I have a Pandas DataFrame of Open, High, Low, Close, Volume for several stocks.
I would like to take only the Close Column for each of the Stock Tickers and create a second separate DataFrame for that - struggling with the Multi-Indexing syntax and understanding; any help would be greatly appreciated! I would like to keep the Data DataFrame untouched for say, CandleStick charts.
import ...
tickers = ['AAPL', 'MSFT', 'INTC', 'AMZN', 'GS', '^GSPC', 'SPY', '^VIX']
data = yf.download(tickers=tickers, start='2010-01-01', end='2020-01-01',
interval='1d',
group_by='ticker',
auto_adjust=True, # auto adjusts OHLC
prepost=True, # download pre/post market hours data
threads=True, # use threads for mass downloading?
proxy=None
)
Many thanks,
On a separate note, as you can see in the Excel output, the date index contains the timestamp "00:00:00"- anyway to remove that within the DataFrame and/or for Excel output? - no need to spend too much time worrying about it, just a thought.
Excel Representation of first 15 rows and some of the stocks
Use the advanced xs method to select from the deeper levels of a MultiIndex.
data.xs('Close', level=1, axis=1)
# AMZN ^VIX SPY ^GSPC MSFT GS INTC AAPL
# Date
# 2010-01-04 133.899994 20.040001 92.246048 1132.989990 24.294369 149.746597 15.251445 26.538483
# 2010-01-05 134.690002 19.350000 92.490204 1136.520020 24.302216 152.394012 15.244140 26.584366
# 2010-01-06 132.250000 19.160000 92.555328 1137.140015 24.153070 150.767426 15.193007 26.161509
# 2010-01-07 130.000000 19.059999 92.946060 1141.689941 23.901886 153.717728 15.046927 26.113146
# 2010-01-08 133.520004 18.129999 93.255348 1144.979980 24.066734 150.810715 15.214921 26.286753
# ... ... ... ... ... ... ... ... ...
# 2019-12-24 1789.209961 12.670000 319.352142 3223.379883 156.951309 228.512817 59.118862 283.596924
# 2019-12-26 1868.770020 12.650000 321.052124 3239.909912 158.237793 229.804916 59.526852 289.223602
# 2019-12-27 1869.800049 13.430000 320.972565 3240.020020 158.527008 229.258255 59.785580 289.113831
# 2019-12-30 1846.890015 14.820000 319.202972 3221.290039 157.160736 228.403488 59.327831 290.829773
# 2019-12-31 1847.839966 13.780000 319.978424 3230.780029 157.270432 228.532684 59.556702 292.954712