为多个股票扩展此 Python 脚本

发布于 2025-01-09 08:41:48 字数 533 浏览 0 评论 0原文

首先，感谢您花时间帮助我。我们正在使用这个 python 脚本从雅虎提取给定时间段的数据。

import datetime as dt
import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import pandas_datareader.data as web

style.use('ggplot')
start = dt.datetime (2007,1,1)
end = dt.datetime(2022,1,31)

df = web.DataReader('AAPL','yahoo', start, end)
df.to_csv('AAPl.csv')

上面的代码从雅虎获取我们设置的日期内 AAPL 股票所需的数据，然后为该股票创建一个 CSV。我们遇到的问题是我们需要对 5000 只不同的股票执行此操作。我们有一个 CSV 文件，其中包含运行该程序所需的所有不同代码。我们如何修改代码以运行 CSV 中的不同股票？而不是我们必须手动运行这个程序 5000 次。

原文

First off, thank you for taking the time to help me. We are using this python script that pulls data from Yahoo for a given time period.

import datetime as dt
import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import pandas_datareader.data as web

style.use('ggplot')
start = dt.datetime (2007,1,1)
end = dt.datetime(2022,1,31)

df = web.DataReader('AAPL','yahoo', start, end)
df.to_csv('AAPl.csv')

The code above grabs the data we need from Yahoo for the AAPL stock for the dates we set, then it creates a CSV for that stock. The problem we are running into is that we have 5000 different stocks we need to do this for. We have a CSV file with all the different tickers we need to run this program over. How can we modify our code to run over the different stocks from our CSV? Instead of us having to run this program manually 5000 times.

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<逆流佳人身旁 2025-01-16 08:41:48

您不必将中间数据帧（针对单个股票代码）写入文件。尝试类似的方法：

tickers = pd.read_csv(r'path_to/symbols.csv')['symbol_column_name'].values
full_df = pd.DataFrame({})
for ticker in tickers:
    df = web.DataReader(ticker,'yahoo', start, end)
    full_df = pd.concat((full_df, df))
#Now write merged table to file
full_df.to_csv('my_output_table.csv')

You don't have to write the intermediate dataframes (for individual stock symbols) to file. Try something like:

tickers = pd.read_csv(r'path_to/symbols.csv')['symbol_column_name'].values
full_df = pd.DataFrame({})
for ticker in tickers:
    df = web.DataReader(ticker,'yahoo', start, end)
    full_df = pd.concat((full_df, df))
#Now write merged table to file
full_df.to_csv('my_output_table.csv')

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