101 numpy exercises for data analysis python

Okay, let's dive into a comprehensive guide to 101 NumPy exercises designed to boost your data analysis skills in Python. We'll cover a wide range of NumPy functionalities with detailed explanations and code examples. This will be a long and detailed response, so get ready to learn!

**Why NumPy?**

NumPy (Numerical Python) is the foundation of numerical computing in Python. It provides:

* **Efficient Array Operations:** NumPy's core is the `ndarray` (n-dimensional array) object, which allows for highly optimized mathematical operations across entire arrays.
* **Broadcasting:** NumPy's broadcasting rules automatically handle operations between arrays with different shapes under certain conditions.
* **Mathematical Functions:** A vast library of built-in mathematical functions (trigonometric, exponential, logarithmic, etc.) are available.
* **Linear Algebra:** Tools for linear algebra operations (matrix multiplication, decomposition, solving linear systems).
* **Random Number Generation:** Capabilities for generating various types of random numbers.
* **Integration with Other Libraries:** Seamlessly integrates with other data science libraries like Pandas, SciPy, and scikit-learn.

**Getting Started**

1. **Install NumPy:** Open your terminal or command prompt and run:



2. **Import NumPy:** In your Python script or Jupyter Notebook, import NumPy using the standard alias:



**Exercises with Detailed Explanations & Code**

I'll present the exercises in logical groups based on related concepts. I'll include explanations and comments within the code.

**1. Array Creation & Basic Attributes**

* **Exercise 1: Create a NumPy array from a Python list.**



* **Exercise 2: Create an array of zeros with shape (3, 4).**



#numpy #numpy #numpy