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Practice

Boolean Masking and Filtering

NumPy lets you filter arrays using boolean conditions — also called masking.

You can compare values in an array, and NumPy will return a new array with only the values that match the condition.


Boolean Arrays​

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

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

print(mask)        # [False  True  True False]

Filtering Values​

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

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

Or write it in one line:

print(arr[arr > 10])  # [12 18]

This is very useful for filtering rows, selecting values in a range, or finding outliers.


Summary​

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

What’s Next​

You’ll now practice filtering arrays using conditions in a Jupyter Notebook.