Find single dataframe row index matching 2 columns conditionals

Question

I have a dataframe with 3 columns and I need to get the index of the row that match the values with 2 columns.

For example, the dataframe below:

Name     City   Country
Pietro   Roma   Italy
Maria    Milan  Italy
Pietro   NY     USA

In this case, I need to get the index of Pietro|Roma|Italy, seaching for Name and City columns only.

I tried doing the code below, but it returns all the rows that matches the 2 columns.

idx = np.where(dataframe[dataframe["Name"] == 'Pietro'],dataframe[dataframe["City"] == 'Roma'])

But it returns an array of indexes [[0,2],[0]] and I need to return index 0 that is where I have Name = 'Pietro' and City = 'Roma'

Updated with Solution

The solution is:

dataframe.index[(dataframe["Name"] == 'Pietro')&(dataframe["City"] == 'Roma')][0]

BENY · Accepted Answer · 2019-02-25 19:37:04Z

4

Using

dataframe.index[(dataframe["Name"] == 'Pietro')&(dataframe["City"] == 'Roma')]

answered Feb 25, 2019 at 19:37

BENY

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2 Comments

This is correct, but returns the index object, (Int64Index([0], dtype='int64'). If you want just the value of the index, add [0] to the end

It worked combining answers: dataframe.index[(dataframe["Name"] == 'Pietro')&(dataframe["City"] == 'Roma')] [0]