如何将文本文件数据插入到python字典中

时间:2015-04-23 09:37:49

标签: python-3.x

我有一个文本文件,它包含以下文字。

marketcap : market capital,market cap,capital,market
e_value:enterprise value,e_value,enterprise,evalue,e value
ret_on_assets:return on assets,assets,return,return assets
tot_cash:total cash
op_cash:operating cash flow,cash flow,operating cash,op cash,
lev_free_cf:levered free cash flow, levered free,free cash,levered cash,levered cash flow
tot_debt:total debt,debt
curr_ratio:current ratio,ratio,current
gross_profit:gross proft,profit,gross
prof_margin:profit margin,margin,porf margin,margin profit
last_trade:last trade,trade last,last
trade_time:trade time,time
prev_close:previous close,prev close,close
high_paid_emp:paid,paid emp,paid employee,highest paid emp,highest paid employee
executive_list:executives,executive,list,executive list
high_pay:payment,pay,highest pay
name:company name,company,name
Address:adress,addr,add,address,Address
phonenum:phone,ph,phone number,phone no
faxnum:fax,fax num,fax no,fax number
website:site,website,url,link,web,web page
Index_mem:member ship,ship,index,index member,index membership,member
sector:sect,sector,
industry:industry,indus,organization,org
full_time:full time,full,full time emp,full time employee
news:news,head lines
bus_summ:business sum,business summary,summary
ticker:ticker,tickers
Finance:finacne,finance details,financial
management:management details,

如何将这些数据存储在字典中,使每个键都有可能的列表?

1 个答案:

答案 0 :(得分:0)

最后我得到了这个。我的代码是

fo = open("keywords.txt","r")
arr = fo.readlines()

keywords = {}

for x in arr:
    x = x.strip()
    y = x.split(':')
    #print(y[0] + " : " +y[1])
    keywords.__setitem__(y[0] , y[1])

print(keywords)

和输出

{'news': 'news,head lines', 'phonenum': 'phone,ph,phone number,phone no', 'website': 'site,website,url,link,web,web page', 'prev_close': 'previous close,prev close,close', 'ticker': 'ticker,tickers', 'gross_profit': 'gross proft,profit,gross', 'sector': 'sect,sector,', 'management': 'management details,', 'e_value': 'enterprise value,e_value,enterprise,evalue,e value', 'op_cash': 'operating cash flow,cash flow,operating cash,op cash,', 'bus_summ': 'business sum,business summary,summary', 'high_paid_emp': 'paid,paid emp,paid employee,highest paid emp,highest paid employee', 'name': 'company name,company,name', 'Address': 'adress,addr,add,address,Address', 'trade_time': 'trade time,time', 'curr_ratio': 'current ratio,ratio,current', 'ret_on_assets': 'return on assets,assets,return,return assets', 'tot_debt': 'total debt,debt', 'marketcap ': ' market capital,market cap,capital,market', 'industry': 'industry,indus,organization,org', 'executive_list': 'executives,executive,list,executive list', 'Index_mem': 'member ship,ship,index,index member,index membership,member', 'faxnum': 'fax,fax num,fax no,fax number', 'prof_margin': 'profit margin,margin,porf margin,margin profit', 'Finance': 'finacne,finance details,financial', 'last_trade': 'last trade,trade last,last', 'tot_cash': 'total cash', 'full_time': 'full time,full,full time emp,full time employee', 'lev_free_cf': 'levered free cash flow, levered free,free cash,levered cash,levered cash flow', 'high_pay': 'payment,pay,highest pay'}