normalize data python pandas
import pandas as pd
from sklearn import preprocessing
x = df.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df = pd.DataFrame(x_scaled)normalize data python pandas
import pandas as pd
from sklearn import preprocessing
x = df.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df = pd.DataFrame(x_scaled)min max normalization in pandas dataframe
# copy the data
df_min_max_scaled = df.copy()
# apply normalization techniques
for column in df_min_max_scaled.columns:
df_min_max_scaled[column] = (df_min_max_scaled[column] - df_min_max_scaled[column].min()) / (df_min_max_scaled[column].max() - df_min_max_scaled[column].min())
# view normalized data
print(df_min_max_scaled)function to scale features in dataframe
# define a method to scale data, looping thru the columns, and passing a scaler
def scale_data(data, columns, scaler):
for col in columns:
data[col] = scaler.fit_transform(data[col].values.reshape(-1, 1))
return dataCopyright © 2021 Codeinu
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