**Overview**

**Problem**: How to check if a given value is `NaN`

?

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

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 `NaN`

value?

`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 `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 `NaN`

.

We have to deal with `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.

**Method 1: Using math.isnan()**

The simplest solution to check for NaN values in Python is to use the mathematical function `math.isnan()`

.

`math.isnan()`

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

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

**Example: **

# 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:**

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

In the above example, since `x`

represents a NaN value, hence, the `isNaN`

method returns `True`

but in case of `y`

, `isNan`

returns `False`

and prints the type of the variable `y`

as an output.

**Method 2: Hack NaN Using != Operator**

The most unique thing about `NaN`

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

value even against itself. Hence, we can use the `!=`

(not equal to) operator to check for the `NaN`

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

, the expression `(x == x)`

will always return `True`

. If it’s not equal, then it is a `NaN`

value.

**Example 1:**

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:**

# 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:**

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

**Method 3: Using numpy.isnan()**

We can also use the `NumPy`

library to check whether the given value is `NaN`

or not. We just need to ensure that we import the library at the start of the program and then use its `np.isnan(x)`

method.

The `np.isnan(number)`

function checks whether the element in a Numpy array is `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 `NaN`

value or not.

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:**

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

**π‘TRIVIA**

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

values and find out what happens to it.

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

**Output:**

```
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 `np.nansum()`

and `np.nanmax()`

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

values in the array.

**Example:**

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

**Output:**

```
70.0
40.0
```

**Method 4: Using pandas.isna()**

**Method 4: Using pandas.isna()**

Another way to solve our problem is to use the `isna()`

method of the Pandas module. `pandas.isna()`

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

value is encountered.

**Example 1:**

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

**Output:**

```
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.

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

**Output:**

```
0 1 2
0 False False False
1 False True False
```

**Method 5: By Checking The Range**

We can check for the `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, `NaN`

values do not fall within this range.

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

and `inf`

. If yes , then it is not a `NaN `

value else it is a `NaN `

value.

**Example:**

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

**Output:**

```
25.87
Not a Number!
```

**Recommended read: Python Infinity**

**Conclusion**

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

, `NumPy`

, and `math`

) in Python to check for the `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**

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