How to standardize/normalize a date with pandas/numpy?

Question

With following code snippet

import pandas as pd
train = pd.read_csv('train.csv',parse_dates=['dates'])
print(data['dates'])

I load and control the data.

My question is, how can I standardize/normalize data['dates'] to make all the elements lie between -1 and 1 (linear or gaussian)??

Answer 1

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import time

def convert_to_timestamp(x):
    """Convert date objects to integers"""
    return time.mktime(x.to_datetime().timetuple())


def normalize(df):
    """Normalize the DF using min/max"""
    scaler = MinMaxScaler(feature_range=(-1, 1))
    dates_scaled = scaler.fit_transform(df['dates'])

    return dates_scaled

if __name__ == '__main__':
    # Create a random series of dates
    df = pd.DataFrame({
        'dates':
            ['1980-01-01', '1980-02-02', '1980-03-02', '1980-01-21',
             '1981-01-21', '1991-02-21', '1991-03-23']
    })

    # Convert to date objects
    df['dates'] = pd.to_datetime(df['dates'])

    # Now df has date objects like you would, we convert to UNIX timestamps
    df['dates'] = df['dates'].apply(convert_to_timestamp)

    # Call normalization function
    df = normalize(df)

Sample:

Date objects that we convert using convert_to_timestamp

       dates
0 1980-01-01
1 1980-02-02
2 1980-03-02
3 1980-01-21
4 1981-01-21
5 1991-02-21
6 1991-03-23

UNIX timestamps that we can normalize using a MinMaxScaler from sklearn

       dates
0  315507600
1  318272400
2  320778000
3  317235600
4  348858000
5  667069200
6  669661200

Normalized to (-1, 1), the final result

[-1.         -0.98438644 -0.97023664 -0.99024152 -0.81166138  0.98536228
  1.        ]

Answer 2

a solution with Pandas

df = pd.DataFrame({
        'A':
            ['1980-01-01', '1980-02-02', '1980-03-02', '1980-01-21',
             '1981-01-21', '1991-02-21', '1991-03-23'] })
df['A'] = pd.to_datetime(df['A']).astype('int64')
max_a = df.A.max()
min_a = df.A.min()
min_norm = -1
max_norm =1
df['NORMA'] = (df.A- min_a) *(max_norm - min_norm) / (max_a-min_a) + min_norm