Turning a matrix to dummy matrix
0
I've generated a list of combination and would like to turn it into "dummies" matrix
import pandas as pd
from itertools import combinations
comb = pd.DataFrame(list(combinations(range(1, 6), 4)))
0 1 2 3
0 1 2 3 4
1 1 2 3 5
2 1 2 4 5
3 1 3 4 5
4 2 3 4 5
would like to turn the above dataframe to a dataframe look like below. Thanks.
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
pandas python-2.7
asked Nov 23 at 1:46
Lafayette
99119
add a comment |
0
I've generated a list of combination and would like to turn it into "dummies" matrix
import pandas as pd
from itertools import combinations
comb = pd.DataFrame(list(combinations(range(1, 6), 4)))
0 1 2 3
0 1 2 3 4
1 1 2 3 5
2 1 2 4 5
3 1 3 4 5
4 2 3 4 5
would like to turn the above dataframe to a dataframe look like below. Thanks.
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
pandas python-2.7
asked Nov 23 at 1:46
Lafayette
99119
add a comment |
0
0
0
I've generated a list of combination and would like to turn it into "dummies" matrix
import pandas as pd
from itertools import combinations
comb = pd.DataFrame(list(combinations(range(1, 6), 4)))
0 1 2 3
0 1 2 3 4
1 1 2 3 5
2 1 2 4 5
3 1 3 4 5
4 2 3 4 5
would like to turn the above dataframe to a dataframe look like below. Thanks.
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
pandas python-2.7
asked Nov 23 at 1:46
Lafayette
99119
I've generated a list of combination and would like to turn it into "dummies" matrix
import pandas as pd
from itertools import combinations
comb = pd.DataFrame(list(combinations(range(1, 6), 4)))
0 1 2 3
0 1 2 3 4
1 1 2 3 5
2 1 2 4 5
3 1 3 4 5
4 2 3 4 5
would like to turn the above dataframe to a dataframe look like below. Thanks.
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
pandas python-2.7
pandas python-2.7
asked Nov 23 at 1:46
Lafayette
99119
asked Nov 23 at 1:46
Lafayette
99119
asked Nov 23 at 1:46
Lafayette
99119
asked Nov 23 at 1:46
Lafayette
99119
asked Nov 23 at 1:46
Lafayette
99119
99119
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
1
You can use MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
lb = MultiLabelBinarizer()
df = pd.DataFrame(lb.fit_transform(comb.values), columns= lb.classes_)
print (df)
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
answered Nov 23 at 1:58
Abhi
2,480320
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
You can use MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
lb = MultiLabelBinarizer()
df = pd.DataFrame(lb.fit_transform(comb.values), columns= lb.classes_)
print (df)
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
answered Nov 23 at 1:58
Abhi
2,480320
add a comment |
1
You can use MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
lb = MultiLabelBinarizer()
df = pd.DataFrame(lb.fit_transform(comb.values), columns= lb.classes_)
print (df)
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
answered Nov 23 at 1:58
Abhi
2,480320
add a comment |
1
1
1
You can use MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
lb = MultiLabelBinarizer()
df = pd.DataFrame(lb.fit_transform(comb.values), columns= lb.classes_)
print (df)
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
answered Nov 23 at 1:58
Abhi
2,480320
You can use MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
lb = MultiLabelBinarizer()
df = pd.DataFrame(lb.fit_transform(comb.values), columns= lb.classes_)
print (df)
1 2 3 4 5
0 1 1 1 1 0
1 1 1 1 0 1
2 1 1 0 1 1
3 1 0 1 1 1
4 0 1 1 1 1
answered Nov 23 at 1:58
Abhi
2,480320
answered Nov 23 at 1:58
Abhi
2,480320
answered Nov 23 at 1:58
Abhi
2,480320
answered Nov 23 at 1:58
Abhi
2,480320
2,480320
add a comment |
add a comment |
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