相关疑难解决方法(0)

我在Pandas中使用布尔索引.问题是为什么声明:

a[(a['some_column']==some_number) & (a['some_other_column']==some_other_number)]

工作正常,而

a[(a['some_column']==some_number) and (a['some_other_column']==some_other_number)]

存在错误？

例:

a=pd.DataFrame({'x':[1,1],'y':[10,20]})

In: a[(a['x']==1)&(a['y']==10)]
Out:    x   y
     0  1  10

In: a[(a['x']==1) and (a['y']==10)]
Out: ValueError: The truth value of an array with more than one element is ambiguous.     Use a.any() or a.all()

我正进入(状态

TypeError:不可用类型:'sl​​ice'

执行以下代码时,在Python中编码分类数据.有人可以帮忙吗？

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('50_Startups.csv')
y=dataset.iloc[:, 4]
X=dataset.iloc[:, 0:4]

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])

假设我有以下数据帧:

          a         b         c         d 
0  0.049531  0.408824  0.975756  0.658347
1  0.981644  0.520834  0.258911  0.639664
2  0.641042  0.534873  0.806442  0.066625
3  0.764057  0.063252  0.256748  0.045850

并且我只想要列0的值在0.5以内的列的子集.我可以做这个:

df2 = df.T
myResult = df2[df2.iloc[:, 0] > 0.5].T

但这感觉就像一个可怕的黑客.是否有更好的方法沿列进行布尔索引？某处我可以指定一个轴参数？