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This is solved in 2 steps:

* Find rows matching remove conditions
* Do anti-left-join on the the composite key `(Die, Cell)`

To filter out the rows:
```
# Read in the data
df = pd.read_csv("data.csv")

# Identify cells with low current
b100 = df[(df["Resistance"] < 100000) & (df["Current"] == 100)]

# Pull out only columns of interest
bad_cells = b100[["Die", "Cell"]]
```

We can take unique values of `bad_cells` here, but I didn't, because it doesn't affect the outcome in the end. Also, taking the column subset is not necessary, but reduces extra junk columns later.

Pandas has no explicit anti-join ("join on *doesn't equal*") so I stole it from https://stackoverflow.com/a/55543744/21703684:
```
# Remove them with anti join
outer_join = df.merge(bad_cells, how="left", indicator=True)
filtered = outer_join[outer_join._merge != "both"].drop("_merge", axis=1)

print(filtered)
```