How to drop all missing values from a numpy array?
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How to drop all missing values from a numpy array?

How to drop all missing values from a numpy array?

This recipe helps you drop all missing values from a numpy array

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Recipe Objective

How to drop all missing values from a numpy array?

Droping the missing values or nan values can be done by using the function "numpy.isnan()" it will give us the indexes which are having nan values and when combined with other function which is "numpy.logical_not()" where the boolean values will be reversed. At last we want the elements which are having non-nan values which can be further filtered out and store it into another array.

Step 1 - Import library

import numpy as np

Step 2 - Take Sample data

Sample_data = np.array([1,2,7,8,np.nan,9,5,np.nan,1,0]) print("This is Sample data with nan values in it:", Sample_data)
This is Sample data with nan values in it: [ 1.  2.  7.  8. nan  9.  5. nan  1.  0.]

Step 3 - Remove Nan values

remove_nan = Sample_data[np.logical_not(np.isnan(Sample_data))]

Step 4 - Print Results

print("This is the original data with nan values:", Sample_data, "\n") print("This is the data without nan values:", remove_nan)
This is the original data with nan values: [ 1.  2.  7.  8. nan  9.  5. nan  1.  0.] 

This is the data without nan values: [1. 2. 7. 8. 9. 5. 1. 0.]

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