Lag A Time Feature Preliminaries import pandas as pd Create Date Data df = pd.DataFrame() 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 df[‘previous_days_stock_price’] = df[‘stock_price’].shift(1) df 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 …
Day: May 23, 2021
Handling Missing Values In Time Series Preliminaries import pandas as pd import numpy as np Create Date Data With Gap In Values time_index = pd.date_range(’01/01/2010′, periods=5, freq=’M’) df = pd.DataFrame(index=time_index) df[‘Sales’] = [1.0,2.0,np.nan,np.nan,5.0] Interpolate Missing Values df.interpolate() Sales 2010-01-31 1.0 2010-02-28 2.0 2010-03-31 3.0 2010-04-30 4.0 2010-05-31 5.0 Forward-fill Missing Values df.ffill() Sales 2010-01-31 1.0 …