How to check np nan in python

import numpy as np

the_array = np.array([np.nan, 2, 3, 4])
array_has_nan = np.isnan(the_array).any()
print(array_has_nan)

the_array = np.array([1, 2, 3, 4])
array_has_nan = np.isnan(the_array).any()
print(array_has_nan)

In NumPy, to replace missing values NaN (np.nan) in ndarray with other numbers, use np.nan_to_num() or np.isnan().

This article describes the following contents.

• Missing value NaN (np.nan) in NumPy
• Specify filling_values argument of np.genfromtxt()
• Replace NaN with np.nan_to_num()
• Replace NaN with np.isnan()

If you want to delete the row or column containing the missing value instead of replacing it, see the following article.

• NumPy: Remove rows/columns with missing value (NaN) in ndarray

Missing value NaN (np.nan) in NumPy

When you read a CSV file with np.genfromtxt(), by default, the missing data is regarded as a missing value NaN (Not a Number).

When outputting with print(), it is printed as nan.

• sample_nan.csv
• numpy.genfromtxt — NumPy v1.21 Manual

import numpy as np

a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')
print(a)
# [[11. 12. nan 14.]
#  [21. nan nan 24.]
#  [31. 32. 33. 34.]]

If you want to generate NaN explicitly, use np.nan or float('nan'). You can also import the math module of the standard library and use math.nan. They are all the same.

a_nan = np.array([0, 1, np.nan, float('nan')])
print(a_nan)
# [ 0.  1. nan nan]

Since comparing missing values with == returns False, use np.isnan() or math.isnan() to check if the value is NaN or not.

• numpy.isnan — NumPy v1.21 Manual
• math.isnan — Mathematical functions — Python 3.10.1 documentation

print(np.nan == np.nan)
# False

print(np.isnan(np.nan))
# True

np.isnan() checks whether each element of ndarray is a NaN or not.

print(a_nan == np.nan)
# [False False False False]

print(np.isnan(a_nan))
# [False False  True  True]

Specify filling_values argument of np.genfromtxt()

If the data of a CSV file is missing, you can fill the missing part with any value by specifying the argument filling_values when reading it with np.genfromtxt().

For example, if you want to fill NaN with 0:

a_fill = np.genfromtxt('data/src/sample_nan.csv', delimiter=',', filling_values=0)
print(a_fill)
# [[11. 12.  0. 14.]
#  [21.  0.  0. 24.]
#  [31. 32. 33. 34.]]

Replace NaN with np.nan_to_num()

You can use np.nan_to_num() to replace NaN.

• numpy.nan_to_num — NumPy v1.21 Manual

Note that np.nan_to_num() also replaces infinity inf. See the following article for details.

• "inf" for infinity in Python

If you specify ndarray as the first argument of np.nan_to_num(), a new ndarray is created with missing values replaced with 0 by default. The original ndarray is not changed.

a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')
print(np.nan_to_num(a))
# [[11. 12.  0. 14.]
#  [21.  0.  0. 24.]
#  [31. 32. 33. 34.]]

print(a)
# [[11. 12. nan 14.]
#  [21. nan nan 24.]
#  [31. 32. 33. 34.]]

If the second argument copy is set to False, the original ndarray is changed.

print(np.nan_to_num(a, copy=False))
# [[11. 12.  0. 14.]
#  [21.  0.  0. 24.]
#  [31. 32. 33. 34.]]

print(a)
# [[11. 12.  0. 14.]
#  [21.  0.  0. 24.]
#  [31. 32. 33. 34.]]

In NumPy version 1.17 or later, the value to be replaced can be specified by the argument nan.

a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')
print(np.nan_to_num(a, nan=-1))
# [[11. 12. -1. 14.]
#  [21. -1. -1. 24.]
#  [31. 32. 33. 34.]]

You can replace NaN with the average of elements that are not missing values with np.nanmean().

• NumPy: Calculate the sum, mean, max, min of ndarray containing np.nan

print(np.nanmean(a))
# 23.555555555555557

print(np.nan_to_num(a, nan=np.nanmean(a)))
# [[11.         12.         23.55555556 14.        ]
#  [21.         23.55555556 23.55555556 24.        ]
#  [31.         32.         33.         34.        ]]

In versions where the nan argument is not implemented, you can replace NaN with a value other than 0 in the following way.

Replace NaN with np.isnan()

You can use np.isnan() to check whether elements of ndarray are NaN or not.

a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')
print(np.isnan(a))
# [[False False  True False]
#  [False  True  True False]
#  [False False False False]]

Using this result, you can assign any value to the missing value element.

If you want to replace NaN with 0:

a[np.isnan(a)] = 0
print(a)
# [[11. 12.  0. 14.]
#  [21.  0.  0. 24.]
#  [31. 32. 33. 34.]]

You can also use np.nanmean() to replace NaN with the average value.

a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')
a[np.isnan(a)] = np.nanmean(a)
print(a)
# [[11.         12.         23.55555556 14.        ]
#  [21.         23.55555556 23.55555556 24.        ]
#  [31.         32.         33.         34.        ]]

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