DatetimeFeatures#
In datasets commonly used in data science and machine learning projects, the variables very often contain information about date and time. Date of birth and time of purchase are two examples of these variables. They are commonly referred to as “datetime features”, that is, data whose data type is date and time.
We don’t normally use datetime variables in their raw format to train machine learning models, like those for regression, classification, or clustering. Instead, we can extract a lot of information from these variables by extracting the different date and time components of the datetime variable.
Examples of date and time components are the year, the month, the week_of_year, the day of the week, the hour, the minutes, and the seconds.
Datetime features with pandas#
In Python, we can extract date and time components through the dt
module of the open-source
library pandas. For example, by executing the following:
data = pd.DataFrame({"date": pd.date_range("2019-03-05", periods=20, freq="D")})
data["year"] = data["date"].dt.year
data["quarter"] = data["date"].dt.quarter
data["month"] = data["date"].dt.month
In the former code block we created 3 features from the timestamp variable: the year, the quarter and the month.
Datetime features with Feature-engine#
DatetimeFeatures()
automatically extracts several date and time features from
datetime variables. It works with variables whose dtype is datetime, as well as with
object-like and categorical variables, provided that they can be parsed into datetime
format. It cannot extract features from numerical variables.
DatetimeFeatures()
uses the pandas dt
module under the hood, therefore automating
datetime feature engineering. In two lines of code and by specifying which features we
want to create with DatetimeFeatures()
, we can create multiple date and time variables
from various variables simultaneously.
DatetimeFeatures()
can automatically create all features supported by pandas dt
and a few more, like, for example, a binary feature indicating if the event occurred on
a weekend and also the semester.
With DatetimeFeatures()
we can choose which date and time features to extract
from the datetime variables. We can also extract date and time features from one or more
datetime variables at the same time.
Through the following examples we highlight the functionality and versatility of DatetimeFeatures()
for tabular data.
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 the 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": ['06/21/2012', '02/10/1998', '08/03/2010', '10/31/2020'],
})
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)
df_transf
With transform()
, the features extracted from the datetime variable are added to the
dataframe.
We see the new features in the following output:
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
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.
Finally, we can obtain the name of the variables in the returned data as follows:
dtfs.get_feature_names_out()
['var_date1',
'var_date2_month',
'var_date2_month_end',
'var_date2_day_of_year']
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.
Note that from version 2.0.0 pandas deprecated the parameter infer_datetime_format
.
Hence, if you want pandas to infer the datetime format and you have different formats,
you need to explicitly say so by passing "mixed"
to the format
parameter as shown
below.
dfts = DatetimeFeatures(features_to_extract=["minute"], format="mixed")
df_transf = dfts.fit_transform(toy_df)
df_transf
We see the new features in the following output:
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 variables in the dataframe that can be cast to datetime and proceeded to extract the requested feature from them.
The variables detected as datetime are stored in the transformer’s variables_
attribute:
dfts.variables_
['var_time1', 'var_time2']
The original datetime variables are dropped from the data by default. This leaves the dataset ready to train machine learning algorithms like linear regression or random forests.
If we want to keep the datetime variables, we just need to indicate drop_original=False
when initializing the transformer.
Finally, if we want to obtain the names of the variables in the output data, we can use:
dfts.get_feature_names_out()
['not_a_dt', 'var_time1_minute', 'var_time2_minute']
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 with 3 datetime variables.
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 from 2 of the datetime
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,
format="mixed",
)
df_transf = dfts.fit_transform(toy_df)
df_transf
We can see the resulting dataframe in the following output:
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 new features are now added to the dataframe.
Time series#
Time series data consists of datapoints indexed in time order. The time is usually in the index of the dataframe. We can extract features from the timestamp index and use them for time series regression or classification, as well as for time series forecasting.
With DatetimeFeatures()
we can also create date and time features from the
dataframe index.
Let’s create a toy dataframe with datetime in the index.
import pandas as pd
X = {"ambient_temp": [31.31, 31.51, 32.15, 32.39, 32.62, 32.5, 32.52, 32.68],
"module_temp": [49.18, 49.84, 52.35, 50.63, 49.61, 47.01, 46.67, 47.52],
"irradiation": [0.51, 0.79, 0.65, 0.76, 0.42, 0.49, 0.57, 0.56],
"color": ["green"] * 4 + ["blue"] * 4,
}
X = pd.DataFrame(X)
X.index = pd.date_range("2020-05-15 12:00:00", periods=8, freq="15min")
X.head()
Below we see the output of our toy dataframe:
ambient_temp module_temp irradiation color
2020-05-15 12:00:00 31.31 49.18 0.51 green
2020-05-15 12:15:00 31.51 49.84 0.79 green
2020-05-15 12:30:00 32.15 52.35 0.65 green
2020-05-15 12:45:00 32.39 50.63 0.76 green
2020-05-15 13:00:00 32.62 49.61 0.42 blue
We can extract features from the index as follows:
from feature_engine.datetime import DatetimeFeatures
dtf = DatetimeFeatures(variables="index")
Xtr = dtf.fit_transform(X)
Xtr
We can see that the transformer created the default time features and added them at the end of the dataframe.
ambient_temp module_temp irradiation color month \
2020-05-15 12:00:00 31.31 49.18 0.51 green 5
2020-05-15 12:15:00 31.51 49.84 0.79 green 5
2020-05-15 12:30:00 32.15 52.35 0.65 green 5
2020-05-15 12:45:00 32.39 50.63 0.76 green 5
2020-05-15 13:00:00 32.62 49.61 0.42 blue 5
2020-05-15 13:15:00 32.50 47.01 0.49 blue 5
2020-05-15 13:30:00 32.52 46.67 0.57 blue 5
2020-05-15 13:45:00 32.68 47.52 0.56 blue 5
year day_of_week day_of_month hour minute second
2020-05-15 12:00:00 2020 4 15 12 0 0
2020-05-15 12:15:00 2020 4 15 12 15 0
2020-05-15 12:30:00 2020 4 15 12 30 0
2020-05-15 12:45:00 2020 4 15 12 45 0
2020-05-15 13:00:00 2020 4 15 13 0 0
2020-05-15 13:15:00 2020 4 15 13 15 0
2020-05-15 13:30:00 2020 4 15 13 30 0
2020-05-15 13:45:00 2020 4 15 13 45 0
We can obtain the name of all the variables in the output dataframe as follows:
dtf.get_feature_names_out()
['ambient_temp',
'module_temp',
'irradiation',
'color',
'month',
'year',
'day_of_week',
'day_of_month',
'hour',
'minute',
'second']
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"],
format="mixed",
)
df_transf = dfts.fit_transform(toy_df)
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 we mistakenly extract the hour and the minute:
dfts = DatetimeFeatures(
features_to_extract=["hour", "minute"],
format="mixed",
)
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, by 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'],
})
Most common features#
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)
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']
All supported features#
We can also extract all supported features automatically, by setting the parameter
features_to_extract
to "all"
:
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 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 line up 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(format="mixed")),
('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.
Working with different timezones#
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,
format="mixed",
)
df_transf = dfts.fit_transform(toy_df)
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, format="mixed")
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)
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.
Missing timestamps#
DatetimeFeatures
has the option to ignore missing timestamps, or raise an error
when a missing value is encountered in a datetime variable.
Additional resources#
You can find an example of how to use DatetimeFeatures()
with a real dataset in
the following Jupyter notebook
For tutorials on how to create and use features from datetime columns, check the following courses:

Feature Engineering for Machine Learning#

Feature Engineering for Time Series Forecasting#
Or read our book:

Python Feature Engineering Cookbook#
Both our book and course are suitable for beginners and more advanced data scientists alike. By purchasing them you are supporting Sole, the main developer of Feature-engine.