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Have lifesat_full.csv and lifesat.csv

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Aurélien Geron 2021-10-19 11:55:02 +13:00
parent 0b8a519395
commit 84f173b600
1 changed files with 4 additions and 3 deletions
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7
01_the_machine_learning_landscape.ipynb
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@ -118,7 +118,7 @@
"import pandas as pd\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"# Load the data\n",
"# Load and prepare the data\n",
"lifesat = pd.read_csv(Path() / \"datasets\" / \"lifesat\" / \"lifesat.csv\")\n",
"X = lifesat[[\"GDP per capita (USD)\"]].values\n",
"y = lifesat[[\"Life satisfaction\"]].values\n",
@ -305,7 +305,7 @@
"outputs": [],
"source": [
"full_country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
"full_country_stats.to_csv(datapath / \"lifesat.csv\")"
"full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
]
},
{
@ -326,6 +326,7 @@
"max_gdp = 62_500\n",
"country_stats = full_country_stats[(full_country_stats[gdppc] >= min_gdp) &\n",
" (full_country_stats[gdppc] <= max_gdp)]\n",
"country_stats.to_csv(datapath / \"lifesat.csv\")\n",
"country_stats.head()"
]
},
@ -373,7 +374,7 @@
"outputs": [],
"source": [
"highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
"highlighted_countries[[\"Life satisfaction\"]].sort_values(by=\"Life satisfaction\")"
"highlighted_countries[[gdppc, \"Life satisfaction\"]].sort_values(by=gdppc)"
]
},
{