SLglider SLglider - 8 months ago 46
Python Question

Python Pandas compare two dataframes to assign country to phone number

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.

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.

Input longlist:

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

Input country_list:


Output should be:

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

I have it solved once like this:

#! /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]]

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

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)



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.


i would do it this way:

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'] = (
    .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')


In [195]: result
    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


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])]))
   .....: )
      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: - 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