Programming Articles - Page 944 of 3366

To return the count of increments applied on the BusinessDay offset, use the BusinessDay.n property in Pandas.At first, import the required libraries −import datetime import pandas as pdSet the timestamp object in Pandas” −timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') Create the BusinessDay Offset. BusinessDay is the DateOffset subclass −bdOffset = pd.tseries.offsets.BusinessDay(n = 6)Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset) Return the count of increments on the given BusinessDay object −print("The count of increments on the BusinessDay object..", bdOffset.n)ExampleFollowing is the code −import datetime import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') ... Read More

To return the rule code applied on the given BusinessDay object, use the BusinessDay.rule_code property in Pandas.At first, import the required libraries −import datetime import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') Create the BusinessDay Offset. BusinessDay is the DateOffset subclass −bdOffset = pd.tseries.offsets.BusinessDay(offset = datetime.timedelta(hours = 8, minutes = 10))Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset) Return the rule code of the frequency applied on the given BusinessDay Offset −print("The rule code of the BusinessDay object..", bdOffset.rule_code)ExampleFollowing is the code −import datetime import pandas as pd # Set the timestamp object ... Read More

To check whether the BusinessDay Offset has been normalized or not, use the BusinessDay.normalize property in Pandas.At first, import the required libraries −import datetime import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') Create the BusinessDay Offset. BusinessDay is the DateOffset subclass. We have normalized the BusinessDay using the "normalize" parameter −bdOffset = pd.tseries.offsets.BusinessDay(offset = datetime.timedelta(hours = 8, minutes = 10), normalize=True)Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset) Check whether the BusinessDay Offset is normalized or not −print("The BusinessDay Offset is normalized..", bdOffset.normalize)ExampleFollowing is the code −import datetime import pandas as pd # ... Read More

To return the name of the frequency applied on the given BusinessDay offset object, use the BusinessDay.name property in Pandas.At first, import the required libraries −import datetime import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') Create the BusinessDay Offset. BusinessDay is the DateOffset subclass −bdOffset = pd.tseries.offsets.BusinessDay(offset = datetime.timedelta(hours = 8, minutes = 10))Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset) Return the name of the frequency applied on the given BusinessDay object −print("The name of the frequency on the BusinessDay object..", bdOffset.name)ExampleFollowing is the code −import datetime import pandas as pd # ... Read More

To display the keyword arguments applied on the given BusinessDay Offset object, use the BusinessDay.kwds property in Pandas.At first, import the required libraries −import datetime import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') Create the BusinessDay Offset. BusinessDay is the DateOffset subclass −bdOffset = pd.tseries.offsets.BusinessDay(offset = datetime.timedelta(hours = 7, minutes = 7))Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset) Display the keyword arguments −print("Keyword arguments on the given BusinessDay Offset...", bdOffset.kwds)ExampleFollowing is the code −import datetime import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-10-30 01:55:02.000045') # ... Read More

To return frequency applied on the given BusinessDay Offset object as a string, use the BusinessDay.freqstr property in Pandas.At first, import the required libraries −import datetime import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-1-1 01:55:02.000045')Create the BusinessDay Offset. BusinessDay is the DateOffset subclass −bdOffset = pd.tseries.offsets.BusinessDay(offset = datetime.timedelta(days = 7, hours = 7, minutes = 7))Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset)Return the frequency applied on the given BusinessDay object as a string −print("Frequency on the given BusinessDay Offset...", bdOffset.freqstr)ExampleFollowing is the code −import datetime import pandas as pd # Set the timestamp ... Read More

To create a BusinessDay offset, use the pd.tseries.offsets.BusinessDay() method in Pandas. At first, import the required libraries −import datetime import pandas as pdCreate the BusinessDay Offset. BusinessDay is the DateOffset subclass −bdOffset = pd.tseries.offsets.BusinessDay(offset = datetime.timedelta(days = 7, hours = 7, minutes = 7))Display the BusinessDay Offset −print("BusinessDay Offset...", bdOffset) Set the timestamp object in Pandas −timestamp = pd.Timestamp('2021-1-1 01:55:02.000045')Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bdOffset) ExampleFollowing is the code −import datetime import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-1-1 01:55:02.000045') # Display the Timestamp print("Timestamp...", timestamp) # Create ... Read More

To check if the given DateOffset is Anchored, use the offset.is_anchored() method in Pandas. At first, import the required libraries −from pandas.tseries.frequencies import to_offset import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-09-26 03:25:02.000045') Create the DateOffset. We are using the Anchored offset i.e. weekly frequency here for Tuesday −offset = to_offset("W-TUE")Display the Updated Timestamp −print("Updated Timestamp...", timestamp + offset) Check whether the DateOffset is anchored −print("Check whether the DateOffset is anchored...", offset.is_anchored())ExampleFollowing is the code −from pandas.tseries.frequencies import to_offset import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-09-26 03:25:02.000045') ... Read More

To return the count of increments applied on the given DateOffset object, use the offset.n property in Pandas. At first, import the required libraries −from pandas.tseries.frequencies import to_offset import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-09-26 03:25:02.000045') Create the DateOffset. We are incrementing the months here using the "M" frequency −offset = to_offset("5M")Display the Updated Timestamp −print("Updated Timestamp...", timestamp + offset) Return the count of increments on the given DateOffset object −print("The count of increments on the DateOffset object..", offset.n)ExampleFollowing is the code −from pandas.tseries.frequencies import to_offset import pandas as pd # Set the timestamp ... Read More

To return the rule code applied on the given DateOffset object, use the offset.rule_code in Pandas. At first, import the required libraries −from pandas.tseries.frequencies import to_offset import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-09-26 03:25:02.000045') Create the DateOffset. We are incrementing the months here using the "M" frequency −offset = to_offset("3M")Display the Updated Timestamp −print("Updated Timestamp...", timestamp + offset) Return the rule code of the frequency applied on the given DateOffset object −print("The rule code of the DateOffset object..", offset.rule_code)ExampleFollowing is the code −from pandas.tseries.frequencies import to_offset import pandas as pd # Set the timestamp ... Read More