问题
This is my first question at Stack Overflow.
I have a DataFrame of Pandas like this.
a b c d
one 0 1 2 3
two 4 5 6 7
three 8 9 0 1
four 2 1 1 5
five 1 1 8 9
I want to extract the pairs of column name and data whose data is 1 and each index is separate at array.
[ [(b,1.0)], [(d,1.0)], [(b,1.0),(c,1.0)], [(a,1.0),(b,1.0)] ]
I want to use gensim of python library which requires corpus as this form.
Is there any smart way to do this or to apply gensim from pandas data?
回答1:
Many gensim functions accept numpy arrays, so there may be a better way...
In [11]: is_one = np.where(df == 1)
In [12]: is_one
Out[12]: (array([0, 2, 3, 3, 4, 4]), array([1, 3, 1, 2, 0, 1]))
In [13]: df.index[is_one[0]], df.columns[is_one[1]]
Out[13]:
(Index([u'one', u'three', u'four', u'four', u'five', u'five'], dtype='object'),
Index([u'b', u'd', u'b', u'c', u'a', u'b'], dtype='object'))
To groupby each row, you could use iterrows:
from itertools import repeat
In [21]: [list(zip(df.columns[np.where(row == 1)], repeat(1.0)))
for label, row in df.iterrows()
if 1 in row.values] # if you don't want empty [] for rows without 1
Out[21]:
[[('b', 1.0)],
[('d', 1.0)],
[('b', 1.0), ('c', 1.0)],
[('a', 1.0), ('b', 1.0)]]
In python 2 the list is not required since zip returns a list.
回答2:
Another way would be
In [1652]: [[(c, 1) for c in x[x].index] for _, x in df.eq(1).iterrows() if x.any()]
Out[1652]: [[('b', 1)], [('d', 1)], [('b', 1), ('c', 1)], [('a', 1), ('b', 1)]]
来源：https://stackoverflow.com/questions/27957112/extract-array-column-name-data-from-pandas-dataframe