要获得设置者和姓名的代码清单，

问题描述

   import pandas as pd
import datetime as dt
from pandas_datareader import data as web
import yfinance as yf
yf.pdr_override()

文件名= r'C：\ Users \ User \ Desktop \ from_python \ data_from_python.xlsx'

yeah = pd.read_excel(filename,sheet_name='entry')
stock = []

stock = list(yeah['name'])
stock = [ s.replace('\xa0','') for s in stock if not pd.isna(s) ]


adj_close=pd.DataFrame([])
high_price=pd.DataFrame([])
low_price=pd.DataFrame([])
volume=pd.DataFrame([])

print(stock)


['^GSPC','NQ=F','AAU','ALB','AOS','APPS','AQB','ASPN','ATHM','AZRE','BCYC','BGNE','CAT','CC','CLAR','CLCT','CMBM','CMT','CRDF','CYD','DE','DKNG','EARN','EMN','FBIO','FBRX','FCX','FLXS','FMC','FMCI','GME','GRVY','HAIN','HBM','HIBB','IEX','IOR','KFS','MAXR','MPX','MRTX','NSTG','NVCR','NVO','OESX','PENN','PLL','PRTK','RDY','REGI','REKR','SBE','SQM','TCON','TCS','TGB','TPTX','TRIL','UEC','VCEL','VOXX','WIT','WKHS','XNCR']

for symbol in stock:
    adj_close[symbol] = web.get_data_yahoo([symbol],start,end)['Adj Close']

我有一个股票行情清单，我有一个收盘价，要如何获得这些股票行情的名称和行业？

对于我在网络上找到的单个自动收录器，可以按以下方式完成

sbux = yf.Ticker("SBUX")
tlry = yf.Ticker("TLRY")

print(sbux.info['sector'])
print(tlry.info['sector'])

我如何才能像dataframe那样以adj的价格将数据放入excel。

非常感谢！

解决方法

它同时处理库存和部门。但是，某些股票没有部门，因此添加了错误对策。由于问题列名称由部门和问题名称组成，因此我们将其更改为分层列，并更新检索到的数据框。最后，我将其保存为CSV格式以将其导入Excel。由于股票数量太多，我只尝试了其中一些股票，所以可能会有一些问题。

import datetime

import pandas as pd
import yfinance as yf
import pandas_datareader.data as web

yf.pdr_override()

start = "2018-01-01"
end = "2019-01-01"

# symbol = ['^GSPC','NQ=F','AAU','ALB','AOS','APPS','AQB','ASPN','ATHM','AZRE','BCYC','BGNE','CAT',#'CC','CLAR','CLCT','CMBM','CMT','CRDF','CYD','DE','DKNG','EARN','EMN','FBIO','FBRX','FCX','FLXS',#'FMC','FMCI','GME','GRVY','HAIN','HBM','HIBB','IEX','IOR','KFS','MAXR','MPX','MRTX','NSTG','NVCR',#'NVO','OESX','PENN','PLL','PRTK','RDY','REGI','REKR','SBE','SQM','TCON','TCS','TGB','TPTX','TRIL',#'UEC','VCEL','VOXX','WIT','WKHS','XNCR']
stock = ['^GSPC','APPS']

adj_close = pd.DataFrame([])

for symbol in stock:
    try:
        sector = yf.Ticker(symbol).info['sector']
        name = yf.Ticker(symbol).info['shortName']
    except:
        sector = 'None'
        name = 'None'
    adj_close[sector,symbol] = web.get_data_yahoo(symbol,start=start,end=end)['Adj Close']
idx = pd.MultiIndex.from_tuples(adj_close.columns)
adj_close.columns = idx
adj_close.head()

                    None    Basic Materials Industrials Technology
            ^GSPC_None  NQ=F_None   AAU_None    ALB_Albemarle Corporation   AOS_A.O. Smith Corporation  APPS_Digital Turbine,Inc.
2018-01-02  2695.810059 6514.75 1.03    125.321663  58.657742   1.79
2018-01-03  2713.060059 6584.50 1.00    125.569397  59.010468   1.87
2018-01-04  2723.989990 6603.50 0.98    124.073502  59.286930   1.86
2018-01-05  2743.149902 6667.75 1.00    125.502716  60.049587   1.96
2018-01-08  2747.709961 6688.00 0.95    130.962250  60.335583   1.96


# for excel 
adj_close.to_csv('stock.csv',sep=',')

您可以使用名为yahooquery的软件包尝试此答案。免责声明：我是该软件包的作者。

from yahooquery import Ticker
import pandas as pd

symbols = ['^GSPC','CC','FMC','NVO','UEC','XNCR']

# Create Ticker instance,passing symbols as first argument
# Optional asynchronous argument allows for asynchronous requests
tickers = Ticker(symbols,asynchronous=True)

data = tickers.get_modules("summaryProfile quoteType")
df = pd.DataFrame.from_dict(data).T

# flatten dicts within each column,creating new dataframes
dataframes = [pd.json_normalize([x for x in df[module] if isinstance(x,dict)]) for module in ['summaryProfile','quoteType']]

# concat dataframes from previous step
df = pd.concat(dataframes,axis=1)

# View columns
df.columns
Index(['address1','address2','city','state','zip','country','phone','fax','website','industry','sector','longBusinessSummary','fullTimeEmployees','companyOfficers','maxAge','exchange','quoteType','symbol','underlyingSymbol','shortName','longName','firstTradeDateEpochUtc','timeZoneFullName','timeZoneShortName','uuid','messageBoardId','gmtOffSetMilliseconds','maxAge'],dtype='object')

# Data you're looking for
df[['symbol','sector']].head(10)
      symbol                      shortName                  sector
0  NQZ20.CME              Nasdaq 100 Dec 20                     NaN
1        ALB          Albemarle Corporation         Basic Materials
2        AOS         A.O. Smith Corporation             Industrials
3       ASPN           Aspen Aerogels,Inc.             Industrials
4        AAU         Almaden Minerals,Ltd.         Basic Materials
5      ^GSPC                        S&P 500                     NaN
6       ATHM                  Autohome Inc.  Communication Services
7        AQB  AquaBounty Technologies,Inc.      Consumer Defensive
8       APPS          Digital Turbine,Inc.              Technology
9       BCYC       Bicycle Therapeutics plc              Healthcare

python yfinance