Find maximum value of a column and return the corresponding row values using Pandas

Assuming df has a unique index, this gives the row with the maximum value:

In [34]: df.loc[df['Value'].idxmax()]
Out[34]: 
Country        US
Place      Kansas
Value         894
Name: 7

Note that idxmax returns index labels. So if the DataFrame has duplicates in the index, the label may not uniquely identify the row, so df.loc may return more than one row.

Therefore, if df does not have a unique index, you must make the index unique before proceeding as above. Depending on the DataFrame, sometimes you can use stack or set_index to make the index unique. Or, you can simply reset the index (so the rows become renumbered, starting at 0):

df = df.reset_index()

df[df['Value']==df['Value'].max()]

This will return the entire row with max value

The country and place is the index of the series, if you don't need the index, you can set as_index=False:

df.groupby(['country','place'], as_index=False)['value'].max()

Edit:

It seems that you want the place with max value for every country, following code will do what you want:

df.groupby("country").apply(lambda df:df.irow(df.value.argmax()))

Use the index attribute of DataFrame. Note that I don't type all the rows in the example.

In [14]: df = data.groupby(['Country','Place'])['Value'].max()

In [15]: df.index
Out[15]: 
MultiIndex
[Spain  Manchester, UK     London    , US     Mchigan   ,        NewYork   ]

In [16]: df.index[0]
Out[16]: ('Spain', 'Manchester')

In [17]: df.index[1]
Out[17]: ('UK', 'London')

You can also get the value by that index:

In [21]: for index in df.index:
    print index, df[index]
   ....:      
('Spain', 'Manchester') 512
('UK', 'London') 778
('US', 'Mchigan') 854
('US', 'NewYork') 562

Edit

Sorry for misunderstanding what you want, try followings:

In [52]: s=data.max()

In [53]: print '%s, %s, %s' % (s['Country'], s['Place'], s['Value'])
US, NewYork, 854