np.in1d() for membership testing in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.in1d() do in numpy?

np.in1d() checks if each element of one array is present in another array. It returns a boolean array showing membership.

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How does np.in1d() differ from Python's in keyword?

np.in1d() works on whole arrays at once and returns an array of booleans, while in checks membership for a single element only.

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What is the output type of np.in1d(array1, array2)?

The output is a numpy boolean array. Each position is True if the element in array1 is found in array2, otherwise False.

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Example: What does np.in1d([1, 2, 3], [2, 3, 4]) return?

It returns [False, True, True] because 1 is not in the second array, but 2 and 3 are.

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Can np.in1d() be used with arrays of strings?

Yes, np.in1d() works with any array type, including strings, to test membership.

What does np.in1d([5, 6, 7], [6, 8, 9]) return?

A [True, False, True]

B [False, True, False]

C [False, False, False]

D [True, True, True]

Only 6 is in the second array, so the output is [False, True, False].

Which of these is true about np.in1d()?

A It returns a boolean array.

B It modifies the original arrays.

C It only works with numeric arrays.

D It returns the common elements.

np.in1d() returns a boolean array indicating membership, it does not modify arrays or return elements.

If np.in1d(a, b) returns all False, what does it mean?

A No elements of <code>a</code> are in <code>b</code>.

B All elements of <code>a</code> are in <code>b</code>.

C Arrays <code>a</code> and <code>b</code> are equal.

D Arrays <code>a</code> and <code>b</code> have the same length.

All False means none of the elements in a are found in b.

Can np.in1d() be used to check membership of strings?

A Only with integers and floats.

B No, only numbers are supported.

C Yes, it works with any data type.

D Only with boolean arrays.

np.in1d() works with any array type, including strings.

What is the shape of the output of np.in1d(array1, array2)?

A 2D array

B Same shape as <code>array2</code>

C Scalar boolean

D Same shape as <code>array1</code>

The output boolean array matches the shape of the first input array.

Explain how np.in1d() helps in checking if elements of one array exist in another.

Think about how you find if items from your shopping list are in the store's stock list.

Describe a real-life example where np.in1d() could be useful.

Imagine you want to find which friends from your contact list are attending a party.