NumPy Data Types (dtypes)

What is a dtype?

A dtype (data type) tells NumPy what kind of values an array contains, such as:

• integers (int32, int64)
• floats (float32, float64)
• booleans (bool)
• strings (<U...) and bytes (|S...)

Because NumPy uses a single dtype for the entire array, it can store values efficiently and run fast computations. A dtype name is really just a hint about memory layout: a type name (int, float) followed by the number of bits per element. A standard double-precision float takes 8 bytes (64 bits) — hence float64.

  flowchart TD
  A["np.array(data)"] --> B{"All values
the same kind?"}
  B -- "All ints" --> C["dtype: int64"]
  B -- "Any float present" --> D["dtype: float64"]
  B -- "Any string present" --> E["dtype: string / object"]
  B -- "Explicit dtype= given" --> F["Use that dtype
(cast or error)"]

Checking dtype

import numpy as np
 
arr = np.array([1, 2, 3])
print(arr.dtype)

Common numeric dtypes

Integers

a = np.array([1, 2, 3], dtype=np.int32)
b = np.array([1, 2, 3], dtype=np.int64)
print(a.dtype, b.dtype)

Floats

a = np.array([1.5, 2.0, 3.25], dtype=np.float32)
b = np.array([1.5, 2.0, 3.25], dtype=np.float64)
print(a.dtype, b.dtype)

Memory usage and dtype

Smaller dtypes use less memory. Every element in a float32 array takes 4 bytes; every element in a float64 array takes 8 bytes — double the space for the same number of values.

import numpy as np
 
arr32 = np.ones(1_000_000, dtype=np.float32)
arr64 = np.ones(1_000_000, dtype=np.float64)
 
print("float32 bytes:", arr32.nbytes)
print("float64 bytes:", arr64.nbytes)

Type conversion

Using .astype()

import numpy as np
 
arr = np.array([1, 2, 3])
arr_f = arr.astype(np.float64)
print(arr_f, arr_f.dtype)

Safe conversion (avoid overflow)

Converting large values into a smaller dtype can overflow.

import numpy as np
 
arr = np.array([300], dtype=np.int16)
print(arr.astype(np.uint8))  # wraps around in many cases

Dtype pitfalls in data analytics

1) Missing values

NumPy numeric arrays can’t store NaN in integer dtype.

import numpy as np
 
# This will upcast to float automatically because of np.nan
arr = np.array([1, 2, np.nan])
print(arr)
print(arr.dtype)

2) Mixed types

If you mix strings and numbers, dtype may become object or strings.

import numpy as np
 
arr = np.array([1, "two", 3])
print(arr)
print(arr.dtype)

Arrays with dtype=object are slower for numerical operations.

Check your understanding

Next

Continue to: Indexing and Slicing Arrays to learn how to select, filter, and extract parts of arrays.

🧪 Try It Yourself

Exercise 1 – Check and Set dtype

Press Run to see what your code prints.

Exercise 2 – Convert with astype()

Press Run to see what your code prints.

Exercise 3 – Memory Cost of a dtype

Press Run to see what your code prints.