diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..5c95e0b 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import random\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
    ]
   },
   {
@@ -29,11 +32,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "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>throw</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    throw\n",
+       "1       6\n",
+       "2       3\n",
+       "3       3\n",
+       "4       5\n",
+       "5       6\n",
+       "6       2\n",
+       "7       4\n",
+       "8       1\n",
+       "9       4\n",
+       "10      5"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def dice_throw(n):\n",
+    "    throws= {i+1:random.randint(1,6) for i in range(n)}\n",
+    "    return pd.DataFrame.from_dict(throws,orient=\"index\",columns=[\"throw\"])\n",
+    "\n",
+    "dice_df=dice_throw(10)\n",
+    "dice_df"
    ]
   },
   {
@@ -45,11 +141,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "dice_df.sort_values(by=\"throw\").plot(kind=\"bar\",y=\"throw\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,22 +169,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "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>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>throw</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count\n",
+       "throw       \n",
+       "1          1\n",
+       "2          1\n",
+       "3          2\n",
+       "4          2\n",
+       "5          2\n",
+       "6          2"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "count_throws=dice_df.groupby(\"throw\").agg({\"throw\":\"count\"})\n",
+    "count_throws.columns=[\"count\"]\n",
+    "count_throws"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "count_throws.plot(kind=\"bar\",y=\"count\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Second graph gives us the frequency distribution of the dice throws - how many times each value on the dice was thrown within 10 throws. First graph is just a plot of all thrown values within 10 throws sorted from min to max*"
    ]
   },
   {
@@ -91,11 +291,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "throw    3.9\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def mean(df):\n",
+    "    return df.sum()/df.count()\n",
+    "mean(dice_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.9"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#just checking values\n",
+    "dice_df[\"throw\"].mean()"
    ]
   },
   {
@@ -107,11 +342,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "throw    1.666667\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "#??? not sure what is being asked but took dataframe with freq distribution from last challenge and applied function from previous exercise\n",
+    "count_throws=dice_df.groupby(\"throw\").agg({\"throw\":\"count\"})\n",
+    "mean(count_throws)"
    ]
   },
   {
@@ -124,11 +373,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "def median(list):\n",
+    "    list.sort()\n",
+    "    if len(list)%2!=0:\n",
+    "        return list[int(round(len(list)/2,0))]\n",
+    "    else:\n",
+    "        return (list[int(round(len(list)/2,0))-1] + list[int(round(len(list)/2,0))])/2"
    ]
   },
   {
@@ -144,7 +398,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "\n"
    ]
   },
   {
@@ -158,21 +412,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "dice_100=pd.read_csv(\"../data/roll_the_dice_hundred.csv\")\n",
+    "dice_100.sort_values(by=\"value\").plot(kind=\"bar\",y=\"value\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n4 and 6 seem to be the more prevailing numbers\\n'"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "4 and 6 seem to be the more prevailing numbers\n",
     "\"\"\""
    ]
   },
@@ -185,11 +463,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean(dice_100[\"value\"])"
    ]
   },
   {
@@ -201,11 +490,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "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>roll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       roll\n",
+       "value      \n",
+       "1        12\n",
+       "2        17\n",
+       "3        14\n",
+       "4        22\n",
+       "5        12\n",
+       "6        23"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "hist=dice_100.groupby(\"value\").agg({\"roll\":\"count\"})\n",
+    "hist"
    ]
   },
   {
@@ -217,21 +580,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "hist.plot(kind=\"bar\",y=\"roll\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nMean is close to the second most frequent value of the dice throw. Set is unimodal, mode=6. Makes sense given the number of possible combinations to achieve 6 and 4 are the highest in the set\\n'"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Mean is close to the second most frequent value of the dice throw. Set is unimodal, mode=6. Makes sense given the number of possible combinations to achieve 6 and 4 are the highest in the set\n",
     "\"\"\""
    ]
   },
@@ -244,21 +630,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 113,
+     "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"
+    "dice_1000=pd.read_csv(\"../data/roll_the_dice_thousand.csv\")\n",
+    "#hist=dice_1000.groupby(\"value\").agg({\"roll\":\"count\"})\n",
+    "#hist.plot(kind=\"bar\",y=\"roll\")\n",
+    "#plt.show()\n",
+    "#too complicated................ why?\n",
+    "dice_1000[\"value\"].hist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 108,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nWith 1000 throws data is more standardised and the frequency trends do not hold anymore.\\n'"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "With 1000 throws data is more standardised and the frequency trends do not hold anymore.\n",
     "\"\"\""
    ]
   },
@@ -274,11 +697,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'observation'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 111,
+     "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"
+    "ages=pd.read_csv(\"../data/ages_population.csv\")\n",
+    "ages.hist()\n"
    ]
   },
   {
@@ -290,21 +735,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 117,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    36.56\n",
+      "dtype: float64\n",
+      "observation    12.8165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(ages.mean())\n",
+    "print(ages.std())\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 118,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nMean corresponds to expectations, so does standard deviation -- most observations are in the central area\\n'"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Mean corresponds to expectations, so does standard deviation -- most observations are in the central area\n",
     "\"\"\""
    ]
   },
@@ -317,11 +785,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 120,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'observation'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 120,
+     "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"
+    "ages2=pd.read_csv(\"../data/ages_population2.csv\")\n",
+    "ages2.hist()"
    ]
   },
   {
@@ -333,13 +823,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nMean around 27.5; data more condensed - std lower?'"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "Mean around 27.5; data more condensed - std lower?\"\"\""
    ]
   },
   {
@@ -351,22 +851,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    27.155\n",
+      "dtype: float64\n",
+      "observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(ages2.mean())\n",
+    "print(ages2.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nEasy to guess mean; did not expect standard deviation to be this low'"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "Easy to guess mean; did not expect standard deviation to be this low\"\"\""
    ]
   },
   {
@@ -381,11 +903,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'observation'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 126,
+     "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"
+    "ages3=pd.read_csv(\"../data/ages_population3.csv\")\n",
+    "ages3.hist()"
    ]
   },
   {
@@ -397,21 +941,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 127,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    41.989\n",
+      "dtype: float64\n",
+      "observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(ages3.mean())\n",
+    "print(ages3.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 130,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nMean age is getting higher, and there seems to be more differentiation within the sample, more age groups included in the study\\n'"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Mean age is getting higher, and there seems to be more differentiation within the sample, more age groups included in the study\n",
     "\"\"\""
    ]
   },
@@ -424,21 +991,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 136,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First quartile is 30.0\n",
+      "First quartile is 40.0\n",
+      "First quartile is 53.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "q1=np.quantile(ages3,0.25)\n",
+    "print(\"First quartile is\",q1)\n",
+    "q2=np.quantile(ages3,0.5)\n",
+    "print(\"First quartile is\",q2)\n",
+    "q3=np.quantile(ages3,0.75)\n",
+    "print(\"First quartile is\",q3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 137,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nObservation re more age groups included in the study confirmed - in age 1 sample, there were very few observations with age of more than 50\\n'"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Observation re more age groups included in the study confirmed - in age 1 sample, there were very few observations with age of more than 50\n",
     "\"\"\""
    ]
   },
@@ -451,21 +1044,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 138,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First quartile is 25.0\n",
+      "First quartile is 27.0\n",
+      "First quartile is 29.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "q1=np.quantile(ages,0.25)\n",
+    "print(\"First quartile is\",q1)\n",
+    "q2=np.quantile(ages,0.5)\n",
+    "print(\"First quartile is\",q2)\n",
+    "q3=np.quantile(ages,0.75)\n",
+    "print(\"First quartile is\",q3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 139,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nIn age 1, majority of observations were very close to the mean. In age 3, age groups are more differentiated\\n'"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "In age 1, majority of observations were very close to the mean. In age 3, age groups are more differentiated\n",
     "\"\"\""
    ]
   },
@@ -500,9 +1119,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "base",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1133,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,