Pandas DataFrame：使用列轴索引添加具有行值总和的列？

Question

查看之前提出的问题，我找不到有用的答案，因为我的专栏是通过混合使用 pytrends 和 yfinance 值生成的。

这是获取相关数据框的代码：

import yfinance as yf

from pytrends.request import TrendReq as tr

ticker = "TER"

pytrends = tr(hl='en-US', tz=360)

# =============================================================================

# Get Stock Information

# These variables are stored as DataFrames

# =============================================================================

stock = yf.Ticker(ticker)

i = stock.info

stock_info = {'Ticker':ticker}

stock_info.update(i)

# =============================================================================

# Get Google Trends Ranking for our Stock

# =============================================================================

longName = stock_info.get('longName')

shortName = stock_info.get('shortName').split(',')[0]

keywords = [ticker, longName, shortName]

pytrends.build_payload(keywords, timeframe='all')

search_rank = pytrends.interest_over_time()

这会为我的 search_rank（第一行）返回一个 pandas 数据框：

date                | TER | Teradyne, Inc. | Teradyne | isPartial

2004-01-01 00:00:00 | 25  | 0              | 1        | False

我想做的是删除 isPartial 列并将其替换为“Rank”列，该列将从第 1、2 和 3 列中获取值并将它们相加，这样它看起来像这样：

date                | TER | Teradyne, Inc. | Teradyne | Rank

2004-01-01 00:00:00 | 25  | 0              | 1        | 26

关于如何完成这项工作的任何想法都将是一个巨大的帮助！

PS：我不想使用实际列名的原因是因为此信息会根据股票行情而改变。另外，我是 python 的极度菜鸟，基本上还在学习 >.<

Answer 1

删除一列del search_rank['isPartial']添加计算列search_rank['Rank'] = df.apply(lambda row: row[0]+row[1] + row[2], axis=1)我用上面的修改测试了你的代码，这里是完整的代码import yfinance as yffrom pytrends.request import TrendReq as trticker = "TER"pytrends = tr(hl='en-US', tz=360)# =============================================================================# Get Stock Information# These variables are stored as DataFrames# =============================================================================stock = yf.Ticker(ticker)i = stock.infostock_info = {'Ticker':ticker}stock_info.update(i)# =============================================================================# Get Google Trends Ranking for our Stock# =============================================================================longName = stock_info.get('longName')shortName = stock_info.get('shortName').split(',')[0]keywords = [ticker, longName, shortName]pytrends.build_payload(keywords, timeframe='all')search_rank = pytrends.interest_over_time()del search_rank['isPartial']search_rank['Rank'] = search_rank.apply(lambda row: row[0]+row[1]+row[2] , axis=1)print(search_rank)输出：&nbsp;Date&nbsp; &nbsp; &nbsp; &nbsp; TER&nbsp; Teradyne, Inc.&nbsp; Teradyne&nbsp; Rank2004-01-01&nbsp; &nbsp;25&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1&nbsp; &nbsp; 262004-02-01&nbsp; &nbsp;25&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1&nbsp; &nbsp; 262004-03-01&nbsp; &nbsp;29&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1&nbsp; &nbsp; 30