SQL-like window functions in PANDAS: Row Numbering in Python Pandas Dataframe

Question

I come from a sql background and I use the following data processing step frequently:

1. Partition the table of data by one or more fields
2. For each parti

Answer 1

You can do this by using groupby twice along with the rank method:

In [11]: g = df.groupby('key1')

Use the min method argument to give values which share the same data1 the same RN:

In [12]: g['data1'].rank(method='min')
Out[12]:
0    1
1    2
2    2
3    1
4    4
dtype: float64

In [13]: df['RN'] = g['data1'].rank(method='min')

And then groupby these results and add the rank with respect to data2:

In [14]: g1 = df.groupby(['key1', 'RN'])

In [15]: g1['data2'].rank(ascending=False) - 1
Out[15]:
0    0
1    0
2    1
3    0
4    0
dtype: float64

In [16]: df['RN'] += g1['data2'].rank(ascending=False) - 1

In [17]: df
Out[17]:
   data1  data2 key1  RN
0      1      1    a   1
1      2     10    a   2
2      2      2    a   3
3      3      3    b   1
4      3     30    a   4

It feels like there ought to be a native way to do this (there may well be!...).