diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 0fc1af6..78a0d60 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -18,10 +18,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -32,19 +35,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "lst = [5.7, 75.2, 74.4, 84.0, 66.5, 66.3, 55.8, 75.7, 29.1, 43.7]"
+    "lst = [5.7, 75.2, 74.4, 84.0, 66.5, 66.3, 55.8, 75.7, 29.1, 43.7]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     5.7\n",
+       "1    75.2\n",
+       "2    74.4\n",
+       "3    84.0\n",
+       "4    66.5\n",
+       "5    66.3\n",
+       "6    55.8\n",
+       "7    75.7\n",
+       "8    29.1\n",
+       "9    43.7\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdseries = pd.Series(lst)\n",
+    "\n",
+    "pdseries\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -57,10 +85,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74.4"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdseries[2]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -71,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,10 +130,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0     1     2     3     4\n",
+      "0  53.1  95.0  67.5  35.0  78.4\n",
+      "1  61.3  40.8  30.8  37.8  87.6\n",
+      "2  20.6  73.2  44.2  14.6  91.8\n",
+      "3  57.4   0.1  96.1   4.2  69.5\n",
+      "4  83.6  20.5  85.4  22.8  35.9\n",
+      "5  49.0  69.0   0.1  31.8  89.1\n",
+      "6  23.3  40.7  95.0  83.8  26.9\n",
+      "7  27.6  26.4  53.8  88.8  68.5\n",
+      "8  96.6  96.4  53.4  72.4  50.1\n",
+      "9  73.7  39.0  43.2  81.6  34.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(b)\n",
+    "\n",
+    "print(df)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -103,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,10 +175,146 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>Score_1</th>\n",
+       "      <th>Score_2</th>\n",
+       "      <th>Score_3</th>\n",
+       "      <th>Score_4</th>\n",
+       "      <th>Score_5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.869224</td>\n",
+       "      <td>0.746609</td>\n",
+       "      <td>0.584444</td>\n",
+       "      <td>0.038943</td>\n",
+       "      <td>0.222773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.406937</td>\n",
+       "      <td>0.274400</td>\n",
+       "      <td>0.688071</td>\n",
+       "      <td>0.525683</td>\n",
+       "      <td>0.180013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.487606</td>\n",
+       "      <td>0.564185</td>\n",
+       "      <td>0.928030</td>\n",
+       "      <td>0.115601</td>\n",
+       "      <td>0.429130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.963187</td>\n",
+       "      <td>0.891227</td>\n",
+       "      <td>0.329746</td>\n",
+       "      <td>0.092960</td>\n",
+       "      <td>0.276190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.171703</td>\n",
+       "      <td>0.796789</td>\n",
+       "      <td>0.908236</td>\n",
+       "      <td>0.527824</td>\n",
+       "      <td>0.888977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.875052</td>\n",
+       "      <td>0.623937</td>\n",
+       "      <td>0.206036</td>\n",
+       "      <td>0.649553</td>\n",
+       "      <td>0.747086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.968347</td>\n",
+       "      <td>0.613830</td>\n",
+       "      <td>0.824405</td>\n",
+       "      <td>0.788321</td>\n",
+       "      <td>0.879799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.493284</td>\n",
+       "      <td>0.555293</td>\n",
+       "      <td>0.065843</td>\n",
+       "      <td>0.511444</td>\n",
+       "      <td>0.745148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.408545</td>\n",
+       "      <td>0.844277</td>\n",
+       "      <td>0.634540</td>\n",
+       "      <td>0.172246</td>\n",
+       "      <td>0.321879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.944799</td>\n",
+       "      <td>0.285153</td>\n",
+       "      <td>0.191065</td>\n",
+       "      <td>0.086497</td>\n",
+       "      <td>0.368352</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Score_1   Score_2   Score_3   Score_4   Score_5\n",
+       "0  0.869224  0.746609  0.584444  0.038943  0.222773\n",
+       "1  0.406937  0.274400  0.688071  0.525683  0.180013\n",
+       "2  0.487606  0.564185  0.928030  0.115601  0.429130\n",
+       "3  0.963187  0.891227  0.329746  0.092960  0.276190\n",
+       "4  0.171703  0.796789  0.908236  0.527824  0.888977\n",
+       "5  0.875052  0.623937  0.206036  0.649553  0.747086\n",
+       "6  0.968347  0.613830  0.824405  0.788321  0.879799\n",
+       "7  0.493284  0.555293  0.065843  0.511444  0.745148\n",
+       "8  0.408545  0.844277  0.634540  0.172246  0.321879\n",
+       "9  0.944799  0.285153  0.191065  0.086497  0.368352"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(np.random.random((10,5)), columns=colnames)\n",
+    "\n",
+    "df"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -126,10 +325,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>Score_1</th>\n",
+       "      <th>Score_3</th>\n",
+       "      <th>Score_5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.869224</td>\n",
+       "      <td>0.584444</td>\n",
+       "      <td>0.222773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.406937</td>\n",
+       "      <td>0.688071</td>\n",
+       "      <td>0.180013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.487606</td>\n",
+       "      <td>0.928030</td>\n",
+       "      <td>0.429130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.963187</td>\n",
+       "      <td>0.329746</td>\n",
+       "      <td>0.276190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.171703</td>\n",
+       "      <td>0.908236</td>\n",
+       "      <td>0.888977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.875052</td>\n",
+       "      <td>0.206036</td>\n",
+       "      <td>0.747086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.968347</td>\n",
+       "      <td>0.824405</td>\n",
+       "      <td>0.879799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.493284</td>\n",
+       "      <td>0.065843</td>\n",
+       "      <td>0.745148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.408545</td>\n",
+       "      <td>0.634540</td>\n",
+       "      <td>0.321879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.944799</td>\n",
+       "      <td>0.191065</td>\n",
+       "      <td>0.368352</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Score_1   Score_3   Score_5\n",
+       "0  0.869224  0.584444  0.222773\n",
+       "1  0.406937  0.688071  0.180013\n",
+       "2  0.487606  0.928030  0.429130\n",
+       "3  0.963187  0.329746  0.276190\n",
+       "4  0.171703  0.908236  0.888977\n",
+       "5  0.875052  0.206036  0.747086\n",
+       "6  0.968347  0.824405  0.879799\n",
+       "7  0.493284  0.065843  0.745148\n",
+       "8  0.408545  0.634540  0.321879\n",
+       "9  0.944799  0.191065  0.368352"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Score_1','Score_3','Score_5']]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -140,10 +451,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5360416639606794"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Score_3'].mean()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -154,10 +478,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7883206597298037"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Score_4'].max()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -168,10 +505,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6188833649256493"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Score_2'].median()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -182,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,10 +553,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>Description</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>UnitPrice</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LUNCH BAG APPLE DESIGN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>1.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SET OF 60 VINTAGE LEAF CAKE CASES</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>13.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>RIBBON REEL STRIPES DESIGN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>1.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
+       "      <td>2880</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>518.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PLAYING CARDS JUBILEE UNION JACK</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>2.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>POPCORN HOLDER</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>5.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BOX OF VINTAGE ALPHABET BLOCKS</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11.95</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PARTY BUNTING</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.95</td>\n",
+       "      <td>19.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JAZZ HEARTS ADDRESS BOOK</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>1.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SET OF 4 SANTA PLACE SETTINGS</td>\n",
+       "      <td>48</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>60.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Description  Quantity  UnitPrice  Revenue\n",
+       "0              LUNCH BAG APPLE DESIGN         1       1.65     1.65\n",
+       "1  SET OF 60 VINTAGE LEAF CAKE CASES         24       0.55    13.20\n",
+       "2         RIBBON REEL STRIPES DESIGN          1       1.65     1.65\n",
+       "3   WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880       0.18   518.40\n",
+       "4    PLAYING CARDS JUBILEE UNION JACK         2       1.25     2.50\n",
+       "5                      POPCORN HOLDER         7       0.85     5.95\n",
+       "6      BOX OF VINTAGE ALPHABET BLOCKS         1      11.95    11.95\n",
+       "7                       PARTY BUNTING         4       4.95    19.80\n",
+       "8            JAZZ HEARTS ADDRESS BOOK        10       0.19     1.90\n",
+       "9       SET OF 4 SANTA PLACE SETTINGS        48       1.25    60.00"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(orders)\n",
+    "\n",
+    "df"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -217,10 +692,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantity ordered :  2978\n",
+      "Revenue Generated :  637.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(\"Quantity ordered : \", df['Quantity'].sum())\n",
+    "print(\"Revenue Generated : \", df['Revenue'].sum())\n",
+    "\n",
+    "\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -231,15 +721,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Most expensive :  11.95\n",
+      "Least expensive :  0.18\n",
+      "The diference between both :  11.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Most expensive : \" , df['UnitPrice'].max())\n",
+    "print(\"Least expensive : \" , df['UnitPrice'].min())\n",
+    "print(\"The diference between both : \", df['UnitPrice'].max() - df['UnitPrice'].min() )"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -253,7 +757,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,