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Practice

Boolean Masking and Filtering

NumPy allows you to filter arrays using boolean conditions, a technique called masking.

You compare values in an array, and NumPy returns a new array with only the values that meet the condition.


Boolean Arrays​

A comparison like arr > 10 produces a new array of True or False values.

Boolean Arrays
arr = np.array([5, 12, 18, 7])
mask = arr > 10

print(mask)        # [False  True  True False]

Filtering Values​

You can use this boolean array as a mask to filter the original array.

Filtering Values
print(arr[mask])   # [12 18]

Or write it more directly:

Filtering Values another way
print(arr[arr > 10])  # [12 18]

Masking is especially useful for filtering rows, selecting ranges, or identifying outliers.


Summary​

  • Use comparisons (>, <, ==, etc.) to create boolean masks
  • Apply the mask to select only the values you want
  • Works with both 1D and 2D arrays

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