Extracting Indices for Data Frame Rows That Have Max Value for Named Field

Find the column name which has the maximum value for each row

You can use idxmax with axis=1 to find the column with the greatest value on each row:

>>> df.idxmax(axis=1)
0    Communications
1          Business
2    Communications
3    Communications
4          Business
dtype: object

To create the new column 'Max', use df['Max'] = df.idxmax(axis=1).

To find the row index at which the maximum value occurs in each column, use df.idxmax() (or equivalently df.idxmax(axis=0)).

Find row where values for column is maximal in a pandas DataFrame

Use the pandas idxmax function. It's straightforward:

>>> import pandas
>>> import numpy as np
>>> df = pandas.DataFrame(np.random.randn(5,3),columns=['A','B','C'])
>>> df
          A         B         C
0  1.232853 -1.979459 -0.573626
1  0.140767  0.394940  1.068890
2  0.742023  1.343977 -0.579745
3  2.125299 -0.649328 -0.211692
4 -0.187253  1.908618 -1.862934
>>> df['A'].idxmax()
3
>>> df['B'].idxmax()
4
>>> df['C'].idxmax()
1

Alternatively you could also use numpy.argmax, such as numpy.argmax(df['A']) -- it provides the same thing, and appears at least as fast as idxmax in cursory observations.
idxmax() returns indices labels, not integers.
Example': if you have string values as your index labels, like rows 'a' through 'e', you might want to know that the max occurs in row 4 (not row 'd').
if you want the integer position of that label within the Index you have to get it manually (which can be tricky now that duplicate row labels are allowed).

HISTORICAL NOTES:

idxmax() used to be called argmax() prior to 0.11
argmax was deprecated prior to 1.0.0 and removed entirely in 1.0.0
back as of Pandas 0.16, argmax used to exist and perform the same function (though appeared to run more slowly than idxmax).
argmax function returned the integer position within the index of the row location of the maximum element.
pandas moved to using row labels instead of integer indices. Positional integer indices used to be very common, more common than labels, especially in applications where duplicate row labels are common.

For example, consider this toy DataFrame with a duplicate row label:

In [19]: dfrm
Out[19]: 
          A         B         C
a  0.143693  0.653810  0.586007
b  0.623582  0.312903  0.919076
c  0.165438  0.889809  0.000967
d  0.308245  0.787776  0.571195
e  0.870068  0.935626  0.606911
f  0.037602  0.855193  0.728495
g  0.605366  0.338105  0.696460
h  0.000000  0.090814  0.963927
i  0.688343  0.188468  0.352213
i  0.879000  0.105039  0.900260

In [20]: dfrm['A'].idxmax()
Out[20]: 'i'

In [21]: dfrm.iloc[dfrm['A'].idxmax()]  # .ix instead of .iloc in older versions of pandas
Out[21]: 
          A         B         C
i  0.688343  0.188468  0.352213
i  0.879000  0.105039  0.900260

So here a naive use of idxmax is not sufficient, whereas the old form of argmax would correctly provide the positional location of the max row (in this case, position 9).

This is exactly one of those nasty kinds of bug-prone behaviors in dynamically typed languages that makes this sort of thing so unfortunate, and worth beating a dead horse over. If you are writing systems code and your system suddenly gets used on some data sets that are not cleaned properly before being joined, it's very easy to end up with duplicate row labels, especially string labels like a CUSIP or SEDOL identifier for financial assets. You can't easily use the type system to help you out, and you may not be able to enforce uniqueness on the index without running into unexpectedly missing data.

So you're left with hoping that your unit tests covered everything (they didn't, or more likely no one wrote any tests) -- otherwise (most likely) you're just left waiting to see if you happen to smack into this error at runtime, in which case you probably have to go drop many hours worth of work from the database you were outputting results to, bang your head against the wall in IPython trying to manually reproduce the problem, finally figuring out that it's because idxmax can only report the label of the max row, and then being disappointed that no standard function automatically gets the positions of the max row for you, writing a buggy implementation yourself, editing the code, and praying you don't run into the problem again.

Get (row,col) indices of max value in dataframe

Use unstack() and extract the top MultiIndex as a tuple using idxmax()

import pandas as pd
data = [[5, 7, 10], [7, 20, 4,], [8, 1, 6,]]
cities = ['Boston', 'Phoenix', 'New York']
df = pd.DataFrame(data, columns=cities, index=cities)

print df.unstack().idxmax()

returns:

('Phoenix', 'Phoenix')

get row with max values in all columns in R

The easiest way to do it is to sort the d column, and them remove duplicates in the A1 column:

df2 <- df[order(df$d,decreasing=T),]
df2[!duplicated(df2$A1),]

This does assume that there is a single unique maximum, you would lose some results if there were more than 1.

how to get all rows with max value of a variable

A reproducible data simulating your matrix:

set.seed(123)
x <- matrix(sample(1:9, 30, T), 10, 3)
row.names(x) <- 1:10
colnames(x) <- LETTERS[1:3]

#    A B C
# 1  3 9 9
# 2  8 5 7
# 3  4 7 6
# ...

In matrix objects, you need to use a binary way to extract element such as data[a, b]. Take the above data for example, x["C"] will return NA and x[, "C"] will return all elements in column C. Therefore, the following two codes are going to generate different outputs.

subset(x, x["C"] == max(x["C"]))
#   A B C (Empty)

subset(x, x[, "C"] == max(x[, "C"]))
#   A B C
# 1 3 9 9
# 4 8 6 9

Get max value from dataframe with other information

You can use idxmax() it will give you the index for maximum value.

index = data['VALUE'][data['PARAMETER'] == 'Param1'].idxmax()

Now you can use this index to get any columns or the entire row.

data.loc[index, 'Object']

Sample Run:

>>import pandas as pd
>>df = pd.DataFrame({'Object': ['A', 'B','C','D', 'E'], 'PARAMETER': ['Param1', 'Param2', 'Param3', 'Param2', 'Param1'], 'VALUE':[1, 2, 3, 4, 5]})
>>df
  Object PARAMETER  VALUE
0      A    Param1      1
1      B    Param2      2
2      C    Param3      3
3      D    Param2      4
4      E    Param1      5

OUTPUT:
The masking:

>>df[df['PARAMETER'] == 'Param1']
  Object PARAMETER  VALUE
0      A    Param1      1
4      E    Param1      5

The idxmax():

>>df[df['PARAMETER'] == 'Param1']['VALUE'].idxmax()
4

From the masking, as you can notice, maximum index is 4, that's what idxmax() yields. Now you can use this index to access any column like this:

>>index = df['VALUE'][df['PARAMETER'] == 'Param1'].idxmax()
>>df.loc[index, 'Object']
'E'

or the entire row:

    >>df.loc[index]
    Object            E
    PARAMETER    Param1
    VALUE             5

Name: 4, dtype: object

Find row-index of highest value in given column of dataframe

Try df['number'].argmax()

import pandas
import numpy as np
df = pandas.DataFrame(np.random.randn(10,3),columns=['Col1','Col2','Col3'])
print df
print df['Col1'].argmax()

output

                Col1      Col2      Col3
0  0.583251 -0.014694  1.516529
1  0.274758  0.438513  0.994992
2  0.601611  1.753035  0.864451
3 -0.971775 -1.461290  0.121570
4  2.239460 -1.099298 -1.953045
5  2.314444  0.215336  0.470668
6 -0.138696  0.422923 -0.624436
7  0.602329 -0.015627  0.023715
8  0.594784  0.739058  1.094646
9 -0.104579  0.557339  1.977929

5

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()

Extract rows with maximum values in pandas dataframe

Close! Groupby the id, then use the value column; return the max for each group.

In [14]: df.groupby('id')['value'].max()
Out[14]: 
id
a     0
b     1
c     2
Name: value, dtype: int64

Op wants to provide these locations back to the frame, just create a transform and assign.

In [17]: df['max'] = df.groupby('id')['value'].transform(lambda x: x.max())

In [18]: df
Out[18]: 
  id  value  max
0  a      0    0
1  b      1    1
2  b      1    1
3  c      0    2
4  c      2    2
5  c      1    2

How to extract a particular column in python dataframe with Max values

I think you need set_index for index from column product name and idxmax for product name by max value of column unit price:

a = Stores.set_index('product name')['unit price'].idxmax()

Sample:

Stores = pd.DataFrame({'product name':['a','b','c'], 'unit price':[3,8,6]})
print (Stores)
  product name  unit price
0            a           3
1            b           8
2            c           6

a = Stores.set_index('product name')['unit price'].idxmax()
print (a)
b