Pandas - 在每行中找到第二大值

Question

早上好！ 我有一个三列 dataframe 并且需要找到每行的第二大值

DATA=pd.DataFrame({"A":[10,11,4,5],"B":[23,8,3,4],"C":[12,7,11,9]})

    A   B   C
0  10  23  12
1  11   8   7
2   4   3  11
3   5   4   9

我尝试使用 nlargest 但它似乎是基于列的，并且找不到针对此问题的 pandas 解决方案。 先感谢您！

Answer 1

import pandas as pd


df=pd.DataFrame({"A":[10,11,4,5],"B":[23,8,3,4],"C":[12,7,11,9]})

# find the second largest value for each row
df['largest2'] = df.apply(lambda x: x.nlargest(2).iloc[1], axis=1)


print(df.head())

结果：

    A   B   C  largest2
0  10  23  12        12
1  11   8   7         8
2   4   3  11         4
3   5   4   9         5

Answer 2

在 Python 列表中

mylist = [1, 2, 8, 3, 12]
print(sorted(mylist, reverse=True)[1])

在 Python Pandas 列表中

import pandas as pd
df=pd.DataFrame({"A":[10,11,4,5],"B":[23,8,3,4],"C":[12,7,11,9]})
print(sorted(df['A'].nlargest(4))[3])
print(sorted(df['B'].nlargest(4))[3])
print(sorted(df['C'].nlargest(4))[3])

在 Python Pandas 列表 mk.2

import pandas as pd
df=pd.DataFrame({"A":[10,11,4,5],"B":[23,8,3,4],"C":[12,7,11,9]})

num_of_rows = len(df.index)
second_highest = num_of_rows - 2
print(sorted(df['A'].nlargest(num_of_rows))[second_highest])
print(sorted(df['B'].nlargest(num_of_rows))[second_highest])
print(sorted(df['C'].nlargest(num_of_rows))[second_highest])

在 Python Pandas 列表 mk.3

import pandas as pd
df=pd.DataFrame({"A":[10,11,4,5],"B":[23,8,3,4],"C":[12,7,11,9]})

col_names
num_of_rows = len(df.index)
second_highest = num_of_rows - 2

for col_name in col_names:
     print(sorted(df[col_name].nlargest(num_of_rows))[second_highest])