How to Use np.nan in Python

The np.nan is a constant representing a missing or undefined numerical value in a NumPy array. It stands for not a number and has a float type.

Syntax

numpy.nan

Example 1: Usage of np.nan() Method

import numpy as np

print(np.nan)

Output

nan

Example 2: Numpy array with nan value

import numpy as np

myarr = np.array([1, 0, np.nan, 3])

print(myarr)

Output

[ 1. 0. nan 3.]

The important thing I would like you to take away from this is that all of our integers have been converted to floats, and that’s because NumPy has defined the NaN data type as a float.

Example 3: Comparing np.nan values

You can use the “double equal(==)” operator to compare two nan values in Python.

import numpy as np

print(np.nan == np.nan)

Output

False

You can see that two np.nan values are not same.

Example 4: Checking for NaN values

To check for NaN values in an array, use the np.isnan() method.

Syntax

np.isnan(arr)

Visual representation

Here is an example.

import numpy as np

arr = np.nan
arr2 = 1

print(np.isnan(np.nan))
print(np.isnan(arr2))

Output

True
False

Example 5: NaN in Pandas Dataframe

In pandas, “NaN” stands for “Not a Number” and is the standard missing data marker. It’s essentially Python marking data as “not present” or “unknown”.

import numpy as np
import pandas as pd

df = pd.DataFrame([(1.0, np.nan, -1.0, 21.0),
                   (np.nan, 12.0, np.nan, 11),
                   (21.0, 15.0, np.nan, 91.0),
                   (np.nan, 14.0, -31.0, 19.0)],
              columns=list('abcd'))

print(df)

Output

Example 6: Checking for NaN values in Pandas DataFrame

You can check for NaN values by using the isnull() method. The output will be a boolean mask with dimensions of the original dataframe.

import numpy as np
import pandas as pd

df = pd.DataFrame([(1.0, np.nan, -1.0, 21.0),
     (np.nan, 12.0, np.nan, 11),
     (21.0, 15.0, np.nan, 91.0),
     (np.nan, 14.0, -31.0, 19.0)],
     columns=list('abcd'))

print(pd.isnull(df))

Output

Example 7: Replacing NaN values in DataFrame

To replace NaN values in a Pandas Dataframe, you can “use the df.fillna() method.”

import numpy as np
import pandas as pd

df = pd.DataFrame([(1.0, np.nan, -1.0, 21.0),
                   (np.nan, 12.0, np.nan, 11),
                   (21.0, 15.0, np.nan, 91.0),
                   (np.nan, 14.0, -31.0, 19.0)],
          columns=list('abcd'))

print(df.fillna(0))

Output

Example 8: Dropping rows containing NaN values

To drop the rows or columns with NaNs, you can use the .dropna() method.

import numpy as np
import pandas as pd

df = pd.DataFrame([(1.0, np.nan, -1.0, 21.0),
  (np.nan, 12.0, np.nan, 11),
  (21.0, 15.0, np.nan, 91.0),
  (np.nan, 14.0, -31.0, 19.0)],
  columns=list('abcd'))

print(df.dropna(axis="columns"))

Output

    d
0  21.0
1  11.0
2  91.0
3  19.0

That’s it.

Krunal

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.