Скачать или смотреть How to Ignore None Values in Python for List of Dictionaries

Discover how to handle `NoneType` values while filtering data in Python dictionaries, ensuring a smooth search for options based on conditions.
---
This video is based on the question https://stackoverflow.com/q/62517499/ asked by the user 'Kam Halil' ( https://stackoverflow.com/u/10055996/ ) and on the answer https://stackoverflow.com/a/62517540/ provided by the user 'Max' ( https://stackoverflow.com/u/10937490/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Ignore None values and skip to next value?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Ignoring None Values in Python Lists of Dictionaries

When working with data in Python, especially when it comes to financial options, you may find yourself grappling with a common issue: how to handle NoneType values. This challenge is particularly evident when filtering through lists of dictionaries, which frequently change in structure. In this guide, we'll clarify the problem at hand and provide an effective solution to ensure you can confidently filter your data without encountering frustrating errors.

The Problem: Filtering Options with None Values

Imagine you have a list of dictionaries representing options in a financial context. Each dictionary contains various fields, such as strike_price, bid_price, and importantly, implied_volatility:

[[See Video to Reveal this Text or Code Snippet]]

You want to search through this list for options with an implied_volatility below 0.90. However, as your code runs, you run into this bothersome error message:

[[See Video to Reveal this Text or Code Snippet]]

This indicates that there are entries where implied_volatility is None, and attempting to convert None to a float causes the error.

The Solution: Skipping None Values

To effectively conduct this search while ignoring any None values, you can utilize a list comprehension that not only checks for the condition you want (implied_volatility < 0.90), but also ensures that the value is not None. Here’s how you can do it:

[[See Video to Reveal this Text or Code Snippet]]

Breaking Down the Solution

Using List Comprehension

List comprehension provides a concise way to create a new list from an existing one by filtering out unwanted values.

It checks for both the existence of a valid implied_volatility and its numerical condition.

Key Points in the Expression

i["implied_volatility"] is not None: This condition filters out any entries where implied_volatility is None.

float(i["implied_volatility"]) < 0.90: This part of the expression ensures that only options with implied_volatility below 0.90 are considered.

Final Implementation

By using the revised code, there’s no need for an explicit for loop. Your filtering will become clearer and more efficient.

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Handling NoneType values in lists of dictionaries can often lead to frustrating errors. However, with a straightforward list comprehension, you can easily filter out those unwanted entries and maintain your search conditions intact. The solution shared here not only resolves the immediate issue but also enhances the readability and efficiency of your code.

Feel free to adapt this method in your own Python projects to streamline data handling in finance and beyond!