CODEWITHSUNDEEP
pythonpandas
Nasia Ntalla
Nasia Ntalla

Reputation: 1789

Pandas read_csv alters the columns when it starts with 0

I have a script where I read from a csv file some zipcodes. The format of the zipcodes are like this:

zipcode
75180
90672
01037
20253
09117
31029
07745
90453
12105
18140
36108
10403
76470
06628
93105
88069
31094
84095
63069

Then I run a script:

import requests
import pandas as pd
import time

file = '/Users/zipcode.csv'
reader = pd.read_csv(file, sep=';', encoding='utf-8-sig')

zipcodes = reader["zipcode"].astype(str)
base_url = "https://api.blabla/?zipcode={zipcode}"
headers = {'Authentication': 'random'}

for zipcode in zipcodes:
    url = base_url.format(zipcode=zipcode)
    r = requests.get(url,
                     headers=headers)
    for r_info in r.json()["data"]:
        print zipcode,r_info["id"]
    time.sleep(0.5)

However, whenever there is a zipcode starting with 0, the result I get is with 4 digits and it can't match with the actual 0. I have formatted my csv to have a text column in it, but still it doesn't work.

The zipcodes I get are like this: 

zipcode
75180
90672
1037
20253
9117
31029
7745
90453
12105
18140
36108
10403
76470
6628
93105
88069
31094
84095
63069

Do you have any idea how can this be resolved?

Upvotes: 2

Views: 438

Answers (1)

EdChum
EdChum

Reputation: 394269

You need to pass the dtype as str:

reader = pd.read_csv(file, sep=';', encoding='utf-8-sig', dtype=str)

to read those values as str:

In [152]:
import pandas as pd
import io
t="""zipcode
75180
90672
01037
20253
09117
31029
07745
90453
12105
18140
36108
10403
76470
06628
93105
88069
31094
84095
63069"""
df = pd.read_csv(io.StringIO(t), dtype=str)
df

Out[152]:
   zipcode
0    75180
1    90672
2    01037
3    20253
4    09117
5    31029
6    07745
7    90453
8    12105
9    18140
10   36108
11   10403
12   76470
13   06628
14   93105
15   88069
16   31094
17   84095
18   63069

by default pandas sniffs the dytpes and in this case it thinks they are numeric so you lose leading zeroes

You can also do this as a post-processing step by casting to str and then using the vectorised str.zfill:

In [154]:
df['zipcode'] = df['zipcode'].astype(str).str.zfill(5)
df

Out[154]:
   zipcode
0    75180
1    90672
2    01037
3    20253
4    09117
5    31029
6    07745
7    90453
8    12105
9    18140
10   36108
11   10403
12   76470
13   06628
14   93105
15   88069
16   31094
17   84095
18   63069

Upvotes: 5

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