多索引 pandas 数据帧的值错误
Value error on multiindex pandas dataframe
我正在使用 here 中的软件包。我想做的是计算每个代码的随机值。
我有以下代码：
import lxml
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
# import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import numpy as np
import ta
html = urlopen('https://en.wikipedia.org/wiki/NIFTY_50')
soup = BeautifulSoup(html,'lxml')
niftylist_raw = soup.find('table', {'class': 'wikitable sortable'})
nifty_symbol =[]
for row in niftylist_raw.findAll('tr')[1:]:
nifty_symbols = row.findAll('td')[1].text
nifty_symbol.append(nifty_symbols)
tickerss = nifty_symbol
df = web.DataReader(tickerss, 'yahoo')
stoch1 = ta.momentum.StochasticOscillator(high= df['High'], low = df['Low'], close = df['Close'], n=14, fillna=False)
上面的代码有效，但是当我尝试时：
stoch1.stoch()
它产生以下错误：
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
我该如何解决这个错误？
似乎方法 StochasticOscillator 需要单列时间序列。但是，当您从 web.DataReader 构建数据框时，它会为您提供与代码一样多的列。
所以你只需要迭代代码，并将输出时间序列附加到一个新的数据帧：
import lxml
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
from pandas_datareader import data as web
import ta
html = urlopen('https://en.wikipedia.org/wiki/NIFTY_50')
soup = BeautifulSoup(html,'lxml')
niftylist_raw = soup.find('table', {'class': 'wikitable sortable'})
tickers = []
for row in niftylist_raw.findAll('tr')[1:]:
nifty_symbols = row.findAll('td')[1].text
tickers.append(nifty_symbols)
print(tickers)
df = web.DataReader(tickers, 'yahoo')
stoch_list = pd.DataFrame(columns = tickers, index = df.index)
for t in tickers:
stoch1 = ta.momentum.StochasticOscillator(
high = df['High'][t],
low = df['Low'][t],
close = df['Close'][t],
n = 14,
fillna = True
)
stoch_list[t] = stoch1.stoch()
print(stoch_list)
输出：
ADANIPORTS.NS ASIANPAINT.NS AXISBANK.NS BAJAJ-AUTO.NS BAJFINANCE.NS BAJAJFINSV.NS BHARTIARTL.NS ... TECHM.NS TITAN.NS ULTRACEMCO.NS UPL.NS VEDL.NS WIPRO.NS ZEEL.NS
Date ...
2015-06-15 21.909922 9.347667 48.704666 91.231693 77.791892 44.191114 30.140809 ... 53.666585 13.368975 14.346124 11.691838 14.367821 56.043549 91.428571
2015-06-16 72.363725 45.265156 74.247676 91.487233 66.564368 40.100098 55.669462 ... 36.333415 64.983097 88.990210 40.529881 21.072792 52.328376 87.301587
2015-06-17 56.363636 66.856091 53.025895 75.153149 91.088110 50.700195 85.667732 ... 21.000163 91.388957 80.205145 86.905950 3.869011 66.513717 92.325610
2015-06-18 56.363636 86.940831 57.636766 89.124719 94.661550 60.670907 71.193579 ... 5.263327 92.608643 78.613103 60.171097 7.142838 84.943018 70.000000
2015-06-19 11.428615 90.558724 66.282281 89.443078 86.336954 60.956522 88.276724 ... 19.378201 87.341779 81.569343 47.826973 22.928220 64.704798 39.333360
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-06-08 91.351874 57.958253 87.042001 82.309507 94.710350 94.247354 60.630541 ... 90.171597 89.217165 71.472360 88.654333 75.702800 98.275850 78.462774
2020-06-09 88.169607 59.439045 77.926708 79.988254 86.386837 85.820652 35.190610 ... 79.905597 77.139145 68.528865 83.201404 73.694768 80.155220 70.400641
2020-06-10 89.285678 55.534959 84.539778 62.649461 85.347188 80.135976 38.385475 ... 84.417652 65.700137 64.559014 73.540428 79.718864 76.537583 64.615390
2020-06-11 71.651770 29.268271 72.028606 62.966014 72.695166 75.927493 15.018624 ... 64.094315 53.441767 41.152918 62.931034 59.437760 65.384598 40.940166
2020-06-12 85.508938 35.945931 66.097784 77.878054 87.243356 84.211742 37.430114 ... 45.712787 62.894536 53.112690 54.729316 68.473904 53.365369 17.986311
[1228 rows x 50 columns]
我正在使用 here 中的软件包。我想做的是计算每个代码的随机值。 我有以下代码：
import lxml
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
# import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import numpy as np
import ta
html = urlopen('https://en.wikipedia.org/wiki/NIFTY_50')
soup = BeautifulSoup(html,'lxml')
niftylist_raw = soup.find('table', {'class': 'wikitable sortable'})
nifty_symbol =[]
for row in niftylist_raw.findAll('tr')[1:]:
nifty_symbols = row.findAll('td')[1].text
nifty_symbol.append(nifty_symbols)
tickerss = nifty_symbol
df = web.DataReader(tickerss, 'yahoo')
stoch1 = ta.momentum.StochasticOscillator(high= df['High'], low = df['Low'], close = df['Close'], n=14, fillna=False)
上面的代码有效，但是当我尝试时：
stoch1.stoch()
它产生以下错误：
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
我该如何解决这个错误？
似乎方法 StochasticOscillator 需要单列时间序列。但是，当您从 web.DataReader 构建数据框时，它会为您提供与代码一样多的列。
所以你只需要迭代代码，并将输出时间序列附加到一个新的数据帧：
import lxml
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
from pandas_datareader import data as web
import ta
html = urlopen('https://en.wikipedia.org/wiki/NIFTY_50')
soup = BeautifulSoup(html,'lxml')
niftylist_raw = soup.find('table', {'class': 'wikitable sortable'})
tickers = []
for row in niftylist_raw.findAll('tr')[1:]:
nifty_symbols = row.findAll('td')[1].text
tickers.append(nifty_symbols)
print(tickers)
df = web.DataReader(tickers, 'yahoo')
stoch_list = pd.DataFrame(columns = tickers, index = df.index)
for t in tickers:
stoch1 = ta.momentum.StochasticOscillator(
high = df['High'][t],
low = df['Low'][t],
close = df['Close'][t],
n = 14,
fillna = True
)
stoch_list[t] = stoch1.stoch()
print(stoch_list)
输出：
ADANIPORTS.NS ASIANPAINT.NS AXISBANK.NS BAJAJ-AUTO.NS BAJFINANCE.NS BAJAJFINSV.NS BHARTIARTL.NS ... TECHM.NS TITAN.NS ULTRACEMCO.NS UPL.NS VEDL.NS WIPRO.NS ZEEL.NS
Date ...
2015-06-15 21.909922 9.347667 48.704666 91.231693 77.791892 44.191114 30.140809 ... 53.666585 13.368975 14.346124 11.691838 14.367821 56.043549 91.428571
2015-06-16 72.363725 45.265156 74.247676 91.487233 66.564368 40.100098 55.669462 ... 36.333415 64.983097 88.990210 40.529881 21.072792 52.328376 87.301587
2015-06-17 56.363636 66.856091 53.025895 75.153149 91.088110 50.700195 85.667732 ... 21.000163 91.388957 80.205145 86.905950 3.869011 66.513717 92.325610
2015-06-18 56.363636 86.940831 57.636766 89.124719 94.661550 60.670907 71.193579 ... 5.263327 92.608643 78.613103 60.171097 7.142838 84.943018 70.000000
2015-06-19 11.428615 90.558724 66.282281 89.443078 86.336954 60.956522 88.276724 ... 19.378201 87.341779 81.569343 47.826973 22.928220 64.704798 39.333360
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-06-08 91.351874 57.958253 87.042001 82.309507 94.710350 94.247354 60.630541 ... 90.171597 89.217165 71.472360 88.654333 75.702800 98.275850 78.462774
2020-06-09 88.169607 59.439045 77.926708 79.988254 86.386837 85.820652 35.190610 ... 79.905597 77.139145 68.528865 83.201404 73.694768 80.155220 70.400641
2020-06-10 89.285678 55.534959 84.539778 62.649461 85.347188 80.135976 38.385475 ... 84.417652 65.700137 64.559014 73.540428 79.718864 76.537583 64.615390
2020-06-11 71.651770 29.268271 72.028606 62.966014 72.695166 75.927493 15.018624 ... 64.094315 53.441767 41.152918 62.931034 59.437760 65.384598 40.940166
2020-06-12 85.508938 35.945931 66.097784 77.878054 87.243356 84.211742 37.430114 ... 45.712787 62.894536 53.112690 54.729316 68.473904 53.365369 17.986311
[1228 rows x 50 columns]