I have a data set in which certain column is a combination of couple of independent values, as in the example below:
id age marks
1 5 3,6,7
2 7 1,2
3 4 34,78,2
Thus the column by itself is composed of multiple values, and I need to pass the vector into a machine learning algorithm , I cannot really combine the values to assign a single value like :
3,6,7 => 1
1,2 => 2
34,78,2 => 3
making my new vector as
id age marks
1 5 1
2 7 2
3 4 3
and then subsequently pass it to the algorithm , as the number of such combination would be infinite and also that might not really capture the real meaning of the data.
how to handle such situation where individual feature is a combination of multiple features.
Note :
the values in column marks are just examples, it could be anything a list of values. it could be list of integer or list of string , string composed of multiple stings separated by commas
UPDATE: I think we can use CountVectorizer in this case:
assuming we have the following DF:
In [33]: df
Out[33]:
id age marks
0 1 5 [3, 6, 7]
1 2 7 [1, 2]
2 3 4 [34, 78, 2]
3 4 11 [3, 6, 7]
In [34]: %paste
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import TreebankWordTokenizer
vect = CountVectorizer(ngram_range=(1,1), stop_words=None, tokenizer=TreebankWordTokenizer().tokenize)
X = vect.fit_transform(df.marks.apply(' '.join))
r = pd.DataFrame(X.toarray(), columns=vect.get_feature_names())
## -- End pasted text --
Result:
In [35]: r
Out[35]:
1 2 3 34 6 7 78
0 0 0 1 0 1 1 0
1 1 1 0 0 0 0 0
2 0 1 0 1 0 0 1
3 0 0 1 0 1 1 0
OLD answer:
you can first convert your list to string and then categorize it:
In [119]: df
Out[119]:
id age marks
0 1 5 [3, 6, 7]
1 2 7 [1, 2]
2 3 4 [34, 78, 2]
3 4 11 [3, 6, 7]
In [120]: df['new'] = pd.Categorical(pd.factorize(df.marks.str.join('|'))[0])
In [121]: df
Out[121]:
id age marks new
0 1 5 [3, 6, 7] 0
1 2 7 [1, 2] 1
2 3 4 [34, 78, 2] 2
3 4 11 [3, 6, 7] 0
In [122]: df.dtypes
Out[122]:
id int64
age int64
marks object
new category
dtype: object
this will also work if marks
is a column of strings:
In [124]: df
Out[124]:
id age marks
0 1 5 3,6,7
1 2 7 1,2
2 3 4 34,78,2
3 4 11 3,6,7
In [125]: df['new'] = pd.Categorical(pd.factorize(df.marks.str.join('|'))[0])
In [126]: df
Out[126]:
id age marks new
0 1 5 3,6,7 0
1 2 7 1,2 1
2 3 4 34,78,2 2
3 4 11 3,6,7 0
Tp access them as either [[x, y, z], [x, y, z]]
or [[x, x], [y, y], [z, z]]
(whatever is most appropriate for the function you need to call) then use:
import pandas as pd
import numpy as np
df = pd.DataFrame(dict(a=[1, 2, 3, 4], b=[3, 4, 3, 4], c=[[1,2,3], [1,2], [], [2]]))
df.values
zip(*df.values)
where
>>> df
a b c
0 1 3 [1, 2, 3]
1 2 4 [1, 2]
2 3 3 []
3 4 4 [2]
>>> df.values
array([[1, 3, [1, 2, 3]],
[2, 4, [1, 2]],
[3, 3, []],
[4, 4, [2]]], dtype=object)
>>> zip(*df.values)
[(1, 2, 3, 4), (3, 4, 3, 4), ([1, 2, 3], [1, 2], [], [2])]
To convert a column try this:
import pandas as pd
import numpy as np
df = pd.DataFrame(dict(a=[1, 2], b=[3, 4], c=[[1,2,3], [1,2]]))
df['c'].apply(lambda x: np.mean(x))
before:
>>> df
a b c
0 1 3 [1, 2, 3]
1 2 4 [1, 2]
after:
>>> df
a b c
0 1 3 2.0
1 2 4 1.5
You can pd.factorize
tuples
Assuming marks
is a list
df
id age marks
0 1 5 [3, 6, 7]
1 2 7 [1, 2]
2 3 4 [34, 78, 2]
3 4 5 [3, 6, 7]
Apply tuple
and factorize
df.assign(new=pd.factorize(df.marks.apply(tuple))[0] + 1)
id age marks new
0 1 5 [3, 6, 7] 1
1 2 7 [1, 2] 2
2 3 4 [34, 78, 2] 3
3 4 5 [3, 6, 7] 1
setup df
df = pd.DataFrame([
[1, 5, ['3', '6', '7']],
[2, 7, ['1', '2']],
[3, 4, ['34', '78', '2']],
[4, 5, ['3', '6', '7']]
], [0, 1, 2, 3], ['id', 'age', 'marks']
)
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.