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

Question

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?

Answer 1

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()

Answer 2

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

这将返回具有最大值的整行

Answer 3

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]

Answer 4

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()))

Answer 5

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

Answer 6

为了打印具有最大值的国家和地区，请使用以下代码行。

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

Answer 7

您可以使用：

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

Answer 8

My solution for finding maximum values in columns:

df.ix[df.idxmax()]

, also minimum:

df.ix[df.idxmin()]

Answer 9

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

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

Answer 10

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.

Answer 11

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!!

Answer 12

Using DataFrame.nlargest .

The dedicated method for this is nlargest which uses algorithm.SelectNFrame on the background, which is a performant way of doing: sort_values().head(n)

   x  y  a  b
0  1  2  a  x
1  2  4  b  x
2  3  6  c  y
3  4  1  a  z
4  5  2  b  z
5  6  3  c  z

df.nlargest(1, 'y')

   x  y  a  b
2  3  6  c  y