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@ -136,7 +136,7 @@
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,2))\n",
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"plt.boxplot(df['Height'].ffill(), vert=False, showmeans=True)\n",
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"plt.boxplot(df['Height'].ffill(), orientation='horizontal', showmeans=True)\n",
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"plt.grid(color='gray', linestyle='dotted')\n",
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"plt.tight_layout()\n",
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"plt.show()"
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@ -272,7 +272,7 @@
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" return m, h\n",
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"\n",
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"for p in [0.85, 0.9, 0.95]:\n",
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" m, h = mean_confidence_interval(df['Weight'].fillna(method='pad'),p)\n",
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" m, h = mean_confidence_interval(df['Weight'].ffill(),p)\n",
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" print(f\"p={p:.2f}, mean = {m:.2f} ± {h:.2f}\")"
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]
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},
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@ -384,7 +384,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"heights = df['Height'].fillna(method='pad')\n",
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"heights = df['Height'].ffill()\n",
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"salaries = 1000+(heights-heights.min())/(heights.max()-heights.mean())*100\n",
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"print(list(zip(heights, salaries))[:10])"
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]
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@ -507,7 +507,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"np.corrcoef(df['Height'].fillna(method='pad'), df['Weight'])"
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"np.corrcoef(df['Height'].ffill(), df['Weight'])"
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]
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},
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{
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