NaN stands for Not a Number and can be represented as a special kind of number that isnt assigned a value. It can appear due to various reasons like dividing by zero, performing mathematical operations on non-numerical types, etc.
NaN (Not A Number) is a unique representation in Python which represents an undefined or uncomputable quantity in the context of numerical calculations. If you’re dealing with data that includes NaN v …
NaN (Not A Number) is a unique representation in Python which represents an undefined or uncomputable quantity in the context of numerical calculations. If you’re dealing with data that includes NaN values, they can make your computations unstable and hard to interpret. In this article, we will learn how to remove them from list in Python. Here’s a sample Markdown format for the problem you described. This isn’t a typical task that a programmer would do in real life, but it is an interesting concept to discuss if you are writing a tutorial or guide for programmers.
Removing NaN values from a List
NaN (Not A Number) is a unique representation in Python which represents an undefined or uncomputable quantity in the context of numerical calculations. If you’re dealing with data that includes NaN values, they can make your computations unstable and hard to interpret. A common example is when you divide by zero, where NaN is returned.
Here are some methods to remove NaN from a list:
- Using List comprehension:
lst = [1,2,'a',3,4,'b','NaN',5]
clean_list = [i for i in lst if not str(i).startswith('NaN')]
print(clean_list)
- Using numpy method:
import numpy as np
lst = [1,2,'a',3,4,'b','NaN',5]
clean_list = list(np.array(lst)[~np.isnan(np.array(lst))])
print(clean_list)
Both of these methods use the fact that NaN is not equal to itself, so it’s easy to filter out from a list or array. The first one uses Python’s list comprehension feature and the second one utilizes numpy library’s built-in function isnan().
Remember, the term “NaN” is case sensitive. If your data has ‘NaN’, it won’t be removed. Make sure you replace all variations of NaN (including capitalization) with actual numbers or string values before filtering.
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