Python 如何处理熊猫中仅包含NaN值的单元格？

我正在建立一个股票价格预测数据集，同时为Ichimoku Cloud Indicator应用以下代码：

from datetime import timedelta
high_9 = df['High'].rolling(window= 9).max()
low_9 = df['Low'].rolling(window= 9).min()
df['tenkan_sen'] = (high_9 + low_9) /2

high_26 = df['High'].rolling(window= 26).max()
low_26 = df['Low'].rolling(window= 26).min()
df['kijun_sen'] = (high_26 + low_26) /2

# this is to extend the 'df' in future for 26 days
# the 'df' here is numerical indexed df
# the problem is here
last_index = df.iloc[-1:].index[0]
last_date = df['Date'].iloc[-1].date()
for i in range(26):
    df.loc[last_index+1 +i, 'Date'] = last_date + timedelta(days=i)

df['senkou_span_a'] = ((df['tenkan_sen'] + df['kijun_sen']) / 2).shift(26)

high_52 = df['High'].rolling(window= 52).max()
low_52 = df['Low'].rolling(window= 52).min()
df['senkou_span_b'] = ((high_52 + low_52) /2).shift(26)

# most charting softwares dont plot this line
df['chikou_span'] = df['Close'].shift(-26)   

上面的代码工作得很好，但问题是当扩展到“senoku span a”和“b”列中接下来的26个时间步（行）时，它会将其他rest列行的值转换为NaN

因此，我需要帮助在我的数据集中生成“Senoku span a”和“Senoku span b”预测行，而无需将其他行变为NaN

电流输出为：

Date      Open    High     Low   Close   Senoku span a    Senoku span b
2019-03-16 50      51       52     53      56.0               55.82
2019-03-17 NaN     NaN     NaN     NaN     55.0               56.42
2019-03-18 NaN     NaN     NaN     NaN     54.0               57.72
2019-03-19 NaN     NaN     NaN     NaN     53.0               58.12
2019-03-20 NaN     NaN     NaN     NaN     52.0               59.52

Date      Open    High     Low   Close   Senoku span a    Senoku span b
2019-03-16  50      51       52     53     56.0               55.82
2019-03-17                                 55.0               56.42
2019-03-18                                 54.0               57.72
2019-03-19                                 53.0               58.12
2019-03-20                                 52.0               59.52

预期产出为：

Date      Open    High     Low   Close   Senoku span a    Senoku span b
2019-03-16 50      51       52     53      56.0               55.82
2019-03-17 NaN     NaN     NaN     NaN     55.0               56.42
2019-03-18 NaN     NaN     NaN     NaN     54.0               57.72
2019-03-19 NaN     NaN     NaN     NaN     53.0               58.12
2019-03-20 NaN     NaN     NaN     NaN     52.0               59.52

Date      Open    High     Low   Close   Senoku span a    Senoku span b
2019-03-16  50      51       52     53     56.0               55.82
2019-03-17                                 55.0               56.42
2019-03-18                                 54.0               57.72
2019-03-19                                 53.0               58.12
2019-03-20                                 52.0               59.52

---

您想要的预期输出是什么？我想要我的数据集中的预测列值，而不将任何其他值转换为NaN.df=df.fillna（“”）是的，您可以在最终数据帧上使用df=df.fillna（“”）。或者您也可以将其应用于特定列，如df['open']=df['open'].fillna（“”）但是这段代码给应用其他代码带来了问题，当应用其他代码时，它会显示错误为：不支持的操作数类型-：“str”和“float”的预期输出是什么您想要我的数据集中的预测列值，而不将任何其他值转换为NaN.df=df.fillna（“”）是的您可以使用df=df.fillna（“”）在最后的数据帧上。或者也可以将其应用于特定列，如df['open']=df['open'].fillna（“”），但此代码会给其他代码的应用带来问题，而在应用其他代码时，它会显示错误为：不支持的操作数类型为-：'str'和'float'