5 Best Ways to Check if an Interval is Open on the Right Side using Python Pandas

by 
interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.

💡 Problem Formulation: When working with data in Python, especially using the Pandas library, users often handle intervals or periods representing ranges of values. It becomes essential to understand the characteristics of these intervals, such as whether they are open or closed on each end. This article outlines methods to check if an interval is open on the right side, which means that the end value is not included in the interval. For instance, given an interval (5, 10], we aim to verify that 10 is not included in this range.

Method 1: Using Interval Attributes

The Interval object in Pandas has attributes like closed, which can be used to determine if the interval is open or closed on each side. Checking if .closed equals ‘right’ confirms if the interval is open on the right side.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.
import pandas as pd
interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.closed != 'right'
print(is_open_on_right)

Output: True

This code snippet creates an interval from 5 to 10 including 10 (closed on the right) and checks whether the interval is not closed on the right side. It returns True if the interval is open on the right, which in this case, it does.

Method 2: Using the open_right() method

Pandas provides a open_right() method specifically to check if an interval is open on the right side. It returns a boolean indicating the openness of the right side of the interval.

Here’s an example:

interval = pd.Interval(5, 10, closed='right')
is_open_on_right = interval.open_right
print(is_open_on_right)

Output: False

The interval is created with the right side closed, so the open_right() method will return False, indicating the interval is not open on the right side.

Method 3: Inspecting the closed_right attribute

Similar to the first method, we can directly check the closed_right attribute of an interval. If this attribute returns False, it implies the interval is open on the right side.

Here’s an example:

interval = pd.Interval(5, 10, closed='both')
is_open_on_right = not interval.closed_right
print(is_open_on_right)

Output: False

Even though the interval is closed on both ends, using not on the closed_right attribute checks if it’s open on the right side. The result is False, as the interval is not open on the right side.

Method 4: Combined Attribute Checking

We can combine checks on various attributes such as closed_left and closed to deduce if an interval is open on the right side. It’s a slightly more complex way that works if the interval might be open on either side.

Here’s an example:

interval = pd.Interval(5, 10, closed='neither')
is_open_on_right = interval.closed_left or interval.closed != 'left'
print(is_open_on_right)

Output: True

This code evaluates to True if the interval is closed on the left or not closed on the left only — both mean that it is open on the right, which is true in this example where the interval has neither end closed.

Bonus One-Liner Method 5: Using eval() and String Formatting

A quick one-liner combines the interval creation and check by using the eval() function with string formatting. This method should be used with caution as it can evaluate arbitrary code.

Here’s an example:

is_open_on_right = eval("pd.Interval(5, 10, closed='right').open_right")
print(is_open_on_right)

Output: False

The one-liner uses Python’s eval() to create and check the interval in a single line. The result is printed out, showing that this interval is not open on the right.

Summary/Discussion

  • Method 1: Attribute closed: Simple and direct. Cons: Requires a negation in logic to derive the answer.
  • Method 2: Method open_right(): Explicit, clear, and logical method designed for this exact purpose. Cons: Less known among users.
  • Method 3: Attribute closed_right: A straight opposite check of what’s needed. Cons: Not as intuitive and requires knowledge of the closed configurations.
  • Method 4: Combined Attribute Checking: Allows for complex logic handling. Cons: Overly complicated for simple checks.
  • Bonus Method 5: eval() One-liner: Quick and compact. Cons: Security risk if misused plus potentially less readable.