Python Pandas比较两个数据框以将国家/地区分配给电话号码 [英] Python Pandas compare two dataframes to assign country to phone number

问题描述

我有两个通过csv读取的数据框.数据框一由电话号码和一些其他数据组成.第二个数据框包含国家/地区代码和国家/地区名称.

I have two dataframes that I read in via csv. Dataframe one consists of a phone number and some additional data. The second dataframe contains country codes and country names.

我想从第一个数据集中获取电话号码，并将其与第二个国家/地区的国家/地区代码进行比较.国家/地区代码的长度可以在1-4位数之间.我从最长的国家代码到最短的国家代码.如果有匹配项，我想将国家/地区名称分配给电话号码.

I want to take the phone number from the first dataset and compare it to the country codes of the second. Country codes can between one to four digits long. I go from the longest country code to the shortest. If there is a match, I want to assign the country name to the phone number.

输入长名单:

phonenumber, add_info    
34123425209, info1
92654321762, info2
12018883637, info3
6323450001, info4
496789521134, info5

输入国家/地区列表:

country;country_code;order_info
Spain;34;1
Pakistan;92;4
USA;1;2
Philippines;63;3
Germany;49;4
Poland;48;1
Norway;47;2

输出应为:

phonenumber, add_info, country, order_info    
34123425209, info1, Spain, 1
92654321762, info2, Pakistan, 4
12018883637, info3, USA, 2
6323450001, info4, Philippines, 3
496789521134, info5, Germany, 4

我曾经这样解决过:

#! /usr/bin/python
import csv
import pandas
with open ('longlist.csv','r') as lookuplist:
with open ('country_list.csv','r') as inputlist:
    with open('Outputfile.csv', 'w') as outputlist:
        reader = csv.reader(lookuplist, delimiter=',')
        reader2 = csv.reader(inputlist, delimiter=';')
        writer = csv.writer(outputlist, dialect='excel')

        for i in reader2:
            for xl in reader:
                if xl[0].startswith(i[1]):
                    zeile = [xl[0], xl[1], i[0], i[1], i[2]]
                    writer.writerow(zeile)
            lookuplist.seek(0)

但是我想用熊猫解决这个问题.我要工作的是: -读取csv文件 -从长名单"中删除重复项 -排序国家/地区代码列表

But I would like to solve this problem, using pandas. What I got to work: - Read in the csv files - Remove duplicates from "longlist" - Sort list of countries / country code

这是我已经在工作的东西:

This is, what I have working already:

import pandas as pd, numpy as np
longlist = pd.read_csv('path/to/longlist.csv', 
                                 usecols=[2,3], names=['PHONENUMBER','ADD_INFO'])
country_list = pd.read_csv('path/to/country_list.csv', 
                           sep=';', names=['COUNTRY','COUNTRY_CODE','ORDER_INFO'], skiprows=[0])

# remove duplicates and make phone number an index
longlist = longlist.drop_duplicates('PHONENUMBER')
longlist = longlist.set_index('PHONENUMBER')

# Sort country list, from high to low value and make country code an index
country_list=country_list.sort_values(by='COUNTRY_CODE', ascending=0)
country_list=country_list.set_index('COUNTRY_CODE')

(...)

longlist.to_csv('path/to/output.csv')

但是对数据集尝试相同的任何方法均行不通.我无法应用startswith(无法遍历对象，也无法将其应用于对象).我将衷心感谢您的帮助.

But any way trying the same with datasets does not work. I cannot apply startswith (cannot iterate through objects and cannot apply it on objects). I would really appreciate your help.

推荐答案

我会这样做:

cl = pd.read_csv('country_list.csv', sep=';', dtype={'country_code':str})
ll = pd.read_csv('phones.csv', skipinitialspace=True, dtype={'phonenumber':str})

lookup = cl['country_code']
lookup.index = cl['country_code']

ll['country_code'] = (
    ll['phonenumber']
    .apply(lambda x: pd.Series([lookup.get(x[:4]), lookup.get(x[:3]),
                                lookup.get(x[:2]), lookup.get(x[:1])]))
    .apply(lambda x: x.get(x.first_valid_index()), axis=1)
)

# remove `how='left'` parameter if you don't need "unmatched" phone-numbers    
result = ll.merge(cl, on='country_code', how='left')

输出:

In [195]: result
Out[195]:
    phonenumber add_info country_code      country  order_info
0   34123425209    info1           34        Spain         1.0
1   92654321762    info2           92     Pakistan         4.0
2   12018883637    info3            1          USA         2.0
3   12428883637   info31         1242      Bahamas         3.0
4    6323450001    info4           63  Philippines         3.0
5  496789521134    info5           49      Germany         4.0
6   00000000000      BAD         None          NaN         NaN

说明:

In [216]: (ll['phonenumber']
   .....:   .apply(lambda x: pd.Series([lookup.get(x[:4]), lookup.get(x[:3]),
   .....:                               lookup.get(x[:2]), lookup.get(x[:1])]))
   .....: )
Out[216]:
      0     1     2     3
0  None  None    34  None
1  None  None    92  None
2  None  None  None     1
3  1242  None  None     1
4  None  None    63  None
5  None  None    49  None
6  None  None  None  None

phones.csv:-我故意添加了一个巴哈马号码(1242...)和一个无效号码(00000000000)

phones.csv: - i've intentionally added one Bahamas number (1242...) and one invalid number (00000000000)

phonenumber, add_info
34123425209, info1
92654321762, info2
12018883637, info3
12428883637, info31
6323450001, info4
496789521134, info5
00000000000, BAD

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