Use read_csv() to download the data directly
Aurélien Geron
parent
85171acd17
commit
ed704b8e34
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@ -111,41 +111,6 @@
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"plt.rc('ytick', labelsize=10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Download `lifesat.csv` from github, unless it's already available locally:"
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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": 5,
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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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"Downloading lifesat.csv\n"
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]
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}
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],
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"source": [
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"from pathlib import Path\n",
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"import urllib.request\n",
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"\n",
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"datapath = Path() / \"datasets\" / \"lifesat\"\n",
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"datapath.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"data_root = \"https://github.com/ageron/data/raw/main/\"\n",
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"filename = \"lifesat.csv\"\n",
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"if not (datapath / filename).is_file():\n",
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" print(\"Downloading\", filename)\n",
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" url = data_root + \"lifesat/\" + filename\n",
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" urllib.request.urlretrieve(url, datapath / filename)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -155,7 +120,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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@ -179,15 +144,14 @@
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}
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],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.linear_model import LinearRegression\n",
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"\n",
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"# Load and prepare the data\n",
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"lifesat = pd.read_csv(Path() / \"datasets\" / \"lifesat\" / \"lifesat.csv\")\n",
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"# Download and prepare the data\n",
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"data_root = \"https://github.com/ageron/data/raw/main/\"\n",
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"lifesat = pd.read_csv(data_root + \"lifesat/lifesat.csv\")\n",
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"X = lifesat[[\"GDP per capita (USD)\"]].values\n",
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"y = lifesat[[\"Life satisfaction\"]].values\n",
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"\n",
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@ -232,7 +196,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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@ -279,10 +243,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"# Where to save the figures\n",
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"IMAGES_PATH = Path() / \"images\" / \"fundamentals\"\n",
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"IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
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@ -312,7 +278,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@ -325,6 +291,12 @@
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}
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],
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"source": [
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"import urllib.request\n",
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"\n",
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"datapath = Path() / \"datasets\" / \"lifesat\"\n",
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"datapath.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"data_root = \"https://github.com/ageron/data/raw/main/\"\n",
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"for filename in (\"oecd_bli.csv\", \"gdp_per_capita.csv\"):\n",
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" if not (datapath / filename).is_file():\n",
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" print(\"Downloading\", filename)\n",
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@ -334,7 +306,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -351,7 +323,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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@ -417,7 +389,7 @@
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"Algeria 10681.679297"
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]
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},
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"execution_count": 11,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -444,7 +416,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@ -717,7 +689,7 @@
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"[5 rows x 24 columns]"
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]
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},
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"execution_count": 12,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -738,7 +710,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -811,7 +783,7 @@
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"Chile 23324.524751 6.5"
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]
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},
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"execution_count": 13,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -834,7 +806,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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@ -907,7 +879,7 @@
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"Hungary 31007.768407 5.6"
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]
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},
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"execution_count": 14,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -923,7 +895,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -933,7 +905,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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@ -984,7 +956,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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@ -1069,7 +1041,7 @@
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"United States 60235.728492 6.9"
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]
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},
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"execution_count": 17,
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1081,7 +1053,7 @@
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{
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"cell_type": "code",
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"metadata": {},
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{
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},
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{
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{
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@ -1152,7 +1124,7 @@
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{
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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": 21,
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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@ -1197,7 +1169,7 @@
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"37655.1803457421"
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]
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},
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"execution_count": 21,
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1209,7 +1181,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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@ -1218,7 +1190,7 @@
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"6.301656332738056"
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]
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},
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"execution_count": 22,
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1230,7 +1202,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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{
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@ -1271,7 +1243,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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@ -1368,7 +1340,7 @@
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"Luxembourg 110261.157353 6.9"
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]
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},
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"execution_count": 24,
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1381,7 +1353,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1400,7 +1372,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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@ -1448,7 +1420,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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@ -1491,7 +1463,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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@ -1505,7 +1477,7 @@
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"Name: Life satisfaction, dtype: float64"
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]
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},
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"execution_count": 28,
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1517,7 +1489,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"Switzerland 68393.306004"
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]
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},
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"execution_count": 29,
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1645,7 +1617,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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@ -1727,9 +1699,9 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "homl3",
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"display_name": "Python 3",
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"language": "python",
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"name": "homl3"
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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": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.9.10"
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},
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"metadata": {
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"interpreter": {
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