# 5 Best Ways to Check If an Interval is Empty Using Python Pandas

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π‘ Problem Formulation: When working with intervals in data analysis, it’s often necessary to determine if a given interval contains any data points. This article explains how to check for empty intervals in Python using the Pandas library. An interval is essentially a range between two points, and in Pandas, it is represented by `pd.Interval`. An empty interval in this context would be one that does not contain any data points or elements. We will explore five different methods to verify the emptiness of an interval.

## Method 1: Using `Interval.length` Property

This method involves checking the length of the interval using the `Interval.length` property. If the length is 0, the interval is empty. This property returns the distance between the endpoints of the interval if it is numeric.

Here’s an example:

```import pandas as pd

# Create a numeric interval
interval = pd.Interval(left=5, right=5)

# Check if the interval is empty by examining its length
is_empty = interval.length == 0

print(is_empty)```

Output: `True`

This snippet creates a numeric interval with both ends at the same point and checks if the length is zero. It prints `True` indicating that the interval is indeed empty.

## Method 2: Using `Interval.empty` Attribute

In Pandas, intervals have an `empty` attribute that can be checked directly to determine if an interval is empty. This attribute returns `True` if the interval is empty and `False` otherwise.

Here’s an example:

```import pandas as pd

# Create a numeric interval
interval = pd.Interval(left=0, right=0, closed='neither')

# Check if the interval is empty by accessing its 'empty' attribute
is_empty = interval.empty

print(is_empty)```

Output: `True`

This code creates an interval with the same start and end points, with a ‘neither’ closing method. The `interval.empty` attribute tells us that the interval contains no elements.

## Method 3: Comparing End Points

Another method to check if an interval is empty is by directly comparing the start and end points. If they are equal, and the interval is closed by neither end, it is empty.

Here’s an example:

```import pandas as pd

# Create an interval
interval = pd.Interval(left=10, right=10, closed='neither')

# Check if the interval is empty by comparing its endpoints
is_empty = (interval.left == interval.right) and (interval.closed == 'neither')

print(is_empty)```

Output: `True`

This example demonstrates checking the interval’s endpoints directly and confirming that the interval’s closure is ‘neither’, which designates an empty interval in this context.

## Method 4: Using `pd.isnull()`

The `pd.isnull()` function can be used to check if an interval is empty when the interval object itself might be `None` or `NaN`. If this returns `True`, the interval can be considered empty or non-existent.

Here’s an example:

```import pandas as pd

# Create an interval that is none
interval = None

# Check if the interval is 'empty' (in the sense of being non-existent)
is_empty = pd.isnull(interval)

print(is_empty)```

Output: `True`

In this code, since the variable `interval` is set to `None`, the `pd.isnull(interval)` function confirms that there is no interval to speak of.

## Bonus One-Liner Method 5: Using `empty` with a Lambda Function

If you’re looking for a compact way to apply an emptiness check across multiple intervals, using the `empty` attribute within a lambda function might be the perfect solution.

Here’s an example:

```import pandas as pd

# Create a list of intervals
intervals = [pd.Interval(left, right, closed='neither') for left, right in [(2, 2), (3, 5), (10, 10)]]

# Check if each interval is empty using a  list comprehension  with a lambda function
intervals_empty = list(map(lambda x: x.empty, intervals))

print(intervals_empty)```

Output: `[True, False, True]`

This example utilizes a list comprehension in conjunction with the `empty` attribute to check whether each interval in the list is empty.

## Summary/Discussion

• Method 1: Checking interval length. Strengths: Direct and intuitive for numeric ranges. Weaknesses: Does not apply to non-numeric intervals.
• Method 2: Using the `empty` attribute. Strengths: Provided by pandas, very explicit. Weaknesses: May not be available in all versions of pandas.
• Method 3: Comparing endpoints. Strengths: Works with custom intervals and boundaries. Weaknesses: Requires more condition checks.
• Method 4: Using `pd.isnull()`. Strengths: Can check for actual non-existence of an interval object. Weaknesses: Doesn’t check for the interval’s semantic emptiness.
• Method 5: Lambda function. Strengths: Suitable for batch operations on a collection of intervals. Weaknesses: Overkill for single interval checks, and readability might suffer.