Pandas DataFrame:使用列轴索引添加具有行值总和的列

Question

浏览之前提出的问题，我找不到有帮助的答案，因为我的列是通过混合使用pytrends和yfnance值生成的。

以下是获取有问题的数据帧的代码：

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()

这为我的search_rank(第一行(返回一个pandas数据帧：

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"；排名"；列，它将从第1、2和3列中获取值并将它们相加，因此它看起来像这样：

date                | TER | Teradyne, Inc. | Teradyne | Rank
2004-01-01 00:00:00 | 25  | 0              | 1        | 26

任何关于我将如何实现这一目标的想法都将是一个巨大的帮助！

附言：我不想使用实际列名的原因是，这些信息会根据股票行情而变化。此外，我是一个极度擅长python的人，基本上仍在学习>lt；

Answer 1

删除列

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是的完整代码

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()
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：

Date        TER  Teradyne, Inc.  Teradyne  Rank
2004-01-01   25               0         1    26
2004-02-01   25               0         1    26
2004-03-01   29               0         1    30