X is a dataframe and can't be accessed via slice terminology like X[:, 3]. You must access via iloc or X.values. However, the way you constructed X made it a copy... so. I'd use values

# 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')

dataset = pd.DataFrame(np.random.rand(10, 10))
y=dataset.iloc[:, 4]
X=dataset.iloc[:, 0:4]

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()

#  I changed this line
X.values[:, 3] = labelencoder_X.fit_transform(X.values[:, 3])

use Values either while creating variable X or while encoding as mentioned above

# 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')

dataset = pd.DataFrame(np.random.rand(10, 10))
y=dataset.iloc[:, 4].values
X=dataset.iloc[:, 0:4].values

While creating the matrix X and Y vector use values.

X=dataset.iloc[:,4].values
Y=dataset.iloc[:,0:4].values

It will definitely solve your problem.

if you use .Values while creating the matrix X and Y vectors it will fix the problem.

y=dataset.iloc[:, 4].values

X=dataset.iloc[:, 0:4].values

when you use .Values it creates a Object representation of the created matrix will be returned with the axes removed. Check the below link for more information

I was getting same error (TypeError: unhashable type: 'slice') with below code:

included_cols = [2,4,10]
dataset = dataset[:,included_cols]  #Columns 2,4 and 10 are included.

Resolved with below code by putting iloc after dataset:

included_cols = [2,4,10]
dataset = dataset.iloc[:,included_cols]  #Columns 2,4 and 10 are included.

Try by changing X[:,3] to X.iloc[:,3] in label encoder

Your x and y values ​​are not running so first of all youre begin to write this point

 import numpy as np
 import pandas as pd
 import matplotlib as plt

 dataframe=pd.read_csv(".\datasets\Position_Salaries.csv")

 x=dataframe.iloc[:,1:2].values 
 y=dataframe.iloc[:,2].values    
 x1=dataframe.iloc[:,:-1].values 

point of value have publish