Data Visualization Thought Process // Python Matplotlib Baby Names Data Visualization
Вставка
- Опубліковано 21 лип 2024
- Join me for a full data viz transformation! In this video, we'll explore the entire data visualization thought process: from collecting and exploring the data to making critical design decisions to convey the main point. Learn how to make design choices like labeling lines directly and code up your own Python data visualization using pandas plotting, Matplotlib code and design, and a bit of seaborn styling. See a data visualization example with a Matplotlib title, Matplotlib text annotations, an updated Matplotlib legend, and use of seaborn despine. I also demo the Matplotlib FuncFormatter to format the tick labels.
0:00 Introduction
0:48 Problem setup
3:09 Data exploration
5:45 Initial plot
8:45 Styling: Figure size & Lines
12:00 Styling: Axes labels & Remove box
16:44 Styling: Label lines directly
21:30 Styling: Title & Annotations
27:50 Styling: Tick labels
33:40 Conclusion
// GitHub CODE //
github.com/kimfetti/Videos/bl...
// Original BLOG //
kimberlyfessel.com/mathematics...
// Related VIDEOS //
Add TEXT to Matplotlib: • Add TEXT to MATPLOTLIB...
Change AXES LIMITS in Matplotlib: • How to change the axes...
Remove outside BOX around Matplotlib: • Remove outside box aro...
Full Matplotlib Tips PLAYLIST: • Matplotlib Tips
#dataviz - Наука та технологія
Hi Kimberly! Thank you so much for providing us with these awesome lectures. I wish you to make full fledge series on Machine Learning, NLP, Time Series Analysis, Computer vision and Deep Learning. Your method of teaching is really awesome too as I pick the concepts readily. Please make those series in such a way that they will cover the stats behind those models, how to train, test and validate those models, how to save those models and deploy them in real world, how to monitor performance of those models and how to make scalable models that can actually work on data streams. Also I have a request if you can please help us with data Engineering concepts like Data Warehousing etc.
Glad I cam across your channel today!
Hello! Kimberly. Your videos and concepts are really comprehensive. Could you make a video on the data set having only categorical and non-numerical data? Could you present the thought process and clustering of that non-numerical data? Will be highly appreciated.
Thank you for the video. It's really helpful. The explanations of why you change the graph AND the python codes
This content is amazing! Thank you for creating it.
so great! thank you Kimberly!
Your videos are good ! You are getting me hooked on your channel !!!
Hello Kimberly, your videos are simply great, I just refer to it as a documentation... Superb!!!
Excellent content! I am starting to learn about data visualization. I really liked the channel! Keep up the videos! Cheers from Brazil!
Oh wonderful - data visualization is so fun! Glad to hear you are enjoying the channel and all the best on your data viz journey.
Great information! Thank you so much!
Happy to hear you enjoyed it!
Hi, thanks for this really helpful video! Excellent and comprehensive content :)
Very educational. Thanks!
excellent video. Thanks!
Most welcome - cheers!
This was very educational. I learnt a lot.
Wonderful! Very glad to hear it was educational 😀
Great video
Again: Very good training ! Unexpected related to my current projects! Wondering if one reason why male baby names are less variant could be that often boys are named after a grandfather ? Also interesting to see that the variation of female baby names is dropping since 2000 again. Maybe the girl names got too exotic and now grandmother names are hipp again - like „Anna“ or „Berta“ or „Klara“ …. ? ..
Awesome - so glad you liked it and that it was related! Yes, I totally agree with you. I think the phenomenon is at least partially driven by family names that are passed down generation after generation for males. One interesting thing about the female names... even though the unique number of names went down a bit, the Gini coefficient has still been trending down (a separate figure I have on my blog). So females names are getting more spread out among relatively fewer names. I would love to spend more time analyzing this data set to see if "grandmother" names are back! 😆
I have a problem knowing when I should use the options in the pandas or seaborn, and when I should use plt. or ax. ? In this video, the plot is a pandas plot and some options are using plt or ax. I have the same trouble when using seaborn. Could help explain to me or point me to where I learn this ? Thanks very much.
Data is not available at github. Can you provide us to do the tutorial by own