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from pandas._libs.tslibs.timestamps import Timestamp | |
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for k in d['Close'].iterkeys(): print (k) | # for k in d['Close'].iterkeys(): print (k) d['Close'][Timestamp('2019-11-22 00:00:00')] d['Close'][Timestamp('2020-02-10 00:00:00')] |
yfinance
yfinance aimes to solve this problem by offering a reliable, threaded, and Pythonic way to download historical market data from Yahoo! finance.
- pip install yfinance --user
1 import yfinance
2 from pandas._libs.tslibs.timestamps import Timestamp
3 data = yfinance.download("AMZN",start="2019-01-01",end="2020-02-12")
4 d = data.to_dict()
5 d['Close'].key
6 # for k in d['Close'].iterkeys(): print (k)
7 d['Close'][Timestamp('2019-11-22 00:00:00')]
8 d['Close'][Timestamp('2020-02-10 00:00:00')]