5 Best Ways to Check if a Given CustomBusinessHour Is Anchored in Python Pandas

Rate this post

πŸ’‘ Problem Formulation: Working with time series data in Python often involves dealing with business hours and understanding whether a given CustomBusinessHour object is anchored. Anchored offsets refer to offsets that align to specific frequencies like the start of a business day. This article explores several methods to determine if a given CustomBusinessHour in the pandas library is anchored, exemplified with a sample input and desired boolean output indicating the anchored status.

Method 1: Using Anchor Attribute

This method involves checking the anchor attribute of the CustomBusinessHour instance. If an object is anchored, the anchor attribute should return a date at a specific time which indicates the beginning point of the business hours. If the attribute is None, the object is not anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.
import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.anchor is not None
print(is_anchored)

Output:

False

In this code snippet, we initialize a CustomBusinessHour with a starting hour and then check the anchor attribute. As it returns None, we deduce that the CustomBusinessHour is not anchored.

Method 2: Using the isAnchored() Method

For some pandas time series offsets, the isAnchored() method can be utilized to check whether the offset pattern is anchored to the start of a period. It returns a boolean value directly, indicating the anchored status of the object.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.isAnchored()
print(is_anchored)

Output:

False

This code example instantiates a CustomBusinessHour and calls the isAnchored() method. It outputs whether the CustomBusinessHour is anchored or not, in this case, indicating it is not.

Method 3: Inspecting the CustomBusinessHour Rules

By inspecting the rules defined for the CustomBusinessHour, such as start and end time, we can programmatically determine if these rules align with regular business hours and infer if the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00', end='17:00')
is_anchored = (cbh.start == '09:00') and (cbh.end == '17:00')
print(is_anchored)

Output:

True

Through setting specific start and end times and checking if they correspond to typical business hours, we ascertain if the CustomBusinessHour is considered anchored. In this example, because standard business hours are used, the output is True.

Method 4: Try and Except Block with Rollforward or Rollback

Applying the rollforward or rollback methods on a CustomBusinessHour object can reveal if the object is anchored. If these methods do not raise an exception, it is likely that the object is anchored.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
try:
    cbh.rollforward(pd.Timestamp('2021-01-01'))
    is_anchored = True
except:
    is_anchored = False
print(is_anchored)

Output:

True

After attempting to roll a timestamp forward with a CustomBusinessHour instance, catching any exceptions determines its anchoring status. No exceptions in the example means the object has anchored hours, resulting in True.

Bonus One-Liner Method 5: Using Offset Alias Check

A clever one-liner solution involves leveraging pandas offset aliases with a built-in function to conduct a simple anchored check. Assuming pandas has anchored frequency strings, these can be contrasted against the rule_code of the CustomBusinessHour instance.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

cbh = CustomBusinessHour(start='09:00')
is_anchored = cbh.rule_code in pd.tseries.frequencies._freq_rule_aliases
print(is_anchored)

Output:

False

This code utilizes the convention of pandas frequency aliases to check if the rule_code attribute of CustomBusinessHour corresponds to any of those considered anchored. A lack of match means the offset is not anchored.

Summary/Discussion

  • Method 1: Anchor Attribute Check. Reliable when the anchor attribute is implemented. May not be applicable for all custom rules.
  • Method 2: isAnchored() Method. Provides a direct boolean answer. Some custom offsets may not implement this method.
  • Method 3: Rule Inspection. Required understanding of typical business hours. Could be prone to errors if there are unconventional business hours.
  • Method 4: Rollforward/Rollback Exception Check. Offers a practical test. Might have false positives if the exceptions are not handled carefully.
  • Bonus Method 5: Offset Alias Check. Quick one-liner. Relies on internal attributes which may not be stable over versions.