Transform pandas Data Frame to use for MultiLabelBinarizer
1
My question is: How can I transform a Data Frame like this to eventually use it in scikit's MulitLabelBinarizer:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D','E']}
df1 = pd.DataFrame(data=d1)
df1
ID km weight label
0 1 80 10 A
1 2 90 20 B
2 2 90 20 C
3 4 100 30 D
It should tourn ot like this:
d2 ={'km':[80,90,100], 'weight':[10,20,30], 'label':['A',('B','C'),'D']}
df2 = pd.DataFrame(data=d2)
df2
km weight label
0 80 10 A
1 90 20 (B, C)
2 100 30 D
So I can juse the data properly in the MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
array([[1, 0, 0, 0],
[0, 1, 1, 0],
[0, 0, 0, 1]])
Note: the raw data has more than 1 million rows.
python dataframe scikit-learn transformation multilabel-classification
asked Nov 27 '18 at 7:41
josnasjosnas
83
add a comment |
1
My question is: How can I transform a Data Frame like this to eventually use it in scikit's MulitLabelBinarizer:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D','E']}
df1 = pd.DataFrame(data=d1)
df1
ID km weight label
0 1 80 10 A
1 2 90 20 B
2 2 90 20 C
3 4 100 30 D
It should tourn ot like this:
d2 ={'km':[80,90,100], 'weight':[10,20,30], 'label':['A',('B','C'),'D']}
df2 = pd.DataFrame(data=d2)
df2
km weight label
0 80 10 A
1 90 20 (B, C)
2 100 30 D
So I can juse the data properly in the MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
array([[1, 0, 0, 0],
[0, 1, 1, 0],
[0, 0, 0, 1]])
Note: the raw data has more than 1 million rows.
python dataframe scikit-learn transformation multilabel-classification
asked Nov 27 '18 at 7:41
josnasjosnas
83
1
So what you want is to check for multiple occurrence in a dictionary? Maybe the title is not clear.
– mikuszefski
Nov 27 '18 at 7:49
yes, excuse me not beeing clear on that.
– josnas
Nov 27 '18 at 8:05
add a comment |
1
1
1
My question is: How can I transform a Data Frame like this to eventually use it in scikit's MulitLabelBinarizer:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D','E']}
df1 = pd.DataFrame(data=d1)
df1
ID km weight label
0 1 80 10 A
1 2 90 20 B
2 2 90 20 C
3 4 100 30 D
It should tourn ot like this:
d2 ={'km':[80,90,100], 'weight':[10,20,30], 'label':['A',('B','C'),'D']}
df2 = pd.DataFrame(data=d2)
df2
km weight label
0 80 10 A
1 90 20 (B, C)
2 100 30 D
So I can juse the data properly in the MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
array([[1, 0, 0, 0],
[0, 1, 1, 0],
[0, 0, 0, 1]])
Note: the raw data has more than 1 million rows.
python dataframe scikit-learn transformation multilabel-classification
asked Nov 27 '18 at 7:41
josnasjosnas
83
My question is: How can I transform a Data Frame like this to eventually use it in scikit's MulitLabelBinarizer:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D','E']}
df1 = pd.DataFrame(data=d1)
df1
ID km weight label
0 1 80 10 A
1 2 90 20 B
2 2 90 20 C
3 4 100 30 D
It should tourn ot like this:
d2 ={'km':[80,90,100], 'weight':[10,20,30], 'label':['A',('B','C'),'D']}
df2 = pd.DataFrame(data=d2)
df2
km weight label
0 80 10 A
1 90 20 (B, C)
2 100 30 D
So I can juse the data properly in the MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
array([[1, 0, 0, 0],
[0, 1, 1, 0],
[0, 0, 0, 1]])
Note: the raw data has more than 1 million rows.
python dataframe scikit-learn transformation multilabel-classification
python dataframe scikit-learn transformation multilabel-classification
asked Nov 27 '18 at 7:41
josnasjosnas
83
asked Nov 27 '18 at 7:41
josnasjosnas
83
edited Nov 27 '18 at 8:00
josnas
asked Nov 27 '18 at 7:41
josnasjosnas
83
asked Nov 27 '18 at 7:41
josnasjosnas
83
asked Nov 27 '18 at 7:41
josnasjosnas
83
83
1
So what you want is to check for multiple occurrence in a dictionary? Maybe the title is not clear.
– mikuszefski
Nov 27 '18 at 7:49
yes, excuse me not beeing clear on that.
– josnas
Nov 27 '18 at 8:05
add a comment |
1
So what you want is to check for multiple occurrence in a dictionary? Maybe the title is not clear.
– mikuszefski
Nov 27 '18 at 7:49
yes, excuse me not beeing clear on that.
– josnas
Nov 27 '18 at 8:05
1
1
So what you want is to check for multiple occurrence in a dictionary? Maybe the title is not clear.
– mikuszefski
Nov 27 '18 at 7:49
So what you want is to check for multiple occurrence in a dictionary? Maybe the title is not clear.
– mikuszefski
Nov 27 '18 at 7:49
yes, excuse me not beeing clear on that.
– josnas
Nov 27 '18 at 8:05
yes, excuse me not beeing clear on that.
– josnas
Nov 27 '18 at 8:05
add a comment |
1 Answer
1
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oldest
votes
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I think you need this:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D']}
df1 = pd.DataFrame(data=d1)
#Groupby and get tuple, like you need
df2 = pd.DataFrame(df1.groupby(['km','weight'])['label'].apply(lambda x: tuple(x.values)))
df2.reset_index(inplace=True)
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
add a comment |
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1 Answer
1
active
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votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
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0
I think you need this:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D']}
df1 = pd.DataFrame(data=d1)
#Groupby and get tuple, like you need
df2 = pd.DataFrame(df1.groupby(['km','weight'])['label'].apply(lambda x: tuple(x.values)))
df2.reset_index(inplace=True)
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
add a comment |
0
I think you need this:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D']}
df1 = pd.DataFrame(data=d1)
#Groupby and get tuple, like you need
df2 = pd.DataFrame(df1.groupby(['km','weight'])['label'].apply(lambda x: tuple(x.values)))
df2.reset_index(inplace=True)
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
add a comment |
0
0
0
I think you need this:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D']}
df1 = pd.DataFrame(data=d1)
#Groupby and get tuple, like you need
df2 = pd.DataFrame(df1.groupby(['km','weight'])['label'].apply(lambda x: tuple(x.values)))
df2.reset_index(inplace=True)
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
I think you need this:
d1 = {'ID':[1,2,3,4], 'km':[80,90,90,100], 'weight':[10,20,20,30], 'label':['A','B','C','D']}
df1 = pd.DataFrame(data=d1)
#Groupby and get tuple, like you need
df2 = pd.DataFrame(df1.groupby(['km','weight'])['label'].apply(lambda x: tuple(x.values)))
df2.reset_index(inplace=True)
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
mlb.fit(df2['label'])
mlb.transform(df2['label'])
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
answered Nov 27 '18 at 8:18
Rudolf MorkovskyiRudolf Morkovskyi
730117
730117
add a comment |
add a comment |
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1
So what you want is to check for multiple occurrence in a dictionary? Maybe the title is not clear.
– mikuszefski
Nov 27 '18 at 7:49
yes, excuse me not beeing clear on that.
– josnas
Nov 27 '18 at 8:05