Arrays from Python Lists

Arrays from Lists

So you want to create arrays from Python lists? That's where it all starts. Think of it like converting your messy notes into a neat spreadsheet.

1D Arrays

The simplest case - just pass a flat list to np.array().

import numpy as np

my_list = [10, 20, 30, 40, 50]
arr_1d = np.array(my_list)

print(f"1D Array: {arr_1d}")
print(f"Shape: {arr_1d.shape}")
print(f"Dimensions: {arr_1d.ndim}")
    

Notice the shape is (5,) - that's a tuple with one element. The comma is important because it tells Python this is a tuple, not just parentheses around a number.

2D Arrays (Matrices)

Use nested lists to create 2D arrays. Each inner list becomes a row.

import numpy as np

nested_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
matrix = np.array(nested_list)

print(f"2D Array:\n{matrix}")
print(f"Shape: {matrix.shape}")
print(f"Dimensions: {matrix.ndim}")
    

Here is the thing - all rows must have the same number of elements. If they don't, NumPy will create an array of arrays instead of a proper 2D array.

3D Arrays

For 3D arrays, you need three levels of nesting. Think of it like a stack of matrices.

import numpy as np

nested_3d = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
cube = np.array(nested_3d)

print(f"3D Array:\n{cube}")
print(f"Shape: {cube.shape}")
print(f"Dimensions: {cube.ndim}")
    

One thing that confused me at first was the shape interpretation. For 3D, shape (2, 2, 2) means 2 blocks, 2 rows per block, 2 columns per row.

Key Takeaways

• Use nested lists to create multi-dimensional arrays
• All rows in 2D arrays must have the same length
• Shape tuple shows dimensions at each level
• 3D arrays are stacks of 2D matrices