select rows in pandas DataFrame using comparisons against two columns

Question

I have a pandas dataframe:

df = pd.DataFrame({'one' : [1, 2, 3, 4] ,'two' : [5, 6, 7, 8]})
   one  two
0    1    5
1    2    6
2    3    7
3    4    8

Column "one" and column "two" together comprise (x,y) coordinates

Lets say I have a list of coordinates: c = [(1,5), (2,6), (20,5)]

Is there an elegant way of obtaining the rows in df with matching coordinates? In this case, given c, the matching rows would be 0 and 1

Related question: Using pandas to select rows using two different columns from dataframe?

And: Selecting rows from pandas DataFrame using two columns

Brent Benson · Accepted Answer · 2016-01-24 20:41:06Z

1

This approaching using pd.merge should perform better than the iterative solutions.

import pandas as pd

df = pd.DataFrame({"one" : [1, 2, 3, 4] ,"two" : [5, 6, 7, 8]})
c = [(1, 5), (2, 6), (20, 5)]
df2 = pd.DataFrame(c, columns=["one", "two"])

pd.merge(df, df2, on=["one", "two"], how="inner")
   one  two
0    1    5
1    2    6

answered Jan 24, 2016 at 20:41

Brent Benson

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Comments

Ami Tavory · Accepted Answer · 2018-04-22 06:20:59Z

0

You can use

>>> set.union(*(set(df.index[(df.one == i) & (df.two == j)]) for i, j in c))
{0, 1}

edited Apr 22, 2018 at 6:20

answered Jan 24, 2016 at 20:02

Ami Tavory

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1 Comment

Chris Withers Over a year ago

This looks to be horribly slow, I wish it wasn't marked as the correct answer. See the merge answer.

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