Defining structured dtypes in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a structured dtype with fields 'name' as string and 'age' as integer.

NumPy

dtype = np.dtype([('name', [1]), ('age', 'i4')])

Drag options to blanks, or click blank then click option'

A'i8'

B'f4'

C'U10'

D'S10'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'S10' which is byte string instead of Unicode string.

Using integer types like 'i8' or 'f4' for string fields.

The field 'name' is a string of length 10, so we use 'U10' for Unicode string of length 10.

2fill in blank

medium

Complete the code to create a structured dtype with fields 'height' as float and 'weight' as float.

NumPy

dtype = np.dtype([('height', [1]), ('weight', 'f8')])

Drag options to blanks, or click blank then click option'

A'f8'

B'i4'

C'U5'

D'S5'

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer types like 'i4' for float fields.

Using string types for numeric fields.

Both 'height' and 'weight' are floats with 8 bytes, so 'f8' is used.

3fill in blank

hard

Fix the error in the dtype definition to correctly define 'id' as integer and 'score' as float.

NumPy

dtype = np.dtype([('id', 'i4'), ('score', [1])])

Drag options to blanks, or click blank then click option'

A'i4'

B'S4'

C'U4'

D'f4'

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer type 'i4' for 'score' field.

Using string types for numeric fields.

The 'score' field should be a float type, so 'f4' (32-bit float) is correct.

4fill in blank

hard

Fill both blanks to define a structured dtype with 'date' as 10-character string and 'temperature' as 64-bit float.

NumPy

dtype = np.dtype([('date', [1]), ('temperature', [2])])

Drag options to blanks, or click blank then click option'

A'U10'

B'f8'

C'i4'

D'S10'

Attempts:

3 left

💡 Hint

Common Mistakes

Using byte string 'S10' instead of Unicode string for 'date'.

Using integer type for 'temperature'.

'date' is a Unicode string of length 10, so 'U10' is used. 'temperature' is a 64-bit float, so 'f8' is used.

5fill in blank

hard

Fill all three blanks to create a structured dtype with 'city' as 15-char string, 'population' as 32-bit int, and 'area' as 32-bit float.

NumPy

dtype = np.dtype([('city', [1]), ('population', [2]), ('area', [3])])

Drag options to blanks, or click blank then click option'

A'U15'

B'i4'

C'f4'

D'S15'

Attempts:

3 left

💡 Hint

Common Mistakes

Using byte string 'S15' instead of Unicode string for 'city'.

Mixing up integer and float types for 'population' and 'area'.

'city' is a Unicode string of length 15 ('U15'), 'population' is a 32-bit integer ('i4'), and 'area' is a 32-bit float ('f4').