Comments (4)
in current version
might try
.values.reshape(-1, 1)
from kaggle_titanic.
import sklearn.preprocessing as preprocessing
'''
Reshape your data either using array.reshape(-1, 1)
if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
'''
scaler = preprocessing.StandardScaler()
age_scale_param = scaler.fit(df['Age'].reshape(-1, 1))
df['Age_scaled'] = scaler.fit_transform(df['Age'].reshape(-1, 1), age_scale_param)
fare_scale_param = scaler.fit(df['Fare'].reshape(-1, 1))
df['Fare_scaled'] = scaler.fit_transform(df['Fare'].reshape(-1, 1), fare_scale_param)
df
from kaggle_titanic.
Reshape your data either using array.reshape(-1, 1) ,if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
from kaggle_titanic.
Or you can try .to_frame()
The way pandas DataFrame object organizing its values is just like the column vectors.
from kaggle_titanic.
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from kaggle_titanic.