# 5 Best Ways to Check if a Pandas Interval is Empty When Closed from the Left Side

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π‘ Problem Formulation: When working with pandas intervals, it is often necessary to determine whether an interval contains any values. Specifically, we want to check if an interval is empty when it is closed from the left side, meaning the starting point is included, but the ending point is not. If an interval is defined as `[a, b)`, we would like to check if there are no points within this interval. This article explores various methods to achieve this check in Pythonβs pandas library.

## Method 1: Using Interval Properties

This method involves checking an interval’s `left` and `right` attributes. In pandas, an interval’s endpoints can be accessed directly. If the intervalβs `left` endpoint is equal to or greater than the `right`, the interval is considered empty when closed from the left side (`[left, right)`).

Here’s an example:

```import pandas as pd

# Create an interval that is closed from the left
interval = pd.Interval(left=5, right=5, closed='left')

# Check if the interval is empty
is_empty = interval.left >= interval.right
print(is_empty)```

Output:

`True`

This snippet creates an interval from 5 to 5, which is closed on the left. It then compares the `left` and `right` attributes, and returns `True` because there are no values between 5 and 5 excluding the endpoint.

## Method 2: Using the `length` Attribute

The `length` attribute of a pandas interval provides the measure of the distance between the `left` and `right` endpoints. An interval is empty when this length is zero or negative, which can occur when an interval is closed on the left side and the `left` endpoint is equal to or greater than the `right` endpoint.

Here’s an example:

```import pandas as pd

# Create an interval
interval = pd.Interval(3, 3, 'left')

# Check if the interval is empty based on length
is_empty = interval.length <= 0
print(is_empty)```

Output:

`True`

In this code, we create an interval similar to Method 1, but this time we use the `length` attribute to determine if the interval is empty. The output confirms the interval is empty since its length is zero.

## Method 3: Using the `empty` Attribute

pandas intervals have an `empty` attribute that returns a boolean directly indicating whether the interval is empty. This is a straightforward method that abstracts the conditional logic seen in the previous methods.

Here’s an example:

```import pandas as pd

# Create an interval
interval = pd.Interval(8, 8, 'left')

# Check if the interval is empty using the 'empty' attribute
print(interval.empty)```

Output:

`True`

The `empty` attribute conveniently tells us whether the interval has any elements, simplifying the check to a single call without the need for comparison.

## Method 4: Custom Function Check

A custom function can be crafted to encapsulate the logic for checking if an interval is empty. This function would take an interval as its parameter and return a boolean value. The function can use attribute checks or any other logic deemed appropriate.

Here’s an example:

```import pandas as pd

def is_interval_empty(interval):
return interval.empty

# Create interval closed on the left
interval = pd.Interval(10, 10, 'left')

# Use the custom function to check if the interval is empty
print(is_interval_empty(interval))```

Output:

`True`

The custom function `is_interval_empty` utilizes the `empty` attribute for this check. However, you could easily extend this function to use other attributes or complex logic as per requirements.

## Bonus One-Liner Method 5: Inline Check

For quick inline checks without the need for creating additional functions or variables, one can directly implement the condition in an if-statement or a lambda expression as a one-liner whenever needed in the code.

Here’s an example:

```import pandas as pd

# Create an interval
interval = pd.Interval(2, 2, 'left')

# Inline check for an empty interval
print('Interval is empty' if interval.empty else 'Interval contains values')```

Output:

`Interval is empty`

This one-liner uses Python’s ternary conditional operator to print a message depending on whether the interval is empty. The check is straightforward and requires no additional setup.

## Summary/Discussion

• Method 1: Using Interval Properties. Direct and clear method. Relies on manual comparison of attributes. It might not be as explicit as using built-in attributes for readability.
• Method 2: Using the `length` Attribute. Also, a direct method. It encapsulates the comparison within the property itself. The semantics can be slightly less clear than `empty` because length can technically be non-zero in other contexts.
• Method 3: Using the `empty` Attribute. The most explicit and readable method. Provides a built-in check with no need for custom logic or interpretation.
• Method 4: Custom Function Check. Useful for encapsulating the check when it is repeated throughout the code. Increases maintainability but adds an extra layer of abstraction.
• Method 5: Inline Check. Great for quick, on-the-spot checks without function or variable overhead. It might be less readable or discoverable in complex expressions.