在使用这段代码后，遇到了 The truth value of a DataFrame is ambiguous.不知道如何解决了(语言-python)

import pandas as pd
from sklearn.linear_model import LinearRegression

X = rideshare_data[["price"]]
y = rideshare_data[["score"]]

reg = LinearRegression()
reg.fit(X, y)
corr = X.corr(y)
print(corr)

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

要计算数据框的两列相关系数，用：corr=df['price'].corr(df['score']),代码改写在如下：

import pandas as pd
from sklearn.linear_model import LinearRegression

X = rideshare_data[["price"]]
y = rideshare_data[["score"]]

reg = LinearRegression()
reg.fit(X, y)
corr = df['price'].corr(df['score'])#计算两列的相关系数
df.corr()#计算整个数据框的相关性
print(corr)

dataframe和series都不能直接做为布尔值判断，根据历史经验，直接做为布尔值判断才会报这种错误的