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.

beginner

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.

beginner

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.

beginner

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.

beginner

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.

Practice

1. What does the np.in1d() function do in NumPy?

easy

A. Finds the unique elements in an array.

B. Sorts the elements of an array in ascending order.

C. Calculates the sum of elements in an array.

D. Checks if elements of one array are present in another array and returns a boolean array.

Solution

1. Step 1: Understand the purpose of np.in1d()

   The function checks membership of each element in the first array against the second array.

2. Step 2: Identify the output type

   It returns a boolean array indicating True where elements are found and False otherwise.

3. Final Answer:

   Checks if elements of one array are present in another array and returns a boolean array. -> Option D

4. Quick Check:

   Membership test = Checks if elements of one array are present in another array and returns a boolean array. [OK]

Hint: Remember: np.in1d returns booleans for membership [OK]

Common Mistakes:

• Confusing np.in1d() with sorting or summing functions
• Expecting np.in1d() to return the matching elements instead of booleans
• Thinking np.in1d() modifies the original arrays

2. Which of the following is the correct syntax to check if elements of array a are in array b using np.in1d()?

easy

A. np.in1d(b, a)

B. np.in1d(a, b)

C. np.in1d(a == b)

D. np.in1d(a, b, axis=1)

Solution

1. Step 1: Recall np.in1d() parameter order

   The first argument is the array to test membership for, the second is the array to check against.

2. Step 2: Evaluate each option

   np.in1d(a, b) uses correct order: np.in1d(a, b). np.in1d(b, a) reverses arrays, np.in1d(a == b) uses invalid syntax, np.in1d(a, b, axis=1) uses unsupported axis parameter.

3. Final Answer:

   np.in1d(a, b) -> Option B

4. Quick Check:

   Correct syntax = np.in1d(a, b) [OK]

Hint: First array is tested, second array is reference [OK]

Common Mistakes:

• Swapping the order of arrays in np.in1d()
• Adding unsupported parameters like axis
• Using comparison operators inside np.in1d()

3. What is the output of the following code?

import numpy as np
x = np.array([1, 3, 5, 7])
y = np.array([3, 4, 5])
result = np.in1d(x, y)
print(result)

medium

A. [False True True False]

B. [True False True False]

C. [False True False False]

D. [True True True True]

Solution

1. Step 1: Check each element of x against y

   1 in y? No (False), 3 in y? Yes (True), 5 in y? Yes (True), 7 in y? No (False).

2. Step 2: Form the boolean array

   Result is [False, True, True, False].

3. Final Answer:

   [False True True False] -> Option A

4. Quick Check:

   Membership booleans = [False True True False] [OK]

Hint: Check each element one by one for membership [OK]

Common Mistakes:

• Mixing up True and False positions
• Assuming np.in1d returns matching elements instead of booleans
• Forgetting to import numpy

4. The following code throws an error. What is the mistake?

import numpy as np
x = [1, 2, 3]
y = np.array([2, 3, 4])
result = np.in1d(x, y, axis=0)
print(result)

medium

A. np.in1d() requires both inputs to be lists.

B. x should be converted to a NumPy array before using np.in1d().

C. np.in1d() does not accept the 'axis' parameter.

D. The arrays x and y must have the same shape.

Solution

1. Step 1: Check np.in1d() parameters

   np.in1d() accepts only two main parameters: the test array and the array to check against. It does not support an 'axis' parameter.

2. Step 2: Identify the error cause

   Passing axis=0 causes a TypeError because it's not a valid argument.

3. Final Answer:

   np.in1d() does not accept the 'axis' parameter. -> Option C

4. Quick Check:

   Invalid parameter = np.in1d() does not accept the 'axis' parameter. [OK]

Hint: np.in1d() only takes two main arguments [OK]

Common Mistakes:

• Trying to use axis parameter with np.in1d()
• Assuming input types must match exactly
• Thinking np.in1d() requires both inputs as arrays

5. You have two arrays:

data = np.array([10, 20, 30, 40, 50])
filter_vals = np.array([20, 40, 60])

You want to create a new array containing only elements from data that are present in filter_vals. Which code snippet correctly achieves this?

hard

A. filtered = data[np.in1d(data, filter_vals)]

B. filtered = filter_vals[np.in1d(filter_vals, data)]

C. filtered = np.in1d(data, filter_vals)

D. filtered = data[filter_vals]

Solution

1. Step 1: Use np.in1d() to get boolean mask

   np.in1d(data, filter_vals) returns a boolean array marking elements of data present in filter_vals.

2. Step 2: Use boolean mask to filter data

   Indexing data with this boolean mask selects only matching elements.

3. Final Answer:

   filtered = data[np.in1d(data, filter_vals)] -> Option A

4. Quick Check:

   Boolean mask indexing = filtered = data[np.in1d(data, filter_vals)] [OK]

Hint: Use np.in1d() mask to index original array [OK]

Common Mistakes:

• Indexing filter_vals instead of data
• Using np.in1d() without indexing
• Trying to index with filter_vals directly