[Tut] Check for NaN Values in Python

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[Tut] Check for NaN Values in Python - xSicKxBot - 04-09-2022

Check for NaN Values in Python

[b]Overview[/b]

Problem: How to check if a given value is

Code:
NaN

?

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

Code:
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

Code:
NaN

value?

Code:
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

Code:
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

Code:
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

Code:
math.isnan()

Code:
math.isnan()

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

Code:
NaN

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

Example:

Code:
# 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

Code:
x

represents a NaN value, hence, the

Code:
isNaN

method returns

Code:
True

but in case of

Code:
y

Code:
isNan

returns

Code:
False

and prints the type of the variable

Code:
y

as an output.

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

The most unique thing about

Code:
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

Code:
!=

(not equal to) operator to check for the

Code:
NaN

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

Code:
NaN

, the expression

Code:
(x == x)

will always return

Code:
True

. If it’s not equal, then it is a

Code:
NaN

value.

Example 1:

Code:
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:

Code:
# 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:

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

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

We can also use the

Code:
NumPy

library to check whether the given value is

Code:
NaN

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

Code:
np.isnan(x)

method.

The

Code:
np.isnan(number)

function checks whether the element in a Numpy array is

Code:
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

Code:
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:

Code:
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

Code:
NaN

values and find out what happens to it.

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

Output:

Code:
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

Code:
np.nansum()

and

Code:
np.nanmax()

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

Code:
NaN

values in the array.

Example:

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

Output:

Code:
70.0
40.0

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

Another way to solve our problem is to use the

Code:
isna()

method of the Pandas module.

Code:
pandas.isna()

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

Code:
NaN

value is encountered.

Example 1:

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

Output:

Code:
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.

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

Output:

Code:
1 2
False False False
False True False

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

We can check for the

Code:
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,

Code:
NaN

values do not fall within this range.

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

Code:
-inf

and

Code:
inf

. If yes , then it is not a

Code:
NaN

value else it is a

Code:
NaN

value.

Example:

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

Output:

Code:
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 (

Code:
pandas

Code:
NumPy

, and

Code:
math

) in Python to check for the

Code:
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.)

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