从 URL 与 Z251D2BBFE9A3B95E56AZ91CEB30DC?6

Question

There is a.csv file contained within a.zip file from a URL I am trying to read into a Pandas DataFrame; I don't want to download the.zip file to disk but rather read the data directly from the URL. I realize that pandas.read_csv() can only do this if the.csv file is the only file contained in the.zip, however, when I run this:

import pandas as pd

# specify zipped comma-separated values url
zip_csv_url = 'http://www12.statcan.gc.ca/census-recensement/2016/geo/ref/gaf/files-fichiers/2016_92-151_XBB_csv.zip'
df1 = pd.read_csv(zip_csv_url)

I get this:

ValueError: Multiple files found in compressed zip file ['2016_92-151_XBB.csv', '92-151-g2016001-eng.pdf', '92-151-g2016001-fra.pdf']

The contents of the.zip appear to be arranged as a list; I'm wondering how I can assign the new DataFrame (df1) as the only available.csv file in the.zip (as the.zip file from the URL I will be using would only ever have one.csv file within it). Thanks!

NB

The corresponding.zip file from a separate URL with shapefiles reads no problem with geopandas.read_file() when I run this code:

import geopandas as gpd

# specify zipped shapefile url
zip_shp_url = 'http://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/files-fichiers/2016/ldb_000b16a_e.zip'
gdf1 = gpd.read_file(zip_shp_url)

Despite having a.pdf file also contained within the.zip, as seen in the image below:

It would appear that the geopandas.read_file() has the ability to only read the requisite shapefiles for creating the GeoDataFrame while ignoring unnecessary data files. Since it is based on Pandas, shouldn't Pandas also have a functionality to only read a.csv within a.zip with multiple other file types? Any thoughts?

Answer 1

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


resp = urlopen(  YOUR_ZIP_LINK  )
files_zip = zipfile.ZipFile(BytesIO(resp.read()))
# files_zip.namelist()
directory_to_extract_to = YOUR_DESTINATION_FOLDER
file = YOUR_csv_FILE_NAME
with files_zip as zip_ref:
    zip_ref.extract(file,directory_to_extract_to)
pd.read_csv(directory_to_extract_to + file)