Find Row Where Values for Column Is Maximal in a Pandas Dataframe

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).

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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.

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()

Find the max value at each row - Pandas data frame

Can you try the code below:

mask = df.filter(regex=r'[A-Z]1').stack().between(1, 5).values
df['MAX'] = df.filter(regex=r'[A-Z]2').stack()[mask].groupby(level=0).max()

The expected result for the first row: 0,9 (1 and 5 values are between A1 and E2). The expected result for the second row: 0,85 (1 and 5 values are between A1 and J2). The expected result for the third row: 0,86 (1 and 5 values are between A1 and C2)

>>> df
  Type    ID  A1     A2  ...  J1      J2   MAX
0    X  1212   1  0.300  ...   9  0.0030  0.90
1    Y  2342   1  0.004  ...   5  0.0001  0.85
2    Z  2421   1  0.860  ...  17  0.3000  0.86

[3 rows x 21 columns]

Find row with min/max value for each day in Pandas DataFrame

Use DataFrameGroupBy.idxmax and
DataFrameGroupBy.idxmin for indices by minimal and maximal values, convert to Series and select original DatetimeIndex by DataFrame.loc:

df1 = (df.loc[df.groupby(df.index.day)['Charge']
               .agg(['idxmin', 'idxmax']).stack()].sort_index())
print (df1)
                     Charge
2022-01-03 13:19:02    99.5
2022-01-03 13:21:02    64.2

If need aggregate new columns:

df2 = df.groupby(df.index.day)['Charge'].agg(['min','max', 'idxmin', 'idxmax'])
print (df2)
    min   max              idxmin              idxmax
3  64.2  99.5 2022-01-03 13:21:02 2022-01-03 13:19:02

How to find the max, min value of ALL Dataframe [ not values by column neither rows ]

You can try:

max_val = df.max().max()

Getting the max value from a list of columns by their index in Pandas

You can find the 'date' columns using a list comprehension which will return the columns that contain /. Then you can use max(axis=1) to create the column which will show the highest value per row, of your date like columns:

date_cols = [c for c in list(df) if '/' in c]
df['max_per_row'] = df[date_cols].max(axis=1)

prints:

   index  ID  Cost  ...  08/01/2021 00:00  15/01/2021 00:00  max_per_row
0      0   1  4000  ...             50.55             60.99        60.99
1      0   1   500  ...             80.55            160.99       160.99
2      0   1  4000  ...            530.55           1660.99      1660.99
3      0   1  5000  ...             90.55          18860.99     18860.99
4      0   1  9000  ...            590.55          73760.99     73760.99

Pandas: sum DataFrame column for max value only

where

df.append(
    df.where(                # only look at values that are max for the row
        df.eq(               # compare max values to all values in row just
                             #     in case there are more than 1
            df.max(axis=1),  # actually get max values
            axis=0
        )
    ).sum().rename('count')
)

          a     b     c     d
0      0.28  0.50  0.33  0.00
1      0.00  0.50  0.70  0.25
2      0.25  0.00  0.25  0.20
3      0.85  0.75  0.20  0.66
4      0.10  0.10  0.50  0.10
count  1.10  0.50  1.45  0.00

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