Python Feature Engineering Cookbook

How to do it...

Let's begin by importing the libraries and getting the data set ready:

1. Let's import the required libraries and function:

import numpy as npimport pandas as pd

import matplotlib.pyplot as pltfrom scipy.signal import find_peaks

2. Let's load the Appliances energy prediction dataset:

data = pd.read_csv('energydata_complete.csv')

3. The data type of the date variable is object; let's change it to datetime:

data['date'] = pd.to_datetime(data['date'])

4. Now, we need to extract the day, month, and hour part from the datetime variable into new columns:

data[['day', 'month', 'hr']] = pd.DataFrame([(x.day, x.month, x.hour) for x in data['date']])

We discussed the code in the preceding step in the Deriving representations of year and ...