Working With Dates and Times in Python

Dates and Calendars

Hurricanes (also known as cyclones or typhoons) hit the U.S. state of Florida several times per year. To start off we will work with date objects in Python, starting with the dates of every hurricane to hit Florida since 1950.

Dates in Python

Which day of the week?

Hurricane Andrew, which hit Florida on August 24, 1992, was one of the costliest and deadliest hurricanes in US history. Which day of the week did it make landfall?

import datetime

hurricane_andrew = datetime.date(1992, 8, 24)
hurricane_andrew.weekday()

0

How many hurricanes come early?

florida_hurricane_dates = [datetime.date(1950, 8, 31), datetime.date(1950, 9, 5), datetime.date(1950, 10, 18),
                           datetime.date(1950, 10, 21), datetime.date(1951, 5, 18), datetime.date(1951, 10, 2),
                           datetime.date(1952, 2, 3), datetime.date(1952, 8, 30), datetime.date(1953, 6, 6),
                           datetime.date(1953, 8, 29), datetime.date(1953, 9, 20), datetime.date(1953, 9, 26),
                           datetime.date(1953, 10, 9), datetime.date(1955, 8, 21), datetime.date(1956, 7, 6),
                           datetime.date(1956, 9, 24), datetime.date(1956, 10, 15), datetime.date(1957, 6, 8),
                           datetime.date(1957, 9, 8), datetime.date(1958, 9, 4), datetime.date(1959, 6, 18),
                           datetime.date(1959, 10, 8), datetime.date(1959, 10, 18), datetime.date(1960, 7, 29),
                           datetime.date(1960, 9, 10), datetime.date(1960, 9, 15), datetime.date(1960, 9, 23), 
                           datetime.date(1961, 9, 11), datetime.date(1961, 10, 29), datetime.date(1962, 8, 26),
                           datetime.date(1963, 10, 21), datetime.date(1964, 6, 6), datetime.date(1964, 8, 27),
                           datetime.date(1964, 9, 10), datetime.date(1964, 9, 20), datetime.date(1964, 10, 5),
                           datetime.date(1964, 10, 14),
 datetime.date(1965, 6, 15),
 datetime.date(1965, 9, 8),
 datetime.date(1965, 9, 30),
 datetime.date(1966, 6, 9),
 datetime.date(1966, 6, 30),
 datetime.date(1966, 7, 24),
 datetime.date(1966, 10, 4),
 datetime.date(1968, 6, 4),
 datetime.date(1968, 6, 18),
 datetime.date(1968, 7, 5),
 datetime.date(1968, 8, 10),
 datetime.date(1968, 8, 28),
 datetime.date(1968, 9, 26),
 datetime.date(1968, 10, 19),
 datetime.date(1969, 6, 9),
 datetime.date(1969, 8, 18),
 datetime.date(1969, 8, 29),
 datetime.date(1969, 9, 7),
 datetime.date(1969, 9, 21),
 datetime.date(1969, 10, 1),
 datetime.date(1969, 10, 2),
 datetime.date(1969, 10, 21),
 datetime.date(1970, 5, 25),
 datetime.date(1970, 7, 22),
 datetime.date(1970, 8, 6),
 datetime.date(1970, 9, 13),
 datetime.date(1970, 9, 27),
 datetime.date(1971, 8, 10),
 datetime.date(1971, 8, 13),
 datetime.date(1971, 8, 29),
 datetime.date(1971, 9, 1),
 datetime.date(1971, 9, 16),
 datetime.date(1971, 10, 13),
 datetime.date(1972, 5, 28),
 datetime.date(1972, 6, 19),
 datetime.date(1972, 9, 5),
 datetime.date(1973, 6, 7),
 datetime.date(1973, 6, 23),
 datetime.date(1973, 9, 3),
 datetime.date(1973, 9, 25),
 datetime.date(1974, 6, 25),
 datetime.date(1974, 9, 8),
 datetime.date(1974, 9, 27),
 datetime.date(1974, 10, 7),
 datetime.date(1975, 6, 27),
 datetime.date(1975, 7, 29),
 datetime.date(1975, 9, 23),
 datetime.date(1975, 10, 1),
 datetime.date(1975, 10, 16),
 datetime.date(1976, 5, 23),
 datetime.date(1976, 6, 11),
 datetime.date(1976, 8, 19),
 datetime.date(1976, 9, 13),
 datetime.date(1977, 8, 27),
 datetime.date(1977, 9, 5),
 datetime.date(1978, 6, 22),
 datetime.date(1979, 7, 11),
 datetime.date(1979, 9, 3),
 datetime.date(1979, 9, 12),
 datetime.date(1979, 9, 24),
 datetime.date(1980, 8, 7),
 datetime.date(1980, 11, 18),
 datetime.date(1981, 8, 17),
 datetime.date(1982, 6, 18),
 datetime.date(1982, 9, 11),
 datetime.date(1983, 8, 28),
 datetime.date(1984, 9, 9),
 datetime.date(1984, 9, 27),
 datetime.date(1984, 10, 26),
 datetime.date(1985, 7, 23),
 datetime.date(1985, 8, 15),
 datetime.date(1985, 10, 10),
 datetime.date(1985, 11, 21),
 datetime.date(1986, 6, 26),
 datetime.date(1986, 8, 13),
 datetime.date(1987, 8, 14),
 datetime.date(1987, 9, 7),
 datetime.date(1987, 10, 12),
 datetime.date(1987, 11, 4),
 datetime.date(1988, 5, 30),
 datetime.date(1988, 8, 4),
 datetime.date(1988, 8, 13),
 datetime.date(1988, 8, 23),
 datetime.date(1988, 9, 4),
 datetime.date(1988, 9, 10),
 datetime.date(1988, 9, 13),
 datetime.date(1988, 11, 23),
 datetime.date(1989, 9, 22),
 datetime.date(1990, 5, 25),
 datetime.date(1990, 10, 9),
 datetime.date(1990, 10, 12),
 datetime.date(1991, 6, 30),
 datetime.date(1991, 10, 16),
 datetime.date(1992, 6, 25),
 datetime.date(1992, 8, 24),
 datetime.date(1992, 9, 29),
 datetime.date(1993, 6, 1),
 datetime.date(1994, 7, 3),
 datetime.date(1994, 8, 15),
 datetime.date(1994, 10, 2),
 datetime.date(1994, 11, 16),
 datetime.date(1995, 6, 5),
 datetime.date(1995, 7, 27),
 datetime.date(1995, 8, 2),
 datetime.date(1995, 8, 23),
 datetime.date(1995, 10, 4),
 datetime.date(1996, 7, 11),
 datetime.date(1996, 9, 2),
 datetime.date(1996, 10, 8),
 datetime.date(1996, 10, 18),
 datetime.date(1997, 7, 19),
 datetime.date(1998, 9, 3),
 datetime.date(1998, 9, 20),
 datetime.date(1998, 9, 25),
 datetime.date(1998, 11, 5),
 datetime.date(1999, 8, 29),
 datetime.date(1999, 9, 15),
 datetime.date(1999, 9, 21),
 datetime.date(1999, 10, 15),
 datetime.date(2000, 8, 23),
 datetime.date(2000, 9, 9),
 datetime.date(2000, 9, 18),
 datetime.date(2000, 9, 22),
 datetime.date(2000, 10, 3),
 datetime.date(2001, 6, 12),
 datetime.date(2001, 8, 6),
 datetime.date(2001, 9, 14),
 datetime.date(2001, 11, 5),
 datetime.date(2002, 7, 13),
 datetime.date(2002, 8, 4),
 datetime.date(2002, 9, 4),
 datetime.date(2002, 9, 14),
 datetime.date(2002, 9, 26),
 datetime.date(2002, 10, 3),
 datetime.date(2002, 10, 11),
 datetime.date(2003, 4, 20),
 datetime.date(2003, 6, 30),
 datetime.date(2003, 7, 25),
 datetime.date(2003, 8, 14),
 datetime.date(2003, 8, 30),
 datetime.date(2003, 9, 6),
 datetime.date(2003, 9, 13),
 datetime.date(2004, 8, 12),
 datetime.date(2004, 8, 13),
 datetime.date(2004, 9, 5),
 datetime.date(2004, 9, 13),
 datetime.date(2004, 9, 16),
 datetime.date(2004, 10, 10),
 datetime.date(2005, 6, 11),
 datetime.date(2005, 7, 6),
 datetime.date(2005, 7, 10),
 datetime.date(2005, 8, 25),
 datetime.date(2005, 9, 12),
 datetime.date(2005, 9, 20),
 datetime.date(2005, 10, 5),
 datetime.date(2005, 10, 24),
 datetime.date(2006, 6, 13),
 datetime.date(2006, 8, 30),
 datetime.date(2007, 5, 9),
 datetime.date(2007, 6, 2),
 datetime.date(2007, 8, 23),
 datetime.date(2007, 9, 8),
 datetime.date(2007, 9, 13),
 datetime.date(2007, 9, 22),
 datetime.date(2007, 10, 31),
 datetime.date(2007, 12, 13),
 datetime.date(2008, 7, 16),
 datetime.date(2008, 7, 22),
 datetime.date(2008, 8, 18),
 datetime.date(2008, 8, 31),
 datetime.date(2008, 9, 2),
 datetime.date(2009, 8, 16),
 datetime.date(2009, 8, 21),
 datetime.date(2009, 11, 9),
 datetime.date(2010, 6, 30),
 datetime.date(2010, 7, 23),
 datetime.date(2010, 8, 10),
 datetime.date(2010, 8, 31),
 datetime.date(2010, 9, 29),
 datetime.date(2011, 7, 18),
 datetime.date(2011, 8, 25),
 datetime.date(2011, 9, 3),
 datetime.date(2011, 10, 28),
 datetime.date(2011, 11, 9),
 datetime.date(2012, 5, 28),
 datetime.date(2012, 6, 23),
 datetime.date(2012, 8, 25),
 datetime.date(2012, 10, 25),
 datetime.date(2015, 8, 30),
 datetime.date(2015, 10, 1),
 datetime.date(2016, 6, 6),
 datetime.date(2016, 9, 1),
 datetime.date(2016, 9, 14),
 datetime.date(2016, 10, 7),
 datetime.date(2017, 6, 21),
 datetime.date(2017, 7, 31),
 datetime.date(2017, 9, 10),
 datetime.date(2017, 10, 29)]

Atlantic hurricane season officially begins on June 1. How many hurricanes since 1950 have made landfall in Florida before the official start of hurricane season?

early_hurricanes = 0

# We loop over dates
for hurricane in florida_hurricane_dates:
    # Check if the month if before june
    if hurricane.month < 6:
        early_hurricanes+=1
early_hurricanes

10

Math with dates

Subtracting dates

# Create a date object for May 9th, 2007
start = datetime.date(2007, 5, 9)

# Create a date object for December 13th, 2007
end = datetime.date(2007, 12, 13)

# Subtract the two dates and print the number of days
(end - start).days

218

Counting events per calendar month

Hurricanes can make landfall in Florida throughout the year. As we’ve already discussed, some months are more hurricane-prone than others. let’s see how hurricanes in Florida were distributed across months throughout the year. We’ve created a dictionary called hurricanes_each_month to hold counts and set the initial counts to zero. We will loop over the list of hurricanes, incrementing the correct month in hurricanes_each_month as we go, and then print the result.

# A dictionary to count hurricanes per calendar month
hurricanes_each_month = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6:0,7: 0, 8:0, 9:0, 10:0, 11:0, 12:0}

# Loop over all hurricanes
for hurricane in florida_hurricane_dates:
    # Pull out the month
    month = hurricane.month
    # Increment the count in your dictionary by one
    hurricanes_each_month[month] += 1
    
print(hurricanes_each_month)

{1: 0, 2: 1, 3: 0, 4: 1, 5: 8, 6: 32, 7: 21, 8: 49, 9: 70, 10: 43, 11: 9, 12: 1}

Turning dates into strings

Printing dates in a friendly format

Let’s see what event was recorded first in the Florida hurricane data set. We will format the earliest date in the florida_hurriance_dates list in two ways so we can decide which one you want to use: either the ISO standard or the typical US style.

# Assign the earliest date to first_date
first_date = min(florida_hurricane_dates)

# Convert to ISO and US formats
iso = "Our earliest hurricane date: " + first_date.isoformat()
us = "Our earliest hurricane date: " + first_date.strftime("%m/%d/%Y")

print("ISO: " + iso)
print("US: " + us)

ISO: Our earliest hurricane date: 1950-08-31
US: Our earliest hurricane date: 08/31/1950

printing out the same date, August 26, 1992 (the day that Hurricane Andrew made landfall in Florida), in a number of different ways, to practice using the .strftime() method.

# Create a date object
andrew = datetime.date(1992, 8, 26)

# Print the date in the format 'YYYY-DDD'
print(andrew.strftime('%Y-%j'))

1992-239

Combining Dates and Times

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., we’ll be working with dates and times together. We’ll parse dates and times from text, analyze peak trip times, calculate ride durations, and more.

Dates and times

Creating Datetimes by hand

# Create a datetime object
dt = datetime.datetime(year=2017, month=10, day=1, hour=15, minute=26, second=26)

# Print the results in ISO 8601 format
print(dt.isoformat())

2017-10-01T15:26:26

# Create a datetime object
dt = datetime.datetime(2017, 12, 31, 15, 19, 13)

# Replace the year with 1917
dt_old = dt.replace(year=1917)

# Print the results in ISO 8601 format
print(dt_old)

1917-12-31 15:19:13

# Pull out the start of the first trip
first_start = onebike_datetimes[0]['start']

# Format to feed to strftime()
fmt = "%Y-%m-%dT%H:%M:%S"

# Print out date with .isoformat(), then with .strftime() to compare
print(first_start.isoformat())
print(first_start.strftime(fmt))

NameError: name 'onebike_datetimes' is not defined

Unix timestamps

Datetimes are sometimes stored as Unix timestamps: the number of seconds since January 1, 1970. This is especially common with computer infrastructure, like the log files that websites keep when they get visitors.

The largest number that some older computers can hold in one variable is 2147483648, which as a Unix timestamp is in January 2038. On that day, many computers which haven’t been upgraded will fail. Hopefully, none of them are running anything critical!

# Starting timestamps
timestamps = [1514665153, 1514664543]

# Datetime objects
dts = []

# Loop
for ts in timestamps:
  dts.append(datetime.datetime.fromtimestamp(ts))
  
# Print results
print(dts)

[datetime.datetime(2017, 12, 30, 23, 19, 13), datetime.datetime(2017, 12, 30, 23, 9, 3)]

Working with durations

Turning pairs of datetimes into durations

Remember that timedelta objects are represented in Python as a number of days and seconds of elapsed time. Be careful not to use .seconds on a timedelta object, since you’ll just get the number of seconds without the days!

onebike_datetimes = [{'end': datetime.datetime(2017, 10, 1, 15, 26, 26),
  'start': datetime.datetime(2017, 10, 1, 15, 23, 25)},
 {'end': datetime.datetime(2017, 10, 1, 17, 49, 59),
  'start': datetime.datetime(2017, 10, 1, 15, 42, 57)},
 {'end': datetime.datetime(2017, 10, 2, 6, 42, 53),
  'start': datetime.datetime(2017, 10, 2, 6, 37, 10)},
 {'end': datetime.datetime(2017, 10, 2, 9, 18, 3),
  'start': datetime.datetime(2017, 10, 2, 8, 56, 45)},
 {'end': datetime.datetime(2017, 10, 2, 18, 45, 5),
  'start': datetime.datetime(2017, 10, 2, 18, 23, 48)},
 {'end': datetime.datetime(2017, 10, 2, 19, 10, 54),
  'start': datetime.datetime(2017, 10, 2, 18, 48, 8)},
 {'end': datetime.datetime(2017, 10, 2, 19, 31, 45),
  'start': datetime.datetime(2017, 10, 2, 19, 18, 10)},
 {'end': datetime.datetime(2017, 10, 2, 19, 46, 37),
  'start': datetime.datetime(2017, 10, 2, 19, 37, 32)},
 {'end': datetime.datetime(2017, 10, 3, 8, 32, 27),
  'start': datetime.datetime(2017, 10, 3, 8, 24, 16)},
 {'end': datetime.datetime(2017, 10, 3, 18, 27, 46),
  'start': datetime.datetime(2017, 10, 3, 18, 17, 7)},
 {'end': datetime.datetime(2017, 10, 3, 19, 52, 8),
  'start': datetime.datetime(2017, 10, 3, 19, 24, 10)},
 {'end': datetime.datetime(2017, 10, 3, 20, 23, 52),
  'start': datetime.datetime(2017, 10, 3, 20, 17, 6)},
 {'end': datetime.datetime(2017, 10, 3, 20, 57, 10),
  'start': datetime.datetime(2017, 10, 3, 20, 45, 21)},
 {'end': datetime.datetime(2017, 10, 4, 7, 13, 31),
  'start': datetime.datetime(2017, 10, 4, 7, 4, 57)},
 {'end': datetime.datetime(2017, 10, 4, 7, 21, 54),
  'start': datetime.datetime(2017, 10, 4, 7, 13, 42)},
 {'end': datetime.datetime(2017, 10, 4, 14, 50),
  'start': datetime.datetime(2017, 10, 4, 14, 22, 12)},
 {'end': datetime.datetime(2017, 10, 4, 15, 44, 49),
  'start': datetime.datetime(2017, 10, 4, 15, 7, 27)},
 {'end': datetime.datetime(2017, 10, 4, 16, 32, 33),
  'start': datetime.datetime(2017, 10, 4, 15, 46, 41)},
 {'end': datetime.datetime(2017, 10, 4, 16, 46, 59),
  'start': datetime.datetime(2017, 10, 4, 16, 34, 44)},
 {'end': datetime.datetime(2017, 10, 4, 17, 31, 36),
  'start': datetime.datetime(2017, 10, 4, 17, 26, 6)},
 {'end': datetime.datetime(2017, 10, 4, 17, 50, 41),
  'start': datetime.datetime(2017, 10, 4, 17, 42, 3)},
 {'end': datetime.datetime(2017, 10, 5, 8, 12, 55),
  'start': datetime.datetime(2017, 10, 5, 7, 49, 2)},
 {'end': datetime.datetime(2017, 10, 5, 8, 29, 45),
  'start': datetime.datetime(2017, 10, 5, 8, 26, 21)},
 {'end': datetime.datetime(2017, 10, 5, 8, 38, 31),
  'start': datetime.datetime(2017, 10, 5, 8, 33, 27)},
 {'end': datetime.datetime(2017, 10, 5, 16, 51, 52),
  'start': datetime.datetime(2017, 10, 5, 16, 35, 35)},
 {'end': datetime.datetime(2017, 10, 5, 18, 16, 50),
  'start': datetime.datetime(2017, 10, 5, 17, 53, 31)},
 {'end': datetime.datetime(2017, 10, 6, 8, 38, 1),
  'start': datetime.datetime(2017, 10, 6, 8, 17, 17)},
 {'end': datetime.datetime(2017, 10, 6, 11, 50, 38),
  'start': datetime.datetime(2017, 10, 6, 11, 39, 40)},
 {'end': datetime.datetime(2017, 10, 6, 13, 13, 14),
  'start': datetime.datetime(2017, 10, 6, 12, 59, 54)},
 {'end': datetime.datetime(2017, 10, 6, 14, 14, 56),
  'start': datetime.datetime(2017, 10, 6, 13, 43, 5)},
 {'end': datetime.datetime(2017, 10, 6, 15, 9, 26),
  'start': datetime.datetime(2017, 10, 6, 14, 28, 15)},
 {'end': datetime.datetime(2017, 10, 6, 16, 12, 34),
  'start': datetime.datetime(2017, 10, 6, 15, 50, 10)},
 {'end': datetime.datetime(2017, 10, 6, 16, 39, 31),
  'start': datetime.datetime(2017, 10, 6, 16, 32, 16)},
 {'end': datetime.datetime(2017, 10, 6, 16, 48, 39),
  'start': datetime.datetime(2017, 10, 6, 16, 44, 8)},
 {'end': datetime.datetime(2017, 10, 6, 17, 9, 3),
  'start': datetime.datetime(2017, 10, 6, 16, 53, 43)},
 {'end': datetime.datetime(2017, 10, 7, 11, 53, 6),
  'start': datetime.datetime(2017, 10, 7, 11, 38, 55)},
 {'end': datetime.datetime(2017, 10, 7, 14, 7, 5),
  'start': datetime.datetime(2017, 10, 7, 14, 3, 36)},
 {'end': datetime.datetime(2017, 10, 7, 14, 27, 36),
  'start': datetime.datetime(2017, 10, 7, 14, 20, 3)},
 {'end': datetime.datetime(2017, 10, 7, 14, 44, 51),
  'start': datetime.datetime(2017, 10, 7, 14, 30, 50)},
 {'end': datetime.datetime(2017, 10, 8, 0, 30, 48),
  'start': datetime.datetime(2017, 10, 8, 0, 28, 26)},
 {'end': datetime.datetime(2017, 10, 8, 11, 33, 24),
  'start': datetime.datetime(2017, 10, 8, 11, 16, 21)},
 {'end': datetime.datetime(2017, 10, 8, 13, 1, 29),
  'start': datetime.datetime(2017, 10, 8, 12, 37, 3)},
 {'end': datetime.datetime(2017, 10, 8, 13, 57, 53),
  'start': datetime.datetime(2017, 10, 8, 13, 30, 37)},
 {'end': datetime.datetime(2017, 10, 8, 15, 7, 19),
  'start': datetime.datetime(2017, 10, 8, 14, 16, 40)},
 {'end': datetime.datetime(2017, 10, 8, 15, 50, 1),
  'start': datetime.datetime(2017, 10, 8, 15, 23, 50)},
 {'end': datetime.datetime(2017, 10, 8, 16, 17, 42),
  'start': datetime.datetime(2017, 10, 8, 15, 54, 12)},
 {'end': datetime.datetime(2017, 10, 8, 16, 35, 18),
  'start': datetime.datetime(2017, 10, 8, 16, 28, 52)},
 {'end': datetime.datetime(2017, 10, 8, 23, 33, 41),
  'start': datetime.datetime(2017, 10, 8, 23, 8, 14)},
 {'end': datetime.datetime(2017, 10, 8, 23, 45, 11),
  'start': datetime.datetime(2017, 10, 8, 23, 34, 49)},
 {'end': datetime.datetime(2017, 10, 9, 0, 10, 57),
  'start': datetime.datetime(2017, 10, 8, 23, 46, 47)},
 {'end': datetime.datetime(2017, 10, 9, 0, 36, 40),
  'start': datetime.datetime(2017, 10, 9, 0, 12, 58)},
 {'end': datetime.datetime(2017, 10, 9, 0, 53, 33),
  'start': datetime.datetime(2017, 10, 9, 0, 37, 2)},
 {'end': datetime.datetime(2017, 10, 9, 1, 48, 13),
  'start': datetime.datetime(2017, 10, 9, 1, 23, 29)},
 {'end': datetime.datetime(2017, 10, 9, 2, 13, 35),
  'start': datetime.datetime(2017, 10, 9, 1, 49, 25)},
 {'end': datetime.datetime(2017, 10, 9, 2, 29, 40),
  'start': datetime.datetime(2017, 10, 9, 2, 14, 11)},
 {'end': datetime.datetime(2017, 10, 9, 13, 13, 25),
  'start': datetime.datetime(2017, 10, 9, 13, 4, 32)},
 {'end': datetime.datetime(2017, 10, 9, 14, 38, 55),
  'start': datetime.datetime(2017, 10, 9, 14, 30, 10)},
 {'end': datetime.datetime(2017, 10, 9, 15, 11, 30),
  'start': datetime.datetime(2017, 10, 9, 15, 6, 47)},
 {'end': datetime.datetime(2017, 10, 9, 16, 45, 38),
  'start': datetime.datetime(2017, 10, 9, 16, 43, 25)},
 {'end': datetime.datetime(2017, 10, 10, 15, 51, 24),
  'start': datetime.datetime(2017, 10, 10, 15, 32, 58)},
 {'end': datetime.datetime(2017, 10, 10, 17, 3, 47),
  'start': datetime.datetime(2017, 10, 10, 16, 47, 55)},
 {'end': datetime.datetime(2017, 10, 10, 18, 0, 18),
  'start': datetime.datetime(2017, 10, 10, 17, 51, 5)},
 {'end': datetime.datetime(2017, 10, 10, 18, 19, 11),
  'start': datetime.datetime(2017, 10, 10, 18, 8, 12)},
 {'end': datetime.datetime(2017, 10, 10, 19, 14, 32),
  'start': datetime.datetime(2017, 10, 10, 19, 9, 35)},
 {'end': datetime.datetime(2017, 10, 10, 19, 23, 8),
  'start': datetime.datetime(2017, 10, 10, 19, 17, 11)},
 {'end': datetime.datetime(2017, 10, 10, 19, 44, 40),
  'start': datetime.datetime(2017, 10, 10, 19, 28, 11)},
 {'end': datetime.datetime(2017, 10, 10, 20, 11, 54),
  'start': datetime.datetime(2017, 10, 10, 19, 55, 35)},
 {'end': datetime.datetime(2017, 10, 10, 22, 33, 23),
  'start': datetime.datetime(2017, 10, 10, 22, 20, 43)},
 {'end': datetime.datetime(2017, 10, 11, 4, 59, 22),
  'start': datetime.datetime(2017, 10, 11, 4, 40, 52)},
 {'end': datetime.datetime(2017, 10, 11, 6, 40, 13),
  'start': datetime.datetime(2017, 10, 11, 6, 28, 58)},
 {'end': datetime.datetime(2017, 10, 11, 17, 1, 14),
  'start': datetime.datetime(2017, 10, 11, 16, 41, 7)},
 {'end': datetime.datetime(2017, 10, 12, 8, 35, 3),
  'start': datetime.datetime(2017, 10, 12, 8, 8, 30)},
 {'end': datetime.datetime(2017, 10, 12, 8, 59, 50),
  'start': datetime.datetime(2017, 10, 12, 8, 47, 2)},
 {'end': datetime.datetime(2017, 10, 12, 13, 37, 45),
  'start': datetime.datetime(2017, 10, 12, 13, 13, 39)},
 {'end': datetime.datetime(2017, 10, 12, 13, 48, 17),
  'start': datetime.datetime(2017, 10, 12, 13, 40, 12)},
 {'end': datetime.datetime(2017, 10, 12, 13, 53, 16),
  'start': datetime.datetime(2017, 10, 12, 13, 49, 56)},
 {'end': datetime.datetime(2017, 10, 12, 14, 39, 57),
  'start': datetime.datetime(2017, 10, 12, 14, 33, 18)},
 {'end': datetime.datetime(2017, 10, 13, 15, 59, 41),
  'start': datetime.datetime(2017, 10, 13, 15, 55, 39)},
 {'end': datetime.datetime(2017, 10, 17, 18, 1, 38),
  'start': datetime.datetime(2017, 10, 17, 17, 58, 48)},
 {'end': datetime.datetime(2017, 10, 19, 20, 29, 15),
  'start': datetime.datetime(2017, 10, 19, 20, 21, 45)},
 {'end': datetime.datetime(2017, 10, 19, 21, 29, 37),
  'start': datetime.datetime(2017, 10, 19, 21, 11, 39)},
 {'end': datetime.datetime(2017, 10, 19, 21, 47, 23),
  'start': datetime.datetime(2017, 10, 19, 21, 30, 1)},
 {'end': datetime.datetime(2017, 10, 19, 21, 57, 7),
  'start': datetime.datetime(2017, 10, 19, 21, 47, 34)},
 {'end': datetime.datetime(2017, 10, 19, 22, 9, 52),
  'start': datetime.datetime(2017, 10, 19, 21, 57, 24)},
 {'end': datetime.datetime(2017, 10, 21, 12, 36, 24),
  'start': datetime.datetime(2017, 10, 21, 12, 24, 9)},
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 {'end': datetime.datetime(2017, 12, 20, 8, 38, 9),
  'start': datetime.datetime(2017, 12, 20, 8, 33, 32)},
 {'end': datetime.datetime(2017, 12, 20, 13, 54, 39),
  'start': datetime.datetime(2017, 12, 20, 13, 43, 36)},
 {'end': datetime.datetime(2017, 12, 20, 19, 6, 54),
  'start': datetime.datetime(2017, 12, 20, 18, 57, 53)},
 {'end': datetime.datetime(2017, 12, 21, 7, 32, 3),
  'start': datetime.datetime(2017, 12, 21, 7, 21, 11)},
 {'end': datetime.datetime(2017, 12, 21, 8, 6, 15),
  'start': datetime.datetime(2017, 12, 21, 8, 1, 58)},
 {'end': datetime.datetime(2017, 12, 21, 13, 33, 49),
  'start': datetime.datetime(2017, 12, 21, 13, 20, 54)},
 {'end': datetime.datetime(2017, 12, 21, 15, 34, 27),
  'start': datetime.datetime(2017, 12, 21, 15, 26, 8)},
 {'end': datetime.datetime(2017, 12, 21, 18, 38, 50),
  'start': datetime.datetime(2017, 12, 21, 18, 9, 46)},
 {'end': datetime.datetime(2017, 12, 22, 16, 21, 46),
  'start': datetime.datetime(2017, 12, 22, 16, 14, 21)},
 {'end': datetime.datetime(2017, 12, 22, 16, 34, 14),
  'start': datetime.datetime(2017, 12, 22, 16, 29, 17)},
 {'end': datetime.datetime(2017, 12, 25, 13, 18, 27),
  'start': datetime.datetime(2017, 12, 25, 12, 49, 51)},
 {'end': datetime.datetime(2017, 12, 25, 14, 20, 50),
  'start': datetime.datetime(2017, 12, 25, 13, 46, 44)},
 {'end': datetime.datetime(2017, 12, 26, 10, 53, 45),
  'start': datetime.datetime(2017, 12, 26, 10, 40, 16)},
 {'end': datetime.datetime(2017, 12, 27, 17, 17, 39),
  'start': datetime.datetime(2017, 12, 27, 16, 56, 12)},
 {'end': datetime.datetime(2017, 12, 29, 6, 12, 30),
  'start': datetime.datetime(2017, 12, 29, 6, 2, 34)},
 {'end': datetime.datetime(2017, 12, 29, 12, 46, 16),
  'start': datetime.datetime(2017, 12, 29, 12, 21, 3)},
 {'end': datetime.datetime(2017, 12, 29, 14, 43, 46),
  'start': datetime.datetime(2017, 12, 29, 14, 32, 55)},
 {'end': datetime.datetime(2017, 12, 29, 15, 18, 51),
  'start': datetime.datetime(2017, 12, 29, 15, 8, 26)},
 {'end': datetime.datetime(2017, 12, 29, 20, 38, 13),
  'start': datetime.datetime(2017, 12, 29, 20, 33, 34)},
 {'end': datetime.datetime(2017, 12, 30, 13, 54, 33),
  'start': datetime.datetime(2017, 12, 30, 13, 51, 3)},
 {'end': datetime.datetime(2017, 12, 30, 15, 19, 13),
  'start': datetime.datetime(2017, 12, 30, 15, 9, 3)}]

# Initialize a list for all the trip durations
onebike_durations = []

for trip in onebike_datetimes:
  # Create a timedelta object corresponding to the length of the trip
  trip_duration = trip['end'] - trip['start']
  
  # Get the total elapsed seconds in trip_duration
  trip_length_seconds = trip_duration.total_seconds()
  
  # Append the results to our list
  onebike_durations.append(trip_length_seconds)
onebike_durations

[181.0,
 7622.0,
 343.0,
 1278.0,
 1277.0,
 1366.0,
 815.0,
 545.0,
 491.0,
 639.0,
 1678.0,
 406.0,
 709.0,
 514.0,
 492.0,
 1668.0,
 2242.0,
 2752.0,
 735.0,
 330.0,
 518.0,
 1433.0,
 204.0,
 304.0,
 977.0,
 1399.0,
 1244.0,
 658.0,
 800.0,
 1911.0,
 2471.0,
 1344.0,
 435.0,
 271.0,
 920.0,
 851.0,
 209.0,
 453.0,
 841.0,
 142.0,
 1023.0,
 1466.0,
 1636.0,
 3039.0,
 1571.0,
 1410.0,
 386.0,
 1527.0,
 622.0,
 1450.0,
 1422.0,
 991.0,
 1484.0,
 1450.0,
 929.0,
 533.0,
 525.0,
 283.0,
 133.0,
 1106.0,
 952.0,
 553.0,
 659.0,
 297.0,
 357.0,
 989.0,
 979.0,
 760.0,
 1110.0,
 675.0,
 1207.0,
 1593.0,
 768.0,
 1446.0,
 485.0,
 200.0,
 399.0,
 242.0,
 170.0,
 450.0,
 1078.0,
 1042.0,
 573.0,
 748.0,
 735.0,
 336.0,
 76913.0,
 171.0,
 568.0,
 358.0,
 917.0,
 671.0,
 1791.0,
 318.0,
 888.0,
 1284.0,
 11338.0,
 1686.0,
 5579.0,
 8290.0,
 1850.0,
 1810.0,
 870.0,
 436.0,
 429.0,
 494.0,
 1439.0,
 380.0,
 629.0,
 962.0,
 387.0,
 952.0,
 190.0,
 739.0,
 1120.0,
 369.0,
 2275.0,
 873.0,
 1670.0,
 643.0,
 572.0,
 1375.0,
 725.0,
 688.0,
 1041.0,
 1707.0,
 1236.0,
 1291.0,
 2890.0,
 -3346.0,
 1213.0,
 331.0,
 1497.0,
 527.0,
 584.0,
 2599.0,
 759.0,
 1291.0,
 916.0,
 161.0,
 806.0,
 838.0,
 644.0,
 374.0,
 678.0,
 137.0,
 659.0,
 386.0,
 745.0,
 448.0,
 558.0,
 888.0,
 662.0,
 663.0,
 362.0,
 513.0,
 655.0,
 221.0,
 469.0,
 430.0,
 192.0,
 324.0,
 1233.0,
 923.0,
 961.0,
 525.0,
 1017.0,
 1216.0,
 747.0,
 668.0,
 1219.0,
 1182.0,
 10262.0,
 1106.0,
 399.0,
 724.0,
 330.0,
 499.0,
 968.0,
 1310.0,
 2629.0,
 427.0,
 839.0,
 258.0,
 396.0,
 238.0,
 745.0,
 613.0,
 710.0,
 2068.0,
 947.0,
 1509.0,
 254.0,
 625.0,
 479.0,
 688.0,
 238.0,
 322.0,
 304.0,
 576.0,
 1035.0,
 661.0,
 276.0,
 1427.0,
 998.0,
 729.0,
 723.0,
 220.0,
 212.0,
 759.0,
 268.0,
 374.0,
 305.0,
 304.0,
 289.0,
 2620.0,
 1288.0,
 212.0,
 2656.0,
 996.0,
 271.0,
 701.0,
 458.0,
 116.0,
 124.0,
 276.0,
 532.0,
 257.0,
 1089.0,
 195.0,
 384.0,
 511.0,
 850.0,
 462.0,
 322.0,
 998.0,
 327.0,
 153.0,
 152.0,
 230.0,
 321.0,
 625.0,
 391.0,
 1298.0,
 1018.0,
 220.0,
 277.0,
 221.0,
 216.0,
 509.0,
 596.0,
 367.0,
 447.0,
 257.0,
 1049.0,
 1367.0,
 933.0,
 151.0,
 153.0,
 1336.0,
 298.0,
 380.0,
 1385.0,
 225.0,
 181.0,
 581.0,
 250.0,
 332.0,
 845.0,
 277.0,
 663.0,
 541.0,
 652.0,
 257.0,
 775.0,
 499.0,
 1744.0,
 445.0,
 297.0,
 1716.0,
 2046.0,
 809.0,
 1287.0,
 596.0,
 1513.0,
 651.0,
 625.0,
 279.0,
 210.0,
 610.0]

Average trip time

# What was the total duration of all trips?
total_elapsed_time = sum(onebike_durations)

# What was the total number of trips?
number_of_trips = len(onebike_durations)
  
# Divide the total duration by the number of trips
print(total_elapsed_time / number_of_trips)

1178.9310344827586

For the average to be a helpful summary of the data, we need for all of our durations to be reasonable numbers, and not a few that are way too big, way too small, or even malformed. For example, if there is anything fishy happening in the data, and our trip ended before it started, we’d have a negative trip length.

The long and the short of why time is hard

Out of 291 trips taken by W20529, how long was the longest? How short was the shortest? Does anything look fishy?

# Calculate shortest and longest trips
shortest_trip = min(onebike_durations)
longest_trip = max(onebike_durations)

# Print out the results
print("The shortest trip was " + str(shortest_trip) + " seconds")
print("The longest trip was " + str(longest_trip) + " seconds")

The shortest trip was -3346.0 seconds
The longest trip was 76913.0 seconds

For at least one trip, the bike returned before it left. Why could that be? Here’s a hint: it happened in early November, around 2AM local time. What happens to clocks around that time each year?

Counting events before and after noon

We will be working with a list of all bike trips for one Capital Bikeshare bike, W20529, from October 1, 2017 to December 31, 2017. This list has been loaded as onebike_datetimes. Each element of the list is a dictionary with two entries: start is a datetime object corresponding to the start of a trip (when a bike is removed from the dock) and end is a datetime object corresponding to the end of a trip (when a bike is put back into a dock). We can use this data set to understand better how this bike was used.

Did more trips start before noon or after noon?

# Create dictionary to hold results
trip_counts = {'AM': 0, 'PM': 0}
  
# Loop over all trips
for trip in onebike_datetimes:
  # Check to see if the trip starts before noon
  if trip['start'].hour < 12:
    # Increment the counter for before noon
    trip_counts['AM'] += 1
  else:
    # Increment the counter for after noon
    trip_counts['PM'] += 1
  
print(trip_counts)

{'AM': 94, 'PM': 196}

It looks like this bike is used about twice as much after noon than it is before noon. One obvious follow up would be to see which hours the bike is most likely to be taken out for a ride.

Printing and Parsing Datetimes

Turning strings into datetimes

# Starting string, in YYYY-MM-DD HH:MM:SS format
s = '2017-02-03 00:00:01'

# Write a format string to parse s
fmt = '%Y-%m-%d %H:%M:%S'

# Create a datetime object d
d = datetime.datetime.strptime(s, fmt)

# Print d
print(d)

2017-02-03 00:00:01

# Starting string, in YYYY-MM-DD format
s = '2030-10-15'

# Write a format string to parse s
fmt = '%Y-%m-%d'

# Create a datetime object d
d = datetime.datetime.strptime(s, fmt)

# Print d
print(d)

2030-10-15 00:00:00

# Starting string, in MM/DD/YYYY HH:MM:SS format
s = '12/15/1986 08:00:00'

# Write a format string to parse s
fmt = '%m/%d/%Y %H:%M:%S'

# Create a datetime object d
d = datetime.datetime.strptime(s, fmt)

# Print d
print(d)

1986-12-15 08:00:00

Parsing pairs of strings as datetimes

onebike_datetime_strings = [('2017-10-01 15:23:25', '2017-10-01 15:26:26'),
 ('2017-10-01 15:42:57', '2017-10-01 17:49:59'),
 ('2017-10-02 06:37:10', '2017-10-02 06:42:53'),
 ('2017-10-02 08:56:45', '2017-10-02 09:18:03'),
 ('2017-10-02 18:23:48', '2017-10-02 18:45:05'),
 ('2017-10-02 18:48:08', '2017-10-02 19:10:54'),
 ('2017-10-02 19:18:10', '2017-10-02 19:31:45'),
 ('2017-10-02 19:37:32', '2017-10-02 19:46:37'),
 ('2017-10-03 08:24:16', '2017-10-03 08:32:27'),
 ('2017-10-03 18:17:07', '2017-10-03 18:27:46'),
 ('2017-10-03 19:24:10', '2017-10-03 19:52:08'),
 ('2017-10-03 20:17:06', '2017-10-03 20:23:52'),
 ('2017-10-03 20:45:21', '2017-10-03 20:57:10'),
 ('2017-10-04 07:04:57', '2017-10-04 07:13:31'),
 ('2017-10-04 07:13:42', '2017-10-04 07:21:54'),
 ('2017-10-04 14:22:12', '2017-10-04 14:50:00'),
 ('2017-10-04 15:07:27', '2017-10-04 15:44:49'),
 ('2017-10-04 15:46:41', '2017-10-04 16:32:33'),
 ('2017-10-04 16:34:44', '2017-10-04 16:46:59'),
 ('2017-10-04 17:26:06', '2017-10-04 17:31:36'),
 ('2017-10-04 17:42:03', '2017-10-04 17:50:41'),
 ('2017-10-05 07:49:02', '2017-10-05 08:12:55'),
 ('2017-10-05 08:26:21', '2017-10-05 08:29:45'),
 ('2017-10-05 08:33:27', '2017-10-05 08:38:31'),
 ('2017-10-05 16:35:35', '2017-10-05 16:51:52'),
 ('2017-10-05 17:53:31', '2017-10-05 18:16:50'),
 ('2017-10-06 08:17:17', '2017-10-06 08:38:01'),
 ('2017-10-06 11:39:40', '2017-10-06 11:50:38'),
 ('2017-10-06 12:59:54', '2017-10-06 13:13:14'),
 ('2017-10-06 13:43:05', '2017-10-06 14:14:56'),
 ('2017-10-06 14:28:15', '2017-10-06 15:09:26'),
 ('2017-10-06 15:50:10', '2017-10-06 16:12:34'),
 ('2017-10-06 16:32:16', '2017-10-06 16:39:31'),
 ('2017-10-06 16:44:08', '2017-10-06 16:48:39'),
 ('2017-10-06 16:53:43', '2017-10-06 17:09:03'),
 ('2017-10-07 11:38:55', '2017-10-07 11:53:06'),
 ('2017-10-07 14:03:36', '2017-10-07 14:07:05'),
 ('2017-10-07 14:20:03', '2017-10-07 14:27:36'),
 ('2017-10-07 14:30:50', '2017-10-07 14:44:51'),
 ('2017-10-08 00:28:26', '2017-10-08 00:30:48'),
 ('2017-10-08 11:16:21', '2017-10-08 11:33:24'),
 ('2017-10-08 12:37:03', '2017-10-08 13:01:29'),
 ('2017-10-08 13:30:37', '2017-10-08 13:57:53'),
 ('2017-10-08 14:16:40', '2017-10-08 15:07:19'),
 ('2017-10-08 15:23:50', '2017-10-08 15:50:01'),
 ('2017-10-08 15:54:12', '2017-10-08 16:17:42'),
 ('2017-10-08 16:28:52', '2017-10-08 16:35:18'),
 ('2017-10-08 23:08:14', '2017-10-08 23:33:41'),
 ('2017-10-08 23:34:49', '2017-10-08 23:45:11'),
 ('2017-10-08 23:46:47', '2017-10-09 00:10:57'),
 ('2017-10-09 00:12:58', '2017-10-09 00:36:40'),
 ('2017-10-09 00:37:02', '2017-10-09 00:53:33'),
 ('2017-10-09 01:23:29', '2017-10-09 01:48:13'),
 ('2017-10-09 01:49:25', '2017-10-09 02:13:35'),
 ('2017-10-09 02:14:11', '2017-10-09 02:29:40'),
 ('2017-10-09 13:04:32', '2017-10-09 13:13:25'),
 ('2017-10-09 14:30:10', '2017-10-09 14:38:55'),
 ('2017-10-09 15:06:47', '2017-10-09 15:11:30'),
 ('2017-10-09 16:43:25', '2017-10-09 16:45:38'),
 ('2017-10-10 15:32:58', '2017-10-10 15:51:24'),
 ('2017-10-10 16:47:55', '2017-10-10 17:03:47'),
 ('2017-10-10 17:51:05', '2017-10-10 18:00:18'),
 ('2017-10-10 18:08:12', '2017-10-10 18:19:11'),
 ('2017-10-10 19:09:35', '2017-10-10 19:14:32'),
 ('2017-10-10 19:17:11', '2017-10-10 19:23:08'),
 ('2017-10-10 19:28:11', '2017-10-10 19:44:40'),
 ('2017-10-10 19:55:35', '2017-10-10 20:11:54'),
 ('2017-10-10 22:20:43', '2017-10-10 22:33:23'),
 ('2017-10-11 04:40:52', '2017-10-11 04:59:22'),
 ('2017-10-11 06:28:58', '2017-10-11 06:40:13'),
 ('2017-10-11 16:41:07', '2017-10-11 17:01:14'),
 ('2017-10-12 08:08:30', '2017-10-12 08:35:03'),
 ('2017-10-12 08:47:02', '2017-10-12 08:59:50'),
 ('2017-10-12 13:13:39', '2017-10-12 13:37:45'),
 ('2017-10-12 13:40:12', '2017-10-12 13:48:17'),
 ('2017-10-12 13:49:56', '2017-10-12 13:53:16'),
 ('2017-10-12 14:33:18', '2017-10-12 14:39:57'),
 ('2017-10-13 15:55:39', '2017-10-13 15:59:41'),
 ('2017-10-17 17:58:48', '2017-10-17 18:01:38'),
 ('2017-10-19 20:21:45', '2017-10-19 20:29:15'),
 ('2017-10-19 21:11:39', '2017-10-19 21:29:37'),
 ('2017-10-19 21:30:01', '2017-10-19 21:47:23'),
 ('2017-10-19 21:47:34', '2017-10-19 21:57:07'),
 ('2017-10-19 21:57:24', '2017-10-19 22:09:52'),
 ('2017-10-21 12:24:09', '2017-10-21 12:36:24'),
 ('2017-10-21 12:36:37', '2017-10-21 12:42:13'),
 ('2017-10-21 13:47:43', '2017-10-22 11:09:36'),
 ('2017-10-22 13:28:53', '2017-10-22 13:31:44'),
 ('2017-10-22 13:47:05', '2017-10-22 13:56:33'),
 ('2017-10-22 14:26:41', '2017-10-22 14:32:39'),
 ('2017-10-22 14:54:41', '2017-10-22 15:09:58'),
 ('2017-10-22 16:40:29', '2017-10-22 16:51:40'),
 ('2017-10-22 17:58:46', '2017-10-22 18:28:37'),
 ('2017-10-22 18:45:16', '2017-10-22 18:50:34'),
 ('2017-10-22 18:56:22', '2017-10-22 19:11:10'),
 ('2017-10-23 10:14:08', '2017-10-23 10:35:32'),
 ('2017-10-23 11:29:36', '2017-10-23 14:38:34'),
 ('2017-10-23 15:04:52', '2017-10-23 15:32:58'),
 ('2017-10-23 15:33:48', '2017-10-23 17:06:47'),
 ('2017-10-23 17:13:16', '2017-10-23 19:31:26'),
 ('2017-10-23 19:55:03', '2017-10-23 20:25:53'),
 ('2017-10-23 21:47:54', '2017-10-23 22:18:04'),
 ('2017-10-23 22:34:12', '2017-10-23 22:48:42'),
 ('2017-10-24 06:55:01', '2017-10-24 07:02:17'),
 ('2017-10-24 14:56:07', '2017-10-24 15:03:16'),
 ('2017-10-24 15:51:36', '2017-10-24 15:59:50'),
 ('2017-10-24 16:31:10', '2017-10-24 16:55:09'),
 ('2017-10-28 14:26:14', '2017-10-28 14:32:34'),
 ('2017-11-01 09:41:54', '2017-11-01 09:52:23'),
 ('2017-11-01 20:16:11', '2017-11-01 20:32:13'),
 ('2017-11-02 19:44:29', '2017-11-02 19:50:56'),
 ('2017-11-02 20:14:37', '2017-11-02 20:30:29'),
 ('2017-11-02 21:35:47', '2017-11-02 21:38:57'),
 ('2017-11-03 09:59:27', '2017-11-03 10:11:46'),
 ('2017-11-03 10:13:22', '2017-11-03 10:32:02'),
 ('2017-11-03 10:44:25', '2017-11-03 10:50:34'),
 ('2017-11-03 16:06:43', '2017-11-03 16:44:38'),
 ('2017-11-03 16:45:54', '2017-11-03 17:00:27'),
 ('2017-11-03 17:07:15', '2017-11-03 17:35:05'),
 ('2017-11-03 17:36:05', '2017-11-03 17:46:48'),
 ('2017-11-03 17:50:31', '2017-11-03 18:00:03'),
 ('2017-11-03 19:22:56', '2017-11-03 19:45:51'),
 ('2017-11-04 13:14:10', '2017-11-04 13:26:15'),
 ('2017-11-04 14:18:37', '2017-11-04 14:30:05'),
 ('2017-11-04 14:45:59', '2017-11-04 15:03:20'),
 ('2017-11-04 15:16:03', '2017-11-04 15:44:30'),
 ('2017-11-04 16:37:46', '2017-11-04 16:58:22'),
 ('2017-11-04 17:13:19', '2017-11-04 17:34:50'),
 ('2017-11-04 18:10:34', '2017-11-04 18:58:44'),
 ('2017-11-05 01:56:50', '2017-11-05 01:01:04'),
 ('2017-11-05 08:33:33', '2017-11-05 08:53:46'),
 ('2017-11-05 08:58:08', '2017-11-05 09:03:39'),
 ('2017-11-05 11:05:08', '2017-11-05 11:30:05'),
 ('2017-11-06 08:50:18', '2017-11-06 08:59:05'),
 ('2017-11-06 09:04:03', '2017-11-06 09:13:47'),
 ('2017-11-06 16:19:36', '2017-11-06 17:02:55'),
 ('2017-11-06 17:21:27', '2017-11-06 17:34:06'),
 ('2017-11-06 17:36:01', '2017-11-06 17:57:32'),
 ('2017-11-06 17:59:52', '2017-11-06 18:15:08'),
 ('2017-11-06 18:18:36', '2017-11-06 18:21:17'),
 ('2017-11-06 19:24:31', '2017-11-06 19:37:57'),
 ('2017-11-06 19:49:16', '2017-11-06 20:03:14'),
 ('2017-11-07 07:50:48', '2017-11-07 08:01:32'),
 ('2017-11-08 13:11:51', '2017-11-08 13:18:05'),
 ('2017-11-08 21:34:47', '2017-11-08 21:46:05'),
 ('2017-11-08 22:02:30', '2017-11-08 22:04:47'),
 ('2017-11-09 07:01:11', '2017-11-09 07:12:10'),
 ('2017-11-09 08:02:02', '2017-11-09 08:08:28'),
 ('2017-11-09 08:19:59', '2017-11-09 08:32:24'),
 ('2017-11-09 08:41:31', '2017-11-09 08:48:59'),
 ('2017-11-09 09:00:06', '2017-11-09 09:09:24'),
 ('2017-11-09 09:09:37', '2017-11-09 09:24:25'),
 ('2017-11-09 13:14:37', '2017-11-09 13:25:39'),
 ('2017-11-09 15:20:07', '2017-11-09 15:31:10'),
 ('2017-11-09 18:47:08', '2017-11-09 18:53:10'),
 ('2017-11-09 23:35:02', '2017-11-09 23:43:35'),
 ('2017-11-10 07:51:33', '2017-11-10 08:02:28'),
 ('2017-11-10 08:38:28', '2017-11-10 08:42:09'),
 ('2017-11-11 18:05:25', '2017-11-11 18:13:14'),
 ('2017-11-11 19:39:12', '2017-11-11 19:46:22'),
 ('2017-11-11 21:13:19', '2017-11-11 21:16:31'),
 ('2017-11-12 09:46:19', '2017-11-12 09:51:43'),
 ('2017-11-13 13:33:42', '2017-11-13 13:54:15'),
 ('2017-11-14 08:40:29', '2017-11-14 08:55:52'),
 ('2017-11-15 06:14:05', '2017-11-15 06:30:06'),
 ('2017-11-15 08:14:59', '2017-11-15 08:23:44'),
 ('2017-11-15 10:16:44', '2017-11-15 10:33:41'),
 ('2017-11-15 10:33:58', '2017-11-15 10:54:14'),
 ('2017-11-15 11:02:15', '2017-11-15 11:14:42'),
 ('2017-11-16 09:27:41', '2017-11-16 09:38:49'),
 ('2017-11-16 09:57:41', '2017-11-16 10:18:00'),
 ('2017-11-16 17:25:05', '2017-11-16 17:44:47'),
 ('2017-11-17 13:45:54', '2017-11-17 16:36:56'),
 ('2017-11-17 19:12:49', '2017-11-17 19:31:15'),
 ('2017-11-18 10:49:06', '2017-11-18 10:55:45'),
 ('2017-11-18 11:32:12', '2017-11-18 11:44:16'),
 ('2017-11-18 18:09:01', '2017-11-18 18:14:31'),
 ('2017-11-18 18:53:10', '2017-11-18 19:01:29'),
 ('2017-11-19 14:15:41', '2017-11-19 14:31:49'),
 ('2017-11-20 21:19:19', '2017-11-20 21:41:09'),
 ('2017-11-20 22:39:48', '2017-11-20 23:23:37'),
 ('2017-11-21 17:44:25', '2017-11-21 17:51:32'),
 ('2017-11-21 18:20:52', '2017-11-21 18:34:51'),
 ('2017-11-21 18:47:32', '2017-11-21 18:51:50'),
 ('2017-11-21 19:07:57', '2017-11-21 19:14:33'),
 ('2017-11-21 20:04:56', '2017-11-21 20:08:54'),
 ('2017-11-21 21:55:47', '2017-11-21 22:08:12'),
 ('2017-11-23 23:47:43', '2017-11-23 23:57:56'),
 ('2017-11-24 06:41:25', '2017-11-24 06:53:15'),
 ('2017-11-24 06:58:56', '2017-11-24 07:33:24'),
 ('2017-11-26 12:25:49', '2017-11-26 12:41:36'),
 ('2017-11-27 05:29:04', '2017-11-27 05:54:13'),
 ('2017-11-27 06:06:47', '2017-11-27 06:11:01'),
 ('2017-11-27 06:45:14', '2017-11-27 06:55:39'),
 ('2017-11-27 09:39:44', '2017-11-27 09:47:43'),
 ('2017-11-27 11:09:18', '2017-11-27 11:20:46'),
 ('2017-11-27 11:31:46', '2017-11-27 11:35:44'),
 ('2017-11-27 12:07:14', '2017-11-27 12:12:36'),
 ('2017-11-27 12:21:40', '2017-11-27 12:26:44'),
 ('2017-11-27 17:26:31', '2017-11-27 17:36:07'),
 ('2017-11-27 18:11:49', '2017-11-27 18:29:04'),
 ('2017-11-27 19:36:16', '2017-11-27 19:47:17'),
 ('2017-11-27 20:12:57', '2017-11-27 20:17:33'),
 ('2017-11-28 08:18:06', '2017-11-28 08:41:53'),
 ('2017-11-28 19:17:23', '2017-11-28 19:34:01'),
 ('2017-11-28 19:34:15', '2017-11-28 19:46:24'),
 ('2017-11-28 21:27:29', '2017-11-28 21:39:32'),
 ('2017-11-29 07:47:38', '2017-11-29 07:51:18'),
 ('2017-11-29 09:50:12', '2017-11-29 09:53:44'),
 ('2017-11-29 17:03:42', '2017-11-29 17:16:21'),
 ('2017-11-29 18:19:15', '2017-11-29 18:23:43'),
 ('2017-12-01 17:03:58', '2017-12-01 17:10:12'),
 ('2017-12-02 07:55:56', '2017-12-02 08:01:01'),
 ('2017-12-02 09:16:14', '2017-12-02 09:21:18'),
 ('2017-12-02 19:48:29', '2017-12-02 19:53:18'),
 ('2017-12-03 14:36:29', '2017-12-03 15:20:09'),
 ('2017-12-03 16:04:02', '2017-12-03 16:25:30'),
 ('2017-12-03 16:40:26', '2017-12-03 16:43:58'),
 ('2017-12-03 17:20:17', '2017-12-03 18:04:33'),
 ('2017-12-04 08:34:24', '2017-12-04 08:51:00'),
 ('2017-12-04 17:49:26', '2017-12-04 17:53:57'),
 ('2017-12-04 18:38:52', '2017-12-04 18:50:33'),
 ('2017-12-04 21:39:20', '2017-12-04 21:46:58'),
 ('2017-12-04 21:54:21', '2017-12-04 21:56:17'),
 ('2017-12-05 08:50:50', '2017-12-05 08:52:54'),
 ('2017-12-06 08:19:38', '2017-12-06 08:24:14'),
 ('2017-12-06 18:19:19', '2017-12-06 18:28:11'),
 ('2017-12-06 18:28:55', '2017-12-06 18:33:12'),
 ('2017-12-06 20:03:29', '2017-12-06 20:21:38'),
 ('2017-12-06 20:36:42', '2017-12-06 20:39:57'),
 ('2017-12-07 05:54:51', '2017-12-07 06:01:15'),
 ('2017-12-08 16:47:18', '2017-12-08 16:55:49'),
 ('2017-12-08 19:15:02', '2017-12-08 19:29:12'),
 ('2017-12-09 22:39:37', '2017-12-09 22:47:19'),
 ('2017-12-09 23:00:10', '2017-12-09 23:05:32'),
 ('2017-12-10 00:39:24', '2017-12-10 00:56:02'),
 ('2017-12-10 01:02:42', '2017-12-10 01:08:09'),
 ('2017-12-10 01:08:57', '2017-12-10 01:11:30'),
 ('2017-12-10 13:49:09', '2017-12-10 13:51:41'),
 ('2017-12-10 15:14:29', '2017-12-10 15:18:19'),
 ('2017-12-10 15:31:07', '2017-12-10 15:36:28'),
 ('2017-12-10 16:20:06', '2017-12-10 16:30:31'),
 ('2017-12-10 17:07:54', '2017-12-10 17:14:25'),
 ('2017-12-10 17:23:47', '2017-12-10 17:45:25'),
 ('2017-12-11 06:17:06', '2017-12-11 06:34:04'),
 ('2017-12-11 09:08:41', '2017-12-11 09:12:21'),
 ('2017-12-11 09:15:41', '2017-12-11 09:20:18'),
 ('2017-12-12 08:55:53', '2017-12-12 08:59:34'),
 ('2017-12-13 17:14:56', '2017-12-13 17:18:32'),
 ('2017-12-13 18:52:16', '2017-12-13 19:00:45'),
 ('2017-12-14 09:01:10', '2017-12-14 09:11:06'),
 ('2017-12-14 09:12:59', '2017-12-14 09:19:06'),
 ('2017-12-14 11:54:33', '2017-12-14 12:02:00'),
 ('2017-12-14 14:40:23', '2017-12-14 14:44:40'),
 ('2017-12-14 15:08:55', '2017-12-14 15:26:24'),
 ('2017-12-14 17:46:17', '2017-12-14 18:09:04'),
 ('2017-12-15 09:08:12', '2017-12-15 09:23:45'),
 ('2017-12-16 09:33:46', '2017-12-16 09:36:17'),
 ('2017-12-16 11:02:31', '2017-12-16 11:05:04'),
 ('2017-12-17 10:09:47', '2017-12-17 10:32:03'),
 ('2017-12-18 08:02:36', '2017-12-18 08:07:34'),
 ('2017-12-18 16:03:00', '2017-12-18 16:09:20'),
 ('2017-12-18 16:30:07', '2017-12-18 16:53:12'),
 ('2017-12-18 19:18:23', '2017-12-18 19:22:08'),
 ('2017-12-18 20:14:46', '2017-12-18 20:17:47'),
 ('2017-12-19 19:14:08', '2017-12-19 19:23:49'),
 ('2017-12-19 19:39:36', '2017-12-19 19:43:46'),
 ('2017-12-20 08:05:14', '2017-12-20 08:10:46'),
 ('2017-12-20 08:15:45', '2017-12-20 08:29:50'),
 ('2017-12-20 08:33:32', '2017-12-20 08:38:09'),
 ('2017-12-20 13:43:36', '2017-12-20 13:54:39'),
 ('2017-12-20 18:57:53', '2017-12-20 19:06:54'),
 ('2017-12-21 07:21:11', '2017-12-21 07:32:03'),
 ('2017-12-21 08:01:58', '2017-12-21 08:06:15'),
 ('2017-12-21 13:20:54', '2017-12-21 13:33:49'),
 ('2017-12-21 15:26:08', '2017-12-21 15:34:27'),
 ('2017-12-21 18:09:46', '2017-12-21 18:38:50'),
 ('2017-12-22 16:14:21', '2017-12-22 16:21:46'),
 ('2017-12-22 16:29:17', '2017-12-22 16:34:14'),
 ('2017-12-25 12:49:51', '2017-12-25 13:18:27'),
 ('2017-12-25 13:46:44', '2017-12-25 14:20:50'),
 ('2017-12-26 10:40:16', '2017-12-26 10:53:45'),
 ('2017-12-27 16:56:12', '2017-12-27 17:17:39'),
 ('2017-12-29 06:02:34', '2017-12-29 06:12:30'),
 ('2017-12-29 12:21:03', '2017-12-29 12:46:16'),
 ('2017-12-29 14:32:55', '2017-12-29 14:43:46'),
 ('2017-12-29 15:08:26', '2017-12-29 15:18:51'),
 ('2017-12-29 20:33:34', '2017-12-29 20:38:13'),
 ('2017-12-30 13:51:03', '2017-12-30 13:54:33'),
 ('2017-12-30 15:09:03', '2017-12-30 15:19:13')]

# Write down the format string
fmt = "%Y-%m-%d %H:%M:%S"

# Initialize a list for holding the pairs of datetime objects
onebike_datetimes = []

# Loop over all trips
for (start, end) in onebike_datetime_strings:
  trip = {'start': datetime.datetime.strptime(start, fmt),
          'end': datetime.datetime.strptime(end, fmt)}
  
  # Append the trip
  onebike_datetimes.append(trip)
onebike_datetimes

[{'start': datetime.datetime(2017, 10, 1, 15, 23, 25),
  'end': datetime.datetime(2017, 10, 1, 15, 26, 26)},
 {'start': datetime.datetime(2017, 10, 1, 15, 42, 57),
  'end': datetime.datetime(2017, 10, 1, 17, 49, 59)},
 {'start': datetime.datetime(2017, 10, 2, 6, 37, 10),
  'end': datetime.datetime(2017, 10, 2, 6, 42, 53)},
 {'start': datetime.datetime(2017, 10, 2, 8, 56, 45),
  'end': datetime.datetime(2017, 10, 2, 9, 18, 3)},
 {'start': datetime.datetime(2017, 10, 2, 18, 23, 48),
  'end': datetime.datetime(2017, 10, 2, 18, 45, 5)},
 {'start': datetime.datetime(2017, 10, 2, 18, 48, 8),
  'end': datetime.datetime(2017, 10, 2, 19, 10, 54)},
 {'start': datetime.datetime(2017, 10, 2, 19, 18, 10),
  'end': datetime.datetime(2017, 10, 2, 19, 31, 45)},
 {'start': datetime.datetime(2017, 10, 2, 19, 37, 32),
  'end': datetime.datetime(2017, 10, 2, 19, 46, 37)},
 {'start': datetime.datetime(2017, 10, 3, 8, 24, 16),
  'end': datetime.datetime(2017, 10, 3, 8, 32, 27)},
 {'start': datetime.datetime(2017, 10, 3, 18, 17, 7),
  'end': datetime.datetime(2017, 10, 3, 18, 27, 46)},
 {'start': datetime.datetime(2017, 10, 3, 19, 24, 10),
  'end': datetime.datetime(2017, 10, 3, 19, 52, 8)},
 {'start': datetime.datetime(2017, 10, 3, 20, 17, 6),
  'end': datetime.datetime(2017, 10, 3, 20, 23, 52)},
 {'start': datetime.datetime(2017, 10, 3, 20, 45, 21),
  'end': datetime.datetime(2017, 10, 3, 20, 57, 10)},
 {'start': datetime.datetime(2017, 10, 4, 7, 4, 57),
  'end': datetime.datetime(2017, 10, 4, 7, 13, 31)},
 {'start': datetime.datetime(2017, 10, 4, 7, 13, 42),
  'end': datetime.datetime(2017, 10, 4, 7, 21, 54)},
 {'start': datetime.datetime(2017, 10, 4, 14, 22, 12),
  'end': datetime.datetime(2017, 10, 4, 14, 50)},
 {'start': datetime.datetime(2017, 10, 4, 15, 7, 27),
  'end': datetime.datetime(2017, 10, 4, 15, 44, 49)},
 {'start': datetime.datetime(2017, 10, 4, 15, 46, 41),
  'end': datetime.datetime(2017, 10, 4, 16, 32, 33)},
 {'start': datetime.datetime(2017, 10, 4, 16, 34, 44),
  'end': datetime.datetime(2017, 10, 4, 16, 46, 59)},
 {'start': datetime.datetime(2017, 10, 4, 17, 26, 6),
  'end': datetime.datetime(2017, 10, 4, 17, 31, 36)},
 {'start': datetime.datetime(2017, 10, 4, 17, 42, 3),
  'end': datetime.datetime(2017, 10, 4, 17, 50, 41)},
 {'start': datetime.datetime(2017, 10, 5, 7, 49, 2),
  'end': datetime.datetime(2017, 10, 5, 8, 12, 55)},
 {'start': datetime.datetime(2017, 10, 5, 8, 26, 21),
  'end': datetime.datetime(2017, 10, 5, 8, 29, 45)},
 {'start': datetime.datetime(2017, 10, 5, 8, 33, 27),
  'end': datetime.datetime(2017, 10, 5, 8, 38, 31)},
 {'start': datetime.datetime(2017, 10, 5, 16, 35, 35),
  'end': datetime.datetime(2017, 10, 5, 16, 51, 52)},
 {'start': datetime.datetime(2017, 10, 5, 17, 53, 31),
  'end': datetime.datetime(2017, 10, 5, 18, 16, 50)},
 {'start': datetime.datetime(2017, 10, 6, 8, 17, 17),
  'end': datetime.datetime(2017, 10, 6, 8, 38, 1)},
 {'start': datetime.datetime(2017, 10, 6, 11, 39, 40),
  'end': datetime.datetime(2017, 10, 6, 11, 50, 38)},
 {'start': datetime.datetime(2017, 10, 6, 12, 59, 54),
  'end': datetime.datetime(2017, 10, 6, 13, 13, 14)},
 {'start': datetime.datetime(2017, 10, 6, 13, 43, 5),
  'end': datetime.datetime(2017, 10, 6, 14, 14, 56)},
 {'start': datetime.datetime(2017, 10, 6, 14, 28, 15),
  'end': datetime.datetime(2017, 10, 6, 15, 9, 26)},
 {'start': datetime.datetime(2017, 10, 6, 15, 50, 10),
  'end': datetime.datetime(2017, 10, 6, 16, 12, 34)},
 {'start': datetime.datetime(2017, 10, 6, 16, 32, 16),
  'end': datetime.datetime(2017, 10, 6, 16, 39, 31)},
 {'start': datetime.datetime(2017, 10, 6, 16, 44, 8),
  'end': datetime.datetime(2017, 10, 6, 16, 48, 39)},
 {'start': datetime.datetime(2017, 10, 6, 16, 53, 43),
  'end': datetime.datetime(2017, 10, 6, 17, 9, 3)},
 {'start': datetime.datetime(2017, 10, 7, 11, 38, 55),
  'end': datetime.datetime(2017, 10, 7, 11, 53, 6)},
 {'start': datetime.datetime(2017, 10, 7, 14, 3, 36),
  'end': datetime.datetime(2017, 10, 7, 14, 7, 5)},
 {'start': datetime.datetime(2017, 10, 7, 14, 20, 3),
  'end': datetime.datetime(2017, 10, 7, 14, 27, 36)},
 {'start': datetime.datetime(2017, 10, 7, 14, 30, 50),
  'end': datetime.datetime(2017, 10, 7, 14, 44, 51)},
 {'start': datetime.datetime(2017, 10, 8, 0, 28, 26),
  'end': datetime.datetime(2017, 10, 8, 0, 30, 48)},
 {'start': datetime.datetime(2017, 10, 8, 11, 16, 21),
  'end': datetime.datetime(2017, 10, 8, 11, 33, 24)},
 {'start': datetime.datetime(2017, 10, 8, 12, 37, 3),
  'end': datetime.datetime(2017, 10, 8, 13, 1, 29)},
 {'start': datetime.datetime(2017, 10, 8, 13, 30, 37),
  'end': datetime.datetime(2017, 10, 8, 13, 57, 53)},
 {'start': datetime.datetime(2017, 10, 8, 14, 16, 40),
  'end': datetime.datetime(2017, 10, 8, 15, 7, 19)},
 {'start': datetime.datetime(2017, 10, 8, 15, 23, 50),
  'end': datetime.datetime(2017, 10, 8, 15, 50, 1)},
 {'start': datetime.datetime(2017, 10, 8, 15, 54, 12),
  'end': datetime.datetime(2017, 10, 8, 16, 17, 42)},
 {'start': datetime.datetime(2017, 10, 8, 16, 28, 52),
  'end': datetime.datetime(2017, 10, 8, 16, 35, 18)},
 {'start': datetime.datetime(2017, 10, 8, 23, 8, 14),
  'end': datetime.datetime(2017, 10, 8, 23, 33, 41)},
 {'start': datetime.datetime(2017, 10, 8, 23, 34, 49),
  'end': datetime.datetime(2017, 10, 8, 23, 45, 11)},
 {'start': datetime.datetime(2017, 10, 8, 23, 46, 47),
  'end': datetime.datetime(2017, 10, 9, 0, 10, 57)},
 {'start': datetime.datetime(2017, 10, 9, 0, 12, 58),
  'end': datetime.datetime(2017, 10, 9, 0, 36, 40)},
 {'start': datetime.datetime(2017, 10, 9, 0, 37, 2),
  'end': datetime.datetime(2017, 10, 9, 0, 53, 33)},
 {'start': datetime.datetime(2017, 10, 9, 1, 23, 29),
  'end': datetime.datetime(2017, 10, 9, 1, 48, 13)},
 {'start': datetime.datetime(2017, 10, 9, 1, 49, 25),
  'end': datetime.datetime(2017, 10, 9, 2, 13, 35)},
 {'start': datetime.datetime(2017, 10, 9, 2, 14, 11),
  'end': datetime.datetime(2017, 10, 9, 2, 29, 40)},
 {'start': datetime.datetime(2017, 10, 9, 13, 4, 32),
  'end': datetime.datetime(2017, 10, 9, 13, 13, 25)},
 {'start': datetime.datetime(2017, 10, 9, 14, 30, 10),
  'end': datetime.datetime(2017, 10, 9, 14, 38, 55)},
 {'start': datetime.datetime(2017, 10, 9, 15, 6, 47),
  'end': datetime.datetime(2017, 10, 9, 15, 11, 30)},
 {'start': datetime.datetime(2017, 10, 9, 16, 43, 25),
  'end': datetime.datetime(2017, 10, 9, 16, 45, 38)},
 {'start': datetime.datetime(2017, 10, 10, 15, 32, 58),
  'end': datetime.datetime(2017, 10, 10, 15, 51, 24)},
 {'start': datetime.datetime(2017, 10, 10, 16, 47, 55),
  'end': datetime.datetime(2017, 10, 10, 17, 3, 47)},
 {'start': datetime.datetime(2017, 10, 10, 17, 51, 5),
  'end': datetime.datetime(2017, 10, 10, 18, 0, 18)},
 {'start': datetime.datetime(2017, 10, 10, 18, 8, 12),
  'end': datetime.datetime(2017, 10, 10, 18, 19, 11)},
 {'start': datetime.datetime(2017, 10, 10, 19, 9, 35),
  'end': datetime.datetime(2017, 10, 10, 19, 14, 32)},
 {'start': datetime.datetime(2017, 10, 10, 19, 17, 11),
  'end': datetime.datetime(2017, 10, 10, 19, 23, 8)},
 {'start': datetime.datetime(2017, 10, 10, 19, 28, 11),
  'end': datetime.datetime(2017, 10, 10, 19, 44, 40)},
 {'start': datetime.datetime(2017, 10, 10, 19, 55, 35),
  'end': datetime.datetime(2017, 10, 10, 20, 11, 54)},
 {'start': datetime.datetime(2017, 10, 10, 22, 20, 43),
  'end': datetime.datetime(2017, 10, 10, 22, 33, 23)},
 {'start': datetime.datetime(2017, 10, 11, 4, 40, 52),
  'end': datetime.datetime(2017, 10, 11, 4, 59, 22)},
 {'start': datetime.datetime(2017, 10, 11, 6, 28, 58),
  'end': datetime.datetime(2017, 10, 11, 6, 40, 13)},
 {'start': datetime.datetime(2017, 10, 11, 16, 41, 7),
  'end': datetime.datetime(2017, 10, 11, 17, 1, 14)},
 {'start': datetime.datetime(2017, 10, 12, 8, 8, 30),
  'end': datetime.datetime(2017, 10, 12, 8, 35, 3)},
 {'start': datetime.datetime(2017, 10, 12, 8, 47, 2),
  'end': datetime.datetime(2017, 10, 12, 8, 59, 50)},
 {'start': datetime.datetime(2017, 10, 12, 13, 13, 39),
  'end': datetime.datetime(2017, 10, 12, 13, 37, 45)},
 {'start': datetime.datetime(2017, 10, 12, 13, 40, 12),
  'end': datetime.datetime(2017, 10, 12, 13, 48, 17)},
 {'start': datetime.datetime(2017, 10, 12, 13, 49, 56),
  'end': datetime.datetime(2017, 10, 12, 13, 53, 16)},
 {'start': datetime.datetime(2017, 10, 12, 14, 33, 18),
  'end': datetime.datetime(2017, 10, 12, 14, 39, 57)},
 {'start': datetime.datetime(2017, 10, 13, 15, 55, 39),
  'end': datetime.datetime(2017, 10, 13, 15, 59, 41)},
 {'start': datetime.datetime(2017, 10, 17, 17, 58, 48),
  'end': datetime.datetime(2017, 10, 17, 18, 1, 38)},
 {'start': datetime.datetime(2017, 10, 19, 20, 21, 45),
  'end': datetime.datetime(2017, 10, 19, 20, 29, 15)},
 {'start': datetime.datetime(2017, 10, 19, 21, 11, 39),
  'end': datetime.datetime(2017, 10, 19, 21, 29, 37)},
 {'start': datetime.datetime(2017, 10, 19, 21, 30, 1),
  'end': datetime.datetime(2017, 10, 19, 21, 47, 23)},
 {'start': datetime.datetime(2017, 10, 19, 21, 47, 34),
  'end': datetime.datetime(2017, 10, 19, 21, 57, 7)},
 {'start': datetime.datetime(2017, 10, 19, 21, 57, 24),
  'end': datetime.datetime(2017, 10, 19, 22, 9, 52)},
 {'start': datetime.datetime(2017, 10, 21, 12, 24, 9),
  'end': datetime.datetime(2017, 10, 21, 12, 36, 24)},
 {'start': datetime.datetime(2017, 10, 21, 12, 36, 37),
  'end': datetime.datetime(2017, 10, 21, 12, 42, 13)},
 {'start': datetime.datetime(2017, 10, 21, 13, 47, 43),
  'end': datetime.datetime(2017, 10, 22, 11, 9, 36)},
 {'start': datetime.datetime(2017, 10, 22, 13, 28, 53),
  'end': datetime.datetime(2017, 10, 22, 13, 31, 44)},
 {'start': datetime.datetime(2017, 10, 22, 13, 47, 5),
  'end': datetime.datetime(2017, 10, 22, 13, 56, 33)},
 {'start': datetime.datetime(2017, 10, 22, 14, 26, 41),
  'end': datetime.datetime(2017, 10, 22, 14, 32, 39)},
 {'start': datetime.datetime(2017, 10, 22, 14, 54, 41),
  'end': datetime.datetime(2017, 10, 22, 15, 9, 58)},
 {'start': datetime.datetime(2017, 10, 22, 16, 40, 29),
  'end': datetime.datetime(2017, 10, 22, 16, 51, 40)},
 {'start': datetime.datetime(2017, 10, 22, 17, 58, 46),
  'end': datetime.datetime(2017, 10, 22, 18, 28, 37)},
 {'start': datetime.datetime(2017, 10, 22, 18, 45, 16),
  'end': datetime.datetime(2017, 10, 22, 18, 50, 34)},
 {'start': datetime.datetime(2017, 10, 22, 18, 56, 22),
  'end': datetime.datetime(2017, 10, 22, 19, 11, 10)},
 {'start': datetime.datetime(2017, 10, 23, 10, 14, 8),
  'end': datetime.datetime(2017, 10, 23, 10, 35, 32)},
 {'start': datetime.datetime(2017, 10, 23, 11, 29, 36),
  'end': datetime.datetime(2017, 10, 23, 14, 38, 34)},
 {'start': datetime.datetime(2017, 10, 23, 15, 4, 52),
  'end': datetime.datetime(2017, 10, 23, 15, 32, 58)},
 {'start': datetime.datetime(2017, 10, 23, 15, 33, 48),
  'end': datetime.datetime(2017, 10, 23, 17, 6, 47)},
 {'start': datetime.datetime(2017, 10, 23, 17, 13, 16),
  'end': datetime.datetime(2017, 10, 23, 19, 31, 26)},
 {'start': datetime.datetime(2017, 10, 23, 19, 55, 3),
  'end': datetime.datetime(2017, 10, 23, 20, 25, 53)},
 {'start': datetime.datetime(2017, 10, 23, 21, 47, 54),
  'end': datetime.datetime(2017, 10, 23, 22, 18, 4)},
 {'start': datetime.datetime(2017, 10, 23, 22, 34, 12),
  'end': datetime.datetime(2017, 10, 23, 22, 48, 42)},
 {'start': datetime.datetime(2017, 10, 24, 6, 55, 1),
  'end': datetime.datetime(2017, 10, 24, 7, 2, 17)},
 {'start': datetime.datetime(2017, 10, 24, 14, 56, 7),
  'end': datetime.datetime(2017, 10, 24, 15, 3, 16)},
 {'start': datetime.datetime(2017, 10, 24, 15, 51, 36),
  'end': datetime.datetime(2017, 10, 24, 15, 59, 50)},
 {'start': datetime.datetime(2017, 10, 24, 16, 31, 10),
  'end': datetime.datetime(2017, 10, 24, 16, 55, 9)},
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  'end': datetime.datetime(2017, 12, 21, 7, 32, 3)},
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  'end': datetime.datetime(2017, 12, 29, 12, 46, 16)},
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  'end': datetime.datetime(2017, 12, 29, 14, 43, 46)},
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  'end': datetime.datetime(2017, 12, 29, 15, 18, 51)},
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  'end': datetime.datetime(2017, 12, 29, 20, 38, 13)},
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  'end': datetime.datetime(2017, 12, 30, 13, 54, 33)},
 {'start': datetime.datetime(2017, 12, 30, 15, 9, 3),
  'end': datetime.datetime(2017, 12, 30, 15, 19, 13)}]

Recreating ISO format with strftime()

Time Zones and Daylight Saving

We confidently tackle the time-related topic that causes people the most trouble: time zones and daylight saving. Continuing with our bike data, we will 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.

UTC offsets

Creating timezone aware datetimes

# Import datetime, timezone
from datetime import datetime, timezone, timedelta

# October 1, 2017 at 15:26:26, UTC
dt = datetime(2017, 10, 1, 15, 26, 26, tzinfo=timezone.utc)

# Print results
print(dt.isoformat())

2017-10-01T15:26:26+00:00

# Import datetime, timedelta, timezone
from datetime import datetime, timedelta, timezone

# Create a timezone for Pacific Standard Time, or UTC-8
pst = timezone(timedelta(hours=-8))

# October 1, 2017 at 15:26:26, UTC-8
dt = datetime(2017, 10, 1, 15, 26, 26, tzinfo=pst)

# Print results
print(dt.isoformat())

2017-10-01T15:26:26-08:00

# Import datetime, timedelta, timezone
from datetime import datetime, timedelta, timezone

# Create a timezone for Australian Eastern Daylight Time, or UTC+11
aedt = timezone(timedelta(hours=11))

# October 1, 2017 at 15:26:26, UTC+11
dt = datetime(2017, 10, 1, 15, 26, 26, tzinfo=aedt)

# Print results
print(dt.isoformat())

2017-10-01T15:26:26+11:00

Did you know that Russia and France are tied for the most number of time zones, with 12 each? The French mainland only has one timezone, but because France has so many overseas dependencies they really add up!

Setting timezones

# Create a timezone object corresponding to UTC-4
edt = timezone(timedelta(hours=-4))

# Loop over trips, updating the start and end datetimes to be in UTC-4
for trip in onebike_datetimes[:10]:
  # Update trip['start'] and trip['end']
  trip['start'] = trip['start'].replace(tzinfo=edt)
  trip['end'] = trip['end'].replace(tzinfo=edt)

Did you know that despite being over 2,500 miles (4,200 km) wide (about as wide as the continential United States or the European Union) China has only one official timezone? There’s a second, unofficial timezone, too. It is used by much of the Uyghurs population in the Xinjiang province in the far west of China.

What time did the bike leave in UTC?

Having set the timezone for the first ten rides that W20529 took, let’s see what time the bike left in UTC.

# Loop over the trips
for trip in onebike_datetimes[:10]:
  # Pull out the start and set it to UTC
  dt = trip['start'].astimezone(timezone.utc)
  
  # Print the start time in UTC
  print('Original:', trip['start'], '| UTC:', dt.isoformat())

Original: 2017-10-01 15:23:25-04:00 | UTC: 2017-10-01T19:23:25+00:00
Original: 2017-10-01 15:42:57-04:00 | UTC: 2017-10-01T19:42:57+00:00
Original: 2017-10-02 06:37:10-04:00 | UTC: 2017-10-02T10:37:10+00:00
Original: 2017-10-02 08:56:45-04:00 | UTC: 2017-10-02T12:56:45+00:00
Original: 2017-10-02 18:23:48-04:00 | UTC: 2017-10-02T22:23:48+00:00
Original: 2017-10-02 18:48:08-04:00 | UTC: 2017-10-02T22:48:08+00:00
Original: 2017-10-02 19:18:10-04:00 | UTC: 2017-10-02T23:18:10+00:00
Original: 2017-10-02 19:37:32-04:00 | UTC: 2017-10-02T23:37:32+00:00
Original: 2017-10-03 08:24:16-04:00 | UTC: 2017-10-03T12:24:16+00:00
Original: 2017-10-03 18:17:07-04:00 | UTC: 2017-10-03T22:17:07+00:00

Did you know that there is no official time zone at the North or South pole? Since all the lines of longitude meet each other, it’s up to each traveler (or research station) to decide what time they want to use.

Timezones database

Putting the bike trips into the right time zone

Instead of setting the timezones for W20529 by hand, let’s assign them to their IANA timezone: ‘America/New_York’. Since we know their political jurisdiction, we don’t need to look up their UTC offset. Python will do that for us.

# Import tz
from dateutil import tz

# Create a timezone object for Eastern Time
et = tz.gettz('America/New_York')

# Loop over trips, updating the datetimes to be in Eastern Time
for trip in onebike_datetimes[:10]:
  # Update trip['start'] and trip['end']
  trip['start'] = trip['start'].replace(tzinfo=et)
  trip['end'] = trip['end'].replace(tzinfo=et)

Time zone rules actually change quite frequently. IANA time zone data gets updated every 3-4 months, as different jurisdictions make changes to their laws about time or as more historical information about timezones are uncovered. tz is smart enough to use the date in your datetime to determine which rules to use historically.

What time did the bike leave? (Global edition)

# Create the timezone object
uk = tz.gettz('Europe/London')

# Pull out the start of the first trip
local = onebike_datetimes[0]['start']

# What time was it in the UK?
notlocal = local.astimezone(uk)

# Print them out and see the difference
print(local.isoformat())
print(notlocal.isoformat())

2017-10-01T15:23:25-04:00
2017-10-01T20:23:25+01:00

# Create the timezone object
ist = tz.gettz('Asia/Kolkata')

# Pull out the start of the first trip
local = onebike_datetimes[0]['start']

# What time was it in India?
notlocal = local.astimezone(ist)

# Print them out and see the difference
print(local.isoformat())
print(notlocal.isoformat())

2017-10-01T15:23:25-04:00
2017-10-02T00:53:25+05:30

# Create the timezone object
sm = tz.gettz('Pacific/Apia')

# Pull out the start of the first trip
local = onebike_datetimes[0]['start']

# What time was it in Samoa?
notlocal = local.astimezone(sm)

# Print them out and see the difference
print(local.isoformat())
print(notlocal.isoformat())

2017-10-01T15:23:25-04:00
2017-10-02T09:23:25+14:00

Did you notice the time offset for this one? It’s at UTC+14! Samoa used to be UTC-10, but in 2011 it changed to the other side of the International Date Line to better match New Zealand, its closest trading partner. However, they wanted to keep the clocks the same, so the UTC offset shifted from -10 to +14, since 24-10 is 14. Timezones… not simple!

Starting Daylight saving time

How many hours elapsed around daylight saving?

Since our bike data takes place in the fall, you’ll have to do something else to learn about the start of daylight savings time.

Let’s look at March 12, 2017, in the Eastern United States, when Daylight Saving kicked in at 2 AM.

# Import datetime, timedelta, tz, timezone
from datetime import datetime, timedelta, timezone
from dateutil import tz

# Start on March 12, 2017, midnight, then add 6 hours
start = datetime(2017, 3, 12, tzinfo = tz.gettz('America/New_York'))
end = start + timedelta(hours=6)
print(start.isoformat() + " to " + end.isoformat())

2017-03-12T00:00:00-05:00 to 2017-03-12T06:00:00-04:00

# Import datetime, timedelta, tz, timezone
from datetime import datetime, timedelta, timezone
from dateutil import tz

# Start on March 12, 2017, midnight, then add 6 hours
start = datetime(2017, 3, 12, tzinfo = tz.gettz('America/New_York'))
end = start + timedelta(hours=6)
print(start.isoformat() + " to " + end.isoformat())

# How many hours have elapsed?
print((end - start).seconds/(60*60))

2017-03-12T00:00:00-05:00 to 2017-03-12T06:00:00-04:00
6.0

# Import datetime, timedelta, tz, timezone
from datetime import datetime, timedelta, timezone
from dateutil import tz

# Start on March 12, 2017, midnight, then add 6 hours
start = datetime(2017, 3, 12, tzinfo = tz.gettz('America/New_York'))
end = start + timedelta(hours=6)
print(start.isoformat() + " to " + end.isoformat())

# How many hours have elapsed?
print((end - start).total_seconds()/(60*60))

# What if we move to UTC?
print((end.astimezone(timezone.utc) - start.astimezone(timezone.utc))\
      .total_seconds()/(60*60))

2017-03-12T00:00:00-05:00 to 2017-03-12T06:00:00-04:00
6.0
5.0

When we compare times in local time zones, everything gets converted into clock time. Remember if you want to get absolute time differences, always move to UTC!

March 29, throughout a decade

Daylight Saving rules are complicated: they’re different in different places, they change over time, and they usually start on a Sunday (and so they move around the calendar).

For example, in the United Kingdom, as of the time this code was written, Daylight Saving begins on the last Sunday in March. Let’s look at the UTC offset for March 29, at midnight, for the years 2000 to 2010.

# Import datetime and tz
from datetime import datetime
from dateutil import tz

# Create starting date
dt = datetime(2000, 3, 29, tzinfo = tz.gettz('Europe/London'))

# Loop over the dates, replacing the year, and print the ISO timestamp
for y in range(2000, 2011):
  print(dt.replace(year=y).isoformat())

2000-03-29T00:00:00+01:00
2001-03-29T00:00:00+01:00
2002-03-29T00:00:00+00:00
2003-03-29T00:00:00+00:00
2004-03-29T00:00:00+01:00
2005-03-29T00:00:00+01:00
2006-03-29T00:00:00+01:00
2007-03-29T00:00:00+01:00
2008-03-29T00:00:00+00:00
2009-03-29T00:00:00+00:00
2010-03-29T00:00:00+01:00

As you can see, the rules for Daylight Saving are not trivial. When in doubt, always use tz instead of hand-rolling timezones, so it will catch the Daylight Saving rules (and rule changes!) for you.

Ending daylight saving time

Finding ambiguous datetimes

we saw something anomalous in our bike trip duration data. Let’s see if we can identify what the problem might be.

Note that tz.datetime_ambiguous() only catches ambiguous datetimes from Daylight Saving changes. Other weird edge cases, like jurisdictions which change their Daylight Saving rules, hopefully should be caught by tz. And if they’re not, at least those kinds of things are pretty rare in most data sets!

Cleaning daylight saving data with fold

As we’ve just discovered, there is a ride in our data set which is being messed up by a Daylight Savings shift. Let’s clean up the data set so we actually have a correct minimum ride length. We can use the fact that we know the end of the ride happened after the beginning to fix up the duration messed up by the shift out of Daylight Savings.

Since Python does not handle tz.enfold() when doing arithmetic, we must put our datetime objects into UTC, where ambiguities have been resolved.

Easy and Powerful: Dates and Times in Pandas

Reading date and time data in Pandas

import pandas as pd

# Load CSV into the rides variable
rides = pd.read_csv('../datasets/capital-onebike.csv', 
                    parse_dates = ['Start date', 'End date'])

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

Start date                        2017-10-01 15:23:25
End date                          2017-10-01 15:26:26
Start station number                            31038
Start station                    Glebe Rd & 11th St N
End station number                              31036
End station             George Mason Dr & Wilson Blvd
Bike number                                    W20529
Member type                                    Member
Name: 0, dtype: object

Making timedelta columns

rides['Duration'] = (rides['End date']-rides['Start date']).dt.total_seconds()
rides.Duration.head()

0     181.0
1    7622.0
2     343.0
3    1278.0
4    1277.0
Name: Duration, dtype: float64

Summarizing datetime data in Pandas

How many joyrides?

Suppose we have a theory that some people take long bike rides before putting their bike back in the same dock. Let’s call these rides “joyrides”. Are there many joyrides? How long were they in our data set? Lets use the median instead of the mean, because we know there are some very long trips in our data set that might skew the answer, and the median is less sensitive to outliers.

# Create joyrides
joyrides = (rides['Start station'] == rides['End station'])

# Total number of joyrides
print("{} rides were joyrides".format(joyrides.sum()))

# Median of all rides
print("The median duration overall was {:.2f} seconds"\
      .format(rides['Duration'].median()))

# Median of joyrides
print("The median duration for joyrides was {:.2f} seconds"\
      .format(rides[joyrides]['Duration'].median()))

6 rides were joyrides
The median duration overall was 660.00 seconds
The median duration for joyrides was 2642.50 seconds

It’s getting cold outside, W20529

Washington, D.C. has mild weather overall, but the average high temperature in October (68ºF / 20ºC) is certainly higher than the average high temperature in December (47ºF / 8ºC). People also travel more in December, and they work fewer days so they commute less.

How might the weather or the season have affected the length of bike trips?

# Import matplotlib
import matplotlib.pyplot as plt

# Resample rides to daily, take the size, plot the results
rides.resample('D', on = 'Start date')\
  .size()\
  .plot(ylim = [0, 15])

# Show the results
plt.show()

Since the daily time series is so noisy for this one bike, change the resampling to be monthly.

# Import matplotlib
import matplotlib.pyplot as plt

# Resample rides to monthly, take the size, plot the results
rides.resample('M', on = 'Start date')\
  .size()\
  .plot(ylim = [0, 150])

# Show the results
plt.show()

As you can see, the pattern is clearer at the monthly level: there were fewer rides in November, and then fewer still in December, possibly because the temperature got colder.

Members vs casual riders over time

Riders can either be “Members”, meaning they pay yearly for the ability to take a bike at any time, or “Casual”, meaning they pay at the kiosk attached to the bike dock.

Do members and casual riders drop off at the same rate over October to December, or does one drop off faster than the other?

# Resample rides to be monthly on the basis of Start date
monthly_rides = rides.resample('M', on='Start date')['Member type']

# Take the ratio of the .value_counts() over the total number of rides
print(monthly_rides.value_counts() / monthly_rides.size())

Start date  Member type
2017-10-31  Member         0.768519
            Casual         0.231481
2017-11-30  Member         0.825243
            Casual         0.174757
2017-12-31  Member         0.860759
            Casual         0.139241
Name: Member type, dtype: float64

.resample() labels Monthly resampling with the last day in the month and not the first. It certainly looks like the fraction of Casual riders went down as the number of rides dropped. With a little more digging, you could figure out if keeping Member rides only would be enough to stabilize the usage numbers throughout the fall.

Combining groupby() and resample()

# Group rides by member type, and resample to the month
grouped = rides.groupby('Member type')\
  .resample('M', on='Start date')

# Print the median duration for each group
print(grouped['Duration'].median())

Member type  Start date
Casual       2017-10-31    1636.0
             2017-11-30    1159.5
             2017-12-31     850.0
Member       2017-10-31     671.0
             2017-11-30     655.0
             2017-12-31     387.5
Name: Duration, dtype: float64

It looks like casual riders consistently took longer rides, but that both groups took shorter rides as the months went by. Note that, by combining grouping and resampling, you can answer a lot of questions about nearly any data set that includes time as a feature. Keep in mind that you can also group by more than one column at once.

### Timezones in Pandas

# Localize the Start date column to America/New_York
rides['Start date'] = rides['Start date'].dt.tz_localize("America/New_York", ambiguous='NaT')

# Print first value
print(rides['Start date'].iloc[0])

2017-10-01 15:23:25-04:00

# Convert the Start date column to Europe/London
rides['Start date'] = rides['Start date'].dt.tz_convert("Europe/London")

# Print the new value
print(rides['Start date'].iloc[0])

2017-10-01 20:23:25+01:00

dt.tzconvert() converts to a new timezone, whereas dt.tzlocalize() sets a timezone in the first place.

How long per weekday?

# Add a column for the weekday of the start of the ride
rides['Ride start weekday'] = rides['Start date'].dt.weekday

# Print the median trip time per weekday
print(rides.groupby('Ride start weekday')['Duration'].median())

Ride start weekday
0.0    922.5
1.0    644.0
2.0    629.0
3.0    659.0
4.0    684.0
5.0    610.0
6.0    625.0
Name: Duration, dtype: float64