在不考虑周末的情况下，如何确保日期显示为 Yahoo Finance/Google Finance 中的日期？

Question

I'm currently using Alpha-Vantage to fetch financial data with 1 min intervals.

amd=ts.get_intraday(symbol=symbol, outputsize='full', interval=interval)
amd=pd.DataFrame(amd[0])
amd.drop(amd.index[-1])

and I'm getting the following output

...
2020-03-02 09:37:00  46.1000  46.1265  45.7427   45.7427   701690.0
2020-03-02 09:36:00  46.0700  46.0700  45.9300   46.0500   725661.0
2020-03-02 09:35:00  46.1100  46.1100  46.1100   46.1100   484583.0
2020-03-02 09:34:00  46.7500  46.8000  46.3000   46.3447   614596.0
2020-03-02 09:33:00  46.9642  47.2300  46.6800   46.7400   528517.0
2020-03-02 09:32:00  47.6100  47.6100  46.7000   46.9800   770555.0
2020-03-02 09:31:00  47.4000  47.6800  47.1000   47.5500  3504998.0
2020-02-28 16:00:00  45.1500  45.5300  45.1400   45.4700   895713.0
2020-02-28 15:59:00  45.0900  45.1600  45.0100   45.1500   411553.0
2020-02-28 15:58:00  44.8750  45.0900  44.8400   45.0800   434739.0
2020-02-28 15:57:00  44.8400  44.9100  44.8100   44.8560   327619.0
2020-02-28 15:56:00  44.7500  44.9100  44.6800   44.8450   363272.0
2020-02-28 15:55:00  44.4800  44.7700  44.4604   44.7400   305512.0
...

As you can see, the date jumps from Feb. 28th to March 2nd, as it should. However, when plotting it in matplotlib...

amd['4. close'].plot()

plt.title('AMD')
plt.show() 

...I'm getting the following graph, where mathplotlib compensates for the lack of data in weekends and at closed market by drawing a straight line from one datapoint to the other.

How can I get a result that displays similar to a Yahoo Finance or Google Finance stock chart, where it ignores mising data ( as in this example ?

Answer 1

Just use this. Done.

import pandas as pd
import datetime
import pandas_datareader.data as web
start=datetime.datetime(2019,3,12)
end=datetime.datetime(2020,3,12)
df=web.DataReader('IBM','yahoo',start,end)
print(df)

Result:

2020-01-30  135.356583  
2020-01-31  142.244659  
2020-02-03  144.758408  
2020-02-04  147.569061  
2020-02-05  154.714447  
2020-02-06  155.139999  
2020-02-07  153.410004  
2020-02-10  154.429993  
2020-02-11  153.479996  
2020-02-12  155.309998  
2020-02-13  154.309998  
2020-02-14  150.699997  
2020-02-18  151.100006  
2020-02-19  150.860001  
2020-02-20  151.220001  
2020-02-21  149.839996  
2020-02-24  146.429993  
2020-02-25  141.710007  
2020-02-26  139.750000  
2020-02-27  133.110001  
2020-02-28  130.149994  
2020-03-02  134.300003  
2020-03-03  128.899994  
2020-03-04  134.220001  
2020-03-05  129.550003  
2020-03-06  127.730003  
2020-03-09  117.809998  
2020-03-10  124.769997  
2020-03-11  117.970001  
2020-03-12  102.809998 

Check this out too.

https://pandas-datareader.readthedocs.io/en/latest/remote_data.html#quandl