ValueError：无法在同一数据帧上没有重叠索引名称的情况下连接

Question

我在 pandas 数据帧中遇到了一个奇怪的问题，where in, where() 失败，抱怨它无法加入重叠的索引名称。

要重现此问题，请尝试以下操作：

import yfinance as yf
from datetime import datetime
startdate=datetime(2022,12,1)
enddate=datetime(2022,12,6)
y_symbols = ['GOOG', 'AAPL', 'MSFT']
data=yf.download(y_symbols, start=startdate, end=enddate, auto_adjust=True, threads=True)
data[data['Close'] > 100]

然后引发的错误如下所示：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
..
  File "lib/python3.9/site-packages/pandas/core/indexes/base.py", line 229, in join
    join_index, lidx, ridx = meth(self, other, how=how, level=level, sort=sort)
  File "lib/python3.9/site-packages/pandas/core/indexes/base.py", line 4658, in join
    return self._join_multi(other, how=how)
  File "lib/python3.9/site-packages/pandas/core/indexes/base.py", line 4782, in _join_multi
    raise ValueError("cannot join with no overlapping index names")
ValueError: cannot join with no overlapping index names

在这里，

data

看起来像：

                 Close                                High                          ...        Open                            Volume
                  AAPL        GOOG        MSFT        AAPL        GOOG        MSFT  ...        AAPL        GOOG        MSFT      AAPL      GOOG      MSFT
Date                                                                                ...
2022-12-01  148.309998  101.279999  254.690002  149.130005  102.589996  256.119995  ...  148.210007  101.400002  253.869995  71250400  21771500  26041500
2022-12-02  147.809998  100.830002  255.020004  148.000000  101.150002  256.059998  ...  145.960007   99.370003  249.820007  65421400  18812200  21522800
2022-12-05  146.630005   99.870003  250.199997  150.919998  101.750000  253.820007  ...  147.770004   99.815002  252.009995  68826400  19955500  23435300

数据框中可能缺少什么而这不起作用？

Answer 1

可能是由多级列引起的，因为

where()

方法需要单级列。先试着把它压平。

startdate=datetime(2022,12,1)
enddate=datetime(2022,12,6)
y_symbols = ['GOOG', 'AAPL', 'MSFT']
data=yf.download(y_symbols, start=startdate, end=enddate, auto_adjust=True, threads=True)
data = data.stack()
filtered_cond = data['Close'] > 100
filtered_data = data.where(filtered_cond).unstack()

Answer 2

这有帮助。从

yfinance

获取结果后设置列名。

理想情况下，希望

yfinance

自己来处理这个问题。

>>> import yfinance as yf
>>> from datetime import datetime
>>> startdate=datetime(2022,12,1)
>>> enddate=datetime(2022,12,6)
>>> y_symbols = ['GOOG', 'AAPL', 'MSFT']
>>> data=yf.download(y_symbols, start=startdate, end=enddate, auto_adjust=True, threads=True)
[*********************100%***********************]  3 of 3 completed
>>> data.columns.names = ["Attributes", "Symbols"]
>>> data[data['Close'] > 100]
Attributes       Close                                High                                 Low                                Open                            Volume
Symbols           AAPL        GOOG        MSFT        AAPL        GOOG        MSFT        AAPL        GOOG        MSFT        AAPL        GOOG        MSFT      AAPL        GOOG      MSFT
Date
2022-12-01  148.309998  101.279999  254.690002  149.130005  102.589996  256.119995  146.610001  100.669998  250.919998  148.210007  101.400002  253.869995  71250400  21771500.0  26041500
2022-12-02  147.809998  100.830002  255.020004  148.000000  101.150002  256.059998  145.649994   99.169998  249.690002  145.960007   99.370003  249.820007  65421400  18812200.0  21522800
2022-12-05  146.630005         NaN  250.199997  150.919998         NaN  253.820007  145.770004         NaN  248.059998  147.770004         NaN  252.009995  68826400         NaN  23435300
>>>

>>> data.where(data['Close'] > 100).where(data['High'] > 120)
Attributes       Close                         High                          Low                         Open                     Volume
Symbols           AAPL GOOG        MSFT        AAPL GOOG        MSFT        AAPL GOOG        MSFT        AAPL GOOG        MSFT      AAPL GOOG      MSFT
Date
2022-12-01  148.309998  NaN  254.690002  149.130005  NaN  256.119995  146.610001  NaN  250.919998  148.210007  NaN  253.869995  71250400  NaN  26041500
2022-12-02  147.809998  NaN  255.020004  148.000000  NaN  256.059998  145.649994  NaN  249.690002  145.960007  NaN  249.820007  65421400  NaN  21522800
2022-12-05  146.630005  NaN  250.199997  150.919998  NaN  253.820007  145.770004  NaN  248.059998  147.770004  NaN  252.009995  68826400  NaN  23435300

Answer 3

Where 方法接受类似列表的参数，其中包含用于过滤的布尔值。你必须向它传递一个 pandas 系列、numpy 数组、python 列表等。但是你向它传递一个数据帧 (df['close']>500) 并且其中方法引发错误 您可以阅读 pandas 文档以获取更多信息