Paper | Step-by-step Notebook | Documentation | PyPI package |External Resources
Are you a policy-maker who wants to see the dynamics of national income inequality? Are you a researcher who wants to visualize economic growth puzzles such as convergence? Or are you simply a curious individual who wants to see your position in the national income distribution? Wonder no more because we present incomevis, a library for income visualization (and more)!
Comparing incomes is complicated. We offer three default deflators (consumer price index, household size, and regional price parities) to adjust nominal household income. The income adjustment process is automatically handled for you. You can further adjust our deflated incomes if you have additional variables that you want to incorporate. Also, if you like interactive visualization, our graph can be displayed using JavaScript's amChart library. If you prefer an animated visualization, we offer a dynamically controlled animation of our graph based on Python Matplotlib library.
Happy visualizing economic complexity!
Installation
incomevis can be installed via pip:
$ pip install incomevis
Gallery
Interactive graph (top) and dynamic graph (bottom) is implement in JavaScript AmChart and Python Matplotlib, respectively. More instant examples of interactive graphs can be found at research.depauw.edu/econ/incomevis. Animated graphs with better control can be generated in a (preferably local) Python environment (for example, see this notebook).
4 levels of deflating income.
Bellow are bootstrap resampling of 50p of DC in 1977 10000 times (bottom right) and 1 million times (bottom left). We also show the relative growth of CA and DC overtime.
A completed and separated page for gallery will be available soon!
Contact
Any question, feedback, or comment can be directed to sttruong@stanford.edu or hbarreto@depauw.edu.
Metadata
Release files for incomevis 0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| incomevis-1.0.tar.gz | 8.8 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| incomevis-1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 18.1 kB
Release files / incomevis-1.0.tar.gz
| Download URL | incomevis-1.0.tar.gz |
|---|---|
| Size | 8.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
0d05e90ba46f9d9a4713a0742da9cd0837771538eea9daeaeb6bebb337d3403e
|
|
BLAKE2b-256 checksum How to use checksums |
6f9ddc04f2eb5d23903dd21a406bf58b1040d1621f02f76ccc350b45f97c186b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/51.1.2 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.6
|
Release files / incomevis-1.0-py3-none-any.whl
| Download URL | incomevis-1.0-py3-none-any.whl |
|---|---|
| Size | 9.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5278917904ebea8e17f1d3036e1e12a10c5f44b1dcab080e7121c9a905556046
|
|
BLAKE2b-256 checksum How to use checksums |
ec318b23ee6aec047d0168651c3a0cb534b8c9918d402d31edbf9176d6a580f4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/51.1.2 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.6
|
Security for incomevis 0.3
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.