tabsplit #19
1 changed files with 246 additions and 23 deletions
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@ -326,12 +326,24 @@
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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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"execution_count": 6,
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"id": "861ac334-14ce-490a-b3c4-877b32789f3e",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(6324, 16)"
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"## your code here\n"
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"## your code here\n",
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"df.shape"
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]
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},
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@ -344,12 +356,24 @@
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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": 7,
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"id": "14f57842-5722-4953-88d6-d7cf3070400c",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(6287, 3)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"## your code here\n"
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"## your code here\n",
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"info.shape"
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]
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},
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{
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@ -364,14 +388,13 @@
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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": 10,
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"id": "35e19a53",
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"metadata": {},
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"outputs": [],
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"source": [
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"## your code here\n",
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"\n",
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"\n"
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"data_diet = pd.merge(df, info, how='inner', left_on=['location-id', 'patient-id'], right_on=['location-id', 'patient-id'])"
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]
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},
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{
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@ -388,7 +411,7 @@
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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": 9,
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"id": "36ce0688-d421-4a07-b00e-0e9b3201f0e0",
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"metadata": {},
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"outputs": [
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@ -456,7 +479,7 @@
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"4 5 Malaga"
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]
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},
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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@ -469,12 +492,195 @@
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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": 11,
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"id": "b636dde4-129a-4dd1-8cbf-c539c9c8a5f2",
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"metadata": {},
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"outputs": [],
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"source": [
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"## your code here:\n"
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"## your code here:\n",
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"data_diet_loc = pd.merge(data_diet, locations, how='inner', left_on='location-id', right_on='location-id')"
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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": 23,
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"id": "e4579355-bdde-40a3-8969-1c27c7bb0eb7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>patient-id</th>\n",
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" <th>location-id</th>\n",
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" <th>sex</th>\n",
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" <th>age</th>\n",
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" <th>smoke</th>\n",
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" <th>bmi</th>\n",
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" <th>waist</th>\n",
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" <th>wth</th>\n",
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" <th>htn</th>\n",
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" <th>diab</th>\n",
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" <th>hyperchol</th>\n",
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" <th>famhist</th>\n",
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" <th>hormo</th>\n",
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" <th>p14</th>\n",
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" <th>toevent</th>\n",
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" <th>event</th>\n",
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" <th>group</th>\n",
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" <th>City</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>436</td>\n",
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" <td>4</td>\n",
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" <td>Male</td>\n",
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" <td>58</td>\n",
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" <td>Former</td>\n",
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" <td>33.53</td>\n",
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" <td>122</td>\n",
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" <td>0.753086</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>10</td>\n",
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" <td>5.374401</td>\n",
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" <td>Yes</td>\n",
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" <td>Control</td>\n",
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" <td>Bilbao</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1130</td>\n",
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" <td>4</td>\n",
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" <td>Male</td>\n",
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" <td>77</td>\n",
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" <td>Current</td>\n",
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" <td>31.05</td>\n",
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" <td>119</td>\n",
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" <td>0.730061</td>\n",
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" <td>Yes</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>10</td>\n",
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" <td>6.097194</td>\n",
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" <td>No</td>\n",
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" <td>Control</td>\n",
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" <td>Bilbao</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1131</td>\n",
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" <td>4</td>\n",
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" <td>Female</td>\n",
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" <td>72</td>\n",
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" <td>Former</td>\n",
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" <td>30.86</td>\n",
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" <td>106</td>\n",
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" <td>0.654321</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>8</td>\n",
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" <td>5.946612</td>\n",
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" <td>No</td>\n",
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" <td>MedDiet + VOO</td>\n",
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" <td>Bilbao</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1132</td>\n",
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" <td>4</td>\n",
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" <td>Male</td>\n",
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" <td>71</td>\n",
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" <td>Former</td>\n",
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" <td>27.68</td>\n",
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" <td>118</td>\n",
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" <td>0.694118</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>8</td>\n",
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" <td>2.907598</td>\n",
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" <td>Yes</td>\n",
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" <td>MedDiet + Nuts</td>\n",
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" <td>Bilbao</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1111</td>\n",
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" <td>2</td>\n",
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" <td>Female</td>\n",
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" <td>79</td>\n",
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" <td>Never</td>\n",
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" <td>35.94</td>\n",
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" <td>129</td>\n",
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" <td>0.806250</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>9</td>\n",
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" <td>4.761123</td>\n",
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" <td>No</td>\n",
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" <td>MedDiet + VOO</td>\n",
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" <td>Valencia</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" patient-id location-id sex age smoke bmi waist wth htn \\\n",
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"0 436 4 Male 58 Former 33.53 122 0.753086 No \n",
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"1 1130 4 Male 77 Current 31.05 119 0.730061 Yes \n",
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"2 1131 4 Female 72 Former 30.86 106 0.654321 No \n",
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"3 1132 4 Male 71 Former 27.68 118 0.694118 Yes \n",
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"4 1111 2 Female 79 Never 35.94 129 0.806250 Yes \n",
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"\n",
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" diab hyperchol famhist hormo p14 toevent event group City \n",
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"0 No Yes No No 10 5.374401 Yes Control Bilbao \n",
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"1 Yes No No No 10 6.097194 No Control Bilbao \n",
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"2 Yes No Yes No 8 5.946612 No MedDiet + VOO Bilbao \n",
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"3 No Yes No No 8 2.907598 Yes MedDiet + Nuts Bilbao \n",
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"4 No Yes No No 9 4.761123 No MedDiet + VOO Valencia "
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]
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},
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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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],
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"source": [
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"data_diet_loc.head()"
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]
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},
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{
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@ -492,7 +698,7 @@
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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": 13,
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"id": "d1d4cc27",
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"metadata": {},
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"outputs": [],
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@ -502,7 +708,7 @@
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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": 14,
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"id": "fbebbd97",
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"metadata": {},
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"outputs": [
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@ -512,7 +718,7 @@
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"(42, 2)"
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]
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},
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"execution_count": 11,
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -523,7 +729,7 @@
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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": 15,
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"id": "8a3c7943",
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"metadata": {},
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"outputs": [
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@ -591,7 +797,7 @@
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"4 4 541"
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]
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},
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"execution_count": 12,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -602,12 +808,20 @@
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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": 21,
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"id": "573687e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# your code here\n"
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"# your code here\n",
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"data_diet_loc_drop = pd.merge(\n",
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" data_diet_loc, \n",
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" dropped, \n",
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" how='left', \n",
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" left_on=['location-id', 'patient-id'], \n",
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" right_on=['location-id', 'patient-id'], \n",
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" indicator=True\n",
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").query('_merge != \"both\"').drop(columns='_merge')"
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]
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},
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{
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@ -622,15 +836,24 @@
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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": 22,
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"id": "85902eea",
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"metadata": {},
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"outputs": [],
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"source": [
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"fname = 'processed_data_predimed.csv'\n",
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"\n",
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"# your code here\n"
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"# your code here\n",
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"data_diet_loc_drop.to_csv(fname)"
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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": null,
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"id": "38e88726-287a-4448-a250-f8616362d070",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -649,7 +872,7 @@
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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": "3.11.5"
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"version": "3.13.7"
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}
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},
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"nbformat": 4,
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