diff --git a/exercises/tabular_join/tabular_join.ipynb b/exercises/tabular_join/tabular_join.ipynb
index 6cf3ed9..9e9640a 100644
--- a/exercises/tabular_join/tabular_join.ipynb
+++ b/exercises/tabular_join/tabular_join.ipynb
@@ -326,12 +326,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "861ac334-14ce-490a-b3c4-877b32789f3e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6324, 16)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## your code here\n"
+    "## your code here\n",
+    "df.shape"
    ]
   },
   {
@@ -344,12 +356,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "14f57842-5722-4953-88d6-d7cf3070400c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6287, 3)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## your code here\n"
+    "## your code here\n",
+    "info.shape"
    ]
   },
   {
@@ -364,14 +388,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "35e19a53",
    "metadata": {},
    "outputs": [],
    "source": [
     "## your code here\n",
-    "\n",
-    "\n"
+    "data_diet = pd.merge(df, info, how='inner', left_on=['location-id', 'patient-id'], right_on=['location-id', 'patient-id'])"
    ]
   },
   {
@@ -388,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "36ce0688-d421-4a07-b00e-0e9b3201f0e0",
    "metadata": {},
    "outputs": [
@@ -456,7 +479,7 @@
        "4            5     Malaga"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,12 +492,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "b636dde4-129a-4dd1-8cbf-c539c9c8a5f2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "## your code here:\n"
+    "## your code here:\n",
+    "data_diet_loc = pd.merge(data_diet, locations, how='inner', left_on='location-id', right_on='location-id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e4579355-bdde-40a3-8969-1c27c7bb0eb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>patient-id</th>\n",
+       "      <th>location-id</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>smoke</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>waist</th>\n",
+       "      <th>wth</th>\n",
+       "      <th>htn</th>\n",
+       "      <th>diab</th>\n",
+       "      <th>hyperchol</th>\n",
+       "      <th>famhist</th>\n",
+       "      <th>hormo</th>\n",
+       "      <th>p14</th>\n",
+       "      <th>toevent</th>\n",
+       "      <th>event</th>\n",
+       "      <th>group</th>\n",
+       "      <th>City</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>436</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>58</td>\n",
+       "      <td>Former</td>\n",
+       "      <td>33.53</td>\n",
+       "      <td>122</td>\n",
+       "      <td>0.753086</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.374401</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>Bilbao</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1130</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>77</td>\n",
+       "      <td>Current</td>\n",
+       "      <td>31.05</td>\n",
+       "      <td>119</td>\n",
+       "      <td>0.730061</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6.097194</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>Bilbao</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1131</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Former</td>\n",
+       "      <td>30.86</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0.654321</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5.946612</td>\n",
+       "      <td>No</td>\n",
+       "      <td>MedDiet + VOO</td>\n",
+       "      <td>Bilbao</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1132</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>71</td>\n",
+       "      <td>Former</td>\n",
+       "      <td>27.68</td>\n",
+       "      <td>118</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.907598</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>MedDiet + Nuts</td>\n",
+       "      <td>Bilbao</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1111</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>79</td>\n",
+       "      <td>Never</td>\n",
+       "      <td>35.94</td>\n",
+       "      <td>129</td>\n",
+       "      <td>0.806250</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4.761123</td>\n",
+       "      <td>No</td>\n",
+       "      <td>MedDiet + VOO</td>\n",
+       "      <td>Valencia</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   patient-id  location-id     sex  age    smoke    bmi  waist       wth  htn  \\\n",
+       "0         436            4    Male   58   Former  33.53    122  0.753086   No   \n",
+       "1        1130            4    Male   77  Current  31.05    119  0.730061  Yes   \n",
+       "2        1131            4  Female   72   Former  30.86    106  0.654321   No   \n",
+       "3        1132            4    Male   71   Former  27.68    118  0.694118  Yes   \n",
+       "4        1111            2  Female   79    Never  35.94    129  0.806250  Yes   \n",
+       "\n",
+       "  diab hyperchol famhist hormo  p14   toevent event           group      City  \n",
+       "0   No       Yes      No    No   10  5.374401   Yes         Control    Bilbao  \n",
+       "1  Yes        No      No    No   10  6.097194    No         Control    Bilbao  \n",
+       "2  Yes        No     Yes    No    8  5.946612    No   MedDiet + VOO    Bilbao  \n",
+       "3   No       Yes      No    No    8  2.907598   Yes  MedDiet + Nuts    Bilbao  \n",
+       "4   No       Yes      No    No    9  4.761123    No   MedDiet + VOO  Valencia  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_diet_loc.head()"
    ]
   },
   {
@@ -492,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "d1d4cc27",
    "metadata": {},
    "outputs": [],
@@ -502,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "fbebbd97",
    "metadata": {},
    "outputs": [
@@ -512,7 +718,7 @@
        "(42, 2)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -523,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "8a3c7943",
    "metadata": {},
    "outputs": [
@@ -591,7 +797,7 @@
        "4            4         541"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -602,12 +808,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "id": "573687e7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "data_diet_loc_drop = pd.merge(\n",
+    "    data_diet_loc, \n",
+    "    dropped, \n",
+    "    how='left', \n",
+    "    left_on=['location-id', 'patient-id'], \n",
+    "    right_on=['location-id', 'patient-id'], \n",
+    "    indicator=True\n",
+    ").query('_merge != \"both\"').drop(columns='_merge')"
    ]
   },
   {
@@ -622,15 +836,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "id": "85902eea",
    "metadata": {},
    "outputs": [],
    "source": [
     "fname = 'processed_data_predimed.csv'\n",
     "\n",
-    "#  your code here\n"
+    "#  your code here\n",
+    "data_diet_loc_drop.to_csv(fname)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38e88726-287a-4448-a250-f8616362d070",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -649,7 +872,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/exercises/tabular_split_apply_combine/split_apply_combine.ipynb b/exercises/tabular_split_apply_combine/split_apply_combine.ipynb
index 190b37c..ccd1996 100644
--- a/exercises/tabular_split_apply_combine/split_apply_combine.ipynb
+++ b/exercises/tabular_split_apply_combine/split_apply_combine.ipynb
@@ -275,12 +275,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "00bb9eb1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here:\n"
+    "# your code here:\n",
+    "event_by_group = df.groupby('group')['event'].sum()"
    ]
   },
   {
@@ -293,12 +294,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "db946d0f-8204-43a3-853c-41981a9811f4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here:\n"
+    "# your code here:\n",
+    "group_size = df.groupby('group').size()"
    ]
   },
   {
@@ -313,12 +315,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "13ad4130-2094-4e7a-a416-f0fd6e810413",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "group\n",
+       "Control           4.761905\n",
+       "MedDiet + Nuts    3.322099\n",
+       "MedDiet + VOO     3.856877\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here:\n"
+    "# your code here:\n",
+    "event_by_group / group_size * 100"
    ]
   },
   {
@@ -352,12 +370,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "id": "5ab4e70e-6261-4a26-8ad9-14eae15be09c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>smoke</th>\n",
+       "      <th>Current</th>\n",
+       "      <th>Former</th>\n",
+       "      <th>Never</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Control</th>\n",
+       "      <td>13</td>\n",
+       "      <td>39</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MedDiet + Nuts</th>\n",
+       "      <td>15</td>\n",
+       "      <td>20</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MedDiet + VOO</th>\n",
+       "      <td>20</td>\n",
+       "      <td>29</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "smoke           Current  Former  Never\n",
+       "group                                 \n",
+       "Control              13      39     44\n",
+       "MedDiet + Nuts       15      20     34\n",
+       "MedDiet + VOO        20      29     34"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "df.pivot_table(index='group', columns='smoke', values='event',aggfunc='sum')"
    ]
   },
   {
@@ -387,12 +474,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 22,
    "id": "196fd111-72bc-4b87-b8fb-293547a8c83d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">std</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th>Female</th>\n",
+       "      <th>Male</th>\n",
+       "      <th>Female</th>\n",
+       "      <th>Male</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Control</th>\n",
+       "      <td>68.009046</td>\n",
+       "      <td>66.400000</td>\n",
+       "      <td>5.979313</td>\n",
+       "      <td>6.605266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MedDiet + Nuts</th>\n",
+       "      <td>67.414591</td>\n",
+       "      <td>65.822665</td>\n",
+       "      <td>5.580050</td>\n",
+       "      <td>6.403373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MedDiet + VOO</th>\n",
+       "      <td>67.668775</td>\n",
+       "      <td>66.080045</td>\n",
+       "      <td>5.816703</td>\n",
+       "      <td>6.621440</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     mean                  std          \n",
+       "sex                Female       Male    Female      Male\n",
+       "group                                                   \n",
+       "Control         68.009046  66.400000  5.979313  6.605266\n",
+       "MedDiet + Nuts  67.414591  65.822665  5.580050  6.403373\n",
+       "MedDiet + VOO   67.668775  66.080045  5.816703  6.621440"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here:\n"
+    "# your code here:\n",
+    "df.pivot_table(index='group', columns='sex', values='age', aggfunc=['mean','std'])\n"
    ]
   }
  ],
@@ -412,7 +583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,