How to split every row in dataframe into two with some features? [closed] - python-3.x

Question

Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
I have this dataframe:
A C1 C2
a1 c1 c3
a2 c2 c4
And columns C1 and C2 has the same type.
And I want get this:
A C
a1 c1
a1 c3
a2 c2
a2 c4
How I can do this?
UPD:
In answers I get this info:
df_final = df.set_index('A').stack().droplevel(1).rename('C').reset_index()
Out[604]:
A C
0 a1 c1
1 a1 c3
2 a2 c2
3 a2 c4
But what I should if I want split in this way?
A B C1 C2 C3 C4
a1 b1 c1 c2 c3 c4
a2 b2 c5 c6 c7 c8
and get this:
A B C1 C2
a1 b1 c1 c2
a1 b1 c3 c4
a2 b2 c5 c6
a2 b2 c7 c8

Answer 1

Edit 2: If you have even number of columns Cx, you may use numpy to make it simple
import numpy as np
cols = ['C1','C2','C3','C4']
df1 = df.loc[df.index.repeat(len(cols) / 2), ['A','B']].reset_index(drop=True)
df_final = df1.join(pd.DataFrame(df[cols].to_numpy().reshape(-1,2), columns=['C1','C2']))
Out[698]:
A B C1 C2
0 a1 b1 c1 c2
1 a1 b1 c3 c4
2 a2 b2 c5 c6
3 a2 b2 c7 c8
Edit for updated sample:
On multiple columns Cx splitting by 2, you need wide_to_long. However, beforing doing it, you need pre-processing columns names to appropriate format to use with wide_to_long
df1 = df.set_index(['A','B'])
stub_cols = (np.arange(df1.columns.size) % 2).astype(str)
suff_cols = (np.arange(df1.columns.size) // 2).astype(str)
d = dict(zip(stub_cols, ['C1', 'C2']))
df1.columns = pd.Series(stub_cols) + '_' + suff_cols
df_final = pd.wide_to_long(df1.reset_index(),
i=['A','B'],
j='num',
stubnames=['0','1'],
sep='_').droplevel(-1).rename(d, axis=1).reset_index()
Out[680]:
A B C1 C2
0 a1 b1 c1 c2
1 a1 b1 c3 c4
2 a2 b2 c5 c6
3 a2 b2 c7 c8
Give this a try
df_final = df.set_index('A').stack().droplevel(1).rename('C').reset_index()
Out[604]:
A C
0 a1 c1
1 a1 c3
2 a2 c2
3 a2 c4

Answer 2

print(
pd.concat([df.A, df[['C1', 'C2']].apply(list, axis=1)], axis=1).explode(0).rename(columns={0:'C'})
)
Prints:
A C
0 a1 c1
0 a1 c3
1 a2 c2
1 a2 c4