diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb
index 01594f2..eb19fee 100644
--- a/lab_pokemon.ipynb
+++ b/lab_pokemon.ipynb
@@ -16,17 +16,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "5923b0d7-c0e0-48fa-b765-4aa6002c2d4f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3"
+       "112"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -37,7 +37,7 @@
     "# printed below the cell. \n",
     "# Then try changing the Python code and re-run it.\n",
     "\n",
-    "1+1+1"
+    "2*56\n"
    ]
   },
   {
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 14,
    "id": "0e2a2927-f6d1-4b13-97ae-ff97416723e9",
    "metadata": {},
    "outputs": [
@@ -62,7 +62,7 @@
        "10"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "id": "69dd7908-b213-4d0f-8016-e46a4a491961",
    "metadata": {},
    "outputs": [
@@ -85,7 +85,7 @@
        "20"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "id": "ba09a0f8-27d9-456f-aeff-3980e3362d5b",
    "metadata": {},
    "outputs": [],
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "id": "a29d508a-2d9a-4d62-9ff6-7a0ecfd5eba4",
    "metadata": {},
    "outputs": [],
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "id": "579d8dda-ca39-48b1-8819-b17651029729",
    "metadata": {},
    "outputs": [
@@ -403,7 +403,7 @@
        "[800 rows x 12 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,12 +426,241 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people"
    ]
   },
   {
@@ -448,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "id": "9afca362-9edc-423c-981b-dc42107d5de0",
    "metadata": {},
    "outputs": [
@@ -465,7 +694,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479",
    "metadata": {},
    "outputs": [
@@ -498,7 +727,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -517,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f",
    "metadata": {},
    "outputs": [
@@ -527,7 +756,7 @@
        "np.float64(0.08125)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,12 +785,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 27,
    "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age\n",
+       "65    0.336326\n",
+       "55    0.206369\n",
+       "45    0.157669\n",
+       "35    0.135527\n",
+       "25    0.108866\n",
+       "18    0.055244\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people.age.value_counts(normalize=True)"
    ]
   },
   {
@@ -574,12 +821,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 30,
    "id": "b7f910c8-3d40-49ae-b270-678734c04100",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "height     1.705082\n",
+       "weight    83.053588\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people[[\"height\", \"weight\"]].mean()"
    ]
   },
   {
@@ -592,12 +852,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "id": "f3891188-a85f-4089-8388-d4d81c7438ad",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.7858014120474688)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "people.exercise.mean()"
    ]
   },
   {
@@ -1592,12 +1863,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "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": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "nodoc=people[people.no_doctor]\n",
+    "nodoc"
    ]
   },
   {
@@ -1618,12 +2119,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 35,
    "id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
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+       "      <th>education</th>\n",
+       "      <th>sexual_orientation</th>\n",
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+       "      <th>exercise</th>\n",
+       "      <th>sleep</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
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+       "      <th>4</th>\n",
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+       "      <td>8</td>\n",
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+       "      <td>1.80</td>\n",
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+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>55</td>\n",
+       "      <td>male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
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+       "      <td>74.84</td>\n",
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+       "      <td>7</td>\n",
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+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166418</th>\n",
+       "      <td>55</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
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+       "      <td>1.57</td>\n",
+       "      <td>63.50</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <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",
+       "      <td>1.52</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</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>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",
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+       "      <td>True</td>\n",
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+       "    <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>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": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "atleastgood=people[people.health >= 3]\n",
+    "atleastgood\n"
    ]
   },
   {
@@ -1643,12 +2374,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 37,
    "id": "315682ae-7d54-4d78-9a63-d23c83ba1576",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "      <th>health</th>\n",
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+       "  <tbody>\n",
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+       "      <th>2</th>\n",
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+       "      <th>146</th>\n",
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+       "    <tr>\n",
+       "      <th>166121</th>\n",
+       "      <td>35</td>\n",
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+       "      <td>55</td>\n",
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+       "      <td>heterosexual</td>\n",
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+       "      <th>166381</th>\n",
+       "      <td>45</td>\n",
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+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
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+       "      <td>1.60</td>\n",
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+       "      <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": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "femalegrads_nodoc=nodoc[(nodoc.sex == \"female\") & (nodoc.education ==4)]\n",
+    "femalegrads_nodoc"
    ]
   },
   {
@@ -1665,6 +2626,257 @@
     "Do different types of pokémon move at different speeds? We'll use `sort_values` to put these in order from slow to fast."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a79d96b5-2e3a-4bf3-b3ce-d41e8c6ea3c8",
+   "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>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>total</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "      <th>special_attack</th>\n",
+       "      <th>special_defense</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>generation</th>\n",
+       "      <th>legendary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bulbasaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>318</td>\n",
+       "      <td>45</td>\n",
+       "      <td>49</td>\n",
+       "      <td>49</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ivysaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>405</td>\n",
+       "      <td>60</td>\n",
+       "      <td>62</td>\n",
+       "      <td>63</td>\n",
+       "      <td>80</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Venusaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>525</td>\n",
+       "      <td>80</td>\n",
+       "      <td>82</td>\n",
+       "      <td>83</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VenusaurMega Venusaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>625</td>\n",
+       "      <td>80</td>\n",
+       "      <td>100</td>\n",
+       "      <td>123</td>\n",
+       "      <td>122</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Charmander</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>309</td>\n",
+       "      <td>39</td>\n",
+       "      <td>52</td>\n",
+       "      <td>43</td>\n",
+       "      <td>60</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>600</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>DiancieMega Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>700</td>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>HoopaHoopa Confined</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Ghost</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>HoopaHoopa Unbound</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Dark</td>\n",
+       "      <td>680</td>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>Volcanion</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Water</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>800 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      name     type subtype  total  hp  attack  defense  \\\n",
+       "0                Bulbasaur    Grass  Poison    318  45      49       49   \n",
+       "1                  Ivysaur    Grass  Poison    405  60      62       63   \n",
+       "2                 Venusaur    Grass  Poison    525  80      82       83   \n",
+       "3    VenusaurMega Venusaur    Grass  Poison    625  80     100      123   \n",
+       "4               Charmander     Fire     NaN    309  39      52       43   \n",
+       "..                     ...      ...     ...    ...  ..     ...      ...   \n",
+       "795                Diancie     Rock   Fairy    600  50     100      150   \n",
+       "796    DiancieMega Diancie     Rock   Fairy    700  50     160      110   \n",
+       "797    HoopaHoopa Confined  Psychic   Ghost    600  80     110       60   \n",
+       "798     HoopaHoopa Unbound  Psychic    Dark    680  80     160       60   \n",
+       "799              Volcanion     Fire   Water    600  80     110      120   \n",
+       "\n",
+       "     special_attack  special_defense  speed  generation  legendary  \n",
+       "0                65               65     45           1      False  \n",
+       "1                80               80     60           1      False  \n",
+       "2               100              100     80           1      False  \n",
+       "3               122              120     80           1      False  \n",
+       "4                60               50     65           1      False  \n",
+       "..              ...              ...    ...         ...        ...  \n",
+       "795             100              150     50           6       True  \n",
+       "796             160              110    110           6       True  \n",
+       "797             150              130     70           6       True  \n",
+       "798             170              130     80           6       True  \n",
+       "799             130               90     70           6       True  \n",
+       "\n",
+       "[800 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 21,
@@ -1967,12 +3179,271 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 41,
+   "id": "38d0c0f1-5b2f-4120-a1cf-c980243a1db8",
+   "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>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": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
    "id": "75c1ac4f-3914-4c0a-a156-2e084002df66",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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\n",
+       "Name: sleep, dtype: float64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "people.groupby(\"income\").sleep.mean().sort_values()"
    ]
   },
   {
@@ -1985,12 +3456,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 54,
    "id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21",
    "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></th>\n",
+       "      <th>income</th>\n",
+       "      <th>health</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th>education</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">female</th>\n",
+       "      <th>1</th>\n",
+       "      <td>3.554701</td>\n",
+       "      <td>2.848040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.049022</td>\n",
+       "      <td>3.315797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.779390</td>\n",
+       "      <td>3.483379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.960652</td>\n",
+       "      <td>3.844340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">male</th>\n",
+       "      <th>1</th>\n",
+       "      <td>4.433009</td>\n",
+       "      <td>3.031525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.742876</td>\n",
+       "      <td>3.440818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.270230</td>\n",
+       "      <td>3.549105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.190582</td>\n",
+       "      <td>3.826512</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    income    health\n",
+       "sex    education                    \n",
+       "female 1          3.554701  2.848040\n",
+       "       2          5.049022  3.315797\n",
+       "       3          5.779390  3.483379\n",
+       "       4          6.960652  3.844340\n",
+       "male   1          4.433009  3.031525\n",
+       "       2          5.742876  3.440818\n",
+       "       3          6.270230  3.549105\n",
+       "       4          7.190582  3.826512"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# YOUR CODE HERE"
+    "sex=people.groupby([\"sex\",\"education\"])\n",
+    "sex[[\"income\", \"health\"]].mean()"
    ]
   },
   {
@@ -2007,7 +3576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 55,
    "id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce",
    "metadata": {},
    "outputs": [],
@@ -2355,12 +3924,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 124,
    "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='height', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 124,
+     "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\", bins=30, color=\"green\")"
    ]
   },
   {
@@ -2373,12 +3963,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "ee30c851-14b1-4901-9182-4304d54d53a6",
+   "execution_count": 132,
+   "id": "1317e77c-8cd9-449e-8ec6-4897914103da",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='age', ylabel='sleep'>"
+      ]
+     },
+     "execution_count": 132,
+     "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"
+    "avgsleep=people.groupby('age')['sleep'].mean().reset_index()\n",
+    "sns.barplot(data=avgsleep, x='age', y='sleep', color='red')"
    ]
   },
   {
@@ -2391,12 +4003,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80",
+   "execution_count": 133,
+   "id": "810870e3-392e-4036-9321-502c6e418d45",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='income', ylabel='exercise'>"
+      ]
+     },
+     "execution_count": 133,
+     "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"
+    "exercise_income=people.groupby('income')['exercise'].mean().reset_index()\n",
+    "sns.barplot(data=exercise_income, x='income', y='exercise', color='purple')"
    ]
   },
   {
@@ -2409,12 +4043,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 118,
    "id": "4ee2eb69-2f9a-42e7-b5d3-9499631bfd06",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='age', ylabel='health'>"
+      ]
+     },
+     "execution_count": 118,
+     "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"
+    "avghealth=people.groupby(['age','exercise'])\n",
+    "avghealth=avghealth.health.mean().reset_index()\n",
+    "\n",
+    "sns.barplot(data=avghealth, x='age', y='health', hue='exercise')"
    ]
   },
   {
@@ -2427,12 +4085,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 138,
    "id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='income', ylabel='count'>"
+      ]
+     },
+     "execution_count": 138,
+     "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"
+    "inc_ed=people.groupby(['education', 'income']).size().reset_index(name='count')\n",
+    "sns.barplot(data=inc_ed, x=\"income\", y=\"count\", hue=\"education\")"
    ]
   },
   {
@@ -2445,12 +4125,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 142,
    "id": "b00dd7d6-226b-469c-86d8-b71b328aa576",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xfa0c34187860>"
+      ]
+     },
+     "execution_count": 142,
+     "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"
+    "sns.relplot(data=people, x='height', y='weight', col='sex', s=20)"
    ]
   },
   {
@@ -2472,19 +4173,81 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99",
+   "cell_type": "markdown",
+   "id": "d8e49878-e2b4-4196-b9bf-6e74142f57bc",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your code here. Feel free to add new text cells and code cells as necessary."
+    "### Research Question\n",
+    "#### Is higher weight associated with lower reported health?\n",
+    "##### Hypothesis: People with higher weight will report lower health ratings on average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "8b4b852b-402c-45d4-b3bb-840e47b249ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(-0.17225826656795756)"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#correlation btwn weight and health\n",
+    "r = people['weight'].corr(people['health'])\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "ef7cbeb1-5fe2-440c-9598-656cac0ee665",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='weight', ylabel='health'>"
+      ]
+     },
+     "execution_count": 150,
+     "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": [
+    "sns.regplot(data=people, x='weight', y='health')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f7306f0-27ff-4997-81f9-240db4f8d445",
+   "metadata": {},
+   "source": [
+    "When searching for a correlation between weigth and health, we get our coefficient, r= -0.172. This mean there is a weak negative correlation, i.e. as weight increases, reported health tends to decrease a little. Since the coefficient is closer to 0, it is a mild to weak relationship and weight alone cannot strongly predict how healthy people are. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b4b852b-402c-45d4-b3bb-840e47b249ed",
+   "id": "5cfa87e0-307e-4c27-8289-1f1f40415584",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2506,7 +4269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,