Communities

Writing
Writing
Codidact Meta
Codidact Meta
The Great Outdoors
The Great Outdoors
Photography & Video
Photography & Video
Scientific Speculation
Scientific Speculation
Cooking
Cooking
Electrical Engineering
Electrical Engineering
Judaism
Judaism
Languages & Linguistics
Languages & Linguistics
Software Development
Software Development
Mathematics
Mathematics
Christianity
Christianity
Code Golf
Code Golf
Music
Music
Physics
Physics
Linux Systems
Linux Systems
Power Users
Power Users
Tabletop RPGs
Tabletop RPGs
Community Proposals
Community Proposals
tag:snake search within a tag
answers:0 unanswered questions
user:xxxx search by author id
score:0.5 posts with 0.5+ score
"snake oil" exact phrase
votes:4 posts with 4+ votes
created:<1w created < 1 week ago
post_type:xxxx type of post
Search help
Notifications
Mark all as read See all your notifications »
Users Search
Help Dashboard Sign In Sign Up
Q&A Code Reviews Meta

Welcome to Software Development on Codidact!

Will you help us build our independent community of developers helping developers? We're small and trying to grow. We welcome questions about all aspects of software development, from design to code to QA and more. Got questions? Got answers? Got code you'd like someone to review? Please join us.

Review Suggested Edit

You can't approve or reject suggested edits because you haven't yet earned the Edit Posts ability.
Return to post

Approved.
This suggested edit was approved and applied to the post 10 months 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 10 months ago by matthewsnyder‭