the brown fox is quick and the blue dog is lazyanimals the sky is very blue and the sky is very beaut weather the dog is lazy but the brown fox is quickanimals the preceding output uses the averaged word vectors based on word embeddings to cluster the documents in our corpus and we can clearly see that it has obtained the right clustersthere are several other schemes of aggregating word vectors like using tf-idf weights along with the word vector representations besides this there have been recent advancements in the field of deep learning where architectures like rnns and lstms are also used for engineering features from text data feature engineering on temporal data temporal data involves datasets that change over period of time and time-based attributes are of paramount importance in these datasets usually temporal attributes include some form of datatimeand timestamp values and often optionally include other metadata like time zonesdaylight savings time informationand so on temporal dataespecially time-series based data is extensively used in multiple domains like stockcommodityand weather forecasting you can load feature_engineering_temporal py directly and start running the examples or use the jupyter notebookfeature engineering on temporal data ipynbfor more interactive experience let' load the following dependencies before we move on to acquiring some temporal data in [ ]import datetime import numpy as np import pandas as pd from dateutil parser import parse import pytz we will now use some sample time-based data as our source of temporal data by loading the following values in dataframe in [ ]time_stamps [ : : + : ' : :: ' : : + : ' : : + : 'df pd dataframe(time_stampscolumns=['time']df out[ ]time : : + : : :: : : + : : : + : of course by defaultthey are stored as strings or text in the dataframe so we can convert time into timestamp objects by using the following code snippet in [ ]ts_objs np array([pd timestamp(itemfor item in np array(df time)]df['ts_obj'ts_objs ts_objs out[ ]array([timestamp( : : + 'tz='utc')