I have the following dataframe:
a1,a2,b1,b2
1,2,3,4
2,3,4,5
3,4,5,6
The desirable output is:
a,b
1,3
2,4
3,5
2,4
3,5
4,6
There is a lot of "a" and "b" named headers in the dataframe, the maximum is a50 and b50. So I am looking for the way to combine them all into just "a" and "b".
I think it's possible to do with concat, but I have no idea how to combine it all, putting all the values under each other. I'll be grateful for any ideas.
First we read the dataframe:
import pandas as pd
from io import StringIO
s = """a1,a2,b1,b2
1,2,3,4
2,3,4,5
3,4,5,6"""
df = pd.read_csv(StringIO(s), sep=',')
Then we stack the columns, and separate the number of the columns from the letter 'a' or 'b':
stacked = df.stack().rename("val").reset_index(1).reset_index()
cols_numbers = pd.DataFrame(stacked
.level_1
.str.split('(\d)')
.apply(lambda l: l[:2])
.tolist(),
columns=["col", "num"])
x = cols_numbers.join(stacked[['val', 'index']])
print(x)
col num val index
0 a 1 1 0
1 a 2 2 0
2 b 1 3 0
3 b 2 4 0
4 a 1 2 1
5 a 2 3 1
6 b 1 4 1
7 b 2 5 1
8 a 1 3 2
9 a 2 4 2
10 b 1 5 2
11 b 2 6 2
Finally, we group by index and num to get two columns a and b, and we fill the first row of the b column with the second value, to get what was expected:
result = (x
.set_index("col", append=True)
.groupby(["index", "num"])
.val
.apply(lambda g:
g
.unstack()
.fillna(method="bfill")
.head(1))
.reset_index(-1, drop=True))
print(result)
col a b
index num
0 1 1.0 3.0
2 2.0 4.0
1 1 2.0 4.0
2 3.0 5.0
2 1 3.0 5.0
2 4.0 6.0
To get rid of the multiindex at the end: result.reset_index(drop=True)