gender_distribution | Analyzing gender distribution | Machine Learning library

by digitalprk Python Version: Current License: No License

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kandi X-RAY | gender_distribution Summary

gender_distribution is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, OpenCV, Neural Network applications. gender_distribution has no bugs, it has no vulnerabilities and it has low support. However gender_distribution build file is not available. You can download it from GitHub.

Analyzing gender distribution in North Korean posters using convolutional neural networks

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gender_distribution has a low active ecosystem.

It has 2 star(s) with 1 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gender_distribution is current.

Quality

gender_distribution has 0 bugs and 0 code smells.

Security

gender_distribution has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gender_distribution code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gender_distribution does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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gender_distribution releases are not available. You will need to build from source code and install.

gender_distribution has no build file. You will be need to create the build yourself to build the component from source.

It has 196 lines of code, 8 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed gender_distribution and discovered the below as its top functions. This is intended to give you an instant insight into gender_distribution implemented functionality, and help decide if they suit your requirements.

Create model .
Generate data .
Generate training data .
Saves bottleneck features .
Resize an image .
Resizes an image .

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gender_distribution Key Features

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gender_distribution Examples and Code Snippets

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Community Discussions

Trending Discussions on gender_distribution

Plotly: How to change legend for a go.pie chart without changing data source?

QUESTION

Plotly: How to change legend for a go.pie chart without changing data source?

Asked 2020-Oct-15 at 20:02

I am practising building a Pie Chart in Plotly Express using Python.
So, this is the Pie Chart that I made;

This chart was build from a file with two columns called

gender with values of [0, 1, 2]
count_genders with values of [total_count_0, total_count_1, total_count_2]

I am planning to add some description to those values; for instance

0 - female
1 - male
2 - undefined

This is where I am currently stuck.
If I remember correctly if you want to change a label in the legend (at least in Choropleth map), you could manipulate the ticks located in colorscale bar. By manipulating them, you could rename the label about the data. Thus I am wondering if you could do the same in Pie chart?

My current code for this graph:

...

ANSWER

Answered 2020-Aug-28 at 15:29

If I'm understanding your question correctly, you'd like to change what's displayed in the legend without changing the names in your data source. There may be more elegant ways of doing this but I've put together a custom function newLegend(fig, newNames) that will do exactly that for you.

So with a figure like this:

...running:

Source https://stackoverflow.com/questions/63631843

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install gender_distribution

You can download it from GitHub.
You can use gender_distribution like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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