The Guided Project: 5. Visualizing The Gender Gap In College Degrees

[drill_n_bass]

Hello

this is my 5th project here. I’m open to all suggestions - as always: the more feedback we get the faster we can develop!

Few things I’d like to mention here:

1. This is another project where I wanted to do more than was in guidelines. But in this project, there isn’t much to add - less than in the last one.
2. I add some images and gifs to this one. But, If you want to see them, you have to download a zip file and open it in Jupyter Notebook. I didn’t find any other way to display it here.
3. I hope there are no logic errors, but If they are, please let me know. As I said, every feedback is welcome
4. I added a new feature to my project: from now, every time I will update my project, the png file called: gender_degrees - will be automatically updated to have the actual date. Thanks to that I will know if I upload the proper png file here.

The last mission screen of the Guided Project: Visualizing The Gender Gap In College Degrees

Visualizing The Gender Gap In College Degrees.ipynb (1019.8 KB)

Click here to view the jupyter notebook file in a new tab

The complete version in a zip file (version with images and gif: Jupyter Notebook needed to open):

5. Visualizing The Gender Gap In College Degrees.zip (2.1 MB)

[sergibtrader]

Hi,
I suppose here should be some piece of code but not notes

"# elif sp == 5:
"# ax.text(2005, 62, ‘Men’)
"# ax.text(2001, 35, ‘Women’)

[brayanopiyo18]

Hi @drill_n_bass,

Thanks for sharing your fifth project on Visualizing The Gender Gap in College Majors. The introduction, the explanations given the markdown cell are so cool. I love the way you have brought out the color bindless , I went through the link you provided and I really enjoyed reading though. Have noticed some of the images you presented aren’t visible, and still carrying out research to somehow get to the root of the problem.
Have got few suggestions;

• consider providing the link of the dataset you are using.
• I think it would more better to put some of the findings in the conclusion, being graphs are self explanatory, with explanations the entire work will just be excellent.
• Also consider specifying the aim of your project, having loaded introduction, then it is always advisable to isolate the aim/objectives/goal, and put them down in an organized manner.
  Congratulations buddy for the good presentation;

Happy learning!

[drill_n_bass]

@sergibtrader

"# elif sp == 5:
"# ax.text(2005, 62, ‘Men’)
"# ax.text(2001, 35, ‘Women’)

I forgot to unhash it. Thank you!

@brayanopiyo18

consider providing the link of the dataset you are using.

I upload all files in zip and attached it to my first message. Now you can see all!

@brayanopiyo18

• I think it would more better to put some of the findings in the conclusion, being graphs are self explanatory, with explanations the entire work will just be excellent.
• Also consider specifying the aim of your project, having loaded introduction, then it is always advisable to isolate the aim/objectives/goal, and put them down in an organized manner.
  Congratulations buddy for the good presentation;

I updated the explanations and conclusion.

Thank You for your feedback.
If you want to see all pictures and gif, just download the zip file and open it via Jupyter Notebook.

[redorbluepill]

Great overview.

I think you need to change this code snippet, as I believe the legend should be the other way around?
E.g. 2005, 82, ‘Women’ and 2002, 15, ‘Men’

Adding legent directly into line charts, 1st column (to the rightmost and leftmost charts):
if sp == 0:
ax.text(2005, 82, ‘Men’)
ax.text(2002, 15, ‘Women’)
elif sp == 5:
ax.text(2005, 86, ‘Men’)
ax.text(2001, 10, ‘Women’)
ax.tick_params(labelbottom=True) # add years on x-axis

I’m not sure if blue for women is the best choice? A lot of people might associate the colour blue with men?

[drill_n_bass]

@redorbluepill

I think you need to change this code snippet, as I believe the legend should be the other way around?
E.g. 2005, 82, ‘Women’ and 2002, 15, ‘Men’

True. Thank you for your vigilance!

@redorbluepill

I’m not sure if blue for women is the best choice? A lot of people might associate the colour blue with men?

All data are updated now. Except for the blue colour for Women.
Why?
You see, for most of the history of humankind, blue was a girl colour! BTW. Pink was for boys…
If it sound’s intriguing, for example, check this out

This reminds me of this painting too:

Edit: I added a new feature to my project: from now, every time I will update my project, the png file called: gender_degrees - will be automatically updated to have the actual date. Thanks to that I will know if I upload the proper png file here.

# Module needed for a date that is included with the file name generated at the end of this cell
from datetime import datetime

today_date = datetime.now().strftime("%d-%m-%Y Time %I_%M_%S_%p")
        
        
# Creating a string with today's date:
file_name = 'gender_degrees - ' + today_date

# file_name # test


# Exporting to a file, format: .png
plt.savefig(file_name + '.png')   

[redorbluepill]

Got ya… makes sense.

Keep up the good work!

[drill_n_bass]

Another hypotetic/possible reason is laziness, but let’s assume that my first answer was the foundation of my choice

Sometimes it’s great to know stuff from history and other branches of knowledge …

[redorbluepill]

100% agree. Always good to question the “common” approach and be educated about the history. There are so many examples nowadays, where people just grab a statistic and jump to conclusions.