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

Using Python Pandas I am trying to find the Country & Place with the maximum value.

This returns the maximum value:

data.groupby(['Country','Place'])['Value'].max()

But how do I get the corresponding Country and Place name?

I'd recommend using nlargest for better performance and shorter code. import pandas

df[col_name].value_counts().nlargest(n=1)

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

This will return the entire row with max value

My solution for finding maximum values in columns:

df.ix[df.idxmax()]

, also minimum:

df.ix[df.idxmin()]

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

You can use:

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

I think the easiest way to return a row with the maximum value is by getting its index. argmax() can be used to return the index of the row with the largest value.

index = df.Value.argmax()

Now the index could be used to get the features for that particular row:

df.iloc[df.Value.argmax(), 0:2]

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()))

import pandas
df is the data frame you create.

Use the command:

df1=df[['Country','Place']][df.Value == df['Value'].max()]

This will display the country and place whose value is maximum.

In order to print the Country and Place with maximum value, use the following line of code.

print(df[['Country', 'Place']][df.Value == df.Value.max()])

I encountered a similar error while trying to import data using pandas, The first column on my dataset had spaces before the start of the words. I removed the spaces and it worked like a charm!!