import pandas as pd
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import ensemble
from sklearn.metrics import mean_absolute_error
from joblib import *
df = pd.read_csv('~/Downloads/Melbourne_housing_FULL.csv')
df.head(n=5)
del df['Address']
del df['Method']
del df['SellerG']
del df['Date']
del df['Postcode']
del df['Lattitude']
del df['Longtitude']
del df['Regionname']
del df['Propertycount']
df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=True)
features_df = pd.get_dummies(df, columns=['Suburb', 'CouncilArea', 'Type'])
X = features_df.as_matrix()
y = df['Price'].as_matrix()
Can anyone please help me I am facing a error as soon as I put X = features_df.as_matrix() y = df['Price'].as_matrix() and I am learning Machine Learning with a book called Machine Learning with python by oliver... Any Help is highly appreciated Thankyou
4 Answers
df.as_matrix() was deprecated after version 0.23.0.
Use df.values instead.
Follow this link for additional information.
Dataframe depricated a lot of attributes such as .ix
Here you need this command:
y = df['Price'].values
Replacing .as_matrix() with .values() also resulted in an error, but replacing it with .to_numpy() worked perfectly
Convert the DataFrame to a NumPy array.
New in version 0.24.0.
As of Pandas 1.0 use .to_numpy() other ways are deprecated according to the docs
