问题
I'm using yfinance to download price history for multiple symbols, which returns a df with multiple indexes. For example:
import yfinance as yf
df = yf.download(tickers = ['AAPL', 'MSFT'], period = '2d')
A similar dataframe could be constructed without yfinance like:
import pandas as pd
pd.options.display.float_format = '{:.2f}'.format
import numpy as np
attributes = ['Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume']
symbols = ['AAPL', 'MSFT']
dates = ['2020-07-23', '2020-07-24']
data = [[[371.38, 202.54], [371.38, 202.54], [388.31, 210.92], [368.04, 202.15], [387.99, 207.19], [49251100, 67457000]],
[[370.46, 201.30], [370.46, 201.30], [371.88, 202.86], [356.58, 197.51 ], [363.95, 200.42], [46323800, 39799500]]]
data = np.array(data).reshape(len(dates),len(symbols) * len(attributes))
cols = pd.MultiIndex.from_product([attributes, symbols])
df = pd.DataFrame(data, index=dates, columns=cols)
df
Adj Close Close High Low Open Volume
AAPL MSFT AAPL MSFT AAPL MSFT AAPL MSFT AAPL MSFT AAPL MSFT
2020-07-23 371.38 202.54 371.38 202.54 388.31 210.92 368.04 202.15 387.99 207.19 49251100.0 67457000.0
2020-07-24 370.46 201.30 370.46 201.30 371.88 202.86 356.58 197.51 363.95 200.42 46323800.0 39799500.0
Once I have this df, I want to restructure it so that I have a row for each symbol and date. I'm currently doing this by looping through a list of symbols and calling the API once each time, and appending the results. I'm sure there must be a more efficient way:
df = pd.DataFrame()
symbols = ['AAPL', 'MSFT']
for x in range(0, len(symbols)):
symbol = symbols[x]
result = yf.download(tickers = symbol, start = '2020-07-23', end = '2020-07-25')
result.insert(0, 'symbol', symbol)
df = pd.concat([df, result])
Example of the desired output:
df
symbol Open High Low Close Adj Close Volume
Date
2020-07-23 AAPL 387.989990 388.309998 368.040009 371.380005 371.380005 49251100
2020-07-24 AAPL 363.950012 371.880005 356.579987 370.459991 370.459991 46323800
2020-07-23 MSFT 207.190002 210.919998 202.149994 202.539993 202.539993 67457000
2020-07-24 MSFT 200.419998 202.860001 197.509995 201.300003 201.300003 39799500
回答1:
This looks like a simple stacking operation. Let's go with
df = yf.download(tickers = ['AAPL', 'MSFT'], period = '2d') # get yer data
df.stack(level=1).rename_axis(['Date', 'symbol']).reset_index(level=1)
symbol Adj Close ... Open Volume
Date ...
2020-07-23 AAPL 371.380005 ... 387.989990 49251100
2020-07-23 MSFT 202.539993 ... 207.190002 67457000
2020-07-24 AAPL 370.459991 ... 363.950012 46323800
2020-07-24 MSFT 201.300003 ... 200.419998 39799500
[4 rows x 7 columns]
来源：https://stackoverflow.com/questions/63106554/pandas-column-multi-index-to-rows