Question

我是Python的初学者，我对从互联网获取数据了解不多。我在这里使用的这种方法在获取和打印IMDB Top 250电影中起作用。所以我想对this coronavirus data做同样的事情。但是与IMDB数据不同，程序不会将项目视为列表。我看不到与IMDB数据有太大差异。那么，如何使用简单的请求和漂亮的汤料至少打印国家的名称？

import requests
from bs4 import BeautifulSoup
url = requests.get("https://www.worldometers.info/coronavirus/")
soup = BeautifulSoup(url.content, "html.parser")
new_soup = soup.find_all("table", {"id":"main_table_countries_today"})
country_table = new_soup[0].contents[3]
country_table = country_table.find_all("tr")
for country in country_table:
    country_name = country.find_all("td", {"style":"font-weight: bold; font-size:15px; text-align:left;"})
    print(country_name[0].text)

Answer 1

我一直从GitHub repository的John Hopkins University中获取数据，据认为这是一个有信誉的来源：

names = ('confirmed', 'deaths', 'recovered')
src_base = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_{name}_global.csv'

可以用requests感染：

import requests


for name, url in src.items():
    response = requests.get(url)

并方便地转换为Pandas数据框：

import io
import pandas


dfs = {}
for name, url in src.items():
    response = requests.get(url)
    dfs[name] = pd.read_csv(io.BytesIO(response.content))
    print(name, url)
    print(dfs[name])

confirmed https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
                Province/State         Country/Region  ...  4/13/20  4/14/20
0                          NaN            Afghanistan  ...      665      714
1                          NaN                Albania  ...      467      475
2                          NaN                Algeria  ...     1983     2070
3                          NaN                Andorra  ...      646      659
4                          NaN                 Angola  ...       19       19
..                         ...                    ...  ...      ...      ...
259  Saint Pierre and Miquelon                 France  ...        1        1
260                        NaN            South Sudan  ...        4        4
261                        NaN         Western Sahara  ...        6        6
262                        NaN  Sao Tome and Principe  ...        4        4
263                        NaN                  Yemen  ...        1        1

[264 rows x 88 columns]
deaths https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
                Province/State         Country/Region  ...  4/13/20  4/14/20
0                          NaN            Afghanistan  ...       21       23
1                          NaN                Albania  ...       23       24
2                          NaN                Algeria  ...      313      326
3                          NaN                Andorra  ...       29       31
4                          NaN                 Angola  ...        2        2
..                         ...                    ...  ...      ...      ...
259  Saint Pierre and Miquelon                 France  ...        0        0
260                        NaN            South Sudan  ...        0        0
261                        NaN         Western Sahara  ...        0        0
262                        NaN  Sao Tome and Principe  ...        0        0
263                        NaN                  Yemen  ...        0        0

[264 rows x 88 columns]
recovered https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv
                Province/State         Country/Region  ...  4/13/20  4/14/20
0                          NaN            Afghanistan  ...       32       40
1                          NaN                Albania  ...      232      248
2                          NaN                Algeria  ...      601      691
3                          NaN                Andorra  ...      128      128
4                          NaN                 Angola  ...        4        5
..                         ...                    ...  ...      ...      ...
245  Saint Pierre and Miquelon                 France  ...        0        0
246                        NaN            South Sudan  ...        0        0
247                        NaN         Western Sahara  ...        0        0
248                        NaN  Sao Tome and Principe  ...        0        0
249                        NaN                  Yemen  ...        0        0

[250 rows x 88 columns]

您最终可能会有一些快速绘图：

可用的完整代码here。

如何解析该站点的冠状病毒数据？

1 个答案: