How can I find the row for which the value of a specific column is maximal?
df.max() will give me the maximal value for each column, I don't know how to get the corresponding row.
You just need the
argmax() (now called
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'].argmax() 3 >>> df['B'].argmax() 4 >>> df['C'].argmax() 1
This function was updated to the name
idxmax in the Pandas API, though as of Pandas 0.16,
argmax still exists and performs the same function (though appears to run more slowly than
You can also just use
numpy.argmax, such as
numpy.argmax(df['A']) -- it provides the same thing as either of the two
pandas functions, and appears at least as fast as
idxmax in cursory observations.
Previously (as noted in the comments) it appeared that
argmax would exist as a separate function which provided the integer position within the index of the row location of the maximum element. For 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'). However, in pandas 0.16, all of the listed methods above only provide the label from the
Index for the row in question, and if you want the position integer of that label within the
Index you have to get it manually (which can be tricky now that duplicate row labels are allowed).
In general, I think the move to
idxmax-like behavior for all three of the approaches (
argmax, which still exists,
numpy.argmax) is a bad thing, since it is very common to require the positional integer location of a maximum, perhaps even more common than desiring the label of that positional location within some index, especially in applications where duplicate row labels are common.
For example, consider this toy
DataFrame with a duplicate row label:
In : dfrm Out: 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 : dfrm['A'].idxmax() Out: 'i' In : dfrm.ix[dfrm['A'].idxmax()] Out: 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.
You might also try
In : df = pandas.DataFrame(np.random.randn(10,3),columns=['A','B','C']) In : df Out: A B C 0 2.001289 0.482561 1.579985 1 -0.991646 -0.387835 1.320236 2 0.143826 -1.096889 1.486508 3 -0.193056 -0.499020 1.536540 4 -2.083647 -3.074591 0.175772 5 -0.186138 -1.949731 0.287432 6 -0.480790 -1.771560 -0.930234 7 0.227383 -0.278253 2.102004 8 -0.002592 1.434192 -1.624915 9 0.404911 -2.167599 -0.452900 In : df.idxmax() Out: A 0 B 8 C 7
In : df.loc[df['A'].idxmax()] Out: A 2.001289 B 0.482561 C 1.579985