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OpenFisca US tutorial.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "OpenFisca US tutorial.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "authorship_tag": "ABX9TyPy/H63ylH8aOYzZbGoPx7n", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/MaxGhenis/f14b2395ceb828d22d0dc07df033246b/openfisca-us-tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# OpenFisca US tutorial\n", | |
| "\n", | |
| "OpenFisca US is [PolicyEngine](https://policyengine.org)'s open source tax and benefit microsimulation model.\n", | |
| "\n", | |
| "This tutorial will teach you how to construct a household, calculate variables, and simulate a range of scenarios, using California's SNAP program as a case study. \n", | |
| "\n", | |
| "*See https://openfisca.us for full documentation.*\n", | |
| "\n", | |
| "## Installation\n", | |
| "\n", | |
| "OpenFisca US is available on PyPI." | |
| ], | |
| "metadata": { | |
| "id": "WSwNepc-Tuui" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "_wQ1Ld9hEMvC", | |
| "outputId": "f3689413-ca41-4c5f-ba31-4fbc4cacd2eb" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting openfisca-us\n", | |
| " Downloading OpenFisca_US-0.37.1-py3-none-any.whl (376 kB)\n", | |
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| "Building wheels for collected packages: pytest-dependency\n", | |
| " Building wheel for pytest-dependency (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for pytest-dependency: filename=pytest_dependency-0.5.1-py3-none-any.whl size=8218 sha256=e6e9e7e71a9c2246cf6c4264594e29abb95262ffb69f60cfb7dbfbdb234db376\n", | |
| " Stored in directory: /root/.cache/pip/wheels/7e/72/eb/c96a0b4b22f42d092914ba8fe7b4c639443ef02b529dbbefcf\n", | |
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| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n", | |
| "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", | |
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| "Successfully installed OpenFisca-Core-35.7.8 OpenFisca-Tools-0.5.0 dpath-2.0.6 flask-cors-3.0.10 gunicorn-20.1.0 matplotlib-label-lines-0.5.1 microdf-python-0.3.0 nptyping-1.4.4 numpy-1.20.3 openfisca-us-0.37.1 openfisca-us-data-0.1.4 pluggy-0.13.1 pytest-5.4.3 pytest-dependency-0.5.1 sortedcontainers-2.2.2 synthimpute-0.1 typish-1.9.3\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.colab-display-data+json": { | |
| "pip_warning": { | |
| "packages": [ | |
| "numpy" | |
| ] | |
| } | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "!pip install openfisca-us" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Constructing households\n", | |
| "\n", | |
| "To calculate taxes and benefits, first define the household via:\n", | |
| "1. The `IndividualSim` constructor.\n", | |
| "2. The `add_person` method.\n", | |
| "\n", | |
| "Let's start with a single person with \\$1,000 monthly employment income (inputs and outputs are all annual)." | |
| ], | |
| "metadata": { | |
| "id": "_uvPNJyRPd3q" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from openfisca_us import IndividualSim\n", | |
| "\n", | |
| "sim = IndividualSim(year=2022)\n", | |
| "sim.add_person(name=\"person\", employment_income=1000 * 12)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "P5NsStTGEQjI", | |
| "outputId": "92ba1dca-c5ac-40a7-8e6d-ffa2bfbb3b0a" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/openfisca_core/parameters/config.py:17: LibYAMLWarning: libyaml is not installed in your environment. This can make OpenFisca slower to start. Once you have installed libyaml, run 'pip uninstall pyyaml && pip install pyyaml --no-cache-dir' so that it is used in your Python environment.\n", | |
| "\n", | |
| " warnings.warn(\" \".join(message), LibYAMLWarning)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Calculating variables\n", | |
| "\n", | |
| "Now we can calculate any variables in the [`openfisca_us/variables` folder](https://github.com/PolicyEngine/openfisca-us/tree/master/openfisca_us/variables) via the `calc` method.\n", | |
| "\n", | |
| "For example, let's recover `employment_income`:" | |
| ], | |
| "metadata": { | |
| "id": "1RTLMDrwRC33" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sim.calc(\"employment_income\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "2f04mTDDRlIH", | |
| "outputId": "613d933b-e0cd-44e5-f3bd-808805216435" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([12000.], dtype=float32)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "**Exercise 1: Calculate SNAP**\n", | |
| "\n", | |
| "Calculate three SNAP-related variables:\n", | |
| "1. `snap_normal_allotment`, which they would receive outside of Covid.\n", | |
| "2. `snap_emergency_allotment`, which bumps up eligible households to the maximum allotment during the pandemic.\n", | |
| "3. `snap`, which sums the two.\n", | |
| "\n", | |
| "Print them as monthly amounts." | |
| ], | |
| "metadata": { | |
| "id": "lpDCSx4oR6cR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Write your solution here." | |
| ], | |
| "metadata": { | |
| "id": "qhUZ2jU0RBPl" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Households with multiple people\n", | |
| "\n", | |
| "To create households with multiple people, we need to assign them to a household unit.\n", | |
| "US tax and benefit programs group people in different ways, and SNAP uses a *SPM unit*, or a group of people that cohabit and share resources (SPM stands for the Supplemental Poverty Measure).\n", | |
| "\n", | |
| "Let's now model a two-person household, still with \\$1,000 monthly employment income, and group them with the `add_spm_unit` method.\n", | |
| "This also enables us to add SPM-unit-level characteristics, like housing costs (which affect SNAP benefits)." | |
| ], | |
| "metadata": { | |
| "id": "9GHwdDfrTd8F" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sim = IndividualSim(year=2022)\n", | |
| "sim.add_person(name=\"parent\", employment_income=1000 * 12)\n", | |
| "sim.add_person(name=\"child\")\n", | |
| "sim.add_spm_unit(\n", | |
| " name=\"spm_unit\", members=[\"parent\", \"child\"], housing_cost=600 * 12\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "id": "6q3mH6bLa8IA" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Now let's recalculate SNAP." | |
| ], | |
| "metadata": { | |
| "id": "UytFo1Kabas2" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(\n", | |
| " \"SNAP normal allotment: \",\n", | |
| " round(sim.calc(\"snap_normal_allotment\")[0] / 12),\n", | |
| ")\n", | |
| "print(\n", | |
| " \"SNAP emergency allotment: \",\n", | |
| " round(sim.calc(\"snap_emergency_allotment\")[0] / 12),\n", | |
| ")\n", | |
| "print(\"Total SNAP: \", round(sim.calc(\"snap\")[0] / 12))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "EMcis19qbfyy", | |
| "outputId": "9c6faba5-bb99-4f3d-e824-fea7453021d9" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "SNAP normal allotment: 354\n", | |
| "SNAP emergency allotment: 105\n", | |
| "Total SNAP: 459\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "**Exercise 2: Customize a household**\n", | |
| "\n", | |
| "Create a new household with a different number of people, or different income or housing costs.\n", | |
| "Recalculate SNAP." | |
| ], | |
| "metadata": { | |
| "id": "ey1n_m0ybjTw" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Write your solution here." | |
| ], | |
| "metadata": { | |
| "id": "lMLmzrqybyYy" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Scenario analysis\n", | |
| "\n", | |
| "Suppose we want to explore a range of inputs, rather than manually specifying one or two.\n", | |
| "We can apply techniques like list comprehension, but openfisca-us makes it easier with the `vary` method.\n", | |
| "\n", | |
| "When we call `sim.vary`, subsequent `calc` calls calculate over the range of inputs specified in `vary`.\n", | |
| "\n", | |
| "Let's vary employment income, going up to \\$3,000 per month in increments of \\$10 per month." | |
| ], | |
| "metadata": { | |
| "id": "pkPuMdkJb01b" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sim.vary(\"employment_income\", max=3000 * 12, step=10 * 12)" | |
| ], | |
| "metadata": { | |
| "id": "xXkUlKRZcpCb" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Now when we call `calc`, we get an array rather than a scalar. Let's start by recovering employment income again." | |
| ], | |
| "metadata": { | |
| "id": "l5dyC7fAcsQl" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sim.calc(\"employment_income\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "2H9VdE3hfX4t", | |
| "outputId": "c93d2ca8-3027-4ab3-d537-2529529375d2" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 0., 120., 240., 360., 480., 600., 720., 840.,\n", | |
| " 960., 1080., 1200., 1320., 1440., 1560., 1680., 1800.,\n", | |
| " 1920., 2040., 2160., 2280., 2400., 2520., 2640., 2760.,\n", | |
| " 2880., 3000., 3120., 3240., 3360., 3480., 3600., 3720.,\n", | |
| " 3840., 3960., 4080., 4200., 4320., 4440., 4560., 4680.,\n", | |
| " 4800., 4920., 5040., 5160., 5280., 5400., 5520., 5640.,\n", | |
| " 5760., 5880., 6000., 6120., 6240., 6360., 6480., 6600.,\n", | |
| " 6720., 6840., 6960., 7080., 7200., 7320., 7440., 7560.,\n", | |
| " 7680., 7800., 7920., 8040., 8160., 8280., 8400., 8520.,\n", | |
| " 8640., 8760., 8880., 9000., 9120., 9240., 9360., 9480.,\n", | |
| " 9600., 9720., 9840., 9960., 10080., 10200., 10320., 10440.,\n", | |
| " 10560., 10680., 10800., 10920., 11040., 11160., 11280., 11400.,\n", | |
| " 11520., 11640., 11760., 11880., 12000., 12120., 12240., 12360.,\n", | |
| " 12480., 12600., 12720., 12840., 12960., 13080., 13200., 13320.,\n", | |
| " 13440., 13560., 13680., 13800., 13920., 14040., 14160., 14280.,\n", | |
| " 14400., 14520., 14640., 14760., 14880., 15000., 15120., 15240.,\n", | |
| " 15360., 15480., 15600., 15720., 15840., 15960., 16080., 16200.,\n", | |
| " 16320., 16440., 16560., 16680., 16800., 16920., 17040., 17160.,\n", | |
| " 17280., 17400., 17520., 17640., 17760., 17880., 18000., 18120.,\n", | |
| " 18240., 18360., 18480., 18600., 18720., 18840., 18960., 19080.,\n", | |
| " 19200., 19320., 19440., 19560., 19680., 19800., 19920., 20040.,\n", | |
| " 20160., 20280., 20400., 20520., 20640., 20760., 20880., 21000.,\n", | |
| " 21120., 21240., 21360., 21480., 21600., 21720., 21840., 21960.,\n", | |
| " 22080., 22200., 22320., 22440., 22560., 22680., 22800., 22920.,\n", | |
| " 23040., 23160., 23280., 23400., 23520., 23640., 23760., 23880.,\n", | |
| " 24000., 24120., 24240., 24360., 24480., 24600., 24720., 24840.,\n", | |
| " 24960., 25080., 25200., 25320., 25440., 25560., 25680., 25800.,\n", | |
| " 25920., 26040., 26160., 26280., 26400., 26520., 26640., 26760.,\n", | |
| " 26880., 27000., 27120., 27240., 27360., 27480., 27600., 27720.,\n", | |
| " 27840., 27960., 28080., 28200., 28320., 28440., 28560., 28680.,\n", | |
| " 28800., 28920., 29040., 29160., 29280., 29400., 29520., 29640.,\n", | |
| " 29760., 29880., 30000., 30120., 30240., 30360., 30480., 30600.,\n", | |
| " 30720., 30840., 30960., 31080., 31200., 31320., 31440., 31560.,\n", | |
| " 31680., 31800., 31920., 32040., 32160., 32280., 32400., 32520.,\n", | |
| " 32640., 32760., 32880., 33000., 33120., 33240., 33360., 33480.,\n", | |
| " 33600., 33720., 33840., 33960., 34080., 34200., 34320., 34440.,\n", | |
| " 34560., 34680., 34800., 34920., 35040., 35160., 35280., 35400.,\n", | |
| " 35520., 35640., 35760., 35880., 36000.],\n", | |
| " [ 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0., 0., 0., 0.,\n", | |
| " 0., 0., 0., 0., 0.]], dtype=float32)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Since `employment_income` is person-level, `calc` returns a two-row array. `vary` varies the first person's variable by default, so the child's income stays fixed at zero.\n", | |
| "\n", | |
| "### Calculating SNAP as income varies\n", | |
| "\n", | |
| "Now we can calculate normal SNAP allotments.\n", | |
| "Each value corresponds to the varied employment income above.\n", | |
| "\n", | |
| "For example, at \\$0 employment income, the household will get \\$5,508 in annual benefits.\n", | |
| "At \\$3,000 monthly employment income, they are not eligible for any SNAP benefits.\n", | |
| "Since `snap_normal_allotment` is at the SPM unit level, `calc` now returns a one-row array." | |
| ], | |
| "metadata": { | |
| "id": "KRzYPtrhfZRV" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sim.calc(\"snap_normal_allotment\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "fgR_DjJPcwxA", | |
| "outputId": "8ffc5666-0bc9-4281-b0a8-a6ef1594481c" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5508. ,\n", | |
| " 5508. , 5508. , 5508. , 5508. , 5508. , 5502.6 ,\n", | |
| " 5459.4 , 5416.2 , 5373. , 5329.8 , 5286.6 , 5243.4 ,\n", | |
| " 5200.2 , 5157. , 5113.8 , 5070.6 , 5027.4 , 4984.2 ,\n", | |
| " 4941. , 4897.8 , 4854.6 , 4811.4 , 4768.2 , 4725. ,\n", | |
| " 4681.8 , 4638.6 , 4595.4 , 4552.2 , 4509. , 4465.8 ,\n", | |
| " 4422.6 , 4379.4 , 4336.2 , 4293. , 4249.8 , 4206.6 ,\n", | |
| " 4163.4 , 4120.2 , 4077. , 4033.7998, 3990.6 , 3947.4 ,\n", | |
| " 3904.2 , 3861. , 3817.7998, 3774.6 , 3731.4 , 3688.2 ,\n", | |
| " 3645. , 3601.7998, 3558.6 , 3515.4 , 3472.2 , 3429. ,\n", | |
| " 3385.7998, 3342.5999, 3299.4 , 3256.2 , 3213. , 3169.7998,\n", | |
| " 3126.5999, 3083.4 , 3040.2 , 2997. , 2953.7998, 2910.5999,\n", | |
| " 2867.4 , 2824.2 , 2781. , 2737.7998, 2694.5999, 2651.4 ,\n", | |
| " 2608.2 , 2565. , 2521.7998, 2478.5999, 2435.4 , 2392.2 ,\n", | |
| " 2348.9998, 2305.7998, 2262.5999, 2219.4 , 2176.2 , 2132.9998,\n", | |
| " 2089.7998, 2046.5999, 2003.3999, 1960.2 , 1916.9998, 1873.7998,\n", | |
| " 1830.5999, 1787.3999, 1744.2 , 1700.9998, 1657.7998, 1614.5999,\n", | |
| " 1571.3999, 1528.2 , 1484.9998, 1441.7998, 1398.5996, 1355.3999,\n", | |
| " 1312.1997, 1269. , 1225.7998, 1184.3999, 1155.5996, 1126.7998,\n", | |
| " 1098. , 1069.1997, 1040.3999, 1011.5996, 982.7998, 954. ,\n", | |
| " 925.1997, 896.3999, 867.5996, 838.7998, 810. , 781.1997,\n", | |
| " 752.3999, 723.5996, 694.7998, 666. , 637.1997, 608.3999,\n", | |
| " 579.5996, 550.7998, 522. , 493.1997, 464.3999, 435.5996,\n", | |
| " 406.7998, 378. , 349.1997, 320.3999, 291.5996, 262.7998,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 240. , 240. , 240. ,\n", | |
| " 240. , 240. , 240. , 0. , 0. , 0. ,\n", | |
| " 0. , 0. , 0. , 0. , 0. , 0. ,\n", | |
| " 0. ]], dtype=float32)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "**Exercise 3: Visualization**\n", | |
| "\n", | |
| "Use your preferred data visualization library to create a plot of employment income on the x axis and normal (non-Covid) SNAP benefits on the y axis.\n", | |
| "\n", | |
| "*Hint: `IndividualSim.calc` returns a list of all households' values. Since we're only working with one household, extract the first with `calc(x)[0]`.*" | |
| ], | |
| "metadata": { | |
| "id": "75v5UVbAb7dU" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Write your solution here." | |
| ], | |
| "metadata": { | |
| "id": "y2mz3743LfNS" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Learn more\n", | |
| "\n", | |
| "Visit [openfisca.us](https://openfisca.us) for examples of other programs, calculating marginal tax rates, and more." | |
| ], | |
| "metadata": { | |
| "id": "qBEWQxKycI5i" | |
| } | |
| } | |
| ] | |
| } |
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