Normalize column 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)

Source: stackoverflow.com

import pandas as pd

df = pd.DataFrame(
    columns=['term', '2002', '2003', '2004', '2005'],
    data=[['climate', 1, 10, 1, 14],
          ['global', 12, 11, 2, 12],
          ['nuclear', 10, 1, 0, 4], ])
normalized = df.select_dtypes('int').apply(lambda x: x / sum(x))
df = df.merge(
    right=normalized,
    left_index=True,
    right_index=True,
    suffixes=['', '_norm']
)

Source: stackoverflow.com

In [5]: %paste                                                                                                                                                                                                                                                                       
cols = ['2002', '2003', '2004', '2005']
df[cols] = df[cols] / df[cols].sum()

## -- End pasted text --

In [6]: df                                                                                                                                                                                                                                                                           
Out[6]: 
      term      2002      2003      2004      2005
0  climate  0.043478  0.454545  0.333333  0.466667
1   global  0.521739  0.500000  0.666667  0.400000
2  nuclear  0.434783  0.045455  0.000000  0.133333

Source: stackoverflow.com

# 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 data

-1

Source: stackoverflow.com

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