Data Transformation in Python

# Performing logarithmic transformation on the feature
cp['price_log']=np.log(cp['price'])

# Reciprocal Transformation – This will inverse values of Price i.e1/Price
cp['price_reciprocal']=np.reciprocal(cp.price)
normality(cp,'price_reciprocal')

# Square Root Transformation
cp['price_sqroot']=np.sqrt(cp.price)
normality(cp,'price_sqroot')

# Exponential Transformation: The exponential value of the Price variable will be taken.
cp['price_exponential']=np.exp(cp.price)
normality(cp,'price_exponential')

# Box-Cox Transformation
cp['price_Boxcox'],parameters=stats.boxcox(cp['price'])