Is it possible to have multiple indexes at once for a single pandas DataFrame?
By index I mean something close to what SQL databases or MongoDB would have to drastically increase the performance of some queries.
Imagine you have a dataframe of orders, and sometimes you would like to index into an order by its ID, and another time by indexing into an order by another key, say warehouse, client ID, time, something like that.
Short Answer : Yes.
Please refer https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.set_index.html
s = pd.Series([1, 2, 3, 4])
df.set_index([s, s**2])
month year sale
1 1 1 2012 55
2 4 4 2014 40
3 9 7 2013 84
4 16 10 2014 31
Another example for multiple index:
dct = {'id' : (0,0,1,1,2,2,3,3,4,4,5,5),
'Store': ('A','A','A','A','A','A','B','B','B','C','C','C'),
'code_num':('INC101','INC102','INC103','INC104','INC105','INC106','INC201','INC202','INC203','INC301','INC302','INC303'),
'days':('4','18','9','15','3','6','10','5','3','1','8','5'),
'products': ('remote','antenna','remote,antenna','TV','display','TV','display,touchpad','speaker','Cell','display','speaker','antenna')
}
df = pd.DataFrame(dct)
df.set_index(['id','Store','code_num'])
df :
