Read specific csv file from zip using pandas

user_11801298 0
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Here is a data I am interested in.

http://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_E_All_Data.zip

It consists of 3 files:

enter image description here

I want to download zip with pandas and create DataFrame from 1 file called Production_Crops_E_All_Data.csv

import pandas as pd
url="http://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_E_All_Data.zip"
df=pd.read_csv(url)

Pandas can download files, it can work with zips and of course it can work with csv files. But how can I work with 1 specific file in archive with many files?

Now I get error

ValueError: ('Multiple files found in compressed zip file %s)

This post doesn't answer my question bcause I have multiple files in 1 zip Read a zipped file as a pandas DataFrame

pandas python
2 Answers

From this link

EDIT: updated for python3 StringIO to io.StringIO

EDIT: updated the import of urllib, changed usage of StringIO to BytesIO. Also your CSV files are not utf-8 encoding, I tried latin1 and that worked.

try this

from zipfile import ZipFile
import io
from urllib.request import urlopen
import pandas as pd

r = urlopen("http://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_E_All_Data.zip").read()
file = ZipFile(io.BytesIO(r))
data_df = pd.read_csv(file.open("Production_Crops_E_All_Data.csv"), encoding='latin1')
data_df_noflags = pd.read_csv(file.open("Production_Crops_E_All_Data_NOFLAG.csv"), encoding='latin1')
data_df_flags = pd.read_csv(file.open("Production_Crops_E_Flags.csv"), encoding='latin1')

Hope this helps!

user_28792150

You could use python's datatable, which is a reimplementation of Rdatatable in python.

Read in data :

from datatable import fread

#The exact file to be extracted is known, simply append it to the zip name:
 url = "Production_Crops_E_All_Data.zip/Production_Crops_E_All_Data.csv"

 df = fread(url)

#convert to pandas

 df.to_pandas()

You can equally work within datatable; do note however, that it is not as feature-rich as Pandas; but it is a powerful and very fast tool.

Update: You can use the zipfile module as well :

from zipfile import ZipFile
from io import BytesIO

with ZipFile(url) as myzip:
    with myzip.open("Production_Crops_E_All_Data.csv") as myfile:
        data = myfile.read()

#read data into pandas
#had to toy a bit with the encoding,
#thankfully it is a known issue on SO
#https://stackoverflow.com/a/51843284/7175713
df = pd.read_csv(BytesIO(data), encoding="iso-8859-1", low_memory=False)
user_71757130
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