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查看以前提出的问题，我找不到有用的答案，因为我的列是通过混合使用 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()

这将为我的 search_rank （第一行）返回一个熊猫数据框：

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，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

任何关于我如何完成这项工作的想法都会是一个巨大的帮助！

PS：我不想使用实际列名的原因是因为此信息会根据股票代码而改变。另外，我是python的一个极端菜鸟，基本上还在学习>.<

4

1 回答 1

0

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 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
于 2020-07-19T02:21:03.627 回答