NumPy data types

Introduction to NumPy

Izzy Weber

Core Curriculum Manager, DataCamp

NumPy vs. Python data types

Sample Python data types:

Sample NumPy data types:

The number 10436 represented in binary is:

An image of the number 10436 in binary

np.int32 can store 4,294,967,296 integers:

A diagram of np.int32 storage capacity

The number 10436 represented in binary is:

An image of the number 10436 in binary

np.int32 can store 4,294,967,296 integers:

A diagram of np.int32 storage capacity

np.array([1.32, 5.78, 175.55]).dtype

dtype('float64')

int_array = np.array([[1, 2, 3], [4, 5, 6]])
int_array.dtype

dtype('int64')

np.array(["Introduction", "to", "NumPy"]).dtype

dtype('<U12')

float32_array = np.array([1.32, 5.78, 175.55], dtype=np.float32)
float32_array.dtype

dtype('float32')

boolean_array = np.array([[True, False], [False, False]], dtype=np.bool_)
boolean_array.astype(np.int32)

array([[1, 0],
       [0, 0]], dtype=int32)

np.array([True, "Boop", 42, 42.42])

array(['True', 'Boop', '42', '42.42'], dtype='<U5')

Adding a float to an array of integers will change all integers into floats:

np.array([0, 42, 42.42]).dtype

dtype('float64')

Adding an integer to an array of booleans will change all booleans in to integers:

np.array([True, False, 42]).dtype

dtype('int64')

Introduction to NumPy