pandas convert lists in multiple columns within DataFrame to separate columns
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0
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I am trying to convert a list within multiple columns of a pandas DataFrame into separate columns.
Say, I have a dataframe like this:
0 1
0 [1, 2, 3] [4, 5, 6]
1 [1, 2, 3] [4, 5, 6]
2 [1, 2, 3] [4, 5, 6]
And would like to convert it to something like this:
0 1 2 0 1 2
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
I have managed to do this in a loop. However, I would like to do this in fewer lines.
My code snippet so far is as follows:
import pandas as pd
df = pd.DataFrame([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
output1 = df[0].apply(pd.Series)
output2 = df[1].apply(pd.Series)
output = pd.concat([output1, output2], axis=1)
pandas dataframe apply
asked Nov 22 at 15:08
Christian
14518
add a comment |
up vote
0
down vote
favorite
I am trying to convert a list within multiple columns of a pandas DataFrame into separate columns.
Say, I have a dataframe like this:
0 1
0 [1, 2, 3] [4, 5, 6]
1 [1, 2, 3] [4, 5, 6]
2 [1, 2, 3] [4, 5, 6]
And would like to convert it to something like this:
0 1 2 0 1 2
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
I have managed to do this in a loop. However, I would like to do this in fewer lines.
My code snippet so far is as follows:
import pandas as pd
df = pd.DataFrame([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
output1 = df[0].apply(pd.Series)
output2 = df[1].apply(pd.Series)
output = pd.concat([output1, output2], axis=1)
pandas dataframe apply
asked Nov 22 at 15:08
Christian
14518
Are you sure you want that output? Duplicate column names are allowed but not particularly useful...
– Jon Clements♦
Nov 22 at 15:13
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns
– Christian
Nov 22 at 15:18
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am trying to convert a list within multiple columns of a pandas DataFrame into separate columns.
Say, I have a dataframe like this:
0 1
0 [1, 2, 3] [4, 5, 6]
1 [1, 2, 3] [4, 5, 6]
2 [1, 2, 3] [4, 5, 6]
And would like to convert it to something like this:
0 1 2 0 1 2
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
I have managed to do this in a loop. However, I would like to do this in fewer lines.
My code snippet so far is as follows:
import pandas as pd
df = pd.DataFrame([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
output1 = df[0].apply(pd.Series)
output2 = df[1].apply(pd.Series)
output = pd.concat([output1, output2], axis=1)
pandas dataframe apply
asked Nov 22 at 15:08
Christian
14518
I am trying to convert a list within multiple columns of a pandas DataFrame into separate columns.
Say, I have a dataframe like this:
0 1
0 [1, 2, 3] [4, 5, 6]
1 [1, 2, 3] [4, 5, 6]
2 [1, 2, 3] [4, 5, 6]
And would like to convert it to something like this:
0 1 2 0 1 2
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
I have managed to do this in a loop. However, I would like to do this in fewer lines.
My code snippet so far is as follows:
import pandas as pd
df = pd.DataFrame([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
output1 = df[0].apply(pd.Series)
output2 = df[1].apply(pd.Series)
output = pd.concat([output1, output2], axis=1)
pandas dataframe apply
pandas dataframe apply
asked Nov 22 at 15:08
Christian
14518
asked Nov 22 at 15:08
Christian
14518
asked Nov 22 at 15:08
Christian
14518
asked Nov 22 at 15:08
Christian
14518
asked Nov 22 at 15:08
Christian
14518
14518
Are you sure you want that output? Duplicate column names are allowed but not particularly useful...
– Jon Clements♦
Nov 22 at 15:13
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns
– Christian
Nov 22 at 15:18
add a comment |
Are you sure you want that output? Duplicate column names are allowed but not particularly useful...
– Jon Clements♦
Nov 22 at 15:13
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns
– Christian
Nov 22 at 15:18
Are you sure you want that output? Duplicate column names are allowed but not particularly useful...
– Jon Clements♦
Nov 22 at 15:13
Are you sure you want that output? Duplicate column names are allowed but not particularly useful...
– Jon Clements♦
Nov 22 at 15:13
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns
– Christian
Nov 22 at 15:18
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns
– Christian
Nov 22 at 15:18
add a comment |
2 Answers
2
active
oldest
votes
up vote
2
down vote
accepted
If you don't care about the column names you could do:
>>> df.apply(np.hstack, axis=1).apply(pd.Series)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:34
w-m
5,9992233
add a comment |
up vote
1
down vote
Using sum
pd.DataFrame(df.sum(1).tolist())
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:50
W-B
97.1k73162
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
If you don't care about the column names you could do:
>>> df.apply(np.hstack, axis=1).apply(pd.Series)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:34
w-m
5,9992233
add a comment |
up vote
2
down vote
accepted
If you don't care about the column names you could do:
>>> df.apply(np.hstack, axis=1).apply(pd.Series)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:34
w-m
5,9992233
add a comment |
up vote
2
down vote
accepted
up vote
2
down vote
accepted
If you don't care about the column names you could do:
>>> df.apply(np.hstack, axis=1).apply(pd.Series)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:34
w-m
5,9992233
If you don't care about the column names you could do:
>>> df.apply(np.hstack, axis=1).apply(pd.Series)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:34
w-m
5,9992233
answered Nov 22 at 15:34
w-m
5,9992233
answered Nov 22 at 15:34
w-m
5,9992233
answered Nov 22 at 15:34
w-m
5,9992233
5,9992233
add a comment |
add a comment |
up vote
1
down vote
Using sum
pd.DataFrame(df.sum(1).tolist())
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:50
W-B
97.1k73162
add a comment |
up vote
1
down vote
Using sum
pd.DataFrame(df.sum(1).tolist())
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:50
W-B
97.1k73162
add a comment |
up vote
1
down vote
up vote
1
down vote
Using sum
pd.DataFrame(df.sum(1).tolist())
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:50
W-B
97.1k73162
Using sum
pd.DataFrame(df.sum(1).tolist())
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
answered Nov 22 at 15:50
W-B
97.1k73162
answered Nov 22 at 15:50
W-B
97.1k73162
answered Nov 22 at 15:50
W-B
97.1k73162
answered Nov 22 at 15:50
W-B
97.1k73162
97.1k73162
add a comment |
add a comment |
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Are you sure you want that output? Duplicate column names are allowed but not particularly useful...
– Jon Clements♦
Nov 22 at 15:13
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns
– Christian
Nov 22 at 15:18