date or other data normalization general solution in pandas
import pandas as pd
import numpy as npfrom sklearn.preprocessing import MinMaxScaler
import time
# of course you can use basic pandas api doing this job, but I'd prefer general solution hereby
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)