无法使用熊猫数据框重命名表的列名答案

【问题标题】：Can not rename the table's column name using pandas dataframe无法使用熊猫数据框重命名表的列名
【发布时间】：2022-11-16 23:38:02
【问题描述】：

我是 jupyter notebook 和 python 的新手。最近我正在使用这段代码，但我找不到问题所在。我想将 "Tesla Quarterly Revenue(Millions of US $)" and "Tesla Quarterly Revenue(Millions of US $).1" 重命名为 "Data" and "Revenue" 但它没有改变。这是我的代码：

!pip install pandas
!pip install requests
!pip install bs4
!pip install -U yfinance pandas 
!pip install plotly
!pip install html5lib
!pip install lxml

import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork23455606-2022-01-01"
html_data  = requests.get(url).text

soup = BeautifulSoup(html_data, 'html5lib')
tesla_revenue = pd.read_html(url, match = "Tesla Quarterly Revenue")[0]
tesla_revenue = tesla_revenue.rename(columns={"Tesla Quarterly Revenue(Millions of US $)":"Date","Tesla Quarterly Revenue(Millions of US $).1":"Revenue"})
tesla_revenue.head()

这是输出：

【问题讨论】：

标签： python pip jupyter-notebook data-science jupyter

【解决方案1】：

无法重现问题，它按预期工作，以防万一（静态）：

tesla_revenue.columns = ['Date','Revenue']

例子

import pandas as pd
url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork23455606-2022-01-01"
tesla_revenue = pd.read_html(url, match = "Tesla Quarterly Revenue")[0]
#tesla_revenue = tesla_revenue.rename(columns={"Tesla Quarterly Revenue(Millions of US $)":"Date","Tesla Quarterly Revenue(Millions of US $).1":"Revenue"})
tesla_revenue.columns = ['Date','Revenue']
tesla_revenue.head()

输出

	Date	Revenue
0	2022-09-30	$21,454
1	2022-06-30	$16,934
2	2022-03-31	$18,756
3	2021-12-31	$17,719
4	2021-09-30	$13,757

【讨论】：