import pandas as pd

file_path='Yourfile.csv' #file name
df = pd.read_csv(file_path) #reading csv file 
df = df.fillna(0) #replace NaN to 0

不幸的是，我不知道有什么干净直接的方法可以声明比csv文件中存在的列更多的列。
但是一旦你有了一个Pandas DataFrame，就很容易添加缺少的列：

# read a CSV file having missing columns:
tmp = pd.read_csv('file.csv')

#create an empty dataframe with all the expected columns
df = pd.DataFrame(columns=['Location', 'Total', 'Open', 'Checkin', 'Closed', 'Cancelled'])

# just copy the data:
df[tmp.columns] = tmp

仅此而已。缺失的列将用NaN值填充。

import pandas as pd

#Read the first excel file content
df1 = pd.read_excel(r'/content/MainFile.xlsx')

#Read the second excel file content where headers are missing
df2 = pd.read_excel(r'/content/ClientFile.xlsx')

现在查找两个文件之间的不同列，并将它们提取到新的数据框中：

extracted_col= df1[df1.columns.difference(df2.columns)]

然后将提取的列加入到缺少的头文件中。

df2=df2.join(extracted_col)