CODEWITHSUNDEEP
pythonpandasdataframe
PJF49
PJF49

Reputation: 103

Combine Multiple Pandas columns into a Single Column

Hi I have a DataFrame for which I have multiple columns I want to combine into 1 with several other columns that I want to be duplicated. An example dataframe:

df = pd.DataFrame(np.random.randint(10, size=60).reshape(6, 10))
df.columns = ['x1', 'x2', 'x3', 'x4', 'x5', 'y1', 'y2', 'y3', 'y4', 'y5']

   x1  x2  x3  x4  x5  y1  y2  y3  y4  y5
0   2   6   9   4   3   8   6   1   0   7
1   1   4   8   7   3   0   5   7   3   1
2   6   7   4   8   1   5   7   7   8   5
3   6   3   4   8   0   8   7   2   3   8
4   8   5   6   1   6   3   2   1   1   4
5   1   3   7   5   1   6   5   3   8   5

I would like a nice way to produce the following DataFrame:

    x1  x2  x3  x4  x5  y
0    2   6   9   4   3  8
1    1   4   8   7   3  0
2    6   7   4   8   1  5
3    6   3   4   8   0  8
4    8   5   6   1   6  3
5    1   3   7   5   1  6
6    2   6   9   4   3  6
7    1   4   8   7   3  5
8    6   7   4   8   1  7
9    6   3   4   8   0  7
10   8   5   6   1   6  2
11   1   3   7   5   1  5
12   2   6   9   4   3  1
13   1   4   8   7   3  7
14   6   7   4   8   1  7
15   6   3   4   8   0  2
16   8   5   6   1   6  1
17   1   3   7   5   1  3
18   2   6   9   4   3  0
19   1   4   8   7   3  3
20   6   7   4   8   1  8
21   6   3   4   8   0  3
22   8   5   6   1   6  1
23   1   3   7   5   1  8
24   2   6   9   4   3  7
25   1   4   8   7   3  1
26   6   7   4   8   1  5
27   6   3   4   8   0  8
28   8   5   6   1   6  4
29   1   3   7   5   1  5

Is there a nice way to produce this DataFrame with Pandas functions or is it more complicated?

Thanks

Upvotes: 0

Views: 2404

Answers (1)

scotscotmcc
scotscotmcc

Reputation: 3113

You can do this with df.melt().

df.melt(
    id_vars = ['x1','x2','x3','x4','x5'],
    value_vars = ['y1','y2','y3','y4','y5'],
    value_name = 'y'
).drop(columns='variable')

df.melt() will have the column called variable that has the value for which column it originally came from (so is that row coming from y1, y2, etc), so you want to drop that as you see above.

Upvotes: 3

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