Working with keys in Pandas Data Frame - r

Question

In R, Setkey can be used to work with keys and i.e. my data table gets sorted automatically when using aggregation functions. The R-Command I use is:
setkey(myData, “Customer”)
Does Python/Pandas also work with keys and Is there an equivalent for the R-Command?
Thanks a lot.

Answer 1

R's data.table setkey() function, as far as I know, doesn't have a direct equivalent in Python. However, there are a few functions that replace this functionality. Note the inplace parameter for these functions. If you don't specify inplace=True, the underlying data is not changed unless you explicitly assign (e.g., `df = df.sort_values('a')
You can use the sort_values() function to sort your data on one or more columns.
import pandas as pd
df = pd.DataFrame({'a': [1,1,2,1,2,2,2],
'b': [1,1,0,2,4,1,5],
'c': [3,4,5,2,6,1,7]})
>>> df
a b c
0 1 1 3
1 1 1 4
2 2 0 5
3 1 2 2
4 2 4 6
5 2 1 1
6 2 5 7
>>> df.sort_values(['a', 'b'])
a b c
0 1 1 3
1 1 1 4
3 1 2 2
2 2 0 5
5 2 1 1
4 2 4 6
6 2 5 7
If you are performing aggregation on a column or series of columns, you can use the groupby() function. This is similar to the by operator in R's data.table.
>>> df.groupby(['a', 'b'])['c'].max()
a b
1 1 4
2 2
2 0 5
1 1
4 6
5 7
You can also set the index to be one or more columns using the set_index() function.
>>> df.set_index('a')
b c
a
1 1 3
1 1 4
2 0 5
1 2 2
2 4 6
2 1 1
2 5 7
# once the index is set, you reference rows on the new index.
df.set_index('a', inplace=True)
df.ix[1]
>>> df.ix[1]
b c
a
1 1 3
1 1 4
1 2 2