CODEWITHSUNDEEP
pandasranking
Felix
Felix

Reputation: 1579

Pandas Ranking based on multiple columns

I am trying to rank data based on several columns in ascending order.
Please see below data frame I am working on:

{'FACILITY': ['AAA', 'AAA', 'AAA', 'AAA', 'AAA'],
 'IN_DATE':
 ['2015-08-30 05:49:05',
  '2015-08-30 05:49:05',
  '2015-08-30 05:49:05',
  '2015-08-30 05:49:05',
  '2015-09-02 20:56:59'],
 'LOT':
 ['N123456', 'N654321', 'N654321', 'N123456', 'N123456'],
 'OPERATION':
 ['100', '100', '100', '100', '100'],
 'TXN_DATE':
 ['2015-08-30 06:04:03',
  '2015-08-30 05:59:57',
  '2015-08-30 06:37:32',
  '2015-08-30 06:30:01',
  '2015-09-02 21:39:44']

I am trying to create new column "ORDER" based on order within lot and operation in ascending order based on TXN_DATE.

Upvotes: 2

Views: 6203

Answers (1)

Andy Hayden
Andy Hayden

Reputation: 375415

You can get the sorted order using the rank method:

In [11]: df
Out[11]:
  FACILITY              IN_DATE      LOT OPERATION             TXN_DATE
0      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 06:04:03
1      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 05:59:57
2      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 06:37:32
3      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 06:30:01
4      AAA  2015-09-02 20:56:59  N123456       100  2015-09-02 21:39:44

In [12]: df["TXN_DATE"].rank()
Out[12]:
0    2
1    1
2    4
3    3
4    5
Name: TXN_DATE, dtype: float64

As a column:

In [13]: df["ORDER"] = df["TXN_DATE"].rank()

In [14]: df
Out[14]:
  FACILITY              IN_DATE      LOT OPERATION             TXN_DATE  ORDER
0      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 06:04:03      2
1      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 05:59:57      1
2      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 06:37:32      4
3      AAA  2015-08-30 05:49:05  N123456       100  2015-08-30 06:30:01      3
4      AAA  2015-09-02 20:56:59  N123456       100  2015-09-02 21:39:44      5

Rank is also a Series groupby method:

In [15]: df.groupby(["LOT", "OPERATION"])["TXN_DATE"].rank()
Out[15]:
0    2
1    1
2    4
3    3
4    5
Name: (N123456, 100), dtype: float64

Note: In this small example the name is from the only group, usually this would not have a name.

Upvotes: 2

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