Python 从url加载压缩（.gz）.csv文件时出现问题

我正在尝试直接从url加载csv文件。csv文件被压缩为.gz文件：

#Importing libraries
import pandas as pd
import requests
import io

#defining the url
url = "https://data.brasil.io/dataset/covid19/caso_full.csv.gz"

错误如下：

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-28-58ebbb6aba80> in <module>
      7 url = "https://data.brasil.io/dataset/covid19/caso_full.csv.gz"
      8 s=requests.get(url).content
----> 9 df=pd.read_csv(io.StringIO(s.decode('utf-8')), sep=',', compression='gzip', index_col=0, quotechar='"')
     10 
     11 #df=pd.read_csv("caso_full.csv.gz")

UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte


    s=requests.get(url).content
    df=pd.read_csv(io.StringIO(s.decode('utf-8')), sep=',', compression='gzip', index_col=0, quotechar='"')

有关于为什么会发生这种情况的提示吗

---

谢谢大家!

问题是您正在解码内容，然后使用

io.StringIO

解决方案是不解码字节并使用

io.BytesIO

请参阅此堆栈溢出回答：

url以GNU ZIP的形式返回内容

pd.read\u csv

需要一个文件路径或缓冲区作为其第一个参数。因为内容是字节，所以必须使用

io.BytesIO

对象。Pandas然后将数据解压缩到CSV文件中

import io
import pandas as pd
import requests

# defining the url
url = "https://data.brasil.io/dataset/covid19/caso_full.csv.gz"
response = requests.get(url)
content = response.content
print(type(content))
df = pd.read_csv(
    io.BytesIO(content), sep=",", compression="gzip", index_col=0, quotechar='"',
)
print(df.head())

输出：

<class 'bytes'>

---

你能发布例外情况吗？您能打印申请的s和返回代码吗？非常感谢！请不要忘记接受答案。答案旁边将出现一个绿色复选标记。

<class 'bytes'>

city_ibge_code        date  epidemiological_week  estimated_population_2019  ...  place_type  state  new_confirmed  new_deaths
city                                                                                    ...
São Paulo       3550308.0  2020-02-25                     9                 12252023.0  ...        city     SP              1           0
NaN                  35.0  2020-02-25                     9                 45919049.0  ...       state     SP              1           0
São Paulo       3550308.0  2020-02-26                     9                 12252023.0  ...        city     SP              0           0
NaN                  35.0  2020-02-26                     9                 45919049.0  ...       state     SP              0           0
São Paulo       3550308.0  2020-02-27                     9                 12252023.0  ...        city     SP              0           0

[5 rows x 16 columns]