Title: Lag A Time Feature
Slug: lag_a_time_feature
Summary: How to lag a dates and times feature for machine learning in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Preprocessing Dates And Times
Authors: Chris Albon

Preliminaries



In [1]:

    
# Load library
import pandas as pd

Create Date Data



In [2]:

    
# Create data frame
df = pd.DataFrame()

# Create data
df['dates'] = pd.date_range('1/1/2001', periods=5, freq='D')
df['stock_price'] = [1.1,2.2,3.3,4.4,5.5]

Lag Time Data By One Row



In [3]:

    
# Lagged values by one row
df['previous_days_stock_price'] = df['stock_price'].shift(1)

# Show data frame
df









    Out[3]:







  
    
      
      dates
      stock_price
      previous_days_stock_price
    
  
  
    
      0
      2001-01-01
      1.1
      NaN
    
    
      1
      2001-01-02
      2.2
      1.1
    
    
      2
      2001-01-03
      3.3
      2.2
    
    
      3
      2001-01-04
      4.4
      3.3
    
    
      4
      2001-01-05
      5.5
      4.4

	dates	stock_price	previous_days_stock_price
0	2001-01-01	1.1	NaN
1	2001-01-02	2.2	1.1
2	2001-01-03	3.3	2.2
3	2001-01-04	4.4	3.3
4	2001-01-05	5.5	4.4