DatetimeFeatures#
DatetimeFeatures() extracts several datetime features from datetime
variables. It works with variables whose original dtype is datetime, and also with
object-like and categorical variables, provided that they can be parsed into datetime
format. It cannot extract features from numerical variables.
Oftentimes, datasets contain information related to dates and/or times at which an event occurred. In pandas dataframes, these datetime variables can be cast as datetime or, more generically, as object.
Datetime variables, in their raw format, are generally not suitable to train machine learning models. Yet, an enormous amount of information can be extracted from them.
DatetimeFeatures() is able to extract many numerical and binary date and
time features from these datetime variables. Among these features we can find the month
in which an event occurred, the day of the week, or whether that day was a weekend day.
With DatetimeFeatures() you can choose which date and time features
to extract from your datetime variables. You can also extract date and time features
from a subset of datetime variables in your data.
Examples#
Through the following examples we highlight the functionality and versatility of
DatetimeFeatures().
Extract date features#
In this example, we are going to extract three date features from a specific variable in the dataframe. In particular, we are interested in the month, the day of the year, and whether that day was the last day of its correspondent month.
First, we will create a toy dataframe with 2 date variables.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"var_date1": ['May-1989', 'Dec-2020', 'Jan-1999', 'Feb-2002'],
"var_date2": ['06/21/12', '02/10/98', '08/03/10', '10/31/20'],
})
Now, we will extract the variables month, month-end and the day of the year from the second datetime variable in our dataset.
dtfs = DatetimeFeatures(
variables="var_date2",
features_to_extract=["month", "month_end", "day_of_year"]
)
df_transf = dtfs.fit_transform(toy_df)
print(df_transf)
var_date1 var_date2_month var_date2_month_end var_date2_day_of_year
0 May-1989 6 0 173
1 Dec-2020 2 0 41
2 Jan-1999 8 0 215
3 Feb-2002 10 1 305
With transform(), the features extracted from the datetime variable are added to the
dataframe.
By default, DatetimeFeatures() drops the variable from which the date and time
features were extracted, in this case, var_date2. To keep the variable, we just need
to indicate drop_original=False when initializing the transformer.
Extract time features#
In this example, we are going to extract the feature minute from the two time variables in our dataset.
First, let’s create a toy dataset with 2 time variables and an object variable.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"not_a_dt": ['not', 'a', 'date', 'time'],
"var_time1": ['12:34:45', '23:01:02', '11:59:21', '08:44:23'],
"var_time2": ['02:27:26', '10:10:55', '17:30:00', '18:11:18'],
})
DatetimeFeatures() automatically finds all variables that can be parsed to
datetime. So if we want to extract time features from all our datetime variables, we
don’t need to specify them.
dfts = DatetimeFeatures(features_to_extract=["minute"])
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
not_a_dt var_time1_minute var_time2_minute
0 not 34 27
1 a 1 10
2 date 59 30
3 time 44 11
The transformer found two variales in the dataframe that can be cast to datetime and proceeded to extract the requested feature from them.
We can find the variables detected as datetime by the transformer in one of its attributes.
dfts.variables_
['var_time1', 'var_time2']
Again, the original datetime variables are dropped from the data by default. If we
want to keep them, we just need to indicate drop_original=False when initializing
the transformer.
Extract date and time features#
In this example, we will combine what we have seen in the previous two examples and extract a date feature - year - and time feature - hour - from two variables that contain both date and time information.
Let’s go ahead and create a toy dataset.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"var_dt1": pd.date_range("2018-01-01", periods=3, freq="H"),
"var_dt2": ['08/31/00 12:34:45', '12/01/90 23:01:02', '04/25/01 11:59:21'],
"var_dt3": ['03/02/15 02:27:26', '02/28/97 10:10:55', '11/11/03 17:30:00'],
})
Now, we set up the DatetimeFeatures() to extract features only from 2 of the
variables. In this case, we do not want to drop the datetime variable after extracting
the features.
dfts = DatetimeFeatures(
variables=["var_dt1", "var_dt3"],
features_to_extract=["year", "hour"],
drop_original=False,
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_dt1 var_dt2 var_dt3 var_dt1_year \
0 2018-01-01 00:00:00 08/31/00 12:34:45 03/02/15 02:27:26 2018
1 2018-01-01 01:00:00 12/01/90 23:01:02 02/28/97 10:10:55 2018
2 2018-01-01 02:00:00 04/25/01 11:59:21 11/11/03 17:30:00 2018
var_dt1_hour var_dt3_year var_dt3_hour
0 0 2015 2
1 1 1997 10
2 2 2003 17
And that is it. The additional features are now added in the dataframe.
Important#
We highly recommend specifying the date and time features that you would like to extract from your datetime variables. If you have too many time variables, this might not be possible. In this case, keep in mind that if you extract date features from variables that have only time, or time features from variables that have only dates, your features will be meaningless.
Let’s explore the outcome with an example. We create a dataset with only time variables.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"not_a_dt": ['not', 'a', 'date', 'time'],
"var_time1": ['12:34:45', '23:01:02', '11:59:21', '08:44:23'],
"var_time2": ['02:27:26', '10:10:55', '17:30:00', '18:11:18'],
})
And now we mistakenly extract only date features.
dfts = DatetimeFeatures(
features_to_extract=["year", "month", "day_of_week"],
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
not_a_dt var_time1_year var_time1_month var_time1_day_of_week var_time2_year \
0 not 2021 12 2 2021
1 a 2021 12 2 2021
2 date 2021 12 2 2021
3 time 2021 12 2 2021
var_time2_month var_time2_day_of_week
0 12 2
1 12 2
2 12 2
3 12 2
The transformer will still create features derived from today’s date (the date of creating the docs).
If instead we have a dataframe with only date variables.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"var_date1": ['May-1989', 'Dec-2020', 'Jan-1999', 'Feb-2002'],
"var_date2": ['06/21/12', '02/10/98', '08/03/10', '10/31/20'],
})
And mistakenly extract the hour and the minute.
dfts = DatetimeFeatures(
features_to_extract=["hour", "minute"],
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_date1_hour var_date1_minute var_date2_hour var_date2_minute
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
The new features will contain the value 0.
Automating feature extraction#
We can indicate which features we want to extract from the datetime variables as we did
in the previous examples, passing the feature names in lists. Alternatively,
DatetimeFeatures() has default options to extract a group of commonly used
features, or all supported features.
Let’s first create a toy dataframe.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"var_dt1": pd.date_range("2018-01-01", periods=3, freq="H"),
"var_dt2": ['08/31/00 12:34:45', '12/01/90 23:01:02', '04/25/01 11:59:21'],
"var_dt3": ['03/02/15 02:27:26', '02/28/97 10:10:55', '11/11/03 17:30:00'],
})
Now, we will extract the most common date and time features from one of the variables.
To do this, we leave the parameter features_to_extract to None.
dfts = DatetimeFeatures(
variables=["var_dt1"],
features_to_extract=None,
drop_original=False,
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_dt1 var_dt2 var_dt3 var_dt1_month \
0 2018-01-01 00:00:00 08/31/00 12:34:45 03/02/15 02:27:26 1
1 2018-01-01 01:00:00 12/01/90 23:01:02 02/28/97 10:10:55 1
2 2018-01-01 02:00:00 04/25/01 11:59:21 11/11/03 17:30:00 1
var_dt1_year var_dt1_day_of_week var_dt1_day_of_month var_dt1_hour \
0 2018 0 1 0
1 2018 0 1
2 2018 0 1 2
var_dt1_minute var_dt1_second
0 0 0
1 0 0
2 0 0
Our new dataset contains the original features plus the new variables extracted from them.
We can find the group of features extracted by the transformer in its attribute.
dfts.features_to_extract_
['month',
'year',
'day_of_week',
'day_of_month',
'hour',
'minute',
'second']
We can also extract all supported features automatically.
dfts = DatetimeFeatures(
variables=["var_dt1"],
features_to_extract='all',
drop_original=False,
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_dt1 var_dt2 var_dt3 var_dt1_month \
0 2018-01-01 00:00:00 08/31/00 12:34:45 03/02/15 02:27:26 1
1 2018-01-01 01:00:00 12/01/90 23:01:02 02/28/97 10:10:55 1
2 2018-01-01 02:00:00 04/25/01 11:59:21 11/11/03 17:30:00 1
var_dt1_quarter var_dt1_semester var_dt1_year \
0 1 1 2018
1 1 1 2018
2 1 1 2018
var_dt1_week var_dt1_day_of_week ... var_dt1_month_end var_dt1_quarter_start \
0 1 0 ... 0 1
1 1 0 ... 0 1
2 1 0 ... 0 1
var_dt1_quarter_end var_dt1_year_start var_dt1_year_end \
0 0 1 0
1 0 1 0
2 0 1 0
var_dt1_leap_year var_dt1_days_in_month var_dt1_hour var_dt1_minute \
0 0 31 0 0
1 0 31 1 0
2 0 31 2 0
var_dt1_second
0 0
1 0
2 0
We can find the group of features extracted by the transformer in its attribute.
dfts.features_to_extract_
['month',
'quarter',
'semester',
'year',
'week',
'day_of_week',
'day_of_month',
'day_of_year',
'weekend',
'month_start',
'month_end',
'quarter_start',
'quarter_end',
'year_start',
'year_end',
'leap_year',
'days_in_month',
'hour',
'minute',
'second']
Extract and select features automatically#
If we have a dataframe with a date variables, time variables and date and time variables,
we can extract all features, or the most common features from all the variables, and then
go ahead and remove the irrelevant features with the DropConstantFeatures() class.
Let’s create a dataframe with a mix of datetime variables.
import pandas as pd
from sklearn.pipeline import Pipeline
from feature_engine.datetime import DatetimeFeatures
from feature_engine.selection import DropConstantFeatures
toy_df = pd.DataFrame({
"var_date": ['06/21/12', '02/10/98', '08/03/10', '10/31/20'],
"var_time1": ['12:34:45', '23:01:02', '11:59:21', '08:44:23'],
"var_dt": ['08/31/00 12:34:45', '12/01/90 23:01:02', '04/25/01 11:59:21', '04/25/01 11:59:21'],
})
Now, we align in a Scikit-learn pipeline the DatetimeFeatures and the
DropConstantFeatures(). The DatetimeFeatures will create date features
derived from today for the time variable, and time features with the value 0 for the
date only variable. DropConstantFeatures() will identify and remove these features
from the dataset.
pipe = Pipeline([
('datetime', DatetimeFeatures()),
('drop_constant', DropConstantFeatures()),
])
pipe.fit(toy_df)
Pipeline(steps=[('datetime', DatetimeFeatures()),
('drop_constant', DropConstantFeatures())])
df_transf = pipe.transform(toy_df)
print(df_transf)
var_date_month var_date_year var_date_day_of_week var_date_day_of_month \
0 6 2012 3 21
1 2 1998 1 10
2 8 2010 1 3
3 10 2020 5 31
var_time1_hour var_time1_minute var_time1_second var_dt_month \
0 12 34 45 8
1 23 1 2 12
2 11 59 21 4
3 8 44 23 4
var_dt_year var_dt_day_of_week var_dt_day_of_month var_dt_hour \
0 2000 3 31 12
1 1990 5 1 23
2 2001 2 25 11
3 2001 2 25 11
var_dt_minute var_dt_second
0 34 45
1 1 2
2 59 21
3 59 21
As you can see, we do not have the constant features in the transformed dataset.
Extract features from time-aware variables#
Time-aware datetime variables can be particularly cumbersome to work with as far
as the format goes. We will briefly show how DatetimeFeatures() deals
with such variables in three different scenarios.
Case 1: our dataset contains a time-aware variable in object format,
with potentially different timezones across different observations.
We pass utc=True when initializing the transformer to make sure it
converts all data to UTC timezone.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
toy_df = pd.DataFrame({
"var_tz": ['12:34:45+3', '23:01:02-6', '11:59:21-8', '08:44:23Z']
})
dfts = DatetimeFeatures(
features_to_extract=["hour", "minute"],
drop_original=False,
utc=True
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_tz var_tz_hour var_tz_minute
0 12:34:45+3 9 34
1 23:01:02-6 5 1
2 11:59:21-8 19 59
3 08:44:23Z 8 44
Case 2: our dataset contains a variable that is cast as a localized datetime in a particular timezone. However, we decide that we want to get all the datetime information extracted as if it were in UTC timezone.
import pandas as pd
from feature_engine.datetime import DatetimeFeatures
var_tz = pd.Series(['08/31/00 12:34:45', '12/01/90 23:01:02', '04/25/01 11:59:21'])
var_tz = pd.to_datetime(var_tz)
var_tz = var_tz.dt.tz_localize("US/eastern")
var_tz
0 2000-08-31 12:34:45-04:00
1 1990-12-01 23:01:02-05:00
2 2001-04-25 11:59:21-04:00
dtype: datetime64[ns, US/Eastern]
We need to pass utc=True when initializing the transformer to revert back to the UTC
timezone.
toy_df = pd.DataFrame({"var_tz": var_tz})
dfts = DatetimeFeatures(
features_to_extract=["day_of_month", "hour"],
drop_original=False,
utc=True,
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_tz var_tz_day_of_month var_tz_hour
0 2000-08-31 12:34:45-04:00 31 16
1 1990-12-01 23:01:02-05:00 2 4
2 2001-04-25 11:59:21-04:00 25 15
Case 3: given a variable like var_tz in the example above, we now want
to extract the features keeping the original timezone localization,
therefore we pass utc=False or None. In this case, we leave it to None which
is the default option.
dfts = DatetimeFeatures(
features_to_extract=["day_of_month", "hour"],
drop_original=False,
utc=None,
)
df_transf = dfts.fit_transform(toy_df)
print(df_transf)
var_tz var_tz_day_of_month var_tz_hour
0 2000-08-31 12:34:45-04:00 31 12
1 1990-12-01 23:01:02-05:00 1 23
2 2001-04-25 11:59:21-04:00 25 11
Note that the hour extracted from the variable differ in this dataframe respect to the one obtained in Case 2.
More details#
You can find additional examples with a real dataset on how to use
DatetimeFeatures() in the following Jupyter notebook.