I created one program in python in which I used collaborative filtering to find Item based CF. My code is,
# Create a placeholder items for closes neighbours to an item
data_neighbours = pd.DataFrame(index=data_ibs.columns,columns=[range(1,13)])
print data_neighbours
# Loop through our similarity dataframe and fill in neighbouring item names
for i in range(0,len(data_ibs.columns)):
##use sort_values(...)
data_neighbours.ix[i,:10] = data_ibs.ix[0:,i].sort_values(ascending=[1, 0])[:10].index
data_neighbours.ix[i,:10] = data_ibs.ix[0:,i].sort_values(ascending=[1, 0])[:10].index
print data_neighbours.ix[i,:10]
Output is in table:
user 1 2 3
0 192. c1s1b1p3 c1s1b1p4 c1s1b1p5
1 192. c1s1b1p3 c1s1b1p4 c1s1b1p5
2 192. c1s1b1p3 c1s1b1p4 c1s1b1p5
3 192. c1s1b1p3 c1s1b1p4 c1s1b1p5
4 192. c1s1b1p7 c1s1b1p8 c1s1b1p10
5 192 c1s1b1p5 c1s1b1p6 c1s1b1p7
6 192. c1s1b1p3 c1s1b1p4 c1s1b1p5
7 192. c1s1b1p8 c1s1b1p9 c1s1b1p4
8 192. c1s1b1p6 c1s1b1p10 c1s1b1p5
9 192. c1s1b1p3 c1s1b1p5 c1s1b1p7
10 192. c1s1b1p6 c1s1b1p8 c1s1b1p9
This is table display on cmd prompt I want to save this table in csv file. How I can create csv file to store this table.
pandas.DataFrame.to_csv. And always try googling first, this looks like a very common and basic question.