>>> df
out[]:
   salary   age   gender
0   10000    23     男
1   15000    34     女
2   23000    21     男
3     0      20     女
4   28500     0     男
5   35000    37     男

>>> df.replace(["男", "女"], [1, 0])
out[]:
   salary   age   gender
0   10000    23     1
1   15000    34     0
2   23000    21     1
3     0      20     0
4   28500     0     1
5   35000    37     1

>>> df = df.loc[~((df['salary'] == 0) | (df['age'] == 0))]
>>> df
out[]:
   salary   age   gender
0   10000    23     1
1   15000    34     0
2   23000    21     1
3   35000    37     1

>>> df.loc[~(df==0).all(axis=1)]

>>> df.loc[(df!=0).any(axis=1)]

>>> new_df = df[df.loc[:]!=0].dropna()

import numpy as np
df = df.replace(0, np.nan)# 把0替換成nan
df = df.dropna(how='all', axis=0)# 刪除所有為nan的行
df = df.replace(np.nan, 0)# 再把nan替換成0

>>> df= df[df['salary'] != 0]

import pandas as pd
import numpy as np
 
np.random.seed(0)
 
df = pd.DataFrame(np.random.randn(5,3),
                  index=['one', 'two', 'three', 'four', 'five'],
                  columns=list('abc'))
 
df.loc[['one', 'three']] = 0 # 把第一行和第三行改為0
 
print(df)
print(df.loc[~df.apply(lambda row: (row==0).all(), axis=1)])

new_df = df[df.loc[:]!=0].dropna()
new_df