.3

Finally, one can drop the columns that one doesn't want or select the one's to display as follows

df = df[['0', '5', '36', '43', '79']]

[Out]:

    0          5   36 43   79
0   A  01JUN2022  1.2  B  1.2
1   C  01JUN2022  1.4  D  1.4
2   E  01JUN2022  1.5  F  1.6
3   G  01JUN2022  1.7  H  1.7
4   I  01JUN2022  1.4  J  1.8
5   K  01JUN2022  1.7  L  1.3
6   A  01JUN2022  1.2  B  1.2
7   C  01JUN2022  1.4  D  1.4
8   E  01JUN2022  1.5  F  1.6
9   G  01JUN2022  1.7  H  1.7
10  I  01JUN2022  1.4  J  1.8
11  K  01JUN2022  1.7  L  1.3

and this gives the desired output.

---

Notes:

• There are additional ways to remove NaN and combine the columns. For that, see: How to remove nan value while combining two column in Panda Data frame?

• There are also additional ways to delete columns in Python. If one wants to learn more about it, see: Delete a column from a Pandas DataFrame