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Load libraries
import pandas as pd
from datetime import timedelta
# Loading dataset and creating duration column
url = 'https://drive.google.com/uc?id=1YV5bKobzYxVAWyB7VlxNH6dmfP4tHBui'
df = pd.read_csv(url, parse_dates = ['pickup_datetime', 'dropoff_datetime', 'dropoff_calculated'])
df["duration"] = pd.to_timedelta(df["duration"])
# Task 1 - filter to only rides with negative durations
df_neg = df[___["___"] < ___(___)]
# Task 2 - iterate over df_neg rows to find inconsistencies
count = 0
for i, row in df_neg.___():
# Compare minutes of dropoff_datetime and dropoff_calculated
if row["___"].___ != row["___"].minute:
# Print these two columns
print(___[["dropoff_datetime", "dropoff_calculated"]])
# Task 3 - count number of rows having hour greater-equal than 12
if row["___"].___ >= ___:
count ___
print(f"There are {count} rows in df_neg having hour greater-equal than 12.")
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from datetime import datetime
dt_string = "12/11/2018 09:15:32"
# Considering date is in dd/mm/yyyy format
dt_object1 = datetime.strptime(dt_string, "%d/%m/%Y %H:%M:%S")
print("dt_object1 =", dt_object1)
# Considering date is in mm/dd/yyyy format
dt_object2 = datetime.strptime(dt_string, "%m/%d/%Y %H:%M:%S")
print("dt_object2 =", dt_object2)
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from datetime import datetime
# EXEMPLE 1
dt_string = "12/11/2018 09:15:32"
# Considering date is in dd/mm/yyyy format
dt_object1 = datetime.strptime(dt_string, "%d/%m/%Y %H:%M:%S")
print("dt_object1 =", dt_object1)
# Considering date is in mm/dd/yyyy format
dt_object2 = datetime.strptime(dt_string, "%m/%d/%Y %H:%M:%S")
print("dt_object2 =", dt_object2)
#EXEMPLE 2
date_string = "21 June, 2018"
print("date_string =", date_string)
print("type of date_string =", type(date_string))
date_object = datetime.strptime(date_string, "%d %B, %Y")
print("date_object =", date_object)
print("type of date_object =", type(date_object))
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# import the datetime module
import datetime
# datetime in string format for may 25 1999
input = '2021/05/25'
# format
format = '%Y/%m/%d'
# convert from string format to datetime format
datetime = datetime.datetime.strptime(input, format)
# get the date from the datetime using date()
# function
print(datetime.date())
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>>> import datetime
>>> datetime.datetime.strptime('24052010', "%d%m%Y").date()
datetime.date(2010, 5, 24)
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from datetime import datetime
my_date_string = "Mar 11 2011 11:31AM"
datetime_object = datetime.strptime(my_date_string, '%b %d %Y %I:%M%p')
print(type(datetime_object))
print(datetime_object)
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from datetime import datetime
date_string = "21 June, 2018"
print("date_string =", date_string)
print("type of date_string =", type(date_string))
date_object = datetime.strptime(date_string, "%d %B, %Y")
print("date_object =", date_object)
print("type of date_object =", type(date_object))
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import pandas as pd
# Define string
date = '04/03/2021 11:23'
# Convert string to datetime format
date1 = pd.to_datetime(date)
# print to_datetime output
print(date1)
# print day, month and year separately from the to_datetime output
print("Day: ", date1.day)
print("Month", date1.month)
print("Year", date1.year)
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import datetime
# str value "Apr 2, 2019" convert into any format.
datetime.datetime.strptime('Apr 2, 2019', '%b %d, %Y').strftime('%a, %d %b %Y')
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date = datetime.datetime.strptime('2019-3-16T5-49-52-595Z','%Y-%m-%dT%H-%M-%S-%f%z')
date_time = date.strftime('%Y-%m-%dT%H:%M:%S.%fZ')
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string CurrentDate = "06/04/2020";
// Creating new CultureInfo Object
// You can use different cultures like French, Spanish etc.
CultureInfo Culture = new CultureInfo("en-US");
//Use of Convert.ToDateTime()
DateTime DateObject = Convert.ToDateTime(CurrentDate, Culture);
Console.WriteLine("The Date is: " + DateObject.Day + " " + DateObject.Month + " " + DateObject.Year);