Gain insights into using Python for scientific and engineering applications. Delve into real-world scenarios, learning NumPy, Matplotlib, audio processing, and more through practical, hands-on projects.

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jupter.tar.gz

notebook

obesity_plot_gender

print_gender_pandas

create_sine_wave

notebook_exercise

range

print_gender_pandas_remove_NaN

print_gender_year

print_data_age

print_data_age_with_year

print_data_age_remove_NaN

obesity_plot_age_simple

obesity_plot_age_clear

print_data_age_under_16

print_data_age_25_34

obesity_plot_children_vs_adults

print_movie_top_rated

print_movies_oldie_teen

print_movies_by_gender

print_movies_for_women

print_movies_by_gender_loc

print_movies_diff_gender

movies_diff_gender_plot

print_u_data

print_movies_oldie_index

print_movies_oldies

print_movies_teens

display_image_nouse

grey_image

blur_image

edge_detection_low

edge_detection_high

plot_sin_wave

create_wave_file

display_again

print_frequencies

count_objects

detect_faces

detect_faces_little_mix

count_exact_lines

webcam_detection

single_thread

find_correlation

webcam_face_detection

motion_detection

motion_detection_live

In this course, you will learn to use Python for real-world scientific/engineering applications. For each topic, there will be a real case scenario where you will build a quick solution in Python to solve the problem.

More specifically, you will cover topics such as creating a word counter, NumPy and Matplotlib, audio processing, and a lot more. Throughout each chapter, you will get hands-on experience in building solutions for complex problems that engineers and scientists face every day.

The Practical Guide to Python for Scientists and Engineers

## How to apply a filter on data?

Let’s compare how the tastes of different age brackets (13–19 vs. 60+) vary. If we want to see all the movies rated by people 60 and older, we’ll execute the code in line 21.

We’ll pass `movie_data.age > 60` to itself, and pandas will return a DataFrame that meets this condition:





# How to apply a filter on data?

Let’s compare how the tastes of different age brackets (13–19 vs. 60+) vary. If we want to see all the movies rated by people 60 and older, we’ll execute the code in line 21.

We’ll pass `movie_data.age > 60` to itself, and pandas will return a DataFrame that meets this condition:





Learn how to apply a filter on the data read.

Printing Top Rated Movies by Viewer Age

Introduction

Create a Word Counter in Python

An Introduction to NumPy and Matplotlib

Python pandas

Audio Processing

Image and Video Processing

MultiThreading vs. Multiprocessing in Python

Machine Learning with an Amazon-like Recommendation Engine

Conclusion

Printing Top Rated Movies by Viewer Age

How to apply a filter on data?