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Merge branch 'main' into french_rev
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‎.github/dependabot.yml

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# To get started with Dependabot version updates, you'll need to specify which
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# package ecosystems to update and where the package manifests are located.
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# Please see the documentation for all configuration options:
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# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
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version: 2
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updates:
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- package-ecosystem: github-actions
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directory: /
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commit-message:
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prefix: ⬆️
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schedule:
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interval: weekly

‎.github/workflows/cache.yml

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runs-on: ubuntu-latest
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- name: Checkout
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uses: actions/checkout@v4
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- name: Setup Anaconda
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uses: conda-incubator/setup-miniconda@v2
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uses: conda-incubator/setup-miniconda@v3
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with:
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auto-update-conda: true
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auto-activate-base: true
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run: |
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jb build lectures --path-output ./ -W --keep-going
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- name: Upload Execution Reports (HTML)
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uses: actions/upload-artifact@v2
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uses: actions/upload-artifact@v4
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if: failure()
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with:
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name: execution-reports
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path: _build/html/reports
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- name: Upload "_build" folder (cache)
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uses: actions/upload-artifact@v2
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uses: actions/upload-artifact@v4
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with:
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‎.github/workflows/ci.yml

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shell: bash -l {0}
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uses: dawidd6/action-download-artifact@v6
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workflow: cache.yml
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branch: main
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name: execution-reports
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path: _build/html/reports
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- name: Preview Deploy to Netlify
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uses: nwtgck/actions-netlify@v2
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uses: nwtgck/actions-netlify@v3.0
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publish-dir: '_build/html/'
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‎.github/workflows/collab.yml

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uses: dawidd6/action-download-artifact@v3
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uses: dawidd6/action-download-artifact@v6
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name: execution-reports
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- name: Preview Deploy to Netlify
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uses: nwtgck/actions-netlify@v2
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uses: nwtgck/actions-netlify@v3.0
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‎.github/workflows/linkcheck.yml

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python-version: ["3.11"]
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steps:
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uses: actions/checkout@v2
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uses: actions/checkout@v4
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- name: Setup Anaconda
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uses: conda-incubator/setup-miniconda@v2
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uses: conda-incubator/setup-miniconda@v3
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with:
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auto-activate-base: true
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Download "build" folder (cache)
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uses: dawidd6/action-download-artifact@v2
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uses: dawidd6/action-download-artifact@v6
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branch: main
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run: jb build lectures --path-output=./ --builder=custom --custom-builder=linkcheck
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- name: Upload Link Checker Reports
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uses: actions/upload-artifact@v2
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uses: actions/upload-artifact@v4
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‎.github/workflows/publish.yml

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uses: actions/checkout@v4
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- name: Setup Anaconda
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uses: conda-incubator/setup-miniconda@v2
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uses: conda-incubator/setup-miniconda@v3
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with:
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auto-update-conda: true
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auto-activate-base: true
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shell: bash -l {0}
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run: pip list
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- name: Download "build" folder (cache)
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uses: dawidd6/action-download-artifact@v2
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uses: dawidd6/action-download-artifact@v6
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with:
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branch: main
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rm -r _build/.doctrees
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NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
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- name: Deploy website to gh-pages
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uses: peaceiris/actions-gh-pages@v3
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uses: peaceiris/actions-gh-pages@v4
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}
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publish_dir: _build/html/
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cname: intro.quantecon.org
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- name: Upload "_build" folder (cache)
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uses: actions/upload-artifact@v2
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uses: actions/upload-artifact@v4
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‎lectures/_config.yml

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colab_url : https://colab.research.google.com
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thebe : false # Add a thebe button to pages (requires the repository to run on Binder)
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intersphinx_mapping:
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intermediate:
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- https://python.quantecon.org/
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- null
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pyprog:
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- https://python-programming.quantecon.org/
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- null

‎lectures/_static/lecture_specific/inequality/data.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "258b4bc9-2964-470a-8010-05c2162f5e05",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: wbgapi in /Users/longye/anaconda3/lib/python3.10/site-packages (1.0.12)\n",
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"Requirement already satisfied: plotly in /Users/longye/anaconda3/lib/python3.10/site-packages (5.22.0)\n",
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"Requirement already satisfied: requests in /Users/longye/anaconda3/lib/python3.10/site-packages (from wbgapi) (2.31.0)\n",
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"Requirement already satisfied: tabulate in /Users/longye/anaconda3/lib/python3.10/site-packages (from wbgapi) (0.9.0)\n",
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"Requirement already satisfied: PyYAML in /Users/longye/anaconda3/lib/python3.10/site-packages (from wbgapi) (6.0)\n",
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"Requirement already satisfied: tenacity>=6.2.0 in /Users/longye/anaconda3/lib/python3.10/site-packages (from plotly) (8.4.1)\n",
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"Requirement already satisfied: packaging in /Users/longye/anaconda3/lib/python3.10/site-packages (from plotly) (23.1)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->wbgapi) (1.26.16)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->wbgapi) (2.0.4)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->wbgapi) (3.4)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->wbgapi) (2024.6.2)\n"
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]
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}
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],
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"source": [
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"!pip install wbgapi plotly\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import random as rd\n",
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"import wbgapi as wb\n",
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"import plotly.express as px\n",
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"\n",
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"url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/SCF_plus/SCF_plus_mini.csv'\n",
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"df = pd.read_csv(url)\n",
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"df_income_wealth = df.dropna()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9630a07a-fce5-474e-92af-104e67e82be5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: quantecon in /Users/longye/anaconda3/lib/python3.10/site-packages (0.7.1)\n",
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"Requirement already satisfied: requests in /Users/longye/anaconda3/lib/python3.10/site-packages (from quantecon) (2.31.0)\n",
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"Requirement already satisfied: numpy>=1.17.0 in /Users/longye/anaconda3/lib/python3.10/site-packages (from quantecon) (1.26.3)\n",
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"Requirement already satisfied: numba>=0.49.0 in /Users/longye/anaconda3/lib/python3.10/site-packages (from quantecon) (0.59.1)\n",
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"Requirement already satisfied: sympy in /Users/longye/anaconda3/lib/python3.10/site-packages (from quantecon) (1.12)\n",
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"Requirement already satisfied: scipy>=1.5.0 in /Users/longye/anaconda3/lib/python3.10/site-packages (from quantecon) (1.12.0)\n",
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"Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in /Users/longye/anaconda3/lib/python3.10/site-packages (from numba>=0.49.0->quantecon) (0.42.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->quantecon) (2024.6.2)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->quantecon) (3.4)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->quantecon) (2.0.4)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/longye/anaconda3/lib/python3.10/site-packages (from requests->quantecon) (1.26.16)\n",
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"Requirement already satisfied: mpmath>=0.19 in /Users/longye/anaconda3/lib/python3.10/site-packages (from sympy->quantecon) (1.3.0)\n"
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]
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}
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],
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"source": [
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"!pip install quantecon\n",
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"import quantecon as qe\n",
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"\n",
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"varlist = ['n_wealth', # net wealth \n",
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" 't_income', # total income\n",
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" 'l_income'] # labor income\n",
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"\n",
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"df = df_income_wealth\n",
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"years = df.year.unique()\n",
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"\n",
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"# create lists to store Gini for each inequality measure\n",
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"results = {}\n",
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"\n",
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"for var in varlist:\n",
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" # create lists to store Gini\n",
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" gini_yr = []\n",
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" for year in years:\n",
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" # repeat the observations according to their weights\n",
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" counts = list(round(df[df['year'] == year]['weights'] ))\n",
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" y = df[df['year'] == year][var].repeat(counts)\n",
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" y = np.asarray(y)\n",
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" \n",
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" rd.shuffle(y) # shuffle the sequence\n",
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" \n",
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" # calculate and store Gini\n",
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" gini = qe.gini_coefficient(y)\n",
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" gini_yr.append(gini)\n",
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" \n",
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" results[var] = gini_yr\n",
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"\n",
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"# Convert to DataFrame\n",
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"results = pd.DataFrame(results, index=years)\n",
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"results.to_csv(\"usa-gini-nwealth-tincome-lincome.csv\", index_label='year')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d59e876b-2f77-4fa7-b79a-8e455ad82d43",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎lectures/_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv

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year,n_wealth,t_income,l_income
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1950,0.8257332034366338,0.44248654139458626,0.5342948198773412
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1953,0.8059487586599329,0.4264544060935945,0.5158978980963702
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1956,0.8121790488050616,0.44426942873399283,0.5349293526208142
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1959,0.795206874163792,0.43749348077061573,0.5213985948309416
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1962,0.8086945076579359,0.4435843103853645,0.5345127915054341
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1965,0.7904149225687935,0.43763715466663444,0.7487860020887753
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1968,0.7982885066993497,0.4208620794438902,0.5242396427381545
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1971,0.7911574835420259,0.4233344246090255,0.5576454812313466
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1977,0.7571418922185215,0.46187678800902543,0.5704448110072049
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1983,0.7494335400643013,0.439345618464469,0.5662220844385915
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1989,0.7715705301674302,0.5115249581654197,0.601399568747142
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1992,0.7508126614055308,0.4740650672076798,0.5983592657979563
14-
1995,0.7569492388110265,0.48965523558400603,0.5969779516716903
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1998,0.7603291991801185,0.49117441585168614,0.5774462841723305
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2001,0.7816118750507056,0.5239092994681135,0.6042739644967272
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2004,0.7700355469522361,0.4884350383903255,0.5981432201792727
18-
2007,0.7821413776486978,0.5197156312086187,0.626345219575322
19-
2010,0.8250825295193438,0.5195972120145615,0.6453653328291903
20-
2013,0.8227698931835303,0.531400174984336,0.6498682917772644
21-
2016,0.8342975903562234,0.5541400068900825,0.6706846793375284
2+
1950,0.8257332034366366,0.44248654139458743,0.534294819877344
3+
1953,0.805948758659935,0.4264544060935942,0.5158978980963682
4+
1956,0.8121790488050612,0.44426942873399367,0.5349293526208106
5+
1959,0.7952068741637912,0.43749348077061534,0.5213985948309414
6+
1962,0.8086945076579386,0.44358431038536356,0.5345127915054446
7+
1965,0.7904149225687949,0.4376371546666344,0.7487860020887701
8+
1968,0.7982885066993503,0.4208620794438885,0.5242396427381534
9+
1971,0.7911574835420282,0.4233344246090255,0.5576454812313462
10+
1977,0.7571418922185215,0.46187678800902554,0.57044481100722
11+
1983,0.749433540064301,0.4393456184644682,0.5662220844385925
12+
1989,0.7715705301674285,0.5115249581654115,0.6013995687471289
13+
1992,0.7508126614055305,0.4740650672076754,0.5983592657979544
14+
1995,0.7569492388110274,0.4896552355840001,0.5969779516717039
15+
1998,0.7603291991801172,0.49117441585168525,0.5774462841723346
16+
2001,0.781611875050703,0.523909299468113,0.6042739644967232
17+
2004,0.7700355469522372,0.48843503839032354,0.5981432201792916
18+
2007,0.782141377648698,0.5197156312086207,0.6263452195753227
19+
2010,0.825082529519342,0.5195972120145641,0.6453653328291843
20+
2013,0.8227698931835299,0.5314001749843426,0.6498682917772886
21+
2016,0.8342975903562537,0.55414000689009,0.6706846793375292

‎lectures/_static/lecture_specific/short_path/Graph-networkx2.ipynb

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