diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb
index 01594f2..c3e699e 100644
--- a/lab_pokemon.ipynb
+++ b/lab_pokemon.ipynb
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "ba09a0f8-27d9-456f-aeff-3980e3362d5b",
    "metadata": {},
    "outputs": [],
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "a29d508a-2d9a-4d62-9ff6-7a0ecfd5eba4",
    "metadata": {},
    "outputs": [],
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "579d8dda-ca39-48b1-8819-b17651029729",
    "metadata": {},
    "outputs": [
@@ -403,7 +403,7 @@
        "[800 rows x 12 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,12 +426,241 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f",
    "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></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>income</th>\n",
+       "      <th>education</th>\n",
+       "      <th>sexual_orientation</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>health</th>\n",
+       "      <th>no_doctor</th>\n",
+       "      <th>exercise</th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1.55</td>\n",
+       "      <td>83.01</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>78.02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>77.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>55</td>\n",
+       "      <td>male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>81.65</td>\n",
+       "      <td>5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>76.66</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166420</th>\n",
+       "      <td>45</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.63</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166421</th>\n",
+       "      <td>25</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166422</th>\n",
+       "      <td>25</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>45.36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166424</th>\n",
+       "      <td>35</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>166425 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age     sex  income  education sexual_orientation  height  weight  \\\n",
+       "0        55  female       5          2              other    1.55   83.01   \n",
+       "1        65  female       8          1       heterosexual    1.65   78.02   \n",
+       "2        35  female       8          4       heterosexual    1.65   77.11   \n",
+       "3        55    male       8          4       heterosexual    1.83   81.65   \n",
+       "4        55  female       8          4       heterosexual    1.80   76.66   \n",
+       "...     ...     ...     ...        ...                ...     ...     ...   \n",
+       "166420   45  female       8          3       heterosexual    1.63   86.18   \n",
+       "166421   25    male       7          2       heterosexual    1.78   86.18   \n",
+       "166422   25  female       1          2       heterosexual    1.91   45.36   \n",
+       "166423   35  female       5          4       heterosexual    1.60   68.04   \n",
+       "166424   35    male       7          2       heterosexual    1.75   86.18   \n",
+       "\n",
+       "        health  no_doctor  exercise  sleep  \n",
+       "0            2       True      True      7  \n",
+       "1            3      False     False      8  \n",
+       "2            4       True      True      7  \n",
+       "3            5      False      True      8  \n",
+       "4            4      False      True      8  \n",
+       "...        ...        ...       ...    ...  \n",
+       "166420       1      False     False      6  \n",
+       "166421       4      False      True      6  \n",
+       "166422       1      False     False      8  \n",
+       "166423       4       True      True      6  \n",
+       "166424       3      False     False      8  \n",
+       "\n",
+       "[166425 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people"
    ]
   },
   {
@@ -448,30 +677,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "id": "9afca362-9edc-423c-981b-dc42107d5de0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "generation\n",
-       "1    166\n",
-       "5    165\n",
-       "3    160\n",
-       "4    121\n",
-       "2    106\n",
-       "6     82\n",
-       "Name: count, dtype: int64"
+       "speed\n",
+       "50     46\n",
+       "60     44\n",
+       "70     37\n",
+       "65     36\n",
+       "30     35\n",
+       "       ..\n",
+       "89      1\n",
+       "49      1\n",
+       "118     1\n",
+       "54      1\n",
+       "123     1\n",
+       "Name: count, Length: 108, dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pokemon.generation.value_counts()"
+    "pokemon.speed.value_counts()"
    ]
   },
   {
@@ -484,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479",
    "metadata": {},
    "outputs": [
@@ -498,7 +732,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -517,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f",
    "metadata": {},
    "outputs": [
@@ -527,7 +761,7 @@
        "np.float64(0.08125)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,12 +790,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age\n",
+       "65    55973\n",
+       "55    34345\n",
+       "45    26240\n",
+       "35    22555\n",
+       "25    18118\n",
+       "18     9194\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people.age.value_counts()"
    ]
   },
   {
@@ -574,12 +826,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "b7f910c8-3d40-49ae-b270-678734c04100",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "height     1.705082\n",
+       "weight    83.053588\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people[[\"height\", \"weight\"]].mean()"
    ]
   },
   {
@@ -592,12 +857,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "f3891188-a85f-4089-8388-d4d81c7438ad",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.7858014120474688)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people.exercise.mean()"
    ]
   },
   {
@@ -615,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "id": "12c0c6c9-c07b-4183-82f6-5e346c74aac9",
    "metadata": {},
    "outputs": [
@@ -855,7 +1131,7 @@
        "[65 rows x 12 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "id": "5d089acf-7b76-4f91-8803-42a4a9a11e3e",
    "metadata": {},
    "outputs": [
@@ -896,7 +1172,7 @@
        "Name: type, Length: 800, dtype: bool"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -915,7 +1191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "id": "510fa0fc-2b38-4725-9bbf-ec57d62792be",
    "metadata": {},
    "outputs": [
@@ -1373,7 +1649,7 @@
        "789              44               46     28           6      False  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1393,7 +1669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "id": "05d4c5c2-c6b4-4795-9799-c884b15445a1",
    "metadata": {},
    "outputs": [
@@ -1562,7 +1838,7 @@
        "676              95              135    105           5      False  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1592,12 +1868,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "id": "198cb0c6-3f43-43c2-9eee-3939c12ea537",
    "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></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>income</th>\n",
+       "      <th>education</th>\n",
+       "      <th>sexual_orientation</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>health</th>\n",
+       "      <th>no_doctor</th>\n",
+       "      <th>exercise</th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1.55</td>\n",
+       "      <td>83.01</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>77.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>35</td>\n",
+       "      <td>male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.73</td>\n",
+       "      <td>94.35</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>81.65</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>45</td>\n",
+       "      <td>female</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.57</td>\n",
+       "      <td>72.57</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166407</th>\n",
+       "      <td>18</td>\n",
+       "      <td>male</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166409</th>\n",
+       "      <td>25</td>\n",
+       "      <td>male</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.57</td>\n",
+       "      <td>58.51</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166414</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.63</td>\n",
+       "      <td>88.45</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166416</th>\n",
+       "      <td>65</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>55.34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13784 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age     sex  income  education sexual_orientation  height  weight  \\\n",
+       "0        55  female       5          2              other    1.55   83.01   \n",
+       "2        35  female       8          4       heterosexual    1.65   77.11   \n",
+       "24       35    male       8          3       heterosexual    1.73   94.35   \n",
+       "50       35  female       4          2       heterosexual    1.78   81.65   \n",
+       "66       45  female       6          4       heterosexual    1.57   72.57   \n",
+       "...     ...     ...     ...        ...                ...     ...     ...   \n",
+       "166407   18    male       5          2       heterosexual    1.68   68.04   \n",
+       "166409   25    male       6          2       heterosexual    1.57   58.51   \n",
+       "166414   55  female       8          3       heterosexual    1.63   88.45   \n",
+       "166416   65  female       5          2       heterosexual    1.50   55.34   \n",
+       "166423   35  female       5          4       heterosexual    1.60   68.04   \n",
+       "\n",
+       "        health  no_doctor  exercise  sleep  \n",
+       "0            2       True      True      7  \n",
+       "2            4       True      True      7  \n",
+       "24           4       True     False      8  \n",
+       "50           4       True     False     10  \n",
+       "66           4       True      True      7  \n",
+       "...        ...        ...       ...    ...  \n",
+       "166407       3       True      True      8  \n",
+       "166409       4       True     False      7  \n",
+       "166414       3       True     False      6  \n",
+       "166416       3       True     False      6  \n",
+       "166423       4       True      True      6  \n",
+       "\n",
+       "[13784 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "no_doctor = people[people.no_doctor]\n",
+    "no_doctor"
    ]
   },
   {
@@ -1618,12 +2124,243 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 28,
    "id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
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+       "    <tr>\n",
+       "      <th>166418</th>\n",
+       "      <td>55</td>\n",
+       "      <td>male</td>\n",
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+       "      <td>8</td>\n",
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+       "    <tr>\n",
+       "      <th>166419</th>\n",
+       "      <td>45</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
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+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
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+       "    <tr>\n",
+       "      <th>166421</th>\n",
+       "      <td>25</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
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+       "    <tr>\n",
+       "      <th>166423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
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+       "      <td>35</td>\n",
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+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
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+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>142249 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age     sex  income  education sexual_orientation  height  weight  \\\n",
+       "1        65  female       8          1       heterosexual    1.65   78.02   \n",
+       "2        35  female       8          4       heterosexual    1.65   77.11   \n",
+       "3        55    male       8          4       heterosexual    1.83   81.65   \n",
+       "4        55  female       8          4       heterosexual    1.80   76.66   \n",
+       "5        55    male       8          4       heterosexual    1.80   74.84   \n",
+       "...     ...     ...     ...        ...                ...     ...     ...   \n",
+       "166418   55    male       7          2       heterosexual    1.57   63.50   \n",
+       "166419   45  female       8          2       heterosexual    1.52   68.04   \n",
+       "166421   25    male       7          2       heterosexual    1.78   86.18   \n",
+       "166423   35  female       5          4       heterosexual    1.60   68.04   \n",
+       "166424   35    male       7          2       heterosexual    1.75   86.18   \n",
+       "\n",
+       "        health  no_doctor  exercise  sleep  \n",
+       "1            3      False     False      8  \n",
+       "2            4       True      True      7  \n",
+       "3            5      False      True      8  \n",
+       "4            4      False      True      8  \n",
+       "5            5      False      True      7  \n",
+       "...        ...        ...       ...    ...  \n",
+       "166418       3      False      True      8  \n",
+       "166419       3      False      True      7  \n",
+       "166421       4      False      True      6  \n",
+       "166423       4       True      True      6  \n",
+       "166424       3      False     False      8  \n",
+       "\n",
+       "[142249 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "people.health >= 3\n",
+    "health = people[people.health >= 3]\n",
+    "health"
    ]
   },
   {
@@ -1643,12 +2380,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "id": "315682ae-7d54-4d78-9a63-d23c83ba1576",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "  <tbody>\n",
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+       "      <th>2</th>\n",
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+       "      <th>166121</th>\n",
+       "      <td>35</td>\n",
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+       "    <tr>\n",
+       "      <th>166423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2321 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age     sex  income  education sexual_orientation  height  weight  \\\n",
+       "2        35  female       8          4       heterosexual    1.65   77.11   \n",
+       "66       45  female       6          4       heterosexual    1.57   72.57   \n",
+       "135      55  female       6          4       heterosexual    1.70   81.65   \n",
+       "146      65  female       5          4       heterosexual    1.55   72.57   \n",
+       "259      65  female       4          4       heterosexual    1.57   56.70   \n",
+       "...     ...     ...     ...        ...                ...     ...     ...   \n",
+       "166121   35  female       8          4       heterosexual    1.65  136.08   \n",
+       "166219   55  female       5          4              other    1.52   59.87   \n",
+       "166272   25  female       8          4       heterosexual    1.52   98.88   \n",
+       "166381   45  female       5          4       heterosexual    1.52   49.90   \n",
+       "166423   35  female       5          4       heterosexual    1.60   68.04   \n",
+       "\n",
+       "        health  no_doctor  exercise  sleep  \n",
+       "2            4       True      True      7  \n",
+       "66           4       True      True      7  \n",
+       "135          4       True      True      7  \n",
+       "146          5       True      True      7  \n",
+       "259          3       True      True      6  \n",
+       "...        ...        ...       ...    ...  \n",
+       "166121       4       True      True      5  \n",
+       "166219       3       True      True      5  \n",
+       "166272       3       True      True      8  \n",
+       "166381       5       True      True      6  \n",
+       "166423       4       True      True      6  \n",
+       "\n",
+       "[2321 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "f_no_doctor_grad = people[(people.no_doctor) & (people.sex == \"female\") & (people.education == 4)]\n",
+    "f_no_doctor_grad"
    ]
   },
   {
@@ -1667,7 +2634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 32,
    "id": "069ea0ab-eff6-4985-9f46-db956fe1df91",
    "metadata": {},
    "outputs": [
@@ -1696,7 +2663,7 @@
        "Name: speed, dtype: float64"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1967,12 +2934,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 35,
    "id": "75c1ac4f-3914-4c0a-a156-2e084002df66",
    "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></th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>income</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.952208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.985627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.055784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.074626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.078495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.079627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.083274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.100286</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           sleep\n",
+       "income          \n",
+       "1       6.952208\n",
+       "2       6.985627\n",
+       "6       7.055784\n",
+       "8       7.074626\n",
+       "7       7.078495\n",
+       "4       7.079627\n",
+       "3       7.083274\n",
+       "5       7.100286"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "sleep_income = people.groupby(\"income\")\n",
+    "sleep_income[[\"sleep\"]].mean().sort_values(\"sleep\")"
    ]
   },
   {
@@ -1985,12 +3036,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 49,
    "id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sex     education\n",
+       "female  1            2.848040\n",
+       "male    1            3.031525\n",
+       "female  2            3.315797\n",
+       "male    2            3.440818\n",
+       "female  3            3.483379\n",
+       "male    3            3.549105\n",
+       "        4            3.826512\n",
+       "female  4            3.844340\n",
+       "Name: health, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "income_health = people.groupby([\"sex\", \"education\"]).health.mean().sort_values()\n",
+    "income_health\n"
    ]
   },
   {
@@ -2007,10 +3079,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 50,
    "id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib is building the font cache; this may take a moment.\n"
+     ]
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "sns.set_theme()"
@@ -2355,12 +3435,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 52,
    "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='height', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "sns.histplot(data=people, x=\"height\")"
    ]
   },
   {
@@ -2373,12 +3474,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 53,
    "id": "ee30c851-14b1-4901-9182-4304d54d53a6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='sleep', ylabel='age'>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "sns.barplot(data=people, x=\"sleep\", y=\"age\")"
    ]
   },
   {
@@ -2391,12 +3513,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 55,
    "id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='exercise', ylabel='income'>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "sns.barplot(data=people, x=\"exercise\", y=\"income\", hue=\"income\", errorbar=None, palette=\"muted\")"
    ]
   },
   {
@@ -2427,12 +3570,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 57,
    "id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='income', ylabel='education'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "sns.barplot(data=people, x=\"income\", y=\"education\")"
    ]
   },
   {
@@ -2445,12 +3609,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 61,
    "id": "b00dd7d6-226b-469c-86d8-b71b328aa576",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x10eeca350>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "gender = people[people.sex.isin([\"male\", \"female\"])]\n",
+    "sns.relplot(data=gender, x=\"height\", y=\"weight\", edgecolor=None, col=\"sex\", alpha=0.1)"
    ]
   },
   {
@@ -2473,12 +3659,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 63,
    "id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='sex', ylabel='income'>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here. Feel free to add new text cells and code cells as necessary."
+    "#Do men with college degrees earn more than women with college degrees?\n",
+    "grad = people[(people.education == 4)]\n",
+    "sns.barplot(data=grad, x=\"sex\", y=\"income\", errorbar=\"sd\")\n",
+    "#the data did answser my question, showing me on average men make more than women even if they both have college degrees, sadly."
    ]
   },
   {
@@ -2506,7 +3716,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,