CODEWITHSUNDEEP
pandas
Ranny
Ranny

Reputation: 315

Pandas: Deleting corresponding rows in 2+ DataFrames

I have 2 DataFrames:

dfA:      dfB:
    n1         n2
a - AA    01 - 0
b - BB    02 - 0
c - CC    03 - 0
d - DD    04 - 1
e - EE    05 - 2
f - FF    06 - 0

Number of rows in dfA and dfB is always equal.

Now, I'm deleting all rows with 0 values from dfB by:

dfB = dfB[dfB['n2']!=0]

(Most likely 0 rows appear only on top or (top and bottom) of DataFrames.)

How to go about deleting corresponding rows from dfA?

Upvotes: 2

Views: 82

Answers (2)

Landmaster
Landmaster

Reputation: 1053

You can extract the value of the index using get_indexer_for, which returns a list of index locations (as opposed to index values)

dfB.index.get_indexer_for(dfB[dfB['n2'] != 0].index)

Then you can use those to drop the corresponding values in dfA as follows

dfA.drop(dfa.index[dfB.index.get_indexer_for(dfB[dfB['n2'] != 0].index)])

Make sure to reassign the df as this operation does not modify existing dataframe. It just makes a copy of it.

Upvotes: 2

piRSquared
piRSquared

Reputation: 294308

Use dfB to make a boolean array. Then use it as a mask for both dataframes.

m = dfB.n2.eq(0).values

dfB = dfB[m]
dfA = dfA[m]

Debugging
Copy/Paste this 

dfB = pd.DataFrame(dict(n2=[0, 0, 0, 1, 2, 0]), '01 02 03 04 05 06'.split())

m = dfB.n2.eq(0).values

dfB[m]

Upvotes: 2

Related Questions