Python Tutorial: Dates in Python

DataCamp · Beginner ·🛠️ AI Tools & Apps ·6y ago

Key Takeaways

The video tutorial covers working with dates in Python, specifically creating date objects, extracting information, and performing basic operations using the datetime package.

Full Transcript

hi my name is max rone I will be your instructor for this course on working with dates and times in Python dates are everywhere in data science stock prices go up and down experiments begin and end people are born politicians take votes and on and on all these events happen at a particular point in time knowing how to analyze data over time is a core science skill this course is divided into four chapters first chapter will be about working with dates and calendars in Chapter two we will add time into the mix and combine dates and times in Chapter three we'll tackle one of the toughest parts of working with time time zones and daylight saving and finally in Chapter four we'll connect what we've learned about working with dates and times to explore how pandas can make answering even complex questions about dates much easier let's begin python has a special date class called date which you will use to represent dates a date like a string or a number or an umpire Ray has special rules for creating it and methods for working with it in this lesson we're going to discuss creating dates and extracting some basic information out of them why do we need a special date class let's have a look to understand how dates work in this chapter you're going to be exploring 67 years of hurricane landfalls in the US state of florida to underscore hurricanes is a list with the dates of two hurricanes represented as strings the last 2016 hurricane on October 7 2016 and the 1st 2017 hurricane on June 21st 2017 represented in the u.s. style with the month than the day than the year suppose you want to do something interesting with these dates how would you figure out how many days had elapsed between them how would you check that they were ordered from earliest to latest how do you know which day of the week each was doing these things manually would be challenging but Python makes all of them easy by the end of this chapter you don't know how to do each of these things yourself to create a date object we start by importing the date class the collection of date and time related classes are stored in the date time package we create a date using the date function here we've created dates corresponding to the two hurricanes now as Python date objects the inputs to date are the Year month and day the first date is October 7th 2016 and the second date is June 21st 2017 the order is easy to remember it goes from biggest to smallest year month day later in this chapter you'll create dates directly from lists of strings but in this lesson you're going to stick to creating dates by hand or using lists have already created dates you can access individual components of a date using the dates attributes you can access the year of the date using the Year attribute like so and the result is 2016 similarly you can access the month and day using the month and day attributes like so you can also ask Python to do more complicated work here we call the weekday method on a date and see that the weekday is for what is forming here Python counts weekdays from 0 starting on Monday 1 is Tuesday 2 is Wednesday and so on up to 6 being a Sunday this date was a Friday in the next few exercises you'll implement what you've seen in this video to see how much you can already do

Original Description

Want to learn more? Take the full course at https://learn.datacamp.com/courses/working-with-dates-and-times-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi! My name is Max Shron, I will be your instructor for this course on working with dates and times in Python. Dates are everywhere in data science. Stock prices go up and down, experiments begin and end, people are born, politicians take votes, and on and on. All these events happen at a particular point in time. Knowing how to analyze data over time is a core data science skill. This course is divided into four chapters. The first chapter will be about working with dates and calendars. In chapter two, we will add time into the mix, and combine dates and times. In chapter three, we'll tackle one of the toughest parts of working with time: time zones and Daylight Saving. And finally, in chapter four, we'll connect what we've learned about working with dates and times to explore how Pandas can make answering even complex questions about dates much easier. Let's begin. Python has a special date class, called "date", which you will use to represent dates. A date, like a string, or a number, or a numpy array, has special rules for creating it and methods for working with it. In this lesson, we're going to discuss creating dates and extracting some basic information out of them. Why do we need a special date class? Let's have a look. To understand how dates work, in this chapter you're going to be exploring 67 years of Hurricane landfalls in the U.S. state of Florida. two_hurricanes is a list with the dates of two hurricanes represented as strings: the last 2016 hurricane (on October 7th, 2016) and the first 2017 hurricane (on June 21st, 2017). The dates are represented in the U.S. style, with the month, then the day, then the year. Suppose you want to do something interesting with these dates. How would you figure out how many days
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from DataCamp · DataCamp · 0 of 60

← Previous Next →
1 SQL Server Tutorial: Date manipulation
SQL Server Tutorial: Date manipulation
DataCamp
2 R Tutorial: Intermediate Interactive Data Visualization with plotly in R
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
3 R Tutorial: Adding aesthetics to represent a variable
R Tutorial: Adding aesthetics to represent a variable
DataCamp
4 R Tutorial: Moving Beyond Simple Interactivity
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
5 Python Tutorial: Why use ML for marketing? Strategies and use cases
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
6 Python Tutorial: Preparation for modeling
Python Tutorial: Preparation for modeling
DataCamp
7 Python Tutorial: Machine Learning modeling steps
Python Tutorial: Machine Learning modeling steps
DataCamp
8 R Tutorial: The prior model
R Tutorial: The prior model
DataCamp
9 R Tutorial: Data & the likelihood
R Tutorial: Data & the likelihood
DataCamp
10 R Tutorial: The posterior model
R Tutorial: The posterior model
DataCamp
11 R Tutorial: An Introduction to plotly
R Tutorial: An Introduction to plotly
DataCamp
12 R Tutorial: Plotting a single variable
R Tutorial: Plotting a single variable
DataCamp
13 R Tutorial: Bivariate graphics
R Tutorial: Bivariate graphics
DataCamp
14 Python Tutorial: Customer Segmentation in Python
Python Tutorial: Customer Segmentation in Python
DataCamp
15 Python Tutorial: Time cohorts
Python Tutorial: Time cohorts
DataCamp
16 Python Tutorial: Calculate cohort metrics
Python Tutorial: Calculate cohort metrics
DataCamp
17 Python Tutorial: Cohort analysis visualization
Python Tutorial: Cohort analysis visualization
DataCamp
18 R Tutorial: Building Dashboards with flexdashboard
R Tutorial: Building Dashboards with flexdashboard
DataCamp
19 R Tutorial: Anatomy of a flexdashboard
R Tutorial: Anatomy of a flexdashboard
DataCamp
20 R Tutorial: Layout basics
R Tutorial: Layout basics
DataCamp
21 R Tutorial: Advanced layouts
R Tutorial: Advanced layouts
DataCamp
22 Python Tutorial: Time Series Analysis in Python
Python Tutorial: Time Series Analysis in Python
DataCamp
23 Python Tutorial: Correlation of Two Time Series
Python Tutorial: Correlation of Two Time Series
DataCamp
24 Python Tutorial: Simple Linear Regressions
Python Tutorial: Simple Linear Regressions
DataCamp
25 Python Tutorial: Autocorrelation
Python Tutorial: Autocorrelation
DataCamp
26 R Tutorial: The gapminder dataset
R Tutorial: The gapminder dataset
DataCamp
27 R Tutorial: The filter verb
R Tutorial: The filter verb
DataCamp
28 R Tutorial: The arrange verb
R Tutorial: The arrange verb
DataCamp
29 R Tutorial: The mutate verb
R Tutorial: The mutate verb
DataCamp
30 R Tutorial: What is cluster analysis?
R Tutorial: What is cluster analysis?
DataCamp
31 R Tutorial: Distance between two observations
R Tutorial: Distance between two observations
DataCamp
32 R Tutorial: The importance of scale
R Tutorial: The importance of scale
DataCamp
33 R Tutorial: Measuring distance for categorical data
R Tutorial: Measuring distance for categorical data
DataCamp
34 Python Tutorial: Plotting multiple graphs
Python Tutorial: Plotting multiple graphs
DataCamp
35 Python Tutorial: Customizing axes
Python Tutorial: Customizing axes
DataCamp
36 Python Tutorial: Legends, annotations, & styles
Python Tutorial: Legends, annotations, & styles
DataCamp
37 Python Tutorial: Introduction to iterators
Python Tutorial: Introduction to iterators
DataCamp
38 Python Tutorial: Playing with iterators
Python Tutorial: Playing with iterators
DataCamp
39 Python Tutorial: Using iterators to load large files into memory
Python Tutorial: Using iterators to load large files into memory
DataCamp
40 SQL Tutorial: Introduction to Relational Databases in SQL
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
41 SQL Tutorial: Tables: At the core of every database
SQL Tutorial: Tables: At the core of every database
DataCamp
42 SQL Tutorial: Update your database as the structure changes
SQL Tutorial: Update your database as the structure changes
DataCamp
43 Python Tutorial: Classification-Tree Learning
Python Tutorial: Classification-Tree Learning
DataCamp
44 Python Tutorial: Decision-Tree for Classification
Python Tutorial: Decision-Tree for Classification
DataCamp
45 Python Tutorial: Decision-Tree for Regression
Python Tutorial: Decision-Tree for Regression
DataCamp
46 Python Tutorial: Census Subject Tables
Python Tutorial: Census Subject Tables
DataCamp
47 Python Tutorial: Census Geography
Python Tutorial: Census Geography
DataCamp
48 Python Tutorial: Using the Census API
Python Tutorial: Using the Census API
DataCamp
49 R Tutorial: A/B Testing in R
R Tutorial: A/B Testing in R
DataCamp
50 R Tutorial: Baseline Conversion Rates
R Tutorial: Baseline Conversion Rates
DataCamp
51 R Tutorial: Designing an Experiment - Power Analysis
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
52 R Tutorial: Introduction to qualitative data
R Tutorial: Introduction to qualitative data
DataCamp
53 R Tutorial: Understanding your qualitative variables
R Tutorial: Understanding your qualitative variables
DataCamp
54 R Tutorial: Making Better Plots
R Tutorial: Making Better Plots
DataCamp
55 SQL Tutorial: OLTP and OLAP
SQL Tutorial: OLTP and OLAP
DataCamp
56 SQL Tutorial: Storing data
SQL Tutorial: Storing data
DataCamp
57 SQL Tutorial: Database design
SQL Tutorial: Database design
DataCamp
58 Python Tutorial: Introduction to spaCy
Python Tutorial: Introduction to spaCy
DataCamp
59 Python Tutorial: Statistical Models
Python Tutorial: Statistical Models
DataCamp
60 Python Tutorial: Rule-based Matching
Python Tutorial: Rule-based Matching
DataCamp

This video tutorial teaches how to work with dates in Python, including creating date objects and extracting information, with applications in data science and real-world scenarios.

Key Takeaways
  1. Import the datetime package
  2. Create a date object using the date function
  3. Access individual components of a date using attributes
  4. Use methods to perform more complicated work
  5. Implement date operations in exercises
💡 Python's datetime package provides a special date class to represent dates, making it easy to perform date-related operations and analysis.

Related AI Lessons

Up next
I Asked ChatGPT to Apply to 500 Jobs (8 Interviews in 48 Hours)
Sabrina Ramonov 🍄
Watch →