PYTHON

string to time python

from datetime import datetime
datetime.strptime(date_string, format)

Python string to datetime object

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)

how to convert string to date object in python

>>> import datetime
>>> datetime.datetime.strptime('24052010', "%d%m%Y").date()
datetime.date(2010, 5, 24)

python string to datetime

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)

python convert string datetime into datetime

# 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())

Python string to datetime object

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))

Convert string to datetime python

 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.")