{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1a4c0c68", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 49, "id": "70b3a793", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('data/fips_states.csv') " ] }, { "cell_type": "code", "execution_count": 51, "id": "c514aad8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fipsstate_name
01Alabama
12Alaska
24Arizona
35Arkansas
46California
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" ], "text/plain": [ " fips state_name\n", "0 1 Alabama \n", "1 2 Alaska \n", "2 4 Arizona \n", "3 5 Arkansas \n", "4 6 California" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "b9ee44d9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.0" } }, "nbformat": 4, "nbformat_minor": 5 }