[Tut] Check for NaN Values in Python

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Check for NaN Values in Python

[b]Overview[/b]

Problem: How to check if a given value is

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NaN

?

Here’s a quick look at the solutions to follow:

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import math import numpy as np import pandas as pd x = float('nan') print(math.isnan(x)) print(x != x) print(np.isnan(x)) print(pd.isna(x)) print(not(float('-inf') < x < float('inf')))

So, what is a

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NaN

value?

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NaN

is a constant value that indicates that the given value is Not a Number. It’s a floating-point value, hence cannot be converted to any other type other than float. We should know that

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NaN

and Null are two different things in Python. The Null values indicate something which does not exist, i.e. is empty. But that is not the case with

Code:

NaN

.

We have to deal with

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NaN

values frequently in Python especially when we deal with array objects or DataFrames. So, without further delay, let us dive into our mission critical question and have a look at the different methods to solve our problem.

[b]Method 1: Using math.isnan()[/b]

The simplest solution to check for NaN values in Python is to use the mathematical function

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math.isnan()

.

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math.isnan()

is a function of the math module in Python that checks for

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NaN

constants in float objects and returns True for every NaN value encountered and returns False otherwise.

Example:

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# Importing the math module import math # Function to check for NaN values def isNaN(a): # Using math.isnan() if math.isnan(a): print("NaN value encountered!") else: print("Type of Given Value: ", type(a)) # NaN value x = float('NaN') isNaN(x) # Floating value y = float("5.78") isNaN(y)

Output:

Code:

NaN value encountered! Type of Given Value: <class 'float'>

In the above example, since

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x

represents a NaN value, hence, the

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isNaN

method returns

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True

but in case of

Code:

y

,

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isNan

returns

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False

and prints the type of the variable

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y

as an output.

[b]Method 2: Hack NaN Using != Operator[/b]

The most unique thing about

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NaN

values is that they are constantly shapeshifting. This means we cannot compare the

Code:

NaN

value even against itself. Hence, we can use the

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!=

(not equal to) operator to check for the

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NaN

values. Thus, the idea is to check if the given variable is equal to itself. If we consider any object other than

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NaN

, the expression

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(x == x)

will always return

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True

. If it’s not equal, then it is a

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NaN

value.

Example 1:

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print(5 == 5) # True print(['a', 'b'] == ['a', 'b']) # True print([] == []) # True print(float("nan") == float("nan")) # False print(float("nan") != float("nan")) # True

Example 2:

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# Function to check for NaN values def not_a_number(x): # Using != operator if x != x: print("Not a Number!") else: print(f'Type of {x} is {type(x)}') # Floating value x = float("7.8") not_a_number(x) # NaN value y = float("NaN") not_a_number(y)

Output:

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Type of 7.8 is <class 'float'> Not a Number!

[b]Method 3: Using numpy.isnan()[/b]

We can also use the

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NumPy

library to check whether the given value is

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NaN

or not. We just need to ensure that we import the library at the start of the program and then use its

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np.isnan(x)

method.

The

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np.isnan(number)

function checks whether the element in a Numpy array is

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NaN

or not. It then returns the result as a boolean array.

Example: In the following example we have a Numpy Array and then we will check the type of each value. We will also check if it is a

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NaN

value or not.

Code:

import numpy as np arr = np.array([10, 20, np.nan, 40, np.nan]) for x in arr: if np.isnan(x): print("Not a Number!") else: print(x, ":", type(x))

Output:

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10.0 : <class 'numpy.float64'> 20.0 : <class 'numpy.float64'> Not a Number! 40.0 : <class 'numpy.float64'> Not a Number!

TRIVIA

Let us try to perform some basic functions on an numpy array that involves

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NaN

values and find out what happens to it.

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import numpy as np arr = np.array([10, 20, np.nan, 40, np.nan]) print(arr.sum()) print(arr.max())

Output:

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nan nan

Now this can be a problem in many cases. So, do we have a way to eliminate the NaN values from our array object and then perform the mathematical operations upon the array elements? Yes! Numpy facilitates us with methods like

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np.nansum()

and

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np.nanmax()

that help us to calculate the sum and maximum values in the array by ignoring the presence of

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NaN

values in the array.

Example:

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import numpy as np arr = np.array([10, 20, np.nan, 40, np.nan]) print(np.nansum(arr)) print(np.nanmax(arr))

Output:

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70.0 40.0

[b][b]Method 4: Using pandas.isna()[/b][/b]

Another way to solve our problem is to use the

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isna()

method of the Pandas module.

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pandas.isna()

is a function that detects missing values in an array-like object. It returns True if any

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NaN

value is encountered.

Example 1:

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import pandas as pd x = float("nan") y = 25.75 print(pd.isna(x)) print(pd.isna(y))

Output:

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True False

Example 2: In the following example we will have a look at a Pandas DataFrame and detect the presence of NaN values in the DataFrame.

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import pandas as pd df = pd.DataFrame([['Mercury', 'Venus', 'Earth'], ['1', float('nan'), '2']]) print(pd.isna(df))

Output:

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0 1 2 0 False False False 1 False True False

[b]Method 5: By Checking The Range[/b]

We can check for the

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NaN

values by using another NaN special property: limited range. The range of all the floating-point values falls within negative infinity to infinity. However,

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NaN

values do not fall within this range.

Hence, the idea is to check whether a given value lies within the range of

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-inf

and

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inf

. If yes , then it is not a

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NaN

value else it is a

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NaN

value.

Example:

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li = [25.87, float('nan')] for i in li: if float('-inf') < float(i) < float('inf'): print(i) else: print("Not a Number!")

Output:

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25.87 Not a Number!

Recommended read: Python Infinity

[b]Conclusion[/b]

In this article, we learned how we can use the various methods and modules (

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pandas

,

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NumPy

, and

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math

) in Python to check for the

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NaN

values. I hope this article was able to answer your queries. Please stay tuned and subscribe for more such articles. 

Authors: SHUBHAM SAYON and RASHI AGARWAL

Do you want to become a NumPy master? Check out our interactive puzzle book Coffee Break NumPy and boost your data science skills! (Amazon link opens in new tab.)