How to convert STRING to DateTime in Python?
DATA MUNGING DATA CLEANING PYTHON MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

How to convert STRING to DateTime in Python?

How to convert STRING to DateTime in Python?

This recipe helps you convert STRING to DateTime in Python

0

Recipe Objective

Have you ever tried to work on datetime features in a dataset? It may look quite complicated to write datetime in its format, we can write date and time in form of strings but how to convert it in DateTime Stamp.

Converting strings to datetime objects in Python has become a common practice for data scientists especially in time series projects. Performing this is often times difficult due to various date formats - different month lengths, timezone variations etc.

To solve this, Python provides a specific data type called "datetime". But in many datasets, the dates might be represented as strings. This recipe demonstrates how to convert date strings to the datetime format.

datetime.strptime is the primary routine for parsing strings into datetimes. datetime.strptime(date_string, format)

Once you have your value in datetime objects, you can then extract specific components of the date such as the month, day, or year, all of which are available as the object's attributes.

References: https://docs.python.org/3/library/datetime.html#datetime.datetime.strptime http://strftime.org/

So this is the recipe on how we can change string to DateTime in Python. In this we will do this by using three different functions.

Step 1 - Import the library

from datetime import datetime from dateutil.parser import parse import pandas as pd

We have imported datetime, parse and pandas. These three modules will be required.

Method 1 - Converting String into DateTime

We have first defined an object called date_start in which we have stored an string in format %Y-%m-%d. Then we have tried to print it as a DateTime Stamp by using function datetime.strptime. date_start = '2020-01-01' print(datetime.strptime(date_start, '%Y-%m-%d'))

Method 2 - Converting String into DateTime

We have created a list of date in the format %m/%d/%y and used parse function on all the values of date_list to convert it in the format of datetime64. date_list = ['2/7/2027', '6/8/2019', '10/25/2020', '6/29/2018', '2/5/2022'] print([parse(x) for x in date_list])

Method 3 - Converting String into DateTime

We have created a dictionary of values and passed in function pd.DataFrame to change it into a DataFrame with columns date and value. Then we have checked the data type in the dataframe (ie object) and to change it to datetime format, we have used pd.to_datetime function. data = {'date': ['2020-05-01 18:47:05.069722', '2016-01-01 18:47:05.119994', '2014-02-05 18:47:05.178768', '2018-04-02 18:47:05.230071', '2018-04-06 18:47:05.230071', '2019-08-02 18:47:05.280592', '2019-07-01 18:47:05.332662', '2011-03-03 18:47:05.385109', '2024-04-09 18:47:05.436523', '2015-04-04 18:47:05.486877'], 'value': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} df = pd.DataFrame(data, columns = ['date', 'value']) print(df.dtypes) print(pd.to_datetime(df['date'])) print(pd.to_datetime(df['date']).dtypes) So the final output of all the methods are

2020-01-01 00:00:00

[datetime.datetime(2027, 2, 7, 0, 0), datetime.datetime(2019, 6, 8, 0, 0), datetime.datetime(2020, 10, 25, 0, 0), datetime.datetime(2018, 6, 29, 0, 0), datetime.datetime(2022, 2, 5, 0, 0)]

date     object
value     int64
dtype: object

0   2020-05-01 18:47:05.069722
1   2016-01-01 18:47:05.119994
2   2014-02-05 18:47:05.178768
3   2018-04-02 18:47:05.230071
4   2018-04-06 18:47:05.230071
5   2019-08-02 18:47:05.280592
6   2019-07-01 18:47:05.332662
7   2011-03-03 18:47:05.385109
8   2024-04-09 18:47:05.436523
9   2015-04-04 18:47:05.486877
Name: date, dtype: datetime64[ns]
datetime64[ns]

Relevant Projects

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.