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

It modifies the existing DataFrame without creating a copy.

It creates and modifies a copy of the original DataFrame.

In the following example, what does `inplace` do?

```
data.set_index("distance", inplace= True)
```

In the following example, what does <code>inplace</code> do?
<pre><code>data.set_index(&quot;distance&quot;, inplace= True)
</code></pre>

4 rows from the top and 2 rows from the bottom will be removed. A total of 4 rows will be printed.

2 rows from the top and 4 rows from the bottom will be removed. A total of 4 rows will be printed.

2 rows from the top and 4 rows from the bottom will be removed. A total of 5 rows will be printed.

No rows will be removed. A total of 10 rows will be printed.

Let's assume we have an excel sheet with ten rows. 

Consider the code given below:
```
data_gender = data.parse(u'7.1', skiprows=2, skipfooter=4, names=columns1) 
print (data_gender)
```

This is a two-part question, so consider the answers carefully. First, how many rows from the top and bottom will be ignored? Second, how many rows will be printed after running the above code?

Let’s assume we have an excel sheet with ten rows.
Consider the code given below:
<pre><code>data_gender = data.parse(u'7.1', skiprows=2, skipfooter=4, names=columns1) 
print (data_gender)
</code></pre>
This is a two-part question, so consider the answers carefully. First, how many rows from the top and bottom will be ignored? Second, how many rows will be printed after running the above code?

Which of the following commands will we use to remove NaN values from an existing dataset?


Which of the following commands will we use to remove NaN values from an existing dataset?

To delete a column, the parameter `axis` of function `drop()` is assigned which of the following values?

To delete a column, the parameter <code>axis</code> of function <code>drop()</code> is assigned which of the following values?

`Employee = Employee.drop('Salaries', axis = 0)`


`Employee = Employee.drop('Salaries', axis = 1)`


`Salaries = Salaries.drop('Employee', axis = 0)`


`Salaries = Salaries.drop('Employee', axis = 1)`


We need to drop the 'Salaries' column from the `Employee` DataFrame.

Which of the following options performs the above function correctly?

We need to drop the ‘Salaries’ column from the <code>Employee</code> DataFrame.
Which of the following options performs the above function correctly?

`pd.read_csv('food.item', sep='|', names=user_columns, usecols=range(3))`

`pd.read_csv('food.item', sep='#', usecols=range(3))`

`pd.read_csv('food.item', sep='#', usecols=range(5))`

`pd.read_csv('user.item', sep='#', usecols=range(5))`

Consider the following scenario:

We have a file named `food.item` in which the values are separated by a `#`. We need to read the first `5` values of each row from the file.

Which of the following lines of code is correct for the above scenario?



Consider the following scenario:
We have a file named <code>food.item</code> in which the values are separated by a <code>#</code>. We need to read the first <code>5</code> values of each row from the file.
Which of the following lines of code is correct for the above scenario?

Which of the following functions is used to join two datasets and merge its data together?

Which of the following functions is used to join two datasets and merge its data together?

`rich_employees = employees.salary > 2000)`

`rich_employees = employees[(employees.salary > 2000)]`

`rich_employees = employees[(salary > 2000)]`

We need a dataset that contains all the employees with salaries greater than 2000 dollars.

Which of the following lines of code is correct for the above scenario? 

We need a dataset that contains all the employees with salaries greater than 2000 dollars.
Which of the following lines of code is correct for the above scenario?

The code reshapes a dataset by creating a new table containing the ratings of all the employees based on gender. Here, the index is the employee's name.

The code creates a new dataset. It contains the ratings of all the employees based on gender. Here, the index is the employee's name.

The code creates a new dataset. It contains the ratings of all the employees based on gender. Here, the index is the employee’s rating.

The code creates a new dataset. It contains the gender of all the employees based on ratings. Here, the index is the employee’s name.

Consider the code below:

`ratings_by_gender = employee.pivot_table('rating', index='employee_name',columns='gender')
`

Which of the following options explains the function of the above code?

Consider the code below:
<code>ratings_by_gender = employee.pivot_table('rating', index='employee_name',columns='gender') </code>
Which of the following options explains the function of the above code?

change the data values of a row to a particular value

Consider the following statement in which  `food` is a DataFrame:

The `food.loc[]` method is used to _____________ here.



Consider the following statement in which <code>food</code> is a DataFrame:
The <code>food.loc[]</code> method is used to _____________ here.

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

Quiz: Python pandas