一次返回所有变量

Question

嘿，我正在做数据验证，如果都满足我当前的代码，我想比较多个正则表达式条件变量

def upload_file():
    if request.method == 'POST':
        print(request.files['file'])
        f = request.files['file']
        data_xls = pd.read_excel(f)
        df2 = data_xls.reset_index()
        name_filter = df2[df2['Pensioner Name'].str.match('^[a-zA-Z\s\.]*$')]
        cnic_filter = df2[df2['CNIC'].str.match('^[\'][0-9+]{5}-[0-9+]{7}-[0-9]{1}[\']$')]
        wallet_filter = df2[df2['Wallet Account No'].astype(str).str.contains('^[0-9+]{8}$')]
        mobile_filter = df2[df2['Mobile Number'].astype(str).str.contains('^[3+][0-9+]{9}$')]
     return name_filter.to_html() 

如何一次返回所有变量，验证所有正则表达式现在它唯一返回的 name_filter

谢谢

Answer 1

我试过了，它对我有用。虽然条件不满足。它仍然有效。

import pandas as pd

df2 = pd.DataFrame(
    {'Pensioner Name' : ['CHANESER','CHANESERS'],
     'CNIC' : ['41207-0800205-9','41207-0800205-9'],
     'Wallet Account No' : ['BDS00229','BDS00229'],
     'MobileNumber' : ['3203501312','3203501312']
     }
)



if df2[df2['Pensioner Name'].str.match('^[a-zA-Z\s\.]*$')].equals(df2['Pensioner Name']) and \
df2[df2['CNIC'].str.match('^[\'][0-9+]{5}-[0-9+]{7}-[0-9]{1}[\']$')].equals(df2['CNIC'])and \
df2[df2['Wallet Account No'].astype(str).str.contains('^[0-9+]{8}$')].equals(df2['Wallet Account No'].astype(str)) and \
df2[df2['Mobile Number'].astype(str).contains('^[3+][0-9+]{9}$')].equals(df2['Mobile Number'].astype(str)):
    print("works!")
else:
    print("not works!")

Answer 2

基本上，您不是验证一个结果，而是验证所有结果并将它们过滤掉。

所以假设你希望所有的结果都通过并且是真的并且它们都是真的情况。

# COMPARE IF THE SAME DATASETS ARE PRESENT, EVEN AFTER PASSING ALL THE CONDITIONS AS TRUE
if df2[df2['Pensioner Name'].str.match('^[a-zA-Z\s\.]*$'].equals(df2['Pensioner Name'])     and df2[df2['CNIC'].str.match('^[\'][0-9+]{5}-[0-9+]{7}-[0-9]{1}[\']$'].equals(df2['CNIC'])     and df[df2['Wallet Account No'].astype(str).str.contains('^[0-9+]{8}$'].equals(df2['Wallet Account No'].astype(str)) and  df2[df2['Mobile Number'].astype(str).contains('^[3+][0-9+]{9}$')].equals(df2[['Mobile Number'].astype(str)):
    return df2.to_html()
else:
    return '''<h2> Error in data </h2>'''