索引、查找和替换

Question

我正在使用 yfinance 数据，它使用股票代码。然而，这些股票代码通常不容易识别。因此，在执行下载功能后，我尝试通过查找列表然后替换数据框中的原始股票代码来替换“股票代码”。我对字典和索引/替换概念相当陌生。谁能指出我正确的方向吗？

import yfinance as yf
import pandas as pd
import csv

tickerCsv = open('Stocks.csv', 'r')
reader = csv.reader(tickerCsv)
next(reader, None)  # skip the headers
tickers = {rows[0] for rows in reader}

data = yf.download(tickers, period='1mo', start="2009-01-01", end="2020-12-31", group_by='ticker')

close_prices = pd.DataFrame()

for ticker in tickers:
    close_prices[ticker] = data[ticker]['Adj Close']
    close_prices[ticker].replace(dict(zip(reader['Symbol'], reader['Name'])))

Stocks.csv 文件如下：

Answer 1

你的代码已经差不多了。我将使用 pandas 读取 csv 文件并生成符号/名称映射：

dd = pd.read_csv('Stocks.csv').set_index('Symbol')['Name'].to_dict()

输出：

{'C38U.SI': 'CICT', 'A17U.SI': 'CLAR', 'M44U.SI': 'MLT', 'ME8U.SI': 'MINT'}

那么

tickers

就是

dd

的键:

tickers = list(dd)

最后，您想要替换

close_prices

中的列名称，您可以这样做：

close_prices.columns = close_prices.columns.map(dd)

总共：

import yfinance as yf
import pandas as pd
import csv

dd = pd.read_csv('Stocks.csv').set_index('Symbol')['Name'].to_dict()
tickers = list(dd)

data = yf.download(tickers, period='1mo', start="2009-01-01", end="2020-12-31", group_by='ticker')

close_prices = pd.DataFrame()

for ticker in tickers:
    close_prices[ticker] = data[ticker]['Adj Close']

close_prices.columns = close_prices.columns.map(dd)

输出：

                CICT      CLAR       MLT      MINT
Date
2009-01-02  0.601655  0.538735  0.144619       NaN
2009-01-05  0.631185  0.600304  0.156840       NaN
2009-01-06  0.631185  0.615697  0.154804       NaN
2009-01-07  0.660713  0.596456  0.158877       NaN
2009-01-08  0.642258  0.577216  0.158877       NaN
...              ...       ...       ...       ...
2020-12-23  1.827041  2.496265  1.674264  2.397144
2020-12-24  1.810045  2.487803  1.691262  2.380439
2020-12-28  1.810045  2.487803  1.691262  2.380439
2020-12-29  1.835539  2.504726  1.691262  2.388792
2020-12-30  1.852535  2.547036  1.708259  2.413848

[3012 rows x 4 columns]