notebook.community

Edit and run



In [7]:

    
import numpy as np
import pandas as pd

from datetime import date



In [16]:

    
df = pd.DataFrame({
    'date': pd.to_datetime([date(2019,1,d) for d in range(1,11)]),
    'reading': np.random.uniform(high=100,size=10)
})



In [17]:

    
df



In [22]:

    
df['reading_d_minus_1']= df['reading'].shift(1)



In [23]:

    
df









    Out[23]:







  
    
      
      date
      reading
      reading_d_minus_1
    
  
  
    
      0
      2019-01-01
      98.884397
      NaN
    
    
      1
      2019-01-02
      50.127106
      98.884397
    
    
      2
      2019-01-03
      18.173642
      50.127106
    
    
      3
      2019-01-04
      55.048161
      18.173642
    
    
      4
      2019-01-05
      76.951160
      55.048161
    
    
      5
      2019-01-06
      53.121246
      76.951160
    
    
      6
      2019-01-07
      4.793864
      53.121246
    
    
      7
      2019-01-08
      12.145948
      4.793864
    
    
      8
      2019-01-09
      89.761799
      12.145948
    
    
      9
      2019-01-10
      83.014272
      89.761799

	date	reading
0	2019-01-01	98.884397
1	2019-01-02	50.127106
2	2019-01-03	18.173642
3	2019-01-04	55.048161
4	2019-01-05	76.951160
5	2019-01-06	53.121246
6	2019-01-07	4.793864
7	2019-01-08	12.145948
8	2019-01-09	89.761799
9	2019-01-10	83.014272