英文:
How to get second max value of each row in a pandas dataframe
问题
这会在pandas数据框的每一行中获取最大值，并创建一个名为'max'的新列，然后从该列创建一个名为maxV的新列表。
df["max"] = df1.max(axis=1)
maxV = df['max'].tolist()
如何在一个名为'sec_max'的新列中获取每一行的第二大值？
英文:
This gets the maximum value for each row in a pandas dataframe in a new column named 'max' and then
creates a new list named maxV from that column.
df["max"] = df1.max(axis=1)
maxV = df['max'].tolist()
How can i get the second max value of each row in a new column named 'sec_max'?
答案1
得分: 1
import pandas as pd
import numpy as np
# 获取每行的第二大值的函数
def get_second_max(row):
# 使用 np.partition 高效地找到第二大的值
second_largest = np.partition(row, -2)[-2]
return second_largest
# 对每行应用该函数，并将结果存储在 'sec_max' 列中
df['sec_max'] = df.apply(get_second_max, axis=1)
# 将 'sec_max' 列转换为名为 maxV 的新列表
sec_maxV = df['sec_max'].tolist()
英文:
import pandas as pd
import numpy as np
# Function to get the second maximum value in each row
def get_second_max(row):
# Use np.partition to efficiently find the second largest value
second_largest = np.partition(row, -2)[-2]
return second_largest
# Apply the function to each row and store the result in 'sec_max' column
df['sec_max'] = df.apply(get_second_max, axis=1)
# Convert the 'sec_max' column to a new list named maxV
sec_maxV = df['sec_max'].tolist()
答案2
得分: 0
另一种解决方案，使用 np.sort：
df[['sec_max', 'maxV']] = np.sort(df, axis=1)[:, -2:]
print(df)
打印：
Col1 Col2 Col3 sec_max maxV
0 1 3 2 2 3
1 6 4 5 5 6
2 7 8 8 8 8
使用的 df：
Col1 Col2 Col3
0 1 3 2
1 6 4 5
2 7 8 8
英文:
Another solution, using np.sort:
df[['sec_max', 'maxV']] = np.sort(df, axis=1)[:, -2:]
print(df)
Prints:
Col1 Col2 Col3 sec_max maxV
0 1 3 2 2 3
1 6 4 5 5 6
2 7 8 8 8 8
df used:
Col1 Col2 Col3
0 1 3 2
1 6 4 5
2 7 8 8
答案3
得分: 0
import pandas as pd
import numpy as np
data = {"col1": [420, 380, 390],
"col2": [50, 40, 45],
"col3": [102, 60, 700]}
df = pd.DataFrame(data)
for i in range(0,len(df.index)):
currentRow=df.iloc[i].values
max1=currentRow.max()
max2=currentRow.min()
for j in range(0,len(currentRow)):
if(currentRow[j]>max2 and currentRow[j]<max1):
max2=currentRow[j]
print(max1,max2)
英文:
here you are:
import pandas as pd
import numpy as np
data = {"col1": [420, 380, 390],
"col2": [50, 40, 45],
"col3": [102, 60, 700]}
df = pd.DataFrame(data)
for i in range(0,len(df.index)):
currentRow=df.iloc[i].values
max1=currentRow.max()
max2=currentRow.min()
for j in range(0,len(currentRow)):
if(currentRow[j]>max2 and currentRow[j]<max1):
max2=currentRow[j]
print(max1,max2)
答案4
得分: 0
df['第二大'] = df.apply(lambda row: row.nlargest(2).values[-1], axis=1)
print(df)
输出：
a b c 第二大
0 1 3 2 2
1 2 4 1 2
2 3 5 10 5
英文:
You can also use this:
df['2nd largest'] = df.apply(lambda row: row.nlargest(2).values[-1],axis=1)
Example Input:
import pandas as pd
df = pd.DataFrame({'a':[1,2,3], 'b':[3,4,5], 'c':[2,1,10]})
print (df)
df:
a b c
0 1 3 2
1 2 4 1
2 3 5 10
code:
df['2nd largest'] = df.apply(lambda row: row.nlargest(2).values[-1],axis=1)
print (df)
Output:
a b c 2nd largest
0 1 3 2 2
1 2 4 1 2
2 3 5 10 5
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