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This suggested edit was approved and applied to the post over 1 year ago by Alexei‭.

187 / 255 
Remove entries by two-column ID everywhere, that meet a condition somewhere
  • # MWE
  • ```py
  • import random
  • import pandas as pd
  • from itertools import product
  • random.seed(12345)
  • dies = [1, 2]
  • cells = list(range(10))
  • currents = [100, 200, 300]
  • dcc = list(product(dies, cells, currents))
  • resistances = random.choices(range(250000 + 1), k=len(dcc))
  • df = pd.DataFrame(dcc, columns=["Die", "Cell", "Current"])
  • df["Resistance"] = resistances
  • b100 = df[(df["Current"] == 100) & (df["Resistance"] < 100000)]
  • ```
  • ```txt
  • df:
  • Die Cell Current Resistance
  • 0 1 0 100 104155
  • 1 1 0 200 2542
  • 2 1 0 300 206302
  • 3 1 1 100 74660
  • 4 1 1 200 92103
  • 5 1 1 300 48415
  • 6 1 2 100 141502
  • 7 1 2 200 40422
  • 8 1 2 300 31066
  • 9 1 3 100 108234
  • 10 1 3 200 140520
  • 11 1 3 300 43586
  • 12 1 4 100 138305
  • 13 1 4 200 88725
  • 14 1 4 300 239517
  • 15 1 5 100 22823
  • 16 1 5 200 244660
  • 17 1 5 300 103030
  • 18 1 6 100 125984
  • 19 1 6 200 37036
  • 20 1 6 300 179742
  • 21 1 7 100 47493
  • 22 1 7 200 85390
  • 23 1 7 300 5880
  • 24 1 8 100 84879
  • 25 1 8 200 241871
  • 26 1 8 300 244700
  • 27 1 9 100 186133
  • 28 1 9 200 863
  • 29 1 9 300 235060
  • 30 2 0 100 217692
  • 31 2 0 200 192709
  • 32 2 0 300 44718
  • 33 2 1 100 24875
  • 34 2 1 200 103633
  • 35 2 1 300 221385
  • 36 2 2 100 144522
  • 37 2 2 200 184146
  • 38 2 2 300 58155
  • 39 2 3 100 130899
  • 40 2 3 200 177347
  • 41 2 3 300 206209
  • 42 2 4 100 201781
  • 43 2 4 200 58077
  • 44 2 4 300 218298
  • 45 2 5 100 54095
  • 46 2 5 200 200475
  • 47 2 5 300 138771
  • 48 2 6 100 46457
  • 49 2 6 200 147152
  • 50 2 6 300 129560
  • 51 2 7 100 239666
  • 52 2 7 200 10384
  • 53 2 7 300 41034
  • 54 2 8 100 245824
  • 55 2 8 200 208052
  • 56 2 8 300 37568
  • 57 2 9 100 57278
  • 58 2 9 200 134785
  • 59 2 9 300 39245
  • ```
  • ```txt
  • b100:
  • Die Cell Current Resistance
  • 3 1 1 100 74660
  • 15 1 5 100 22823
  • 21 1 7 100 47493
  • 24 1 8 100 84879
  • 33 2 1 100 24875
  • 45 2 5 100 54095
  • 48 2 6 100 46457
  • 57 2 9 100 57278
  • ```
  • # Problem
  • If a cell is below 100k resistance, at 100 current, I want to remove
  • that cell everywhere, within that die. `b100` above shows that I want to
  • remove all entries for cells 1, 5, 7, and 8 in die 1, and all entries
  • for cells 1, 5, 6, and 9 in die 2.
  • Cells 6 and 9 should not be removed from die 1, and cells 7 and 8 should not be removed from die 2.
  • How would I do this programatically?
  • # Tried
  • Tried:
  • ```py
  • df = df.mask((df["Die"].isin(b100["Die"])) & (df["Cell"].isin(b100["Cell"])))
  • ```
  • But this removes every cell marked, in either die:
  • ```txt
  • Die Cell Current Resistance
  • 0 1.0 0.0 100.0 104155.0
  • 1 1.0 0.0 200.0 2542.0
  • 2 1.0 0.0 300.0 206302.0
  • 3 NaN NaN NaN NaN
  • 4 NaN NaN NaN NaN
  • 5 NaN NaN NaN NaN
  • 6 1.0 2.0 100.0 141502.0
  • 7 1.0 2.0 200.0 40422.0
  • 8 1.0 2.0 300.0 31066.0
  • 9 1.0 3.0 100.0 108234.0
  • 10 1.0 3.0 200.0 140520.0
  • 11 1.0 3.0 300.0 43586.0
  • 12 1.0 4.0 100.0 138305.0
  • 13 1.0 4.0 200.0 88725.0
  • 14 1.0 4.0 300.0 239517.0
  • 15 NaN NaN NaN NaN
  • 16 NaN NaN NaN NaN
  • 17 NaN NaN NaN NaN
  • 18 NaN NaN NaN NaN
  • 19 NaN NaN NaN NaN
  • 20 NaN NaN NaN NaN
  • 21 NaN NaN NaN NaN
  • 22 NaN NaN NaN NaN
  • 23 NaN NaN NaN NaN
  • 24 NaN NaN NaN NaN
  • 25 NaN NaN NaN NaN
  • 26 NaN NaN NaN NaN
  • 27 NaN NaN NaN NaN
  • 28 NaN NaN NaN NaN
  • 29 NaN NaN NaN NaN
  • 30 2.0 0.0 100.0 217692.0
  • 31 2.0 0.0 200.0 192709.0
  • 32 2.0 0.0 300.0 44718.0
  • 33 NaN NaN NaN NaN
  • 34 NaN NaN NaN NaN
  • 35 NaN NaN NaN NaN
  • 36 2.0 2.0 100.0 144522.0
  • 37 2.0 2.0 200.0 184146.0
  • 38 2.0 2.0 300.0 58155.0
  • 39 2.0 3.0 100.0 130899.0
  • 40 2.0 3.0 200.0 177347.0
  • 41 2.0 3.0 300.0 206209.0
  • 42 2.0 4.0 100.0 201781.0
  • 43 2.0 4.0 200.0 58077.0
  • 44 2.0 4.0 300.0 218298.0
  • 45 NaN NaN NaN NaN
  • 46 NaN NaN NaN NaN
  • 47 NaN NaN NaN NaN
  • 48 NaN NaN NaN NaN
  • 49 NaN NaN NaN NaN
  • 50 NaN NaN NaN NaN
  • 51 NaN NaN NaN NaN
  • 52 NaN NaN NaN NaN
  • 53 NaN NaN NaN NaN
  • 54 NaN NaN NaN NaN
  • 55 NaN NaN NaN NaN
  • 56 NaN NaN NaN NaN
  • 57 NaN NaN NaN NaN
  • 58 NaN NaN NaN NaN
  • 59 NaN NaN NaN NaN
  • ```
  • Tried:
  • ```py
  • rm_dies = b100["Die"].to_list()
  • rm_cells = b100["Cell"].to_list()
  • for die, cell in zip(rm_dies, rm_cells):
  • df = df.mask((df["Die"] == die) & (df["Cell"] == cell))
  • ```
  • This works, but is very slow on large dataframes and is not
  • elegant.
  • ```txt
  • Die Cell Current Resistance
  • 0 1.0 0.0 100.0 104155.0
  • 1 1.0 0.0 200.0 2542.0
  • 2 1.0 0.0 300.0 206302.0
  • 3 NaN NaN NaN NaN
  • 4 NaN NaN NaN NaN
  • 5 NaN NaN NaN NaN
  • 6 1.0 2.0 100.0 141502.0
  • 7 1.0 2.0 200.0 40422.0
  • 8 1.0 2.0 300.0 31066.0
  • 9 1.0 3.0 100.0 108234.0
  • 10 1.0 3.0 200.0 140520.0
  • 11 1.0 3.0 300.0 43586.0
  • 12 1.0 4.0 100.0 138305.0
  • 13 1.0 4.0 200.0 88725.0
  • 14 1.0 4.0 300.0 239517.0
  • 15 NaN NaN NaN NaN
  • 16 NaN NaN NaN NaN
  • 17 NaN NaN NaN NaN
  • 18 1.0 6.0 100.0 125984.0
  • 19 1.0 6.0 200.0 37036.0
  • 20 1.0 6.0 300.0 179742.0
  • 21 NaN NaN NaN NaN
  • 22 NaN NaN NaN NaN
  • 23 NaN NaN NaN NaN
  • 24 NaN NaN NaN NaN
  • 25 NaN NaN NaN NaN
  • 26 NaN NaN NaN NaN
  • 27 1.0 9.0 100.0 186133.0
  • 28 1.0 9.0 200.0 863.0
  • 29 1.0 9.0 300.0 235060.0
  • 30 2.0 0.0 100.0 217692.0
  • 31 2.0 0.0 200.0 192709.0
  • 32 2.0 0.0 300.0 44718.0
  • 33 NaN NaN NaN NaN
  • 34 NaN NaN NaN NaN
  • 35 NaN NaN NaN NaN
  • 36 2.0 2.0 100.0 144522.0
  • 37 2.0 2.0 200.0 184146.0
  • 38 2.0 2.0 300.0 58155.0
  • 39 2.0 3.0 100.0 130899.0
  • 40 2.0 3.0 200.0 177347.0
  • 41 2.0 3.0 300.0 206209.0
  • 42 2.0 4.0 100.0 201781.0
  • 43 2.0 4.0 200.0 58077.0
  • 44 2.0 4.0 300.0 218298.0
  • 45 NaN NaN NaN NaN
  • 46 NaN NaN NaN NaN
  • 47 NaN NaN NaN NaN
  • 48 NaN NaN NaN NaN
  • 49 NaN NaN NaN NaN
  • 50 NaN NaN NaN NaN
  • 51 2.0 7.0 100.0 239666.0
  • 52 2.0 7.0 200.0 10384.0
  • 53 2.0 7.0 300.0 41034.0
  • 54 2.0 8.0 100.0 245824.0
  • 55 2.0 8.0 200.0 208052.0
  • 56 2.0 8.0 300.0 37568.0
  • 57 NaN NaN NaN NaN
  • 58 NaN NaN NaN NaN
  • 59 NaN NaN NaN NaN
  • ```
  • # Notes
  • Ultimately I want to remove these cells from the dataframe. `dropna()`
  • excluded from above calls for illustrative purposes.
  • I have a dataset showing electrical current and resistance measurements on various cells of different dies. There are multiple measurements for each cell.
  • If a cell is ever observed to have resistance <100k while current = 100, that cell is considered a "bad cell". I want to remove all bad cells of that die only (not other dies).
  • How can I do this efficiently (without multiple for loops)?
  • # Example
  • In the dataset below:
  • * On die 1, cells 1, 5, 7, 8 are bad and should be removed.
  • * On die 2, cells 1, 5, 6, 9 are bad and should be removed.
  • * Cells 6, 9 should **not** be removed from die 2 (or others, besides die 1).
  • * Cells 7, 8 should **not** be removed from die 1 (or others, besides die 2).
  • Tried:
  • ```py
  • rm_dies = b100["Die"].to_list()
  • rm_cells = b100["Cell"].to_list()
  • for die, cell in zip(rm_dies, rm_cells):
  • df = df.mask((df["Die"] == die) & (df["Cell"] == cell))
  • ```
  • This works, but is very slow on large dataframes and is not
  • elegant.
  • ```txt
  • Die Cell Current Resistance
  • 0 1.0 0.0 100.0 104155.0
  • 1 1.0 0.0 200.0 2542.0
  • 2 1.0 0.0 300.0 206302.0
  • 3 NaN NaN NaN NaN
  • 4 NaN NaN NaN NaN
  • 5 NaN NaN NaN NaN
  • 6 1.0 2.0 100.0 141502.0
  • 7 1.0 2.0 200.0 40422.0
  • 8 1.0 2.0 300.0 31066.0
  • 9 1.0 3.0 100.0 108234.0
  • 10 1.0 3.0 200.0 140520.0
  • 11 1.0 3.0 300.0 43586.0
  • 12 1.0 4.0 100.0 138305.0
  • 13 1.0 4.0 200.0 88725.0
  • 14 1.0 4.0 300.0 239517.0
  • 15 NaN NaN NaN NaN
  • 16 NaN NaN NaN NaN
  • 17 NaN NaN NaN NaN
  • 18 1.0 6.0 100.0 125984.0
  • 19 1.0 6.0 200.0 37036.0
  • 20 1.0 6.0 300.0 179742.0
  • 21 NaN NaN NaN NaN
  • 22 NaN NaN NaN NaN
  • 23 NaN NaN NaN NaN
  • 24 NaN NaN NaN NaN
  • 25 NaN NaN NaN NaN
  • 26 NaN NaN NaN NaN
  • 27 1.0 9.0 100.0 186133.0
  • 28 1.0 9.0 200.0 863.0
  • 29 1.0 9.0 300.0 235060.0
  • 30 2.0 0.0 100.0 217692.0
  • 31 2.0 0.0 200.0 192709.0
  • 32 2.0 0.0 300.0 44718.0
  • 33 NaN NaN NaN NaN
  • 34 NaN NaN NaN NaN
  • 35 NaN NaN NaN NaN
  • 36 2.0 2.0 100.0 144522.0
  • 37 2.0 2.0 200.0 184146.0
  • 38 2.0 2.0 300.0 58155.0
  • 39 2.0 3.0 100.0 130899.0
  • 40 2.0 3.0 200.0 177347.0
  • 41 2.0 3.0 300.0 206209.0
  • 42 2.0 4.0 100.0 201781.0
  • 43 2.0 4.0 200.0 58077.0
  • 44 2.0 4.0 300.0 218298.0
  • 45 NaN NaN NaN NaN
  • 46 NaN NaN NaN NaN
  • 47 NaN NaN NaN NaN
  • 48 NaN NaN NaN NaN
  • 49 NaN NaN NaN NaN
  • 50 NaN NaN NaN NaN
  • 51 2.0 7.0 100.0 239666.0
  • 52 2.0 7.0 200.0 10384.0
  • 53 2.0 7.0 300.0 41034.0
  • 54 2.0 8.0 100.0 245824.0
  • 55 2.0 8.0 200.0 208052.0
  • 56 2.0 8.0 300.0 37568.0
  • 57 NaN NaN NaN NaN
  • 58 NaN NaN NaN NaN
  • 59 NaN NaN NaN NaN
  • ```
  • # Notes
  • Ultimately I want to remove these cells from the dataframe. `dropna()`
  • excluded from above calls for illustrative purposes.
python pandas
python pandas

Suggested over 1 year ago by matthewsnyder‭