How to rank and reassign values to subset of Python dataframe

Drawing a blank, please advise. Have a dataframe, d: Position Operation Side Price Size 0 0 0 1 0.7288 -17 1 8 0 1 0.7297 -14 2 7 0 1 0.7296 -8 3 6 0 1 0.7295 -426 4 5 0 1 0.7294 -16 5 4 0 1 0.7293 -16 6 3 0 1 0.7292 -15 7 2 0 1 0.7291 -267 8 1 0 1 0.729 -427 9 0 0 1 0.7289 -16 10 0 0 0 0.7299 6 11 1 0 0 0.73 34 12 2 0 0 0.7301 7 13 3 0 0 0.7302 9 14 4 0 0 0.7303 16 15 5 0 0 0.7304 15 16 6 0 0 0.7305 429 17 7 0 0 0.7306 16 18 8 0 0 0.7307 265 19 9 0 0 0.7308 18 That I'd like to filter based on column value, such as: d.loc[(d.Side==1)] '' Position Operation Side Price Size 0 0 0 1 0.7288 -17 1 8 0 1 0.7297 -14 2 7 0 1 0.7296 -8 3 6 0 1 0.7295 -426 4 5 0 1 0.7294 -16 5 4 0 1 0.7293 -16 6 3 0 1 0.7292 -15 7 2 0 1 0.7291 -267 8 1 0 1 0.729 -427 9 0 0 1 0.7289 -16 I'd then like to rank based on Price, and assign the rank to a column in the subset dataframe, specifically Position. For instance, filtering based on Side = 1, and then ranking based on Price, I'd like my updated dataframe, d, to look like this. Position Operation Side Price Size 0 0 0 1 0.7288 -17 1 9 0 1 0.7297 -14 2 8 0 1 0.7296 -8 3 7 0 1 0.7295 -426 4 6 0 1 0.7294 -16 5 5 0 1 0.7293 -16 6 4 0 1 0.7292 -15 7 3 0 1 0.7291 -267 8 2 0 1 0.729 -427 9 1 0 1 0.7289 -16 10 0 0 0 0.7299 6 11 1 0 0 0.73 34 12 2 0 0 0.7301 7 13 3 0 0 0.7302 9 14 4 0 0 0.7303 16 15 5 0 0 0.7304 15 16 6 0 0 0.7305 429 17 7 0 0 0.7306 16 18 8 0 0 0.7307 265 19 9 0 0 0.7308 18 Where '0' is the lowest value and '9' is the largest--df.rank returns a one-indexed float rank, but it's easy enough to get from there to the int 0-9 values I need. Much appreciated

Marayaty Marayaty
Aug 28, 2024 - 03:37
0 2
How to rank and reassign values to subset of Python dataframe

Drawing a blank, please advise.

Have a dataframe, d: 

    Position    Operation   Side    Price   Size
0   0   0   1   0.7288  -17
1   8   0   1   0.7297  -14
2   7   0   1   0.7296  -8
3   6   0   1   0.7295  -426
4   5   0   1   0.7294  -16
5   4   0   1   0.7293  -16
6   3   0   1   0.7292  -15
7   2   0   1   0.7291  -267
8   1   0   1   0.729   -427
9   0   0   1   0.7289  -16
10  0   0   0   0.7299  6
11  1   0   0   0.73    34
12  2   0   0   0.7301  7
13  3   0   0   0.7302  9
14  4   0   0   0.7303  16
15  5   0   0   0.7304  15
16  6   0   0   0.7305  429
17  7   0   0   0.7306  16
18  8   0   0   0.7307  265
19  9   0   0   0.7308  18

That I'd like to filter based on column value, such as: 

d.loc[(d.Side==1)]

'' 

    Position    Operation   Side    Price   Size
0   0   0   1   0.7288  -17
1   8   0   1   0.7297  -14
2   7   0   1   0.7296  -8
3   6   0   1   0.7295  -426
4   5   0   1   0.7294  -16
5   4   0   1   0.7293  -16
6   3   0   1   0.7292  -15
7   2   0   1   0.7291  -267
8   1   0   1   0.729   -427
9   0   0   1   0.7289  -16

I'd then like to rank based on Price, and assign the rank to a column in the subset dataframe, specifically Position.

For instance, filtering based on Side = 1, and then ranking based on Price, I'd like my updated dataframe, d, to look like this. 

    Position    Operation   Side    Price   Size
0   0   0   1   0.7288  -17
1   9   0   1   0.7297  -14
2   8   0   1   0.7296  -8
3   7   0   1   0.7295  -426
4   6   0   1   0.7294  -16
5   5   0   1   0.7293  -16
6   4   0   1   0.7292  -15
7   3   0   1   0.7291  -267
8   2   0   1   0.729   -427
9   1   0   1   0.7289  -16
10  0   0   0   0.7299  6
11  1   0   0   0.73    34
12  2   0   0   0.7301  7
13  3   0   0   0.7302  9
14  4   0   0   0.7303  16
15  5   0   0   0.7304  15
16  6   0   0   0.7305  429
17  7   0   0   0.7306  16
18  8   0   0   0.7307  265
19  9   0   0   0.7308  18

Where '0' is the lowest value and '9' is the largest--df.rank returns a one-indexed float rank, but it's easy enough to get from there to the int 0-9 values I need.

Much appreciated

Read More