Python：将自定义函数应用于数据框中的多个指定列

我创建了一个函数，用于查找数据帧中缺少的值.

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

# Create a sample dataset
iris = load_iris()

df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                 columns= iris['feature_names'] + ['target'])
df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)

# Here we replace all values of setosa with 'missing_value'
df = df.applymap(lambda x: 'missing_value' if x == 'setosa' else x)

# Here we want to create a flag for the missing values
def add_missing_value_flags(mydf, column):
    
    # Generate the new column name
    new_col = "missing_" + column
    
    # Create flags where the data is missing
    # that has put in a holder to represent a missing value
    mydf[new_col]= np.where(mydf[column] == 'missing_value', True,
                            np.where(mydf[column] == '', True,
                            np.where(mydf[column] == 'N/A', True,  
                            np.where(mydf[column] == 'N\A', True, 
                            np.where(mydf[column] == 'NA', True,
                            np.where(mydf[column] == 'N.A.', True,
                            np.where(mydf[column] == 'NONE', True,
                            np.where(mydf[column] == '.', True, 
                            np.where(mydf[column].str.len() == 1, True, 
                            np.where(mydf[column] == '..', True, False))))))))))
    
    return(mydf)


add_missing_value_flags(df, 'species')

     sepal length (cm)  sepal width (cm)  ...        species  missing_species
0                  5.1               3.5  ...  missing_value             True
1                  4.9               3.0  ...  missing_value             True
2                  4.7               3.2  ...  missing_value             True
3                  4.6               3.1  ...  missing_value             True
4                  5.0               3.6  ...  missing_value             True
..                 ...               ...  ...            ...              ...
145                6.7               3.0  ...      virginica            False
146                6.3               2.5  ...      virginica            False
147                6.5               3.0  ...      virginica            False
148                6.2               3.4  ...      virginica            False
149                5.9               3.0  ...      virginica            False

Is there a method in python where I can apply my function to the rest of my columns similiar to: mydf[mydf.columns[mydf.columns.str.contains('species|plant|earth')]].apply...