Time Series Features

changing_dtype.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')
data.dtypes

date_features.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')

data['year']=data['Datetime'].dt.year 
data['month']=data['Datetime'].dt.month 
data['day']=data['Datetime'].dt.day

data['dayofweek_num']=data['Datetime'].dt.dayofweek  
data['dayofweek_name']=data['Datetime'].dt.weekday_name

data.head()

expanding_window.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')

data['expanding_mean'] = data['Count'].expanding(2).mean()
data = data[['Datetime','Count', 'expanding_mean']]
data.head(10)

lag_feature.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')

data['lag_1'] = data['Count'].shift(1)
data = data[['Datetime', 'lag_1', 'Count']]
data.head()

lag_seven.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')

data['lag_1'] = data['Count'].shift(1)
data['lag_2'] = data['Count'].shift(2)
data['lag_3'] = data['Count'].shift(3)
data['lag_4'] = data['Count'].shift(4)
data['lag_5'] = data['Count'].shift(5)
data['lag_6'] = data['Count'].shift(6)
data['lag_7'] = data['Count'].shift(7)

data = data[['Datetime', 'lag_1', 'lag_2', 'lag_3', 'lag_4', 'lag_5', 'lag_6', 'lag_7', 'Count']]
data.head(10)

loading_data.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data.dtypes

rolling_mean.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')

data['rolling_mean'] = data['Count'].rolling(window=7).mean()
data = data[['Datetime', 'rolling_mean', 'Count']]
data.head(10)

time_features.py

import pandas as pd
data = pd.read_csv('Train_SU63ISt.csv')
data['Datetime'] = pd.to_datetime(data['Datetime'],format='%d-%m-%Y %H:%M')

data['Hour'] = data['Datetime'].dt.hour 
data['minute'] = data['Datetime'].dt.minute 

data.head()