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Tags : PythonPython List

97

This tutorial will look into various methods to find and remove the `NaN`

values from the list in Python. The `NaN`

value in programming means `Not a Number`

, which means the variable’s value is not a number.

If a `NaN`

value occurs in an array or a list, it can create problems and errors in the calculations. We will also look into ways to remove the string values `nan`

from the list in this tutorial. We can remove the `NaN`

or `'nan'`

values from the list, by using the following methods.

`NaN`

From the List in Python Using the `math.isnan()`

MethodThe `math.isnan(value)`

method takes a number `value`

as input and returns `True`

if the `value`

is a `NaN`

value and returns `False`

otherwise. Therefore we can check if there a `NaN`

value in a list or array of numbers using the `math.isnan()`

method.

We need the `math.isnan()`

method because `if float('NaN') == float('NaN')`

returns `False`

in Python or we can say that two `NaN`

values are not equal in Python. The below example code demonstrates how to use the `math.isnan()`

method to remove the `NaN`

value from the list.

`import math mylist = [1,2,float('nan'),8,6,4,float('nan')] print(mylist) newlist = [x for x in mylist if math.isnan(x) == False] print(newlist) `

Output:

`[1, 2, nan, 8, 6, 4, nan] [1, 2, 8, 6, 4] `

`NaN`

From the List in Python Using the `numpy.isnan()`

MethodThe `np.isnan(array)`

method, takes the `array`

as input and returns `True`

for the corresponding index if it is `NaN`

value and returns `False`

otherwise.

The below example code demonstrates how to remove the `NaN`

values from the list using the `numpy.isnan()`

method:

`import numpy as np mylist = [1,2,float('nan'),8,6,4,float('nan')] print(mylist) newlist = [x for x in mylist if np.isnan(x) == False] print(newlist) `

Output:

`[1, 2, nan, 8, 6, 4, nan] [1, 2, 8, 6, 4] `

`NaN`

From the List of Strings in PythonNow, let’s suppose that the number list is converted to string type, and we want to check if it contains any `NaN`

values. After converting into the string type, the `NaN`

value becomes a string equal to `'nan'`

and can be easily detected and remove by comparing it with `'nan'`

.

The below example code demonstrates how we can remove the `NaN`

value from the list of string data type:

`mylist = [1,2,'nan',8,6,4,'nan'] mylist = [str(x) for x in mylist] print(mylist) newlist = [x for x in mylist if x != 'nan'] print(newlist) `

Output:

`['1', '2', 'nan', '8', '6', '4', 'nan'] ['1', '2', '8', '6', '4'] `

`NaN`

From the List in Python Using the `pandas.isnull()`

MethodThe `pandas.isnull(obj)`

takes a scalar or an array-like `obj`

as input and returns `True`

if the value is equal to `NaN`

, `None`

, or `NaT`

; otherwise, it returns `False`

.

The example code demonstrates how to use the `pandas.isnull()`

method to remove the `NaN`

values from Python’s list.

`import pandas as pd mylist = [1,2,float('nan'),8,float('nan'),4,float('nan')] print(mylist) newlist = [x for x in mylist if pd.isnull(x) == False] print(newlist) `

Output:

`[1, 2, nan, 8, nan, 4, nan] [1, 2, 8, 4] `

Now suppose we do not know the type of the list or if the list contains the data of various data types. In this case, we can check and remove the `NaN`

values and `'nan'`

values from the list using the `pandas.isnull()`

method by comparing each value of the list with the `'nan'`

value.

We can use the `pandas.isnull()`

method because, unlike the previously mentioned methods, the `pandas.isnull()`

method does not return an error if the string data type is given as input. Therefore we can use the `pandas.isnull()`

method to remove the `NaN`

and `'nan'`

value from the list or an array in Python.

The below example code demonstrates how to use the `pandas.isnull()`

method and the `'nan'`

value to remove `NaN`

and `'nan'`

values from the list in Python.

`import pandas as pd mylist = ['John',23,'nan','New York',float('nan')] print(mylist) newlist = [x for x in mylist if pd.isnull(x) == False and x != 'nan'] print(newlist) `

Output:

`['John', 23, 'nan', 'New York', nan] ['John', 23, 'New York'] `