Array Attributes
Every NumPy array comes with useful metadata. Think of it like checking the specs of a car before buying it. These attributes tell you everything about your array.
Shape - The Dimensions
Shape tells you the size of each dimension. It's a tuple that's super important for understanding your data.
import numpy as np
arr_1d = np.array([1, 2, 3, 4, 5])
arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f"1D Shape: {arr_1d.shape}")
print(f"2D Shape: {arr_2d.shape}")
For 1D, shape is (5,) - note the comma. For 2D, it's (2, 3) meaning 2 rows, 3 columns.
Size & Ndim
Size gives total elements, ndim gives number of dimensions.
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(f"Size: {arr.size}")
print(f"Dimensions: {arr.ndim}")
Here is the thing - size is just the product of all shape values. 2 * 3 = 6.
Dtype - Data Type
Every array has a dtype that tells you what type of data it holds.
import numpy as np
int_arr = np.array([1, 2, 3])
float_arr = np.array([1.0, 2.0, 3.0])
print(f"Int dtype: {int_arr.dtype}")
print(f"Float dtype: {float_arr.dtype}")
Common dtypes: int32, int64, float32, float64, bool, string_
Itemsize - Memory per Element
Itemsize tells you how many bytes each element uses.
import numpy as np
int_arr = np.array([1, 2, 3])
float_arr = np.array([1.0, 2.0, 3.0])
print(f"Int itemsize: {int_arr.itemsize} bytes")
print(f"Float itemsize: {float_arr.itemsize} bytes")
One thing that confused me at first was why itemsize matters. It affects memory usage and performance. Larger dtypes use more memory.
Key Takeaways
- shape shows dimensions as a tuple
- size gives total number of elements
- ndim shows number of dimensions
- dtype shows data type, itemsize shows bytes per element