Label Not In List and KeyError

Question

I'm trying to get the value of a specific cell.

  main_id   name  code
  0    1345  Jones    32
  1    1543   Jack    62
  2    9874   Buck    86
  3    2456   Slim    94

I want the cell that says code=94, as I already know the main_id but nothing else.

raw_data = {'main_id': ['1345', '1543', '9874', '2456'],
        'name': ['Jones', 'Jack', 'Buck', 'Slim'],
        'code': [32, 62, 86, 94]}

    df=pd.DataFrame(raw_data, columns = ['main_id', 'name', 'code'])


    v=df.loc[str(df['main_id']) == str(2456)]['code'].values
    print(df.loc['name'])

The print(df.loc['name']) claims the label is not in index

And the v=df.loc[str(df['main_id']) == str(2456)]['code'].values says 'KeyError False'

Solution 1

df.loc['name'] raises a KeyError because name is not in the index; it is in the columns. When you use loc, the first argument is for index. You can use df['name'] or df.loc[:, 'name'].

You can also pass boolean arrays to loc (both for index and columns). For example,

df.loc[df['main_id']=='2456']
Out:
   main_id  name    code
3  2456     Slim    94

You can still select a particular column for this, too:

df.loc[df['main_id']=='2456', 'code']
Out:
3    94
Name: code, dtype: int64

With boolean indexes, the returning object will always be a Series even if you have only one value. So you might want to access the underlying array and select the first value from there:

df.loc[df['main_id']=='2456', 'code'].values[0]
Out:
94

But better way is to use the item method:

df.loc[df['main_id']=='2456', 'code'].item()
Out:
94

This way, you'll get an error if the length of the returning Series is greater than 1 while values[0] does not check that.

Solution 2

Alternative solution:

In [76]: df.set_index('main_id').at['2456','code']
Out[76]: 94

Comments

[Danny W]

Thanks for your help guys, very helpful.

[D.S.]

I know this is an old question, but there is a speedier way to get first you need to set main_id as an index of the dataframe and then you can do this df.loc['2456'] and it is much faster than the approach suggested in the answer