Loading a csv file in Pandas

The capital_onebike.csv file covers the October, November and December rides of the Capital Bikeshare bike W20529.

Here are the first two columns:

Start date	End date	...
2017-10-01 15:23:25	2017-10-01 15:26:26	...
2017-10-01 15:42:57	2017-10-01 17:49:59	...

Bu egzersiz

Working with Dates and Times in Python

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Import Pandas.
Complete the call to read_csv() so that it correctly parses the date columns Start date and End date.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import pandas
import ____ as pd

# Load CSV into the rides variable
rides = pd.read_csv('capital-onebike.csv', 
                    parse_dates = ____)

# Print the initial (0th) row
print(rides.iloc[0])

Kodu Düzenle ve Çalıştır

Bu egzersiz

Working with Dates and Times in Python

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Hurricanes (also known as cyclones or typhoons) hit the U.S. state of Florida several times per year. To start off this course, you'll learn how to work with date objects in Python, starting with the dates of every hurricane to hit Florida since 1950. You'll learn how Python handles dates, common date operations, and the right way to format dates to avoid confusion.

Exercise 1: Dates in Python Exercise 2: Which day of the week?Exercise 3: How many hurricanes come early?Exercise 4: Math with dates Exercise 5: Subtracting dates Exercise 6: Counting events per calendar month Exercise 7: Putting a list of dates in order Exercise 8: Turning dates into strings Exercise 9: Printing dates in a friendly format Exercise 10: Representing dates in different ways

Bike sharing programs have swept through cities around the world -- and luckily for us, every trip gets recorded! Working with all of the comings and goings of one bike in Washington, D.C., you'll practice working with dates and times together. You'll parse dates and times from text, analyze peak trip times, calculate ride durations, and more.

Exercise 1: Dates and times Exercise 2: Creating datetimes by hand Exercise 3: Counting events before and after noon Exercise 4: Printing and parsing datetimes Exercise 5: Turning strings into datetimes Exercise 6: Parsing pairs of strings as datetimes Exercise 7: Recreating ISO format with strftime()Exercise 8: Unix timestamps Exercise 9: Working with durations Exercise 10: Turning pairs of datetimes into durations Exercise 11: Average trip time Exercise 12: The long and the short of why time is hard

In this chapter, you'll learn to confidently tackle the time-related topic that causes people the most trouble: time zones and daylight saving. Continuing with our bike data, you'll learn how to compare clocks around the world, how to gracefully handle "spring forward" and "fall back," and how to get up-to-date timezone data from the dateutil library.

Exercise 1: UTC offsets Exercise 2: Creating timezone aware datetimes Exercise 3: Setting timezones Exercise 4: What time did the bike leave in UTC?Exercise 5: Time zone database Exercise 6: Putting the bike trips into the right time zone Exercise 7: What time did the bike leave? (Global edition)Exercise 8: Starting daylight saving time Exercise 9: How many hours elapsed around daylight saving?Exercise 10: March 29, throughout a decade Exercise 11: Ending daylight saving time Exercise 12: Finding ambiguous datetimes Exercise 13: Cleaning daylight saving data with fold

To conclude this course, you'll apply everything you've learned about working with dates and times in standard Python to working with dates and times in Pandas. With additional information about each bike ride, such as what station it started and stopped at and whether or not the rider had a yearly membership, you'll be able to dig much more deeply into the bike trip data. In this chapter, you'll cover powerful Pandas operations, such as grouping and plotting results by time.

Exercise 1: Reading date and time data in Pandas Exercise 2: Loading a csv file in Pandas

Geçerli Egzersiz

Exercise 3: Making timedelta columns Exercise 4: Summarizing datetime data in Pandas Exercise 5: How many joyrides?Exercise 6: It's getting cold outside, W20529 Exercise 7: Members vs casual riders over time Exercise 8: Combining groupby() and resample()Exercise 9: Additional datetime methods in Pandas Exercise 10: Timezones in Pandas Exercise 11: How long per weekday?Exercise 12: How long between rides?Exercise 13: Wrap-up