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NumPy lessons (11/28)

NumPy — Array Reshape

Key attributes

import numpy as np

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

print(f"Shape: {arr.shape}")      # (2, 3)
print(f"Size: {arr.size}")        # 6
print(f"Ndim: {arr.ndim}")        # 2
print(f"Dtype: {arr.dtype}")      # int64
print(f"Itemsize: {arr.itemsize}") # 8 bytes

reshape

arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.reshape(2, 3)
print(reshaped)

flatten

arr_2d = np.array([[1, 2], [3, 4]])
flat = arr_2d.flatten()
print(flat)  # [1, 2, 3, 4]

T (transpose)

arr = np.array([[1, 2], [3, 4]])
print(arr.T)

dtype

arr = np.array([1, 2, 3], dtype=np.float32)
print(arr.dtype)  # float32

Mini Practice

  1. Check shape and size
  2. Reshape arrays
  3. Transpose matrices
  4. Work with dtypes

Up Next

Continue with Indexing - Accessing elements.

Related Topics

Frequently Asked Questions about Array Reshape

What is Array Reshape in NumPy?

Array Reshape is a fundamental concept in NumPy. This lesson explains it step by step with clear examples, making it easy for beginners to understand.

How do I learn Array Reshape?

Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Array Reshape.

Why is Array Reshape important in NumPy?

Array Reshape is essential for NumPy development. Understanding this concept will help you write better code and solve real-world problems more effectively.