从表数据中获取 Pandas 中的空 DataFrame
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【中文标题】从表数据中获取 Pandas 中的空 DataFrame【英文标题】：Getting Empty DataFrame in pandas from table data 【发布时间】：2022-01-23 09:05:57 【问题描述】：我正在使用打印命令获取数据，但在 Pandas DataFrame 中，结果为：Empty DataFrame,Columns: [],Index: [`]
脚本：
from bs4 import BeautifulSoup
import requests
import re
import json
import pandas as pd
url='http://financials.morningstar.com/finan/financials/getFinancePart.html?&callback=jsonp1640132253903&t=XNAS:AAPL'
req=requests.get(url).text
#print(req)
data=re.search(r'jsonp1640132253903\((\.*\)\)',req).group(1)
json_data=json.loads(data)['componentData']
#print(json_data)
# with open('index.html','w') as f:
# f.write(json_data)
soup=BeautifulSoup(json_data,'lxml')
for tr in soup.select('tr'):
row_data=[td.get_text(strip=True) for td in tr.select('td,th') if td.text]
if not row_data:
continue
if len(row_data) < 12:
row_data = ['Particulars'] + row_data
#print(row_data)
df=pd.DataFrame(row_data)
print(df)
打印结果：
['详情', '2012-09', '2013-09', '2014-09', '2015-09', '2016-09', '2017-09', '2018-09', '2019-09'、'2020-09'、'2021-09'、'TTM'] ['RevenueUSD Mil', '156,508', '170,910', '182,795', '233,715', '215,639', '229,234', '265,595', '260,174', '274,515', '365,817', '365']8 ['毛利率%', '43.9', '37.6', '38.6', '40.1', '39.1', '38.5', '38.3', '37.8', '38.2', '41.8', '41.8' ] ['营业收入百万美元', '55,241', '48,999', '52,503', '71,230', '60,024', '61,344', '70,898', '63,930', '66,288', '108,949', '108,949' ] ['营业利润率%', '35.3', '28.7', '28.7', '30.5', '27.8', '26.8', '26.7', '24.6', '24.1', '29.8', '29.8' ] ['净收入百万美元', '41,733', '37,037', '39,510', '53,394', '45,687', '48,351', '59,531', '55,256', '57,411', '94,680', '94,680'] ['每股收益USD', '1.58', '1.42', '1.61', '2.31', '2.08', '2.30', '2.98', '2.97', '3.28', '5.61', '5.61'
预期输出：
2012-09 2013-09 2014-09 2015-09 2016-09 2017-09 2018-09 2019-09 2020-09 2021-09 TTM
Revenue USD Mil 156,508 170,910 182,795 233,715 215,639 229,234 265,595 260,174 274,515 365,817 365,817
Gross Margin % 43.9 37.6 38.6 40.1 39.1 38.5 38.3 37.8 38.2 41.8 41.8
Operating Income USD Mil 55,241 48,999 52,503 71,230 60,024 61,344 70,898 63,930 66,288 108,949 108,949
Operating Margin % 35.3 28.7 28.7 30.5 27.8 26.8 26.7 24.6 24.1 29.8 29.8
Net Income USD Mil 41,733 37,037 39,510 53,394 45,687 48,351 59,531 55,256 57,411 94,680 94,680
Earnings Per Share USD 1.58 1.42 1.61 2.31 2.08 2.30 2.98 2.97 3.28 5.61 5.61
Dividends USD 0.09 0.41 0.45 0.49 0.55 0.60 0.68 0.75 0.80 0.85 0.85
Payout Ratio % * — 27.4 28.5 22.3 24.8 26.5 23.7 25.1 23.7 16.3 15.2
Shares Mil 26,470 26,087 24,491 23,172 22,001 21,007 20,000 18,596 17,528 16,865 16,865
Book Value Per Share * USD 4.25 4.90 5.15 5.63 5.93 6.46 6.04 5.43 4.26 3.91 3.85
Operating Cash Flow USD Mil 50,856 53,666 59,713 81,266 65,824 63,598 77,434 69,391 80,674 104,038 104,038
Cap Spending USD Mil -9,402 -9,076 -9,813 -11,488 -13,548 -12,795 -13,313 -10,495 -7,309 -11,085 -11,085
Free Cash Flow USD Mil 41,454 44,590 49,900 69,778 52,276 50,803 64,121 58,896 73,365 92,953 92,953
Free Cash Flow Per Share * USD 1.58 1.61 1.93 2.96 2.24 2.41 2.88 3.07 4.04 5.57 —
Working Capital USD Mil 19,111 29,628 5,083 8,768 27,863 27,831 14,473 57,101 38,321 9,355
预期列：
'Particulars', '2012-09', '2013-09', '2014-09', '2015-09', '2016-09', '2017-09', '2018-09', '2019-09', '2020-09', '2021-09', 'TTM'
【问题讨论】：
【参考方案1】：@QHarr 的回答是迄今为止最直接的，但如果您想知道您的代码出了什么问题，那就是您正在为循环的每次迭代重置变量 row_data。
为了使您的代码正常工作，您可以将每一行作为一个元素存储在列表中。然后构建一个DataFrame，你可以将这个行列表和列名传递给pd.DataFrame：
data = []
soup=BeautifulSoup(json_data,'lxml')
for tr in soup.select('tr'):
row_data=[td.get_text(strip=True) for td in tr.select('td,th') if td.text]
if not row_data:
continue
elif len(row_data) < 12:
columns = ['Particulars'] + row_data
else:
data.append(row_data)
df=pd.DataFrame(data, columns=columns)
结果：
>>> df
Particulars 2012-09 2013-09 2014-09 2015-09 2016-09 2017-09 2018-09 2019-09 2020-09 2021-09 TTM
0 RevenueUSD Mil 156,508 170,910 182,795 233,715 215,639 229,234 265,595 260,174 274,515 365,817 365,817
1 Gross Margin % 43.9 37.6 38.6 40.1 39.1 38.5 38.3 37.8 38.2 41.8 41.8
2 Operating IncomeUSD Mil 55,241 48,999 52,503 71,230 60,024 61,344 70,898 63,930 66,288 108,949 108,949
3 Operating Margin % 35.3 28.7 28.7 30.5 27.8 26.8 26.7 24.6 24.1 29.8 29.8
4 Net IncomeUSD Mil 41,733 37,037 39,510 53,394 45,687 48,351 59,531 55,256 57,411 94,680 94,680
5 Earnings Per ShareUSD 1.58 1.42 1.61 2.31 2.08 2.30 2.98 2.97 3.28 5.61 5.61
6 DividendsUSD 0.09 0.41 0.45 0.49 0.55 0.60 0.68 0.75 0.80 0.85 0.85
7 Payout Ratio % * — 27.4 28.5 22.3 24.8 26.5 23.7 25.1 23.7 16.3 15.2
8 SharesMil 26,470 26,087 24,491 23,172 22,001 21,007 20,000 18,596 17,528 16,865 16,865
9 Book Value Per Share *USD 4.25 4.90 5.15 5.63 5.93 6.46 6.04 5.43 4.26 3.91 3.85
10 Operating Cash FlowUSD Mil 50,856 53,666 59,713 81,266 65,824 63,598 77,434 69,391 80,674 104,038 104,038
11 Cap SpendingUSD Mil -9,402 -9,076 -9,813 -11,488 -13,548 -12,795 -13,313 -10,495 -7,309 -11,085 -11,085
12 Free Cash FlowUSD Mil 41,454 44,590 49,900 69,778 52,276 50,803 64,121 58,896 73,365 92,953 92,953
13 Free Cash Flow Per Share *USD 1.58 1.61 1.93 2.96 2.24 2.41 2.88 3.07 4.04 5.57 —
14 Working CapitalUSD Mil 19,111 29,628 5,083 8,768 27,863 27,831 14,473 57,101 38,321 9,355 —
【讨论】：
【参考方案2】：使用 read_html 创建 DataFrame，然后删除 na 行
json_data=json.loads(data)['componentData']
pd.read_html(json_data)[0].dropna(axis=0, how='all')
【讨论】：
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