NumPy - 12 NumPy Masking

NumPy masking refers to the technique of filtering or selecting elements in an array based on a condition using Boolean masks. It is commonly used for conditional operations and efficiently filtering data. NumPy masking is a powerful tool for filtering, modifying, and handling missing data.

1. Creating a Boolean Mask

A mask is a Boolean array (same shape as the original array) where:

True indicates that the corresponding element satisfies the condition.
False indicates that it does not.

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> mask = arr > 25  # Mask for values greater than 25
>>> print(mask)
[False False  True  True  True]

2. Using Masking to Filter Data

You can use the Boolean mask to extract elements that meet a condition.

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> mask = arr > 25  # Mask for values greater than 25
>>> print(mask)
[False False  True  True  True]
>>> filtered = arr[mask]  # Extract elements where mask is True
>>> print(filtered)
[30 40 50]

3. Applying Multiple Conditions

You can use logical operators like:

& (AND)
| (OR)
~ (NOT)

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> mask = (arr > 15) & (arr < 45)  # Values between 15 and 45
>>> filtered = arr[mask]
>>> print(filtered)
[20 30 40]

4. Modifying Elements Using a Mask

You can modify elements that satisfy a condition.

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> arr[arr > 30] = 100  # Replace values greater than 30 with 100
>>> print(arr)
[ 10  20  30 100 100]

5. Masking with ’np.where()’

np.where(condition, x, y) returns an array where:

x is placed where the condition is True
y is placed where the condition is False

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> new_arr = np.where(arr < 25, 1, 0)  # Replace values < 25 with 1, others with 0
>>> print(new_arr)
[0 0 1 1 1]

6. Masked Arrays with ’numpy.ma’

NumPy provides a numpy.ma module for handling masked (missing or invalid) data.

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> masked_arr = np.ma.array(arr, mask=(arr < 25))  # Mask values less than 25
>>> print(masked_arr)
[10 20 -- -- --]

7. Counting and Summing Masked Values

You can count or sum elements using masks.

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> count = np.sum(arr > 25)  # Count values greater than 25
>>> total = np.sum(arr[arr > 25])  # Sum of values greater than 25
>>> print(count)  # Output: 3
3
>>> print(total)  # Output: 120
120

8. Masking in 2D Arrays

You can apply the same masking techniques to 2D arrays.

>>> import numpy as np
>>> arr2D = np.array([[10, 20, 30], [40, 50, 60]])
>>> mask2D = arr2D > 25  # Mask for values greater than 25
>>> print(arr2D[mask2D])
[30 40 50 60]

9. Using ’np.where()’ for Indexing

np.where() is also useful for obtaining the indices of elements that satisfy a given condition.

>>> import numpy as np
>>> arr = np.array([10, 20, 30, 40, 50])
>>> indices = np.where(arr > 25)  # Returns indices where the condition is True
>>> print(indices)
(array([2, 3, 4]),)  # Indices of 30, 40, 50

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