Congratulations!

1. Congratulations!

You made it!

2. Welcome to team NumPy

You're now a card-carrying member of Team NumPy. I hope I've convinced you that NumPy is amazing.

3. NumPy is amazing!

It leverages the immense calculating power of C while keeping the friendly syntax of Python. In some places, it even makes Python syntax simpler by vectorizing operations!

4. What's next?

What's next on your NumPy journey? You could check out one of the many libraries built on top of NumPy's API. Perhaps you'd like to explore data analysis with pandas, data visualization with Seaborn, parallel programming with Dask, machine learning with scikit-learn, or deep learning with TensorFlow. Every one of these libraries is built on top of NumPy, and DataCamp has courses dedicated to all of them.

5. Congratulations

No matter where you take your NumPy knowledge, thanks for taking this course all the way to the end. I'm Izzy Weber, and on behalf of all of us at DataCamp, congratulations on your wide new array of NumPy skills!

6. Thank you!

Thanks again!

This exercise is part of the course

Introduction to NumPy

BeginnerSkill Level

4.8+

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Meet the incredible NumPy array! Learn how to create and change array shapes to suit your needs. Finally, discover NumPy's many data types and how they contribute to speedy array operations.

Exercise 1: Introducing arrays Exercise 2: Your first NumPy array Exercise 3: Creating arrays from scratch Exercise 4: A range array Exercise 5: Array dimensionality Exercise 6: 3D array creation Exercise 7: The fourth dimension Exercise 8: Flattening and reshaping Exercise 9: NumPy data types Exercise 10: The dtype argument Exercise 11: Anticipating data types Exercise 12: A smaller sudoku game

Sharpen your NumPy data wrangling skills by slicing, filtering, and sorting New York City’s tree census data. Create new arrays by pulling data based on conditional statements, and add and remove data along any dimension to suit your purpose. Along the way, you’ll learn the shape and dimension compatibility principles to prepare for super-fast array math.

Exercise 1: Indexing and slicing arrays Exercise 2: Slicing and indexing trees Exercise 3: Stepping into 2D Exercise 4: Sorting trees Exercise 5: Filtering arrays Exercise 6: Filtering with masks Exercise 7: Fancy indexing vs. np.where()Exercise 8: Creating arrays from conditions Exercise 9: Adding and removing data Exercise 10: Compatible or not?Exercise 11: Adding rows Exercise 12: Adding columns Exercise 13: Deleting with np.delete()

Leverage NumPy’s speedy vectorized operations to gather summary insights on sales data for American liquor stores, restaurants, and department stores. Vectorize Python functions for use in your NumPy code. Finally, use broadcasting logic to perform mathematical operations between arrays of different sizes.

Exercise 1: Summarizing data Exercise 2: Sales totals Exercise 3: Plotting averages Exercise 4: Cumulative sales Exercise 5: Vectorized operations Exercise 6: Tax calculations Exercise 7: Projecting sales Exercise 8: Vectorizing .upper()Exercise 9: Broadcasting Exercise 10: Broadcastable or not?Exercise 11: Broadcasting across columns Exercise 12: Broadcasting across rows

NumPy meets the art world in this final chapter as we use image data from a Monet masterpiece to explore how you can use to augment image data. You’ll use flipping and transposing functionality to quickly transform our masterpiece. Next, you’ll pull the Monet array apart, make changes, and reconstruct it using array stacking to see the results.

Exercise 1: Saving and loading arrays Exercise 2: Loading .npy files Exercise 3: Getting help Exercise 4: Update and save Exercise 5: Array acrobatics Exercise 6: Augmenting Monet Exercise 7: Transposing your masterpiece Exercise 8: Stacking and splitting Exercise 9: 2D split and stack Exercise 10: Splitting RGB data Exercise 11: Stacking RGB data Exercise 12: Congratulations!

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