Drop the entire row if a particular sub-row does not fulfill condition

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I have a pandas df with subentries. I would like to make a condition for a particular subentry, and if this condition is not fulfilled, I would like to drop the entire row so I could update the df.

For example, I would like to check each subentry 0 for all the entries and give a condition that if pt<120 then drop the entire entry.

               pt
entry subentry
0     0        100
      1        200
      2        300
1     0        200
      1        300
2     0        80
      1        300
      3        400
      4        300
...   ...      ...

So, the entry 0 and 2 (with all the subentries) should be deleted.

               pt
entry subentry
1     0        200
      1        300
...   ...      ...

I tried using:

df.loc[(slice(None), 0), :]["pt"]>100

but it creates a new series and I cannot pass it to the original df because it does not match the entries/subentries. Thank you.

pandas python
2 Answers

Try this:

# Count the number of invalid `pt` per `entry`
invalid = df['pt'].lt(120).groupby(df['entry']).sum()

# Valid `entry` are those whose `invalid` count is 0
df[df['entry'].isin(invalid[invalid == 0].index)]
user_25389390

One solution is to groupby "entry" and then calculate using transform to create a minimum that can then be used with loc to index the correct rows

df = pd.DataFrame({'entry': [0, 0, 1, 1, 2, 2], 
           'subentry': [1, 2, 1, 2, 1, 2], 
           'pt': [100, 300, 200, 300, 80, 300]})

Initial df:

   entry  subentry   pt
0      0         1  100
1      0         2  300
2      1         1  200
3      1         2  300
4      2         1   80
5      2         2  300

Use loc to find select only the rows matching conditional:

df.loc[df.groupby('entry').transform('min')['pt']>120]

Output:

   entry  subentry   pt
2      1         1  200
3      1         2  300
user_151267630
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