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.

Python for Scientists and Engineers_468 x 60 copy 2.png

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

# Introduction to Matplotlib

**Matplotlib** is a visualization library in Python for plotting 2D array plots. It is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It can be used to plot all sorts of graphs like line, bar, scatter, histogram, and so on.

We’ll import Matplotlib into our code like so:

```
from matplotlib import pyplot as plt
or
import matplotlib.pyplot as plt
``` 

The new thing here is: `import matplotlib.pyplot as plt`. We are importing it as `plt` for user ease.

>Matplotlib is a vast library. So, we’ll only import the `pyplot` section. This is useful in saving memory and speeding up code. Otherwise, we’ll be importing gigabytes of libraries every time we want to print “Hello World” to the screen.

Learning to use Python skillfully means using a lot of libraries and functions. But we don’t have to remember them all. We can just google them on runtime whenever we need any function that belongs to a Python library.

Let’s work with one of the functions provided by NumPy, `linspace()`.

**`linspace()`** generates evenly spread out values. In the example above, it will generate 1000 values between 0 and 20. Printing 1000 values will take a lot of space here, so let’s see what happens when we generate only 10 values:


 



Learn how to plot sin and cos waves using matplotlib in Python.

Plotting Sin and Cos Waves

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

Plotting Sin and Cos Waves

Introduction to Matplotlib