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

Let’s see which movies are top-rated by females in the movie lens database.

Look at the highlighted line 26 of the code widget below. We’re sorting `ratings_by_gender` in the 'F’ column, which stands for female:

import pandas as pd
import matplotlib.pyplot as plt

def movie(nogui=False, movielenspath=''):
    user_columns = ['user_id', 'age', 'gender']
    users = pd.read_csv(movielenspath + 'movie_lens/u.user', sep='|', names=user_columns, usecols=range(3))

    rating_columns = ['user_id', 'movie_id', 'rating']
    ratings = pd.read_csv(movielenspath + 'movie_lens/u.data', sep='\t', names=rating_columns,  usecols=range(3))

    movie_columns = ['movie_id', 'title']
    movies = pd.read_csv(movielenspath + 'movie_lens/u.item', sep='|', names=movie_columns, usecols=range(2), encoding="iso-8859-1")

    # create one merged DataFrame
    movie_ratings = pd.merge(movies, ratings)
    movie_data = pd.merge(movie_ratings, users)
    
    ratings_by_title = movie_data.groupby('title').size()
    popular_movies = ratings_by_title.index[ratings_by_title >= 250]

    ratings_by_gender = movie_data.pivot_table('rating', index='title',columns='gender')


    ratings_by_gender = ratings_by_gender.loc[popular_movies]

    top_movies_individuals_tagged_as_female = ratings_by_gender.sort_values(by='F', ascending=False)

    print("Top rated movies by individuals_tagged_as_female \n", top_movies_individuals_tagged_as_female.head())
    print("\n")

if __name__ == "__main__":
    movie()


As we can see, most top-rated movies by women were also highly rated by men. 

We’re going to add a new column to our table, which will calculate the difference between the ratings in line 26 of the code block below.

Here is an example of what the `diff` column should look like in the widget below:

Learn how to filter the movie lens database by gender.

Comparing Ratings by Gender and Plotting Them

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

Comparing Ratings by Gender and Plotting Them