Welcome to The Coding College! This study plan will help you learn and master NumPy, the fundamental Python library for numerical computing, in just 7 days. By following this structured approach, you’ll gain practical skills and confidence to use NumPy effectively in data analysis, machine learning, and scientific computing.
Study Plan Overview
- Duration: 7 Days (Flexible, adjust as per your pace).
- Goal: Understand core concepts, master array manipulations, and solve real-world problems using NumPy.
- Prerequisite: Basic Python knowledge.
- Resources:
- Python installed on your computer (or use online platforms like Google Colab).
- Tutorials and exercises from The Coding College.
Day 1: Getting Started with NumPy
Objectives:
- Learn what NumPy is and why it’s important.
- Install NumPy and understand its basic setup.
- Create and manipulate basic arrays.
Tasks:
- Introduction to NumPy:
- What is NumPy?
- Advantages of using NumPy over Python lists.
- Installation:
- Install NumPy with
pip install numpy
.
- Install NumPy with
- Creating Arrays:
- One-dimensional arrays.
- Two-dimensional arrays (matrices).
- Multi-dimensional arrays.
- Practice Exercises:
- Create arrays of integers, floats, and mixed types.
- Print array attributes like
shape
,ndim
, anddtype
.
Day 2: Array Basics and Operations
Objectives:
- Understand NumPy array indexing, slicing, and data types.
- Perform basic mathematical operations on arrays.
Tasks:
- Indexing and Slicing:
- Access elements using indices.
- Slice arrays to extract subarrays.
- Data Types:
- Learn about NumPy data types (
int32
,float64
, etc.). - Convert array data types.
- Learn about NumPy data types (
- Mathematical Operations:
- Add, subtract, multiply, and divide arrays.
- Use broadcasting for operations on arrays of different shapes.
- Practice Exercises:
- Perform element-wise arithmetic operations.
- Extract specific rows/columns from a matrix.
Day 3: Array Manipulation
Objectives:
- Learn to reshape, join, and split arrays.
- Differentiate between copies and views of arrays.
Tasks:
- Reshaping Arrays:
- Reshape 1D arrays into 2D or 3D arrays.
- Flatten multi-dimensional arrays into 1D arrays.
- Joining and Splitting:
- Concatenate arrays along different axes.
- Split arrays into smaller subarrays.
- Copy vs View:
- Understand the difference between copying data and viewing a reference.
- Practice Exercises:
- Reshape a 3×3 matrix into a 1D array.
- Split an array into equal parts.
Day 4: Working with Random Numbers
Objectives:
- Generate random numbers and understand data distributions.
- Create reproducible random data.
Tasks:
- Random Numbers:
- Generate random integers and floating-point numbers.
- Create arrays with random values.
- Random Distributions:
- Understand normal (Gaussian), uniform, and binomial distributions.
- Generate data samples using these distributions.
- Practice Exercises:
- Create a random 3×3 matrix.
- Generate an array of 100 numbers following a Gaussian distribution.
Day 5: Linear Algebra and Mathematical Functions
Objectives:
- Perform linear algebra operations.
- Use NumPy’s mathematical and trigonometric functions.
Tasks:
- Linear Algebra:
- Dot product, matrix multiplication, and transposition.
- Compute determinants and inverses.
- Solve systems of linear equations.
- Mathematical Functions:
- Compute logarithms, exponential, and summations.
- Use trigonometric and hyperbolic functions.
- Practice Exercises:
- Solve a system of equations using
np.linalg.solve()
. - Create a sine wave using NumPy.
- Solve a system of equations using
Day 6: File Input/Output and Performance Optimization
Objectives:
- Learn to save and load NumPy arrays from files.
- Optimize performance with vectorized operations.
Tasks:
- File Input/Output:
- Save arrays to binary files using
np.save()
andnp.load()
. - Save and load arrays as text files with
np.savetxt()
andnp.loadtxt()
.
- Save arrays to binary files using
- Performance Optimization:
- Replace Python loops with NumPy vectorized operations.
- Measure performance improvements with
timeit
.
- Practice Exercises:
- Save a NumPy array to a file and reload it.
- Optimize a calculation using vectorization.
Day 7: Advanced Topics and Real-World Applications
Objectives:
- Dive into advanced NumPy topics.
- Solve real-world problems using NumPy.
Tasks:
- Broadcasting:
- Perform operations on arrays with mismatched shapes.
- Use broadcasting to add a scalar to a matrix.
- Universal Functions (ufuncs):
- Learn about NumPy’s built-in ufuncs.
- Create custom universal functions.
- Real-World Applications:
- Use NumPy for data preprocessing and analysis.
- Apply NumPy in financial modeling or image processing.
- Practice Exercises:
- Preprocess a dataset using NumPy.
- Perform element-wise operations using a custom ufunc.
Bonus: NumPy Quizzes and Certification Practice
After completing the study plan, test your skills with the NumPy Quiz and attempt advanced exercises to prepare for NumPy certification exams.