DataFrames Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Every DataFrames objects on Pandas"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAC5CAYAAAAxpaayAAAgAElEQVR4nOydd3gUVdfAfzOzJckmC6kkgYQQAwakhKaAKBYEFUWMggURwQaviPqKqKg0QUWxAoriKwiCojT9QIpURUAgSA09DdLJpiebLTPfH5sMWZJAUCAQ5vc8eZ5Mu3N25t577j33nDPC7NmzlSeeeIILwcCBAy9IORoaGhoaGlcb8+fP/9dlCIqiKNUdSE5O5sSJE5w6dYrCwkKcTue/vll9QZZlRFGsazE0NDQ0NK5CJEnCx8eHgIAAwsLCaNq0qXqsilLPyckhPT2dsrKySy7o5Y4syyxatIhp06Zx8803M27cOAwGQ12LpVGPWLt2LXv37qVfv35ERkbWtTgaGhpXAEajkZCQEPz9/d2VemJiIhaLpU6EcjgcFBUVkZ+fD4BOp8PHxwdvb+/LZlacmZlJnz591O1nnnmGZ555pg4l0jgfZFlWB6tGo/GyqVcVzJ8/n48//hhwjcR//vlngoOD61gqDQ2NKwU/Pz90FRtHjx6loKDgkgtRUlLC8uXLmT17NtnZ2VWON2nShPvvv5+77rqLwMBABEG45DJWkJKS4ra9Z8+eOpJEo7bYbDYOHTrEnDlzSEpKUt9haGgooaGh3Hvvvdxwww34+fnVuZJfvXq1+r/T6eTYsWOaUr+C2bdvH8XFxWc9x8fHh8DAQMxmMx4eHpdIskvLjh07znv5Njg4mIiIiIskUf3FYrG4Zup1NUM/fvw4Q4YMoaSkpFbnd+nShffffx8vL6+LLFn1FBYW0qtXL+x2OwBTp07llltuqRNZNM5NUlISTzzxBEVFRec8t02bNowbN46mTZvW2cDxnXfeYcmSJQCIosh3331HixYt6kQWjX/PkCFD2LdvX63P79u3LyNGjMDX1/ei1sHCwkJ1AicIAiEhIRf1frfcckut2mBlBgwYwOjRoy+SRPUbMScnp04UusViqaLQBUFAkiQkSap21vTXX39htVovpZhu+Pj4sHr1au6++26+/vprTaFfxiQkJPDggw/WujPZt28fL730EjX4jV4SxowZwy233IKPjw8TJkzQFPpVxi+//EKvXr2YO3cusixftPssWbKE++67j/vuu4+HHnoIh8Nx0e6lcenRpaen18mNZ82a5abQfX19mTZtGtHR0SiKQm5uLsuWLWPOnDnqef369cPPz69O5K3AbDYzceLEOpVB49yMHz/ebdtgMPCf//yH5s2bU1xczKpVq9i8eTM2m00959lnn61zE/zUqVPr9P4aF4+a6taZCnzatGkcOHCAKVOmXBKr0aW2TNWmjel0unOeo1E9urrycv/777/dtmfMmKHOTARBwM/Pj6FDh/LEE0+QlJTEoEGDGDZs2FnLlGWZY8eOsXHjRqxWKxEREXTp0oWgoKAar/nzzz/ZuXOnWrGfeuop1byfkpLCmjVrePzxxzEYDJSVlTFjxgy1wvXo0YN27drVWLbT6eTw4cNs2LABp9NJy5Yt6dSpE76+vud8PrIss3//fjZt2oQsy3Tu3Jm2bdvi7e19zmuvdlJSUoiPj1e3TSYTGzZscOtMbrvtNmRZ5uTJk7z88stYLBZuvfXWc5atKAqFhYWAy3Jzrg6xuLhYXU+UJAmTyVSlLLPZDLjW/ytbomrjJCrLsmqNqCintjgcDkpKShBFUatXF5nQ0FCWLVtWZb+iKGRlZTFs2DBSU1PV/evXr2fGjBmMGDHiUop50WnQoAGrV68+Z72uS9+pK506Gw5VniGdDVEUiYyM5Pfff6+xIpSVlfHGG2+wZcuWKuUKgoCXlxdjxoyhd+/eVa6Ni4tj3rx56vbAgQPZv38/o0aNorS0FL1ez6OPPqoq9QULFqjnNmrUqFqlXlhYyMsvv8y+ffvU9ffKv8fb25spU6bQuXPnKtcqisILL7zAjh073K6dN28eoiji6enJwIEDNa/7s3Cmyb0mT3dRFAkPD2fhwoUUFRVhNBqrLc/hcDBr1iy+//577Ha7aq7U6/Xo9Xoee+wxnnzyyWrvMXToUBISEgBo2bIlc+fO5ciRI4wcOZKCggI8PDxYu3YtoiiyePFiPvroI8BVD3788cdqw9rKysr4+OOP+fXXX6vI4+HhwfDhw3nwwQer7RgdDgefffYZS5cuxW63qwMOvV6Pp6cnDzzwAMOHD9c61YtATf1XSEgIy5YtY+nSpbz77rvqEtDcuXO57bbbaNWqVZVrLBYL//vf/9iwYQOFhYXqNWazmfvuu4+hQ4ei1+vdrtm/fz9paWkcPnxY3ed0OlmzZo0qm6Io3HTTTVUGeUVFRcydO5dVq1ZRUFCg1jmz2cztt9/O888/X+vwXlEUa20Rczqd9OzZU+0Lw8PDWbBgAbIsM336dFasWEGzZs2YOXOmeo3VamXx4sX89NNPFBQUqDrBx8eHjh078sYbb+Dp6Vnt/R577DGSkpIA6NSpE5988gklJSV89tlnbNiwQXV8NJvNPP300/Tr18+trZw4cYK3336bY8eOYbPZ0Ol0hIWFMX78eK655ppz/t5169Yxc+ZMsrOzcTgciKJIgwYNeOCBBxg4cGCVd1oddabUz/T0HD9+PHPmzKmxYkiSVO3+lJQUhg4dSl5eXrXHFUWhuLiYN954g4MHD/Liiy+eVa6dO3cyduzYf5xsZ+/evYwYMaJG5z9ZlikoKOC5555j1KhRDBgwwO34wIEDOXLkSI3XFhcX8/XXX9OrVy/NO7QGzjTd5efnc/jwYa699tpqzxdFscZZbkpKCk888US1kSE2mw2bzcaXX37Jzz//zPz582nQoEGV8yo6XEEQ+O2333jjjTdUk2vldiAIwjnX9Pft28dzzz1Xbf2qkGfKlCksXryY2bNnu3VehYWFPP7445w4caLGa7/55hu+//571q1bp+VguIQIgkBsbCxJSUnqxEGWZb799lumTJminnf48GHeeustkpKSql13t1qtzJo1i/nz5/PVV18RHR2tHluyZAm//PKL2/k2m42xY8e6ybFw4UJVqaenp/Paa69x+PDhatferVYr33//PUuXLmX8+PH07Nnz3z2IarDb7aoFy263k5yczJAhQ9Q22bhxY8A10Jk4cSLbt2+vdtJotVpZtWoVa9eu5YUXXuCRRx6pco7D4XC7V3x8PM8++yylpaVVypo8eTLz5s3jhx9+wGAw8Pnnn/Ptt99W0R0HDx7k0Ucf5YknnmD48OHV/sbU1FSGDBlSrX9bSUkJ06dP55tvvmHKlCl07dr1rM+rzhYQu3fv7rZ95MgR7rjjDsaNG+dmhjobZWVljBgxwk2h33vvvSxYsIClS5fy6aefEhoaqh777rvvOH78eI3lCYLApEmT1Jei0+nOyyxpsVj473//q3a4oijy6KOP8sMPP7BkyRKmTJlCw4YNAVeDnTp1qpuy2Lx5M0ePHlW3R44cyZIlS1i4cCGvv/46TZo0ASAmJkZT6GehcePGbhESTqeTxx9/nLFjx55X2GZaWhqPPfaY2zUtWrTgmWee4amnnqJZs2bq/oyMDEaNGnVWB6eTJ08yduzYf+wEdeTIEYYNG+am0GNiYhg2bBhDhw6lUaNG6v5jx44xadIkt+u//PJLVaGLokjv3r0ZNmwYsbGx+Pj4qOcNGjRIU+h1xMiRIwkMDFS3N23a5NbRr127loSEhHPWoZKSEp566ql/7QS9bt06Dhw4cE5nOqvVyvjx48nJyflX9zsXgiAwbNiwatvxvn37qvjJVIfD4eDDDz9062urIyMjgxEjRlRR6JVJSUlh6tSpbNy4kTlz5tQ4GXQ6ncyZM6faMOjU1FT69+/v9q6MRiO+vr5u/VhJSQkvv/wy+/fvP6vcdTZTHzJkCJs3b3ablRYXF7NixQpWrlyJv78/0dHRqnNTdcyYMYO0tDR1+/333+e2225Tt8PCwli8eDEjR45kx44dALz22mssXLiwWvOPoiiUlpYSGBjIxIkTiY6OxmQy1dpU9N5776kDDEmS+OKLL+jQoYN6PDw8nK5duzJ48GASExORZZkxY8Ywffp0wNWAK2ZqzZs3Z9CgQapp55prrqFfv34cPXq0zkL6rhRMJhOjRo1yc2h0Op38+uuvrFmzhkaNGtG9e3ceeeQRdaBUHVOmTFEVqMFgYNq0aXTo0EF9J88++yx//vknL7/8Mk6nk7///pudO3dy/fXXV1teRWIlDw8PRo8eTadOnWqdBEeWZSZOnKgmz/H29uarr75y85AfNmwYv/zyC++88w6yLLN69WqGDBlCVFQUgNoGAB5++GH++9//qtujR48mNTWVDz/8kKFDh55THo2Lg06nIyYmht9++w1wKaCUlBTVQfjhhx9m/vz5+Pj48PTTT9OlSxd12ahiVp2VlQW4FO3atWtVa+CgQYPo06cPq1atYunSpYCrXn/00UeqJVRRFEJCQlR5HnnkEebPn09ZWRmDBw/m5ptvxtvbG0VRyM7OZurUqWrYntVq5dNPP62VI/G5LFI1Lf8kJiaq17Zt25Z+/fqp9bt79+60a9eOI0eO0L9/f+6++27MZjOCIFBYWMjnn3/Oxo0b1bLef/99vvrqqxrvlZycDEBgYCBPP/00TZs2JTU1lenTp7sp4GXLlrFhwwZkWaZbt248+OCDeHh4sHnzZhYuXKgqeqfTycyZM/niiy/Uax0OB48++qg6ENHpdEybNo02bdpgNBqx2+2kpKQwatQoTp48ic1m44MPPuDbb7+t8dnVmVL39PRk5syZTJgwgU2bNrkdk2WZ7OxssrOz2bx5MxEREUyePLlKiM/vv/+u/n/bbbe5KfQK9Ho9kydPplevXoCrUhQXF7vNTCoTGhrK3Llz1Rl1bXE4HOzatUvdHjBggJtCr8DLy4sPPviABx98EEA1FRkMBrfKlZiYyMqVK7nrrrvU/ZIkuZnTNGrmnnvuoaioSF2jrsDhcJCamsrChQtZtGgRYWFhDBkyxC1TILhmulu3blW3hw4dSseOHd3OEQSB7t2707NnTzVxzPLly2tU6uAa2M2ePbtaM/3Z2LVrF4cOHVK3R48eXaU9iKJIv379WL58Obt37wZgzZo1aqdX2UejYt2wAp1OR9OmTfn000+19fQ6pnPnzqpSB5fFKCYmBgB/f3/mz59PaGhoFR+QwMBAFi1aRO/evdXZ5ebNm1Wl3qxZM5o1a+Y205MkiU6dOtXobS5JEjNmzCA4OLjKZCIoKIjp06cTGxurztDPlZCroKCA7t2711jHFEWha9eufPjhhzUe1+l0zJw5k7Zt27oNiCVJ4t1338VoNFZpX4GBgbz//vv06dNHTXK2Z88eSktLzzpJiomJ4bPPPlPP6dixIz179uSee+5RrQWyLJObm8trr71GbGysKtP1119Ply5dGDlypFpexUChglWrVrklKPr6669p3bq1um0wGIiKiuLTTz/l4Ycfxm63c/DgQRISEmpMI12n8Ttms5kPPviAH3/8kSeffLJar3BFUUhMTOSxxx5zU/5paWlus/Tu3btjsVjIycmp8nfmqLDCe7k6nn766fNW6ADx8fFuywA33HBDtbLk5OS4rXPKsqx2tn369FEru8PhYNy4cdx777289dZbbN++/aLGrtY3RFHkkUceYdGiRaqj45k4nU6SkpIYN24cr7zyipvZbvfu3erz9vb2ZtCgQTXeq3///m7X1WSCq5jtn69CB9i2bZv6f1BQEHfeeWeN5/br10/9f+fOner/YWFh6v9btmyhb9++fPDBB25mSE2h1z1n1o8zTd/NmjWr0anTy8uLgIAAdftcGe1qQ2RkZI2Kz2QyceONN6rb5/JFUhQFm81GWVlZtX82m+2cZcyYMYOYmJhqLVxBQUE1ti9RFImNjVW3ZVk+67KCXq/nrbfeqvLbvby8ePPNN932RUZG0q9fvyoydevWzc0huqysTNVHsizzzTffqMfCw8Np2bJltbI0bdqUG264Qb3uzIlwZeo8GLDCu3348OE8+eSTJCQksH37dpYtW+aWllWWZV5//XWWL1+On58fmZmZbkrufGLH09LS3NbaK1Pd7Lo2nOkHcC6HvMoUFBRgMplo164d999/v5pVTFEUMjIyWLlyJStXriQ4OJj77ruPp59++h/JeLUhCAIRERH897//ZdiwYRw5coT169ezdu1a1URZwYYNG5g5c6Y6qq5c90pKSnj00UdrNBlW7hgyMzMpLi6u1vGucePGqlPP+XLy5En1/9zcXPr371+jPJXDVBMSElRL0DPPPENcXJzqCJSWlsbChQv58ccfadKkCTfddBMjRozQ1tPrmDNnc2f69aSkpDB//nyOHz9eZc1cEAQyMjIuqDyZmZnMnz+fQ4cOcerUqSr3u9Dr6GebvHh5edGmTZsaj+fn57Nw4UL+/vtvMjMzqxw/cy3+bIPYqKgot6+fVaZdu3Zujq0DBgyo0drRvHlzdelLURQURUEQBBwOh5vTqtPpZNy4cdW2a1EU3fqkmpyp4TJQ6pUxGAxER0cTHR3NwIEDWbduHWPGjFGP22w24uLiuOOOO/5V5q+zOVL80/zLF0qeMWPG0KFDBzX0qTIZGRl8+eWXxMfHqx/+0KgdXl5exMTEEBMTw8iRI9m1axfvvPOOm7L86aefeOKJJzCbzW6KUZblKh1tTTidzhq/cFhTGE1tqFxHKjyAa4PVasXhcGAwGLjuuuv44osveO+999zCmhRF4cSJEyxYsIBff/2VOXPmnNXXQOPiUnnZB9wtLN9++y0zZ86sEip7sVi6dCkfffTRWZ3FzgeTycTHH398VmV6NktWUFBQjYPOrVu38tZbb9UYCXW+1LREC1UHA5Xf0ZmcLelQZb2RmppaayfxMwdXlakzpR4fH19t/GUFkiTRq1cv/vrrL37++Wd1f2JiIgABAQFuI6W+ffue09W/gouxLl3Z8xhcTiln+30VKIpS5do777yTXr16kZCQwLZt21i1ahWHDx9Wf+sff/zBmjVrVD8BDXdkWcZms9U4QNPpdFx//fV8++23xMbGqg5spaWlpKenYzab3Wbaer3ebWnkXPe+GI6MlWdrHh4e3H333bUaSIqi6Bbb2qZNG+bNm8eBAwf4448/WL9+vdv6el5eHo899hi//fZbrWJiNS4sp06dchtwBQcHq7PFuLg4pk2bph7z8/Nzc5QDl7JZvXq1anb/p6G5ANnZ2XzwwQfqgNJsNtOlSxe3BEqCIBAXF6cOMs9VJyscAf9p5saa2mBubq6bQvfw8KBbt25VBgiHDx92S0xVl2mhz/wtRqOx1tFWZxv41IlSLywsZOTIkXTv3p0XX3zxrGvYZ5rJK0wcjRs3JiAgQHV6SEhI4M0336yxslSYPC4WLVu2xMvLS/WWTk5O5oUXXqjx/HPJI4oiUVFRREVFMXDgQDZt2sTrr7+ujtAr8kRrVOXQoUOMHz+eKVOmuIWdnUmDBg3w9/dXlTqcdiarHHHhcDi4+eabufnmmy+e0OegwtmtQp7Y2Nh/PDgVRZE2bdrQpk0bhg0bxv79+/nkk0/Yu3cv4Eo0kpOTo30hrg4YPXq0W8hibGysOjNduXKluj80NJQff/yx2oHrzp07L8ha+rx581SF7u3tzU8//YS/v3+V8yZNmlRry9HF4o8//lAVuqenJ3Pnzq227c+ZM8dNqdcler0es9msLgmYTCaWL1/+rwfTdeIoN2/ePPLy8li+fDn9+/fn22+/rbLGCa41v++++85tX4UXqCRJ3HTTTer+/fv3895771VrJjp27BiDBg06a4z6v8XLy4tOnTqp27///juff/55FTOZoijs3buXgQMHVlnz2bJlC9OmTaviyCcIAj169HAz8WgfYageRVGYM2cOCQkJDBw4kM8//7zGNb+TJ0+6vQNJktSUwh06dFDN5YqiMHny5GrrKLjq6fz58y+qI+NNN92khh05HA7efPPNGmPujx8/zrJly6rMQlavXs2Z33oQRZG2bdvy3nvvue3Pzc29gNJrnIusrCyGDx+uDqzAZf2riJIB3NbKg4ODa7REncv8fOZkoqZ6e+zYMbf7VafQ7XY7Bw4cOOv9LgWV63XDhg2rVeiKorhFTNU1Fc68FVgsFtauXVvj+ampqbXyX6iTmXrlcI3c3FymTZvGzJkziYqKIjo6Gp1OR2ZmJps3b3arcBWx6xUMHz6cjRs3qs4iS5YsYfPmzfTq1YvIyEgsFgu7du1i69atKIrCI488wo8//njREre89NJLxMXFqaPkb775hrVr13L77bfTpEkTMjMz2blzpxr61r9/f5YtW6bGoH7yySckJCSwdOlS+vTpQ/v27YmIiMBut7Np0yY13SigfSGuBiqc4QA1Q9p3333HTTfdRLdu3TCbzSiKwtGjR1mwYIHbjOaGG25QlXpAQACDBg3iq6++AiAnJ4fY2FiGDx9OTEwMJpOJ5ORk4uLiWLRoETabjfz8fJ555pmL8jGKZs2acc8996hLUUlJSfTr14/nnnuO1q1bYzQaSUhIYOvWrfzf//0fDocDm83Ggw8+iCiKFBYWMnXqVGw2Gw888AC333474eHhmEwmrFar27fcAW2WfoGxWq1s3LixykCrtLSUnTt3smLFCjdTucFg4N1333VbBqqsVJOSkqo4/Obk5DBx4sRzJliqvFZstVpZsWIF9913H3l5eezatUvNCtekSRO2b9+u3u/gwYNu3tkWi4UZM2acM4nLpaCyx392djZ//PGH26SvtLSUefPmuQ2aLgcGDx7MV199pdaLt956i+TkZPr27UtwcDCyLJOVlcWGDRv43//+R3BwMDNmzDjr90PqRKl//PHHjB8/3i1esiL+7uDBg9VeYzAYmD59utt6pa+vLx988AEvv/yyOjrNysqqMruvIDQ0tFYfU/mnhIWFMWHCBMaNG6cqi5SUFGbPnl3j+RVrKHl5eepMsKCggO+//57vv/++2uvCw8OrpJfVcBEWFkafPn1YuXKlOiC02WysW7eOdevW1XhdgwYNGDVqlNu+wYMHs2vXLjUszGq1ntVBcePGjTz22GPn/WGV2vLCCy9w8OBB1YGyoKCAd999t8bzV61aRd++ffHw8GDJkiXq7Hvu3LnMnTsXo9GIv78/BQUFbvnyIyMj/1HYnUbNWCyWKvWrJsxmM++99x5t27Z123/nnXeyYsUKtbz+/ftz88034+fnx8GDB4mPj6+VBa9yDnJFUXjnnXeYPXs2mZmZiKLI7bffjiAIDBkyhP/7v/9Tvy8wdOhQunbtSmhoKElJSezdu7fGdNiXmh49evDZZ59RXFyMw+Fg1KhRxMTE0Lx5c7KysoiLi3NbZrtcMBgMjBs3jokTJ6r91ddff83XX3/tptQrKCgoYOrUqUyePLnGMuvE/B4REcGcOXN45513aNmy5TlnNm3atGHRokXVZpZr164ds2fPpmvXrjV6RZrNZvr27cuSJUsuemd1yy23MGvWLNq3b1/j7/Lz82PQoEHMnz9flblhw4YsXLiQW2+9tUavS71ez4033sjs2bPr/BOhlyteXl5MmDCBGTNm0KpVq1o9p8jISL766ivCw8Pd9huNRj777DP69Olz1jAvSZK44447+Oabby6aQgdXPZ41axY333xzjd9CAFdHERsbyxdffKGaaB966CFiY2PdnJzKyspIS0tTFbogCLRu3ZrvvvtOq191gCRJ3HjjjSxatKjaBEZdu3Z1y0FQVlbGb7/9xsKFC9m7dy8Oh4OmTZvWGK5bQdu2bbn99tvVbUVRSEtLw+l0YrfbVe/7kJAQBgwYoNYFu93O77//zg8//MC2bdsoKSmhSZMmVVJ+1wUBAQG88MILap/rdDqJi4vjhx9+YP369eTn5+Pt7e2WU+Jy4Z577uHVV1+tspySkZHhptBFUeS2225j3LhxZy2vTkPaevXqRa9evcjKymL9+vUcP35cXfdu1KgRYWFh9O3b95zhNWFhYUybNg2LxcLGjRvV3MhGo5H27dtz44031tgJdu3aFS8vL0RRRFGUs4YdeXh4uJlXa4qXbNGiBbNmzSIzM5P169dz8uRJFEXBZDLRuXNnOnfuXK2TXKNGjVRv07i4OHbt2kVxcTGCINC4cWN69ux51s/Iapymc+fOzJ07l5SUFH766Sd2795Nfn6+qsACAgIIDQ3lvvvuo0ePHjUqMYPBwIQJE3juueeYP3++moUKXAOI9u3b079/f7fUmpVp3ry5qkjPtezj5+fnNjurbs3UZDLx0UcfkZKSwg8//MCBAwewWq0oioK3tzedO3fmoYceUpd0Kpc1ZswYnn/+eVauXMm2bdvIyMjA6XQiSRLh4eHce++9bolENP4dnp6e5xzkeXt706BBA9q3b8+jjz56zmWPN954g5YtW7Jw4UIyMjJUh9vAwEB69OjBiBEjeOqpp9R6XlMkxttvv02jRo347bffKCwsRBAE9StmlZc4X3rpJa677jrmzp3LiRMnkGUZQRDw9fXlxhtv5LnnnmPWrFmqWbvyoLECHx8ftX2dLUzsbM+oYlBdXfkVxMbG0rRpU2bMmEFiYiJ2u139Xe3bt+fVV19l7dq1Z30nXl5e6vFzhaBW/i1nm5gajUa1zJq82x944AFuvfVWpk+fzq5du7BYLOq7NZlMNG/enMGDB1fJalkdws6dO+vOp19DQ0ND4x9ht9ux2+3qJ4D/CRXfuxBF8Zw5Oir8NHQ63WWfoEiWZaxWK5Ik1Zh973Km4t3W5r2cyWWVfEZDQ0NDo3b8G2VegSAItc6roNPpLooT6MVAFMUr+sNX/+bdagtnGhoaGhoa9QRNqWtoaGhoaNQTNKWuoaGhoaFRT9CUuoaGhoaGRj1BU+oaGhoaGhr1BE2pa2hoaGho1BM0pa6hoaGhoVFP0JS6hoaGhoZGPUFT6hoaGhoaGvUETalraGhoaGjUEzSlrqGhoaGhUU/QlLqGhoaGhkY9QVPqGhoaGhoa9QRNqWtoaNarVMkAACAASURBVGhoaNQTNKWuoaGhoaFRT9CUuoaGhoaGRj1BU+oaGhoaGhr1BE2pa2hoaGho1BN0F6tgWZFxOBwXq3gNQK/TIQhX57hMlp04nU6EuhaknqIAer0ervInbHfYURSlrsWovygy4lXah10sLppSLy4u4u+9O3EK1ot1i6saSfGgY/vrMXl617UodUJGejqpyfFIorOuRamHKJTYTNzQtVu5Yr962bdvLwXWTDS1fuEREDAoJjzE4roWpV5x0ZQ6AuTrD5FrOMDVPtq/8CgE2DqC0rmuBakzZESu8f4Lsz5b63AvMKIgE5fTt67FuDwQnaR4rQStll1wJEVHK1tv2vis1J7uBeTiKXUARARFQlPqFxYFBRTtmcpIyMpFrsJXITKy1smqCIiK5GpzGhcUl24AWdMRFxRtMUNDQ0NDQ6OeoCl1DQ0NDQ2NeoKm1DU0NDQ0NOoJmlLX0NDQ0NCoJ2hKXUNDQ0NDo56gKXUNDQ0NDY16ghYPVAOCAKLONeZx2uU6lkZDQ0Pj/BElAUEUUJwKsqyF5V0NaEq9BorybCyZegC9h8Qjb7VD0RqEhobGFYQgCvzxYxIJuy1Edw2ky73hOJ3aBKW+oyn1GpCdCpb0EoxeEgJaPikNDY0rCwEozrdhSS+hpMBe1+JoXCKuKKUuCAKSXnTlHhJAUVym8YoPLoiigCAJILtMTZJeQhBcCtrpOD1ClXQiCCA7ZARRQJLKzewO+ZwmKkFALdd1fydnfu9B0ouIgnBaRoeszfQvMyQdCJLoeqGyjNOuuL1Hnc71/hwOBZ1eBBFkm4wsgyiCqC+/FsAp47ArlcoWEARwOhW3ZRycCg6Hez3Q6QWQRFAUnHYZSXTd1+lUUOTqz1OcMs4zvpUk6UDQiVRUOsWp4HRode5yRJIEBEl0VR8FZPmM/knvqi+yQ0aURERJqNKPnLk8KBlcfY6iKDhs556NS+XlAsjlda8yal9bXsUVWcFxuS1DCuXtQhTLO1oZt2+ICeXtWAGnrCAZJJBPPx9XmxKoaDOyQ0Z2Vr3W1Qecbn8Om+w+yxNAZyjvD8r7Ap3+9LUViFL5OxNcOsphr6ac8r7GpaCqOacWXDFKXRAFju+ysH1FCoU5NkDB1MBAu9tCaNMjGEVROLYrh93r0mhybQN8/IxsX3ESm9VJo6be9H2+JQguxb/mmyPkZ1u5+aFI9m3MIGFPDqIk0OL6QLo/GFGjAhZFgb0bM9i9No3SIjue3npadgui092N1Q44N72Ujd8nYEkvQXYqeHrrubZLIF3uDcPp1DrZywGbQ2byzCMcOFpAaZmTQF8jsT1D6XdrsKq0359zlKSTJTz1QFOmfnuMgiIHIx6J5I5ugXz6XQJb9lgoLHIgitC4kQevDm1BRKgXkiTw+Q+JHDhWwND7w9m0M4dNcadwOhXaXduACf+JRihPiVlS5uDV9+JJPFmCp4fEI32akJxaTGJqCU/FNqVtiwaIIuw5XMC0BQmkZVnx8BDp1MqX0UOaI5Z3uLKi8MbHhziYWIDNpuDlKdH2WjOjhzTHIGm+sJcTiqywes5RUo8UYLM6ESUBvxAv7n72WowmHZJOZMXnhygpsHHrwGvYsiSZtGOFGL0k2t8RSsxtoTidMiUFdlZ+dRijl44eDzdj+eeHKThlxexvpOcTzQkM86pRBlupk3VzD5N2vADFCf6NPbnl0WvwC/FEUVyTnm2/pHB4WzalRQ4EERoGeXD741H4hXhdFl+tk3QCm+JymPtzCmnZVnSSQIsIb14adA0hAR6gQFq2lfe+PoK/r4F2zRswa0kygX4G5r7TkbTMUt6ZdYSk9BJkJ3ibdHRt58uIRyKRBAFLgZ3xMw4S5G9k8L3hTPzyMGnZpTT01jOobzh3dQ9CkV2Kf9Fv6SxcfZKCIjthwZ68PDiKGd8n4u2l4+0RLVVL77QFiWzccYpSq5NGAUYG3RvGbdcHIDtdfc4fuyzMXpZMRk4ZKBDkb2DwfeHc0jkA5Ty+WyU988wz4y/GQ7fZy0jK3odVyuZC5PUtPFXGT+/vo8hSRsg1PugNEpnJRSTutRDR1hfvhgZOHMxjz/oMTp0o5vjfFoIifMjPKiUntYTD20/R6c4mgMKWpcmkHy8ieX8up1JLCGhi4tSJYldDK3UQ2dYfa4mDv39LQ6cX6Xx3GAD7NmWwbu5xSgvthLVsyKmUIhL35SI7oWmrhjjtCt9P2k32iWL8G5vw8TeSfaKYlAN56D0kQqN8/vVzqMDLGUJEUCsMeuMFK/NKoqCwEF3pboxiyXldJ4gwYNQO9hzOx9NDpHm4DwcTC9m08xQOp0LnNg0RBPhmWQq7D+ezdW8uqZlWSstknn4wgqXr0vn2lxSsZU5ios1Y8u0knCxhw/ZT9O8Vit4g8f2vJ/hrXy47DuSSlFZCWLAXx1KKSThZwqk8Ozd39MchKzz43x0cP1GM0SjRPMzE4t/SOZpSxImMUrp39CcixIt9Rwt5ccpe0k9ZaRnpTXGpzM4DucTF59LvthAUBZ4ev5sdB3IJaGgkKtyL5LRSDhwr5GSGldtuCDjvZysICumlzQlp3AxJks77+vpEemYa2ey7IGUJosBvc44SvzkLQRQIucaH/GwrOWklHN9tIeb2EERJYNMPiWSnFHM0Lgeb1enqR5KLSdqfh6mBnkbNvLEWO9m4IIG8rDIO/JGJt68BBIFTJ4o5sDmL625qhNFTx7FdOZw6UUxwpA/N2vrhdMj8MGkPJw/n42U24ONv5OShAg5ucV2jN0oc2pLFhgUJOG1OQqLMOOwyWUlFHNl+itY3N3LNJi8AIiKBciRBhuOcj44QBFiyPp1JXx4hI8dKiwhvUBT2HClgzZYsenQKxOytI8tSxowfEklJL+XP3RZKrU4UGQbdH07/l7aTcLKEZo1NhAQaOZpczN4jBfj66Gnd3ExekZ2P5x3nZGYpv27OIsDXgM2ukJJRypbdFgb2aYIkCqzcnMV73xwhr8BOq0gf8oscLP4tjcS0EgpLHDx8V2MkSWDSV4dZsjYNAYiO9GHvkQLWbz9FdIQPYSGeZOfaeGbCbrLzbLRubsbbSyI+oYj1f52iSxs/An1r389fMTP17BPFRLX3I7S5mY53NkGnF5gzZheZiYVknyimUbPTCtNuk+k/ujWNWzSgMKeMWS/vIC+zlLRj+YREnj7PYVN4fFJ7vBoYOPxXNis+P8TejRl07hNW5f5Oh8KG+QkIosCgie0JDDdRZCnjm9E72bHiBB16hZKbUUpRrg3fYC8GvN4Go6fE0Z2niFudhtOhIIqCNluvSwSYviCR9Gwr14SZ+HpCezw9JfYfKeC5yXuY938nuKNbEFFNTs9y7A6Fj0a3Ib/ITrMmXvzfRjudrmvIW89GE9LIk5IyJ72f2oylwEZ+qRNPr9NNyuSh43+TOuDtpeOTucdY8OtJdh/KR2eQWPdHJjl5NnSSyNJPrsfTU2LXgTyef3ever2kE/jyp0RKy2SGDWjGkPvDKSl1MvC1new7WsDOA7lc16IB8QmFCAJ8NSEGP18PsixWxn4Sj49JoswuY9Bps/XLAdkho9NJhLdqyD0jWmLy0VNgsfLVi9vJzSitsoznF+xJ7KjW6AwiW5cks/XnFOJWpdL21pDTZTpl2t4Syk0PRYKsMG/cLrJTivlzcTJ3PX2te4ECHPgzk5y0EoIjfXj4jbaIksjmn5LYvuIEa789Rr8Xr2P3+nRQoMcj19D+DtfAcdkn8QgilBTYMZp0detkJMLXi5JxOGXefPZa7unRCFmGSTMP8evmLD6ae4zPxrRVT7c7ZHp09Oehe8MpKrBxKruM61v7Ulrm5L3/XofBqOOdLw+x5Lc0DiYUIoinBxilZTJDH4jg8XvDKChy0PvZLdgdMgeOFdC5nR+fzD+OLCs8278ZT9wfDorCi+/t4699uYBrqHIkuYhf/8jE12xgwQed8DPr2bY3lxfe3cuELw+z7n83snZbFg6nS84PXm2DKAjMmH+c4ynF5OSXIYg+bstxZ+OKUepRHfxpcX0gmUlF7Fp9ktQjBVjSXN/hddhkt3FewyAPmlzbELvNiVcDPY2vNXPyUD4Ht2YTEmlWz2vZLRBPbz1Ou0xUB3+8fQ0U5drISi7CN9jT7f5FeWXITgVREsnLtJKdUoxOLyLqRBSbTE5qKX6hnuiNErkZJXz54l+ERJppcq2ZXkOb0yDIE4dN+/Z3XaLXiyxdnwbA8AERGA0isl2hbXQDrm3qzd6jBfy1x0KLcJN6Tbd2vnSL8UWRQXYovPhYFKUOhV3xefy8MY3dhwqwl6+bOc7wLI6JboDJQ8Rhl2kd5ap3TqcCosCmnacA6NHJH6PeJUfntn74NzCQZSkDoMwmcySpyFWYJPDrH1kgCoQEepCWZWX1lmxiWjWkgbee/CI79z63jZaRPrSOMvPMwEhiosw4yi6zddCrGEEUuGNIFGWlTk4czCP1SAEnD+eryvzMZb+2twa7JgJ2mZY3BrHtlxQKLGUU5Zap50g6kS79wnHaXX3L9feEseLzQyTvz0WW3d+9pBM4cSAPAE9vHUe2u+qgzuga9KUfK0SSBMKiG5CRUMjab48StyaVkEgfWnYLIjLGr1o5LzX7DheQW2DDyyjR84ZA5PJ19P53NeHXzVnEHy+ktPR0X6vXiYx/riVGnYhQvvw++cXrSMksZeUfmew5mM+feywAlJ3hNyCKAnd1C8JpVzAZJTwMIiVWJyWlMrJdpqT8PvfeEoxS3g/cc2tIJaUusOdwAQDeXhJ/HchDcSiUlvcVxSUO7A6ZmOgGiIJrSeGeYVtpE2WmYztfHr4njIZeuvPyj7lilHpxno3FH+4nJ9VlbjU1NKgjKlEU3AaOOoOkbgsCmP1cposzPUBNZoP6vyQKGDx1kGvDZq2qfMuKXftkp8zyLw65HRNFgdJCGyazmdsfv4Y/FydRaLGRuNdC4l4Lfy5JodNdjbkxtqkWK1qXiIKqgIP8jKdnG04Fb5OrKZzKsyEIp4eI4SFeaqeBAGu2ZvPu14cpLnUiCNA0xEsdUIqCuwnRx6RXO2y97vQxRVHIL3bVxQBfQ6UrFPSVZtWlVifWcqeer39MonLxOkkkN9+GThCYObYd42Yc4khyEbsP5bP7UD7fLT9Btxg/3nvhOnSS9lnLywFBENi5MpU/lyThsMmIooBviKfb8cp4eOnUT77qjZIab+6wyaf7Psnl9Faxzm32d/V19rKqDrwAZaWuypy8P4/k+Hx1v6gTkWWXs+iNDzQlL6uU47ssWNJKsKSVcGBzJmZ/I/e/fB0NgzyrFnwJsRTYUQAfb51be/H30QNQWuakuPS0x5zRIOJt0mEvk1Fk1zN74d09bN2TiywreHlImDxdy0yS6G7V0kkCXp6nl6A8PSRKyvWD04n6jCu3sQbeldSqALkFNgBSM0uZ/Plp3aHXiSgKlJY4uC7Kh4H3NGHx2jSyLGWs257Nuu3ZTP/uOGOfvZYeHQOqfZ/VcUUodUkS+WX6QXJSSwhv1ZAeD0cSGG7ix3f3cvJwPpJeoPIvtpU41I5WUaCwfObj3VDvVm5eZqn6v9OpYCuvCEbPqo/FUPHS9SIvzLoRW/kITay05CjLCte09ye8VUPys60k7s0l+UAumYlF7F6bxvV9wlyyatQJilPGoBexO2TSs61ENfV2KXZJIK98wNfI3+g28PIxna4LdqfCm9PiEQR47pFIencLItjfyK1Pbqa41Kl6E1cg1mD1FgSB8Eae/EUu8ccLEV2Orsiy4tYZeRglDAYBuwO+Gh9Ds1DPcmdPV/SGUO5d28jPyJTRbSgutrN9j4Ud+/PYvi+XLbstHDheQLtrG2gxmZcBRbllbPohAVES6TWkOc1i/DB6Skx7diuKoiCcUV8Kc10DTEVRsFudKLKCqBPRG0U12sLpcPVbeg9XR1SQ4+rr9EYdQjVdjcHDVZ/b3R5S7hTs8jOpONdhcyLLCncMbcEtjzpJPZRH0v48jv2dQ0FOGTt/TaX3ky3qNN69kb8RAcgvslPmkPEoV+wZuS7l6eUp4eOlIyfPtS2J7g9i8dp0/vzbgq9Zz6SRrWgd6cO3/5fCN0tT0OsFt+V9sSLUqhp0BsHV/zuhoMiB2UuHIMLxE8Vu55lNLr3TMtLMl+NjsNucCIIrKRAC6AQBpxMG9wtnwJ1NOJRcyPbdFv7cbSEty8oXPybRo1NArdvwFbHYJkoCuRkuBdysrR9BEd4UF9jUfbIMlWtwXraVw9uzkfQixXl2ThzKBwGuuymYyk8mfmsW+aesSHqRo3GnKMq1YTCKNIrwriKDt6/RNZp1yBz/Owejl4S12M73k/awYuYhHDaZ+D+z+PLFv/j+7T34hXpx84Bm9BkeXS6jgnwZeI1ezTgcCvfdGgzAjB+SKC5xIOkF/tpj4VhKMQa9yI0d/Gv07rWWd2QCAnfdGkxQgJE//s6htMLEXcvXqygK/W5zrYvuO1rA9oP5FFodTPn6KHmFp61JnkaR6AiXD8iK3zPw9NShKPDshN2M+eQACSeLySuyc+d/thI7YisGvchjfcOZ+mY7/Bq4OhK7FtZ22VBa5Bqw6QwC0d2CMDUwcGBzllrfzqx2catTsVmdSDqRA39moSjQINADUyWnKdmpsG7ecSRJQBQF/volBYCoDn6uwV8lnE6F8OsaApC0NxdFVjB4SWxZmsyyT+PZuSoVSS8y+7WdzHx+Gwc2ZxLdrRF9hkdzTYw/gCvEqo7nJdGR3viaDVjLZH79PRNBAgfww4qTAHRo2QCjsWbVFle+BNE4yJP2rRoi6ER27HftOy9LqgwRoS7/m/e+OcKpQjtHTxTz/YoT6imKotChZQMAElOLSc0oxcukIy4+j/+8vYcpXx/BaBSZOPMQfYZvZdz0eLq19mP0Uy14ekAzAOx2udazdLhCZupOh8w1HfyI35zFpoUJHNqejeVksSveHCjItlJ5MCaKAr/OPEzAihOcOuky1wc0NhEY7l0+AnAh6UTmvbULv1AvspNdo6v2vRrjadKpZqqKZ6nTC9w8IIKNCxL4+dN4QiJ9OHWyGLtNpmGgBwZPiegugcStSiUnrYQ5r8XRMMiTnHRXuU1b++LhpXOLR9W4xCgwcmAkf+zKISWjhNgXt9Mk2JMjyUXIssLzj0TSJMhDPfdMvPQSDc168grs9B+xjWZh3hxNLkISBWRZoaDARrB/LbxUFYgMM9GtnR9b9lgYOXkP4DLFmzwlisutQA6nwtMPNOXAsQIW/5ZG3IE8SsucZOaUER7qxbXlg88urX35fVcOj7y8g5aRPqSfspKT55KldZSPNku/TDD7GTB4SNhKncx6aTs+fkYsGSVUxDzZbbIr3rmcorwyZr+6E1MDPadOliCKAl3uDUOu1IcIgkDiXguzX4vDYXdSaLGhN4p0vb+pa7BQ8e4VQIZW3Rqxe206lvQS/jd6Jz6+BrJSXH3pjbFNcdhleg6OYtkn8WxZkszRHa5191MnixElgXa3hyDXdVY6GYY/FMEHs48xdc5RlqxNpajESZaljCA/Ay8PjqqSD6IysT1D2LjjFPuPFTDotZ3k5tnVx5Seba3GZ0Cp9l+HQ+GD/7bm0Vd3sCs+j77PbQWgRVNvsiw29fTmTb3p1S2INVuyePyNOK4JM3EoweUr06OTPzabzIuPR7EpLoddB/PpP2o7vmYDRxJd5zx4RyiiyOkY+nNwRYS0KQq06BiAJb0Uu00Gp0JMz1A63tmEkkI7Hl46Itr4kpFQSNK+XALDTNw26BqS9+ci6USatvblwVdaIztd61h7N6RTUmDnhnvDCGxiIiulGC8fAx3vbEyXvuGuWbVD4VRaCf7BnjS/PhBFgdBrzJgDPCjKLaOkwI6n2UCrbkH0fKI5iuJqYC27NaKkyI61yEFZsR1Ps55WNwbRc3DUBXUw0ULa/llIm6LAg3c0pqDITn6hncISB2HBXox+Iop7egQjO11Gn8TUEswmHTe08SWskWsNURSgd7cg9h8rxCEreBgk3h7RkgbeOkweOhoHexIR6kVSqivuvEPLBkSFuxRvcamTbIuNZk286NHJH9kBd98SjL+fAdmh0DXGj6mvtGH5xgwKSxzcc3MwjYM8CQn0oFNrX1KzrOQV2tFJIp1b+zL15dboJREU6NU9CBSXCTC/0I6HQaJjq4Z8+EprvDzOf9yuhbSd5kKGtOkMEuHXNSQ7pRjZoeDhrSf25euwFjrw9jMSFOGNt6+RXatTKStxcOdT1yI7IS/Lio+fgR4PRxLdJRBZBpvV6Qq5NYg8/nZHEvflYrM6CQg18cDLrV0hbggUnLIiSCKhzX1oFOGNIEKrbkEU59uwFjqwlzkJCDPR+8kWhESZQYGGwV6ERvlQkOPq55x2Gf/GJno/eS2NW5jPa9Z4Nv5pSBtAy0hvYlo2IMtiI7fAjl4n0q2dH1Neak1Db5eVymaTSU4vpWmIFz3LnxtAWLAHsgKn8uzY7DKdWjVk8vMtSc0sxcMo0a2dHwgCiSdKaBrqye3XB6om/OMnimgUYKRbjCvMzOQp0adHCHaHTEMfPUPub0q39n6s2ZKF2VvPI3c1QZGhR6cABAEs+XYKiuyEBHkwNLYpj97dBFkGo16kd7cgTmaWklfooKTUQeMgT56MbcqAXo2Rz2McJezcufOijOMLiwvYGL+APH08F8peI4kCksHlXOCwO0FxeZSiuMwce9ans2F+AkHhJgZP7uhStCI4bTLOcoUqSQLzxu4i+0QJXe8Lp+v9TZF0glqmGjZQnqimIuNTBYLgyiYmiQKKDA6Hs0qogSiezsZU0zn/BgWFAFt7erR+EJPXhYt9v5I4mZqGR84czLpT/+h6UQJRL7mqpkPB4XDP3CRJuJI6yVR5dzq9ADrRlSHOJiOKrnpWcW7FtiJXMgwJ5WW6El+xfV8uC1enEuxvZOTjUZiMIsUOhfv/s5W8Qjtz3+lIVJPTXvg6nQB6lxJX7E6cZ4zaBbE8E1lFe3DI/zijnCDI7LLcTYfrb0Wv15/7gnrMrj07iFfmqw5r/5ryrGEV4a1Ou6z6YiiygqQTmfXyDgpOWek7shVRHfxd/ZMM9vI+D6Ao18b/XtmBziDy3OddkfQikiTgdLhnpxPF05ktK08qKvdRFXKcSUWZQJVyLwSSoqOlvSfXea/hn+oIna68LVJNu6jc5qprLwZXBsaK66Ty8a/TUfO1Ff2C0wkGvcijo3cSHGCge4cA+vUMRZEV5q04wfT5CbRpbubrie1xlvs/uLVRuZrMdJV/j4Crf6nPGeXAlerPeYZnuuI8nTZR3YerEttrEUKmyAo2azWVVXGtV1XZrZQPEs5SpiwryGVa+NrljOwE+cyWXomzHHI5KdlPnyDLQKUqdOY24OocKqWwjG7mw/Z9uThlhXXbTxER6snxE8UUlzoJa+RJs8buGcEcDgUcNQulyGjha1cCClXSuFbXz5w+VjUlcHU47TLOatK717RGXJs+qqYyLyfO2i7OaHNuh2RwnNHvu51bw7WV+wW7QyYy3ItVm7P4fZeFRb+5wmWPlTvK3XtLsNu7rU0bPVc7rw1XlFI/F55mA0HhJhoGe9bolKYo4B9qQhAFTA0N1Z6joXGxMXvr+N/E9oydcYiCYjvJ6S7TX5vmDZj0fEuk6lyXNeo9iuLKs+HhJWH01NU4SZMkAf9QLzXGXOPSo8gwcUQrzCYdv+/MIdPiSu/ayM/I433Due+W4LNODi4W9UapKwq06OTPtZ0DUKj5G+iyrHDXs9eWX6N9Y1ijjlCgRbg3iz7sjGAsX7d2gmx31ElHoHF54HTIPDCqNeDqn2pav/Y063l8UgfgPD22NS4oDpvMS49F8crQFuoyAHYZu12us3Zcb5Q6lK8b1cKLQ2sEGpcLLnNbLeyrGlcNte2ftH7s8kBRwF4mw2Wy/KXZbjQ0NDQ0NOoJmlLX0NDQ0NCoJ2hKXUNDQ0NDo56gKXUNDQ0NDY16wkVzlBMFEQEBAamuUwXXOxQUhKv8qbq+aOX609yFLjTaWL8CQRAQFFGrZRcBAVfyF1cbvrr7swuJLi4uDp1OVyuv8fNBlmUa0YHGyvUXtFwNF3bs6KTLP9vX9u3bMRgufD4Am82GJPVCdGoK6GLg4PzS79YlFouFhIQEdLoLP0cpszpoZRx0wcvVAFAok20cKNOe74VEBxAdHY3zAgfVFRYWYrfbadKkyQUtV8NFUlISwpnfarwMMRgMtGjR4oKWKQgCiYmJNGnSBLPZfEHL1nCxffv2uhah1siyTFBQEIGBgRd8cnLo0CFiYmKqfOtc49/jdDrZv38/7dq1q2tR6hUXVStoDUFDQ0NDQ+PScflP9TQ0NDQ0NDRqhabUNTQ0NDQ06gmaUtfQ0NDQ0KgnaEpdQ0NDQ0OjnqApdQ0NDQ0NjXrCOQM7BUHAbrdz7NgxysrKiIiIwM/P74KHwNUVFouFY8eOYTAYiI6OxsPDo65FuqoQRRGbzcbRo0ex2+00a9aMhg0b1pv6lZ+fz+HDh5EkiejoaEwmU12LdFWi0+nIysoiOTkZk8lEixYtEAThgofA1QWyLHPs2DFycnJo3LgxYWFhWuTRBUSWZRITE8nKyiIoKIhrrrmmrkU6K+dU6ikpKbz00ktkZ2cDIEkS//nPf3jooYeu+I53xYoVvPvuu5SVlQEQFBTE22+/TYcOHepYsqsDQRA4ePAgr776KhaLBUEQ0Ov1jBw5kvvvv/+Kr19r1qzh7bffxmq1AuDj48OUKVPo3LlzHUt2dSEIAjNnzmTu3LmIoogsyzRu3JgZM2bg7+9/RSv2srIyXnnlFbZs2aL+tl69ejF+/PiLkvTpaqOkpIQxY8awefNmdRDYrVs3PvnkE0Tx8jR0n1Uqo9HI66+/jpeXF7NmzWLZsmX06tWL6dOnc/z48Usl40UhKyuLyZMn0717d1asWMHChQtp1KgRpHjstQAAIABJREFUY8eOxWaz1bV4VwWKovD2228TEBDAnDlzWLx4MbfccgvTpk2jsLCwrsX7V2RnZzNp0iQ6d+7Mzz//zMKFC4mIiODNN9+8opXIlYYgCBw/fpzvvvuOgQMHsnz5cmbOnElpaSlvvPFGXYv3r/n444/ZsWMHkydPZv369UycOJENGzbw008/1bVo9YIff/yRuLg4xo4dy7p16xg7dizbtm1j1qxZdS1ajZxVqScnJ5OamsqECRO49tpr8fX15c0336RJkyYsX778oqRlvFSsXLkSHx8fxowZQ1BQEJGRkQwfPpyMjAzS09PrWryrgn379nHy5EnefPNNIiMjCQgI4PXXX0cQBObPn39FmxDXrVuHJEm8/fbbhIaGEhkZyaRJk8jJyeGvv/6qa/GuGgRBYO7cufj7+/PCCy/g7e1Nq1atmDhxIvHx8eTm5ta1iP+YsrIytm3bRmxsLL1798bb25u7776bpk2bsnr16roW74rH6XSyePFievfuTd++fTGbzfTt25eBAwcyb968y3Zwflalvnv3bhRFISQkRP0BgiAQHR3NkSNHLlvzQ20YPHgwq1evxmw2I8syBQUFrFq1isaNGxMSElLX4tV7RFEkPj6ehg0bEhERoe7X6/W0b9+e7du3X9GDxv379xMZGem2hh4QEICfnx9//PFHHUp2dSHLMnFxcbRv315dBgFo27YtHh4epKSkXLGDR6PRyLJly3jllVcA129NTk4mLS2NPn361LF0Vz45OTmkp6fTpUsXt/29e/fGZrNdtkq9xl5TEAQyMjLw9PR061wVRcHX15ejR49eEgEvBU/+f3t3Hh1FlT58/HurqpPOHiCJCVsAMQgThSg6KGgEVPQ1CCKIDsiMGkBQFmUYAUXCBJXIjAvKCIgsiqLgijO8oCKIKC4gGVlVCGEPEEgge3ctvz8yiUQgRO1Od3Xu5xzOIV2heLr7Vj333rrLffexfft2wsPDmTt3rnwWVQ8URaGgoIDo6GhUVa1+fi6EID4+nt27d6OqKm6328eR/janTp2icePGNV4LCgoiOjqaAwcO+CiqhscwDMrKympUHAGcTicOh6N6LIe/3qDr6tixY/Tt25eKigpuvPFGBgwY4OuQbK/qEWB8fHyN1+Pi4oDKsuWPDdtaIyovLz+jFmtZFpqm2X4Q0+n69OnDQw89RGRkJOnp6Rw8eNDXITUI5xq74HBU7j5n1xYUgK7rZ33dzhUVu9J1/YyyVPXz2Y7ZUUVFBRMmTKBfv36sWbOGjIwMTNP0dVi2VnUN/7J8VO1q6q8VwVqTelRU1Bk3ICEEpaWlhISEeDWw+tS3b1/uvPNOli5dSkhICC+99JKvQwp4lmURFhaG2+0+4+IoKioiODjY1jclp9N5xrVjmiZlZWU0atTIR1E1PEIIgoKCKC2tuZWs2+3GNE3Cw8P99ub8azRv3pzevXszadIkpk6dyooVK2SP0O9UNb359Mc2AMXFxQghUFXVF2Gd1zmTumVZtG/fnoqKihoXhKIoHDlyhJiYmHoJ0FsOHTrEqlWralzQDoeDtm3bkp+f78PIGgbDMGjVqhXHjh2rUb6q5twmJCScs7VrBy1atODQoUM1yld5eTlHjx71+Fa00rmpqkpCQgJbt26t0eIqLi6moqKC+Ph42yZ10zT56quv2LdvX43Xr7rqKjRN4+TJkz6KLDDExMTgcDjOmOm1Z88ehBB+28NTa0s9JSWF0NBQ1q1bh6ZpCCE4fPgwX3/9NZ07d7b1TXfRokU8+uijNWqz+fn5bNy4kbZt2/owsoajU6dOVFRUsHr1ahwOB4qikJeXx08//URaWpqtH/FcccUV5OTksGXLlurXtmzZgmEY3HjjjT6MrGERQtC7d2927NhBcHAwUDm2Yfbs2ei6TtOmTW2b1MvKyhg1ahQZGRnV78GyLBYtWoSu68TGxvo4QnsLCwsjJSWF5cuXV/caGobBjBkziI2N9dukXuvwYkVR6NatGzNmzOCdd94hNjaWTZs20aRJE/r162fr7tFHHnmENWvW0L9/f/74xz+i63r1exs5cqSvw2sQ4uPjueGGG3j66ad55513aNy4MRs3biQuLo6bbrrpjG4vO+natSsdOnQgPT2dTp06YRgG33//PZdccomcXVGPTNOkb9++zJw5k6uuuoquXbuSm5vLvn37GDlyJCEhIbatPIaFhTFt2jQeffRRbr75Zjp16sTWrVvJy8tj2LBhZwzwkn69YcOGkZ6ezvXXX88VV1zBd999x8mTJ3n77bf9NqmrvXv3zoiLiztrbdWyLHr06EGTJk349ttv2b9/P7169SIrK+u8041cLhemaRIZGemt2H8XIQQDBw6ktLSU9evXc/LkSQYNGsRTTz1li/EChYWFRERE+O1znSp5eXk0adLkrMdM0yQ1NZWoqCg2btzI4cOHueWWW3jmmWdq7QUSQlBYWEhkZGR168vfCCG4+eabMQyDL7/8kuLiYu655x4mT57slyNmf+ngwYMkJCT4ffmCylW/3G73OZfgVVWVO++8kwMHDvDll18SEhLCxIkTueWWW87bMMnPzychIcFvb+AXXngh1113HVu2bOG7774jMTGRp556iptvvtlvY65iWRZHjx7168rHBRdcQPfu3dm+fTubN2/moosuYubMmWfMpvAnYs6cOVZycnKttVVFUaoLiGmadequKioqQtd1mjVr5rFgvaXqwrbDzbZKbm4uTZs29fvpd9nZ2ed9hiyEQFGU6hGl5ytfQgj27NlD8+bN/bbSeDo7lq9vvvmGlJSU6pkI/iw/P5/S0lJiY2NrLTuqqtbopq7LfWznzp2kpKT4fYKEynJmpzJmGAZbt26lY8eOvg6lTuzy+dZpdQ87d7PXhR2+qEBmWZZtu0DrQpYv/xDIZQxkOfM2u3y+9ohSkiRJkqTzkkldkiRJkgKETOqSJEmSFCBkUpckSZKkACGTuiRJkiQFCM00TY4cOeLxE5eXl2MYhi2mgthRcUmJr0OoE7fb7ZVldysqKsjPz6e4uNjj55bsN1LcW+XAMHQOHz7slXM3dJZl4na7OXTokK9DCSgaVE4p8uS0NSEEhmmgmxWUu4uw5yKM/ksAulHh6zDqrK5rG9RV1VaZllGKqcsdzzxNgK2WThVCYJqmx8sZgGVBmfsUlZ+K5EmVawWYmHqRr0MJKJqiKMTHx3t8LnrhyQLKXEXExssdqTxOgG6TlpTD4eCCCy7w+HlLS0uJbRJGZIT/r/5nO0Jw8PAJX0dRZ5ZlERkZ6fG1zi3LoqDgBLEJjWVK9wLTtCgpKKF500bIlp/nVC8+440aLsjvyhuEzT5UT5ctIQRYFhY/lzPJc4TdCthpvNPDYGHJtO551av7IROFB8mBcpIkSZIUIGRSlyRJkqQAIZO6JEmSJAUImdQlSZIkKUD8qqRetUVmIBGAKhRUocihMD4WiOULAQjlf398HYwENbeSDhSKENX3MckLhPjfNez/5aZOW6/Cz3tYb9iwgcGDB9tucYqzsmDzpmzWf/4Vuq5zTWpXLru8I0KVF0Z9E0Kwa9cuNmzYwF/+8hd0Xfd1SL+bbsGmbzfz6doviI6KIO2WXrRoEQ+BvZOxX9M0jdmzZ9O7d2+vTLWsbwIoOlnMp6vX8uOPOcTGNiHt1puIjW0ii5knCDhRWMyK/6xi376DXN75Uq697lpCNP/NEXVK6kIISkpKGDduHImJiQwZMsT2Sd2hagy5ayh7c/cTEREOwL+Xr6Rt2zbMnv9cZa1MqhdCCI4dO8aIESNISkrinnvu8XVIv5slBA8//Bhff7OZqMgIDNNg/sK3GP3gfQwZ1N9Wi7sEClVV+eCDD3j11VdJTU0lPj7e1t+DAH76YTfD7hmFM8RJSIiTinIXi155ncl/f4SeN1yHaeP353MCftiVy+C7RxIS4iTE6WTFqk8RTz7Pmk/fR/PTeXjnzVyapvHf//6Xvn37cvz48fqIqV68u2w5e3L28q+Xn2Pl6vdZufp9nnsxi10/7WbZm+/LntJ6omkaGzZsoH///rhcLl+H4xFCwKqP1/LV19/xjxkZfPzJe3z66XKGDLqdha++BaosXfVNURSmTJnCjBkzfB2Kx1jAX8c+ygUJcXz++Uf856O3+Wzd/+eyzp3IfDyLspIyX4dob4pCZuY/ad2qJZ9/vpKPPn6HVavew+Vy8eQT//R1dOd03pb64sWLeemllxg1ahQbN24MiBuvIgSR0ZFc1fVKktq3pcxVDkCnyzrS7uIkdv+Ug6ppAdEFXFe6rlNaWoplWURFRdXL/6mqKgsWLGDevHk89NBDfPbZZ7jdAbDsqxAEOxxc3+Marr3mKixXOQi4rntX3nr7Q0zdQAR4tbGkpKTOvXmWZREWFoam1flp4K8ihKB79+6oqspHH31Er169vPL/1DdD17mq65Wk9uhGkaty7ftyXDw0/gH+fNdwTp06RWyo08dR2pe7wsWFbRLpctUVWK5SAMKcCvEXxLF370H+twaW3znvVXT11Vdzxx13EBoayrffflsfMXmdaVmk9uhG957X1OieKi4uJjd3H92vT8VoQAkdYPv27TzzzDO4XC4WL15cLwPWTNOkR48e3H333TgcDj755BOv/5/1wTItelx3NT26d8XS3QhFkJ29jbF/ncLll12KomlYur0fX53PrFmz2LlzZ51+V9d1xo4dy2WXXeaVWCzLYtasWXTs2NH2jw1Pp2oaEx57GPh5QTYBrP9sA6ZpEhUV7bPYAoFD0/j7lL8ClZ+v260ze+6r5B05xitz/+mXCR3qkNRbtmwJ2GuDh7o6PaE7tSD+OmESkVER3D7gVj99WuJd9T0q2LIsmjVrVv33QGL9bw1b3TLoe+u9HD2WT0REOGNHDwMP77Pgj3Rdr3NPl67rXv/+27dvT0VFhdd6A3zll5/asaPHmf/yazwwZhghoU4MK/DLmjdZFiDg22+zeXDsY5imyQ09ryU2Ph7L7Z+91oFVwn8jBcHQ9NHk5uTyyqKXUFSlQSZ1yfNUobBkyRwsCxbOf507B93P7FnTSemY7OvQvGr06NF1TuqWZREeHu7VeAKt0vhLAti5/QfGjPwb3Xtew4A7b5MJ3VMs6NQpmU8+eZfCghMMGjSSG3v1Z9XKZeCHu0Q26CHeAsjPO8af+t/LsaP5LHrjZS5IiJUJXfr9FIHQNASCiBAnkaFORoy8j0aNopn98msBP20yPDyc6OjoOv1p1KgRDofD1yHblkPTWLL4bR4YNo7+d97GpCnj5ah3DxGahlAUHJpGhDOIFk3jmTNnBgUFJykr9s8tYxtsS10I+HbDd0x5dBodUy5h6rRHCXIGNYiE7nK52Lt3b3VXuxCCvLw8oHLwWm5ubo2teIOCgqofw0h1oKrMnrOQPbn7eHp6Btb/avOKAoqA8vIKWyxiIfk/DZW/jpnEt19/xxNZU7i62x9lC91Djp0oZPzfMvjzn++i+zV/rH492BEM4LfjrhpsUj98II/JE/9Ov9v7MOrBkainfRK6ZVBh+OfzEk84cuQIGRkZBAUFVb8mhEAIgcPhIDMzs8bvx8fH8+STTwbcKlzeIiyTiPBw1q7dwMqVn3DTTb0wTYN3lr7HsfwTjB83MuAHyn3xxRfk5+fX6XdN06RLly4kJCR4OarAoghB1lPPsnnT9yx5cxGt27TEOq0yXqqXYzWIZop3REdFcPx4IbNnL6DLlZcRGhbFqcJjjH8kgyCHg7DoaPDDxN4gk3qQpjFt6tNUVLhY/cka1qz5rPqYZUG3a69izLgH0A3/+8I8wbIs3G53jSStKAoOhwMhBC6Xq8axhjS1zxMs02LAgD7897/beDxjBi/Mmo9pGOTnn+DOO/rQo+c1AZ/U169fX+fR74ZhkJiYKJP6r3Qiv4D/LF+JoiiMHTse67QWumnCi3P/QUxsjA8jtDeHpvLUU4/xwIOPcNMtdxEdHUFBwUnA4vXF/0L46X2xzkndMAzS0tIwTdP200JMy+KmW27kuh7XnPV4YqsWmKa932Nt4uPjmT59eo1pazk5OSxevBiXy8W0adNqJPWqZO9Npmly++23A9i+fAEEqQpPPfkYW7ftZOeOH3A4NJIvvYR2F7UK+IQOlRVHs46j/OtzEJtpmowcOZLY2Fj7D54TFiNHDz3n4bDQ0HoMJgBZkNy+Le+9s4jvNm3m6NF8mjWLp9NlHYkKDz1z6oGfqHNS13Wd1NRUhBC2v+nqhkGf2/7fOY9blhXQA02CgoJo06ZNjddOnToFVN70EhMT631jFdM0uf76639VMvB3qoCOl1xMx44dAAtMC8sIjPd2PqNHj/5V94mQkBAvRvMz0zS56667MAzD9kk9unEjBv7p9nMeNwLkOvIpCxpHhXP99ddWjoOxzMq9G/y46Pyq7vdAudmCLPD+yO6VxbOygAaSyE8X6setxEB6nCTvY/XEtPDrTH6awJ5XI0mSJEkNSIMcKCedSdM0wsPDCQ4O9nUokiRJ0m8kk7oEQIcOHZg5c6avw5AkSZJ+B9n9LkmSJEkBQoOfFx7xpKrTyeVKPM9ua8B4vmwJEJWblwphl+ErNmK3AnYaT5a1qoVbAn2bXJ+pXtFSXsOepBmGwc6dOz0+vcMwDEzLoKiw1KPnlSq5XG5o7usozq+8vJwff/zR4+etqKggZ28ZaoCvoe4TAty6fUZVCyE4cuQIBQUFHj+3y+UmZ+dBj59XqlTucrNlu/x8PUlTFIU2bdp4fLpaUVERhmEQHx/v0fNKlTex/fv3+zqMOgkKCqJ169YeP+++ffto2rSp13f3aogURWHTpk2+DqPOLMsiJiaGJk2aePzcu3btov3Ff/D4eaXKKdI7duygnfx8PUqrWu/b03OEq/YtPn19cclz6ntxmN9KURSP72EthKg+ryxf3mG3df5VVcXhcHhlQZn6WFGxITIMA0VR5DXsYfbIDJIkSZIknZdM6pIkSZIUIGRSlyRJkqQAIZO6JEmSJAUImdQlSZIkKUDUaVhy1QhFIQS6ruN2u70dl9SAVI1cluVL8qagoCBUVcWyLFwuV0DtOilJVeqU1CdOnMi2bdswDINGjRoxfvx4OnXqZPutMl0uFxMmTGDLli0AXHrppUydOlXOfa5H5eXlTJkyhZ07d2KaJjExMUyaNImkpCRb73edmZnJZ599dtZjbdq0Ye7cufUcUcOlKAqbN28mKyuLoqIiFEUhJSWFjIwMX4fmEd988w3Tpk2jtLSU4OBgRowYQVpamq/Dsr28vDwGDx58zuNvvvkmMTEx9RhR3dSa1B0OBwMHDiQvL4+BAwcSGxvLsmXLGDt2LFlZWVxxxRW2vvHec8897N69m/vuuw/Lsli4cCEPPPAAixYt8nVoDYLL5WLw4MEUFxdzxx13EB0dzVtvvcX999/Pyy+/TJs2bXwd4m/Wq1cv/vCHmotq5OfnM3fuXHr27OmjqBoeIQSbNm1izJgxtG7dmuHDh7Nv3z6WLVvGoEGDWLx4sa3noO/evZsHH3yQCy+8kPT0dFasWEFGRgbh4eFcd911vg7P1iIjIxk5cmSN1xRFYcGCBeTl5REdHe2jyGpXa1Jft24d+/fvZ/78+SQlJQHQp08fhg4dytKlS7n66qtxuVz1Eqinbdu2jZ9++ol58+Zx6aWXAnDllVeSnp7Opk2buPzyy30cYWATQvDxxx9z/PhxXnvtNRITEwHo3bs3AwYM4IUXXuCFF15A13UfR/rbXHnllVx55ZXVP5eUlDBw4EBSU1MZOHCgDyNrWCzLYsaMGSQmJvL6669XN0K6d+/OiBEj2LVrl617hbKysmjatClvvPEGQghuvfVWRo8ezbPPPku3bt08vvBTQxIaGkq/fv1qvLZ+/Xry8vJ47733/PazrTWqlJQUnnnmGVq3bl3d1R4UFESrVq04cuRIvQToLf/5z38ICQnh4osvrn6tY8eOtGzZki+++EImdS+zLIvU1FSaN29O8+bNq8uX0+kkKSmJwsJCW7egfmnevHm43W4yMzN9HUqDk5WVRVlZWY0KYvv27QkODqasrMyHkf0+paWlZGdnM2bMmBrXypAhQ7j//vspKCggNjbWhxEGlsLCQiZOnEj//v1p2rSpr8M5p1pHvzudTjp37lxjSdKCggI2bNhA586dbf1M/cCBA7Ru3brGEoVCCOLj4zl4UG4wUB/Cw8Pp1KlT9c9CCI4fP87GjRvp1atXwAyYy8nJ4Y033mD48OGEhob6OpwGp0WLFrRr1676Z1VVWbx4MWVlZVx44YW2baWXlZVhmibt27ev8XqbNm1QVZWSkhIfRRaY5s6di8Ph4OGHH/Z1KLU675S20wt8WVkZY8eOJS4ujkGDBtk6qZeUlJx1zWGn0xkwycQOqsqXEIJTp07xwAMP0KpVq4Dqon7ttddo1qwZffv29XUoDVZVOdM0jbVr1zJ//nwmTJhARESEjyP77XRdRwhBcHBwjdfDwsJQFMXW92d/c/ToUd5++22GDx+Oqqq+DqdWdZqnrigKeXl5DB06FF3XefbZZ/32eUJdBQcHn3VKi67rfv+lBRpVVdm/fz/p6emEhITwr3/9y7bP0n/p6NGjrFq1ij59+thmE55A5XA4eOONN5g8eTJDhw4lLS3N1tPaqu5Tv0zeLpcLy7JkefOgVatWER4eTv/+/X0dynmd91vXNI1169aRnp5OQkICL730Eo0aNaqP2Lzqggsu4NChQ2dc1CdOnJDPoeqRpml89NFHDBs2jIsuuojZs2fjdDp9HZbHfPXVV2iaJlvpPiaEYPLkybz44otMmjSJu+++29YJHSobJpZlnbEN8+HDhzEMI6CuI19yu90sXbqUjh072qLBV2tSV1WVd999l8cff5y0tDRmzJhBSEiIbZ9Bna5Hjx4cPXqUvLy86tcOHz7Mnj17SE5O9mFkDYemaSxZsoTMzEz69+/P9OnTbd8D9EtfffUVbdu2JSoqytehNFimaTJ06FC+/vprFixYwM033xwQPUFhYWG0atWK5cuX13j9ww8/xOl0+u2UK7s5evQohw8fZsCAAb4OpU5qvYPu37+fmTNnkpqaSs+ePfnxxx+rjzmdThITE22b4K+++mqaNGnCI488wogRI1AUhVmzZhEVFUVqaqqvwwt4Qgiys7OZNWsWN954I926dWP79u3Vx4OCgmjdurVtyxdUJpOtW7fStWtXX4fSYAkhePrpp9m1axdZWVkYhsGOHTuAyufsLVq0sO3gRUVRGDFiBBMnTmT27Nl069aNHTt28Pbbb3PfffcREhLi6xADwsGDBwkODq4xqNefnTOpq6rKiy++iMvlYvXq1axevbrG8fbt27NgwQLbzlNXVZXMzEymT5/O2LFjgcpRsk8++SRhYWE+ji7waZrGW2+9hWmarFy5kpUrV9Y4npiYyNKlS6moqPBRhL9fUVERx48fp3Xr1r4OpcEqLS1lxYoVAIwbN+6M4//4xz/o0qWLbbviU1NTSUtLY8mSJcybNw+n08k111zDkCFDfB1awMjNzSUsLMw2lT8xZ84cKzk5+YzBFlXTi841EtzhcNCkSZNztqSKiorQdZ1mzZp5PGhPMk2TI0eOIIQgLi7ONoNLcnNzadq06VlH8PuT7Ozs6oWLTieE4MSJE+esFGqaRkxMzFnLlxCCPXv20Lx5cyIjIz0es6eYpsmhQ4do3LixbW4IVb755htSUlJwOBy+DuW88vPzKS0tJTY29ozyYllW9fV9No0aNar1Gtq5cycpKSl+v2ZCWVkZ+fn5REdH22ZEv2EYbN26lY4dO/o6lFoVFRVRVFTk13PTT3fOlrplWTRu3LjWf2znrtEqiqKQkJDg6zAaHMuyzjvg0u7lS1EUmjdv7uswGrSqtScCXUhICC1atPB1GAEpIiLCNhUlkFuvSpIkSVLAkEldkiRJkgKETOqSJEmSFCBkUpckSZKkACGTuiRJkiQFCA0qR+l6cqSxEKJ6Cohd539KnqOqqsfLF1SOjpflS4LKMuHp+1jVeS3Lsv1MDH9U9ZnKa9izxKxZsyxv7uZjWfIL8wYhFDp37uz389S/+OILL87xtUDebL3CMC26dOlii3nqJ06cYPv27V5bY0Lew7xHCAXk5+tRmqZppKSkeHybvsKThZS5ThGX0Ejedz1MCDi0P9/XYdRJWFhYjb2sPUEIQU5ODs3iw4iMDAFZvjxLgW837fZ1FHVmmiYtW7YkLi7Owy1qix07dnBRspz/7Q2WabFn5wEuTW4hr2EP0gCvdC9Vd63IL8vzbPaZeqXrsuqcspLvccLCdmUMvHMfA7CwAP9eUc6OLMuqLGc2LW/+Sg6UkyRJkqQAIZO6JEmSJAUImdQlSZIkKUDIpC5JkiRJAUImdUmSJEkKEHVK6kII9u7dS25ubuU/ssme479G8ali8o/kyzGu9axqDntubi65ubk1Fi6yO6EoVOg6P+zK5Yddubh0A5TAeG92o2kaxcXF7Nq1i/z8fL9f3+G3UBWF3T/kYMnFXDxL1TiWf4LtP+Rw8PBRhObfZeec+6lX2bVrF5MnT+bQoUMAxMbGMmrUKHr06OHxue2+YukW40ZP5JJOf2DsuAfQA+R9+TtFUfj+++/JzMzk8OHDCCGIi4vj4YcfpmvXrrZeaUqoCvMXvsXi15dx6lQxQgiioyNJv3cQd9zRB2QZqzeKojBz5kzeffddXC4XVWtzPPHEEzidTl+H5xGKEGRv+p6HRk1g+aqlhIaF+jqkgHCsoJCJE6exbfsP6LqBqqp0aH8Rzz/3BBGhIb4O76xqbXKrqsr48eMJCwtj9uzZLFy4kA4dOvDkk09y5MiR+orRa1RF4cTREzx4/0P89ONu2UqvZ0VFRUyaNInGjRszb948XnnlFdq2bcu0adNwu92+Du+3E7Du86/510sLuL7HtSx+9SUWzn+BK6/oxHMz5/LTj/ZZ2MULx9oHAAAHB0lEQVTuhBCsW7eOpUuXMmTIEN58802mT5/O1q1bycrKCoheR03R+HjlGsY++IitK8L+xhKCjClPs3ffATL//ghL35pPZuYEcnL28Zd7RoF23jaxT9Raordt20ZxcTFTp06lQ4cOtG7dmnHjxlFeXs7WrVvrK0avUIRg+fsrGPKnoTSJaUJMbBO5/kE9+/7779F1nSlTppCUlETbtm0ZN24cRUVF7Nmzx9fh/WZCVdm4MZukpAuZOOkh2rVNpEO7NkycOA5VVflg+UoC5AmDLbz//vu0aNGC9PR0mjZtSpcuXbjppptYs2aN7XsbLd3koVGPkPXEM9w1uL+vwwkohSeL2LV7D8OG/Znre1xD6xbx3Ni9G1MzxnPgYB6Wofs6xLOqtarRoUMH/v3vfxMcHIyqqui6zscff4zT6eTiiy+urxi9QghB3qE8/jphLDf16sWdd9zt65AanK5du/L+++8TFBSEpmnous6aNWsIDw+nZcuWvg7vN7N0g7FjhlNeXgrGzz0OQlR2BUdHR8kKZD2xLIt//vOflJWVVXe9nzp1ik2bNtGtWzevbAJTnwzDJKpRFEveWURZWSlvvLbM1yEFjEZR4fx7+euVa9P/b2lUCwtVVSsr5YoGfpjYa03qpmkSHByMpmk8//zzrFmzhuPHj/PYY4/RvHlzdN3/3lBdGabJ8AfSMQwdhL1r63ZlmmZ1Qn/++ef59NNPOXnyJJmZmYSEhNi6FSUsk5Bg58/LX6oK7733bwD6D+gtl8WsR5Zl4XQ6EUIwePBg9u/fT1xcHFOnTrV1QgfQgjWmZk7Cbejsyy32dTiBxQKHqlDdoS2gtNxF5hPPcEXnTgg/XaO6zg+UWrZsSb9+/WjXrh0zZswgOzvb9qOUdUOX91Y/YBgGzZo147bbbqNVq1ZMnTqVnJwcX4flMZYQLF26nFmz5vPQmGE0iozwdUgNksvlomfPntx2220UFBQwfvz4gHgG7fbD1mLAEVBYVMLQ4Q/jdAbz/HPTwPDPsnPepK4oCrqu06dPHwYNGsT8+fNp1aoVL774oi22ZZT8W1X3Z79+/RgyZAjz5s0jIiKCjIyMgJh2VKGbPDdzLjNfnMff/vYgt912C5bc5aheVU2TdDgcDBkyhDFjxjB79mw2b97Md999FxCD5SQvUgQ5ew9z113DMXSDpW/OQ/HjHp5aS/OpU6f47LPPME0T0zSxLAvTNElOTubYsWMBUcuVfEMIQUFBAV9++SVCiOoypqoqXbp0obCwEFVVfR3mbyegoKiY4fePY8WK1Tz/7DRu63MTlo0fWdnVjh072LJlC1DZFW8YBu3atSM0NJR9+/bZvsdR8iJFsPrTL7nzrnQuuqg1by6ZS7Cm4s+79tWa1LOzs3nsscfYvn07qqqiqirl5eVs3LiRFi1ayBqu9Lt8/vnn/O1vf2Pbtm2oqoqiKJSUlLB27VoSEhJsPWajtKycoUMfpriklCVL5nF5yqVYJiAU5ND3+pWRkcHkyZNxOBzV97Gvv/6akpISkpKSZONEOiuhCD5Z/TmPTn6CwX/qzwsvzKgcMCeUyj9+qtaBcjfccAMLFy5k1KhR3HrrrURGRvLhhx9SWlrKo48+au+5xJJPWZZFWloay5YtY8yYMaSlpREaGsoHH3yAYRi2nqsuNJUXXnyFvfsOcNfAPrz/7vLqYxYW7S66kNRru+DHPXgBQwhBRkYG999/P7fffjtpaWns2bOH1atXk5yczCWXXGL7wXKSd5woPEXG3/9BTJMmREeF8/Kc+dXHLNMkPX0Qwg9b7LUm9YqKChYvXsxTTz3F+vXrMQyDtm3bMnHiRGJiYgLqYggLDyUkQFaXsgtFUXj11VfJyspi7dq1WJZFcnIyjz/+OKGhNl4Ry7LYtv1HGjeKYtVHa2seAm7ocQ2pqVchs7r3WZZFhw4dWLx4MRkZGSxbtozg4GDuvfde7r33Xlv3Bv2SAKIbRcmOIE8QsGP7D4SGhuB2u3h9ybu/OK6Qnj7IN7Gdh5gzZ46VnJxc6/QhIQTBwcEIIXC5XHWaalR4spBydxEx8Y08Ga+XWIRoIRgYuHT/bx0K4PCBfFo1b+v3g8mys7NJSkqq9XcURSEoKKjO5UsIwZ6cHJolhBMZ7odLNQoQWi0VRMvA8uNyJhT4ZtNuUi7rbIvBsPn5+ZSWlhIbG1trQ0PTNBwOB5ZlUV5eXqdz79y5g7bJzfHnZ6hVFCEI0ZyUustsMavHMkxydx7k0uQW/jnFU1UQSjBnD05gucvqO6I6qdM6d7/mIrAnQZkeyO/Pv5mmGVjlywLLHUDvJ0Douh5QLfNfMi2LEj9NNLZkmFiG/T5PJSoqytcxSJIkSZLkAUpKSoqvY5AkSZIkyQOUpKQkYmNjfR2HJEmSJEm/0/8Bbmj4BxBbSEUAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Un index par colonne."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "fandango.csv : fandango contient la note moyenne des utilisateurs et des critiques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lire le dataset 'fandango_score_comparison' dans le dataframe fandango.\n",
    "- Retourner un DataFrame contenant seulement la première et la dernière ligne et assigner le résultat à la variable first_last.\n",
    "- Afficher le résultat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>271107</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>249</td>\n",
       "      <td>65709</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627</td>\n",
       "      <td>103660</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.20</td>\n",
       "      <td>...</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31</td>\n",
       "      <td>3136</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.40</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>88</td>\n",
       "      <td>19560</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Water Diviner (2015)</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.10</td>\n",
       "      <td>...</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>34</td>\n",
       "      <td>39373</td>\n",
       "      <td>397</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Irrational Man (2015)</td>\n",
       "      <td>42</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.65</td>\n",
       "      <td>...</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>17</td>\n",
       "      <td>2680</td>\n",
       "      <td>252</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Top Five (2014)</td>\n",
       "      <td>86</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.20</td>\n",
       "      <td>...</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>124</td>\n",
       "      <td>16876</td>\n",
       "      <td>3223</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shaun the Sheep Movie (2015)</td>\n",
       "      <td>99</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.95</td>\n",
       "      <td>4.10</td>\n",
       "      <td>...</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>62</td>\n",
       "      <td>12227</td>\n",
       "      <td>896</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Love &amp; Mercy (2015)</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>80</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.35</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54</td>\n",
       "      <td>5367</td>\n",
       "      <td>864</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
       "0  Avengers: Age of Ultron (2015)              74                   86   \n",
       "1               Cinderella (2015)              85                   80   \n",
       "2                  Ant-Man (2015)              80                   90   \n",
       "3          Do You Believe? (2015)              18                   84   \n",
       "4   Hot Tub Time Machine 2 (2015)              14                   28   \n",
       "5        The Water Diviner (2015)              63                   62   \n",
       "6           Irrational Man (2015)              42                   53   \n",
       "7                 Top Five (2014)              86                   64   \n",
       "8    Shaun the Sheep Movie (2015)              99                   82   \n",
       "9             Love & Mercy (2015)              89                   87   \n",
       "\n",
       "   Metacritic  Metacritic_User  IMDB  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0          66              7.1   7.8             5.0                   4.5   \n",
       "1          67              7.5   7.1             5.0                   4.5   \n",
       "2          64              8.1   7.8             5.0                   4.5   \n",
       "3          22              4.7   5.4             5.0                   4.5   \n",
       "4          29              3.4   5.1             3.5                   3.0   \n",
       "5          50              6.8   7.2             4.5                   4.0   \n",
       "6          53              7.6   6.9             4.0                   3.5   \n",
       "7          81              6.8   6.5             4.0                   3.5   \n",
       "8          81              8.8   7.4             4.5                   4.0   \n",
       "9          80              8.5   7.8             4.5                   4.0   \n",
       "\n",
       "   RT_norm  RT_user_norm  ...  IMDB_norm  RT_norm_round  RT_user_norm_round  \\\n",
       "0     3.70          4.30  ...       3.90            3.5                 4.5   \n",
       "1     4.25          4.00  ...       3.55            4.5                 4.0   \n",
       "2     4.00          4.50  ...       3.90            4.0                 4.5   \n",
       "3     0.90          4.20  ...       2.70            1.0                 4.0   \n",
       "4     0.70          1.40  ...       2.55            0.5                 1.5   \n",
       "5     3.15          3.10  ...       3.60            3.0                 3.0   \n",
       "6     2.10          2.65  ...       3.45            2.0                 2.5   \n",
       "7     4.30          3.20  ...       3.25            4.5                 3.0   \n",
       "8     4.95          4.10  ...       3.70            5.0                 4.0   \n",
       "9     4.45          4.35  ...       3.90            4.5                 4.5   \n",
       "\n",
       "   Metacritic_norm_round  Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "0                    3.5                         3.5              4.0   \n",
       "1                    3.5                         4.0              3.5   \n",
       "2                    3.0                         4.0              4.0   \n",
       "3                    1.0                         2.5              2.5   \n",
       "4                    1.5                         1.5              2.5   \n",
       "5                    2.5                         3.5              3.5   \n",
       "6                    2.5                         4.0              3.5   \n",
       "7                    4.0                         3.5              3.5   \n",
       "8                    4.0                         4.5              3.5   \n",
       "9                    4.0                         4.5              4.0   \n",
       "\n",
       "   Metacritic_user_vote_count  IMDB_user_vote_count  Fandango_votes  \\\n",
       "0                        1330                271107           14846   \n",
       "1                         249                 65709           12640   \n",
       "2                         627                103660           12055   \n",
       "3                          31                  3136            1793   \n",
       "4                          88                 19560            1021   \n",
       "5                          34                 39373             397   \n",
       "6                          17                  2680             252   \n",
       "7                         124                 16876            3223   \n",
       "8                          62                 12227             896   \n",
       "9                          54                  5367             864   \n",
       "\n",
       "   Fandango_Difference  \n",
       "0                  0.5  \n",
       "1                  0.5  \n",
       "2                  0.5  \n",
       "3                  0.5  \n",
       "4                  0.5  \n",
       "5                  0.5  \n",
       "6                  0.5  \n",
       "7                  0.5  \n",
       "8                  0.5  \n",
       "9                  0.5  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "fandango = pd.read_csv('fandango_score_comparison.csv')\n",
    "fandango.head(10)\n",
    "    # RangeIndex(start=0, stop=146, step=1) : first and final index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146, 22)\n",
      "\n",
      "RangeIndex(start=0, stop=146, step=1)\n"
     ]
    }
   ],
   "source": [
    "print(fandango.shape)\n",
    "print()\n",
    "print(fandango.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Tomorrowland (2015)</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.65</td>\n",
       "      <td>...</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>262</td>\n",
       "      <td>42937</td>\n",
       "      <td>8077</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>The SpongeBob Movie: Sponge Out of Water (2015)</td>\n",
       "      <td>78</td>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.75</td>\n",
       "      <td>...</td>\n",
       "      <td>3.05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>196</td>\n",
       "      <td>26046</td>\n",
       "      <td>4493</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               FILM  RottenTomatoes  \\\n",
       "45                              Tomorrowland (2015)              50   \n",
       "90  The SpongeBob Movie: Sponge Out of Water (2015)              78   \n",
       "\n",
       "    RottenTomatoes_User  Metacritic  Metacritic_User  IMDB  Fandango_Stars  \\\n",
       "45                   53          60              6.4   6.6             4.0   \n",
       "90                   55          62              6.5   6.1             3.5   \n",
       "\n",
       "    Fandango_Ratingvalue  RT_norm  RT_user_norm  ...  IMDB_norm  \\\n",
       "45                   3.7      2.5          2.65  ...       3.30   \n",
       "90                   3.3      3.9          2.75  ...       3.05   \n",
       "\n",
       "    RT_norm_round  RT_user_norm_round  Metacritic_norm_round  \\\n",
       "45            2.5                 2.5                    3.0   \n",
       "90            4.0                 3.0                    3.0   \n",
       "\n",
       "    Metacritic_user_norm_round  IMDB_norm_round  Metacritic_user_vote_count  \\\n",
       "45                         3.0              3.5                         262   \n",
       "90                         3.5              3.0                         196   \n",
       "\n",
       "    IMDB_user_vote_count  Fandango_votes  Fandango_Difference  \n",
       "45                 42937            8077                  0.3  \n",
       "90                 26046            4493                  0.2  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango[0:5] # 5 first lines\n",
    "fandango[141:146]   # 5 latest lines\n",
    "fandango.iloc[50] # index lines 50\n",
    "fandango.iloc[[45,90]] # lines between 45 and 90\n",
    "# When selecting a row, pandas returns a Series",
    "# When selecting multiple rows, pandas returns a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>271107</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Kumiko, The Treasure Hunter (2015)</td>\n",
       "      <td>87</td>\n",
       "      <td>63</td>\n",
       "      <td>68</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.35</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>3.35</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19</td>\n",
       "      <td>5289</td>\n",
       "      <td>41</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
       "0        Avengers: Age of Ultron (2015)              74                   86   \n",
       "145  Kumiko, The Treasure Hunter (2015)              87                   63   \n",
       "\n",
       "     Metacritic  Metacritic_User  IMDB  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0            66              7.1   7.8             5.0                   4.5   \n",
       "145          68              6.4   6.7             3.5                   3.5   \n",
       "\n",
       "     RT_norm  RT_user_norm  ...  IMDB_norm  RT_norm_round  RT_user_norm_round  \\\n",
       "0       3.70          4.30  ...       3.90            3.5                 4.5   \n",
       "145     4.35          3.15  ...       3.35            4.5                 3.0   \n",
       "\n",
       "     Metacritic_norm_round  Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "0                      3.5                         3.5              4.0   \n",
       "145                    3.5                         3.0              3.5   \n",
       "\n",
       "     Metacritic_user_vote_count  IMDB_user_vote_count  Fandango_votes  \\\n",
       "0                          1330                271107           14846   \n",
       "145                          19                  5289              41   \n",
       "\n",
       "     Fandango_Difference  \n",
       "0                    0.5  \n",
       "145                  0.0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first and last line\n",
    "first = 0\n",
    "last = fandango.shape[0] - 1\n",
    "first_last = fandango.iloc[[first,last]]\n",
    "first_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use custom index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Utiliser la méthode set_index pour indéxer notre DataFrame fandango avec la colonne 'FILM'. A préciser que nous souhaitons conserver la colonne 'FILM'. De plus nous souhaitons garder le DataFrame d'origine et en créer un nouveau que nous assignerons à la variable fandango_films.\n",
    "- Afficher l'index du DataFrame fandango_films à l'iade de l'attribut index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Avengers: Age of Ultron (2015)', 'Cinderella (2015)', 'Ant-Man (2015)',\n",
      "       'Do You Believe? (2015)', 'Hot Tub Time Machine 2 (2015)',\n",
      "       'The Water Diviner (2015)', 'Irrational Man (2015)', 'Top Five (2014)',\n",
      "       'Shaun the Sheep Movie (2015)', 'Love & Mercy (2015)',\n",
      "       ...\n",
      "       'The Woman In Black 2 Angel of Death (2015)', 'Danny Collins (2015)',\n",
      "       'Spare Parts (2015)', 'Serena (2015)', 'Inside Out (2015)',\n",
      "       'Mr. Holmes (2015)', ''71 (2015)', 'Two Days, One Night (2014)',\n",
      "       'Gett: The Trial of Viviane Amsalem (2015)',\n",
      "       'Kumiko, The Treasure Hunter (2015)'],\n",
      "      dtype='object', name='FILM', length=146)\n"
     ]
    }
   ],
   "source": [
    "fandango_films = fandango.set_index('FILM', drop=False)\n",
    "print(fandango_films.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select values from an index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sélectionner les films suivants depuis le DataFrame fandango_films (dans l'ordre suivant) et assigner les à la variable some_movies:\n",
    ">- \"Top Five (2014)\"\n",
    ">- \"Black Sea (2015\"\n",
    ">- \"Taken 3 (2015)\"\n",
    "- Afficher le résultat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Top Five (2014)</th>\n",
       "      <td>Top Five (2014)</td>\n",
       "      <td>86</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>124</td>\n",
       "      <td>16876</td>\n",
       "      <td>3223</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black Sea (2015)</th>\n",
       "      <td>Black Sea (2015)</td>\n",
       "      <td>82</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37</td>\n",
       "      <td>16547</td>\n",
       "      <td>218</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taken 3 (2015)</th>\n",
       "      <td>Taken 3 (2015)</td>\n",
       "      <td>9</td>\n",
       "      <td>46</td>\n",
       "      <td>26</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>104235</td>\n",
       "      <td>6757</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
       "FILM                                                                      \n",
       "Top Five (2014)    Top Five (2014)              86                   64   \n",
       "Black Sea (2015)  Black Sea (2015)              82                   60   \n",
       "Taken 3 (2015)      Taken 3 (2015)               9                   46   \n",
       "\n",
       "                  Metacritic  Metacritic_User  IMDB  Fandango_Stars  \\\n",
       "FILM                                                                  \n",
       "Top Five (2014)           81              6.8   6.5             4.0   \n",
       "Black Sea (2015)          62              6.6   6.4             4.0   \n",
       "Taken 3 (2015)            26              4.6   6.1             4.5   \n",
       "\n",
       "                  Fandango_Ratingvalue  RT_norm  RT_user_norm  ...  IMDB_norm  \\\n",
       "FILM                                                           ...              \n",
       "Top Five (2014)                    3.5     4.30           3.2  ...       3.25   \n",
       "Black Sea (2015)                   3.5     4.10           3.0  ...       3.20   \n",
       "Taken 3 (2015)                     4.1     0.45           2.3  ...       3.05   \n",
       "\n",
       "                  RT_norm_round  RT_user_norm_round  Metacritic_norm_round  \\\n",
       "FILM                                                                         \n",
       "Top Five (2014)             4.5                 3.0                    4.0   \n",
       "Black Sea (2015)            4.0                 3.0                    3.0   \n",
       "Taken 3 (2015)              0.5                 2.5                    1.5   \n",
       "\n",
       "                  Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "FILM                                                            \n",
       "Top Five (2014)                          3.5              3.5   \n",
       "Black Sea (2015)                         3.5              3.0   \n",
       "Taken 3 (2015)                           2.5              3.0   \n",
       "\n",
       "                  Metacritic_user_vote_count  IMDB_user_vote_count  \\\n",
       "FILM                                                                 \n",
       "Top Five (2014)                          124                 16876   \n",
       "Black Sea (2015)                          37                 16547   \n",
       "Taken 3 (2015)                           240                104235   \n",
       "\n",
       "                  Fandango_votes  Fandango_Difference  \n",
       "FILM                                                   \n",
       "Top Five (2014)             3223                  0.5  \n",
       "Black Sea (2015)             218                  0.5  \n",
       "Taken 3 (2015)              6757                  0.4  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = [\"Top Five (2014)\",\"Black Sea (2015)\",\"Taken 3 (2015)\"]\n",
    "some_movies = fandango_films.loc[movies]\n",
    "some_movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Méthode Apply() sur les colonnes d'un DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Obtenir le dataframe float_df que j'ai réalisé en exemple (qui retourne les colonnes décimales float64)\n",
    "- Utiliser la méthode apply() sur ce dataframe float_df afin de diviser par 2 chaque valeur et assigner le résultat à la variable exo_df.\n",
    "- Afficher les premières lignes du résultat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FILM                           object\n",
      "RottenTomatoes                  int64\n",
      "RottenTomatoes_User             int64\n",
      "Metacritic                      int64\n",
      "Metacritic_User               float64\n",
      "IMDB                          float64\n",
      "Fandango_Stars                float64\n",
      "Fandango_Ratingvalue          float64\n",
      "RT_norm                       float64\n",
      "RT_user_norm                  float64\n",
      "Metacritic_norm               float64\n",
      "Metacritic_user_nom           float64\n",
      "IMDB_norm                     float64\n",
      "RT_norm_round                 float64\n",
      "RT_user_norm_round            float64\n",
      "Metacritic_norm_round         float64\n",
      "Metacritic_user_norm_round    float64\n",
      "IMDB_norm_round               float64\n",
      "Metacritic_user_vote_count      int64\n",
      "IMDB_user_vote_count            int64\n",
      "Fandango_votes                  int64\n",
      "Fandango_Difference           float64\n",
      "dtype: object\n"
     ]
    },
    {
     "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>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>Metacritic_norm</th>\n",
       "      <th>Metacritic_user_nom</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron (2015)</th>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinderella (2015)</th>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ant-Man (2015)</th>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.20</td>\n",
       "      <td>4.05</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Do You Believe? (2015)</th>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.20</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hot Tub Time Machine 2 (2015)</th>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr. Holmes (2015)</th>\n",
       "      <td>7.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'71 (2015)</th>\n",
       "      <td>7.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.85</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.15</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two Days, One Night (2014)</th>\n",
       "      <td>8.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.85</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.40</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gett: The Trial of Viviane Amsalem (2015)</th>\n",
       "      <td>7.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kumiko, The Treasure Hunter (2015)</th>\n",
       "      <td>6.4</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.35</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.35</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Metacritic_User  IMDB  \\\n",
       "FILM                                                               \n",
       "Avengers: Age of Ultron (2015)                         7.1   7.8   \n",
       "Cinderella (2015)                                      7.5   7.1   \n",
       "Ant-Man (2015)                                         8.1   7.8   \n",
       "Do You Believe? (2015)                                 4.7   5.4   \n",
       "Hot Tub Time Machine 2 (2015)                          3.4   5.1   \n",
       "...                                                    ...   ...   \n",
       "Mr. Holmes (2015)                                      7.9   7.4   \n",
       "'71 (2015)                                             7.5   7.2   \n",
       "Two Days, One Night (2014)                             8.8   7.4   \n",
       "Gett: The Trial of Viviane Amsalem (2015)              7.3   7.8   \n",
       "Kumiko, The Treasure Hunter (2015)                     6.4   6.7   \n",
       "\n",
       "                                           Fandango_Stars  \\\n",
       "FILM                                                        \n",
       "Avengers: Age of Ultron (2015)                        5.0   \n",
       "Cinderella (2015)                                     5.0   \n",
       "Ant-Man (2015)                                        5.0   \n",
       "Do You Believe? (2015)                                5.0   \n",
       "Hot Tub Time Machine 2 (2015)                         3.5   \n",
       "...                                                   ...   \n",
       "Mr. Holmes (2015)                                     4.0   \n",
       "'71 (2015)                                            3.5   \n",
       "Two Days, One Night (2014)                            3.5   \n",
       "Gett: The Trial of Viviane Amsalem (2015)             3.5   \n",
       "Kumiko, The Treasure Hunter (2015)                    3.5   \n",
       "\n",
       "                                           Fandango_Ratingvalue  RT_norm  \\\n",
       "FILM                                                                       \n",
       "Avengers: Age of Ultron (2015)                              4.5     3.70   \n",
       "Cinderella (2015)                                           4.5     4.25   \n",
       "Ant-Man (2015)                                              4.5     4.00   \n",
       "Do You Believe? (2015)                                      4.5     0.90   \n",
       "Hot Tub Time Machine 2 (2015)                               3.0     0.70   \n",
       "...                                                         ...      ...   \n",
       "Mr. Holmes (2015)                                           4.0     4.35   \n",
       "'71 (2015)                                                  3.5     4.85   \n",
       "Two Days, One Night (2014)                                  3.5     4.85   \n",
       "Gett: The Trial of Viviane Amsalem (2015)                   3.5     5.00   \n",
       "Kumiko, The Treasure Hunter (2015)                          3.5     4.35   \n",
       "\n",
       "                                           RT_user_norm  Metacritic_norm  \\\n",
       "FILM                                                                       \n",
       "Avengers: Age of Ultron (2015)                     4.30             3.30   \n",
       "Cinderella (2015)                                  4.00             3.35   \n",
       "Ant-Man (2015)                                     4.50             3.20   \n",
       "Do You Believe? (2015)                             4.20             1.10   \n",
       "Hot Tub Time Machine 2 (2015)                      1.40             1.45   \n",
       "...                                                 ...              ...   \n",
       "Mr. Holmes (2015)                                  3.90             3.35   \n",
       "'71 (2015)                                         4.10             4.15   \n",
       "Two Days, One Night (2014)                         3.90             4.45   \n",
       "Gett: The Trial of Viviane Amsalem (2015)          4.05             4.50   \n",
       "Kumiko, The Treasure Hunter (2015)                 3.15             3.40   \n",
       "\n",
       "                                           Metacritic_user_nom  IMDB_norm  \\\n",
       "FILM                                                                        \n",
       "Avengers: Age of Ultron (2015)                            3.55       3.90   \n",
       "Cinderella (2015)                                         3.75       3.55   \n",
       "Ant-Man (2015)                                            4.05       3.90   \n",
       "Do You Believe? (2015)                                    2.35       2.70   \n",
       "Hot Tub Time Machine 2 (2015)                             1.70       2.55   \n",
       "...                                                        ...        ...   \n",
       "Mr. Holmes (2015)                                         3.95       3.70   \n",
       "'71 (2015)                                                3.75       3.60   \n",
       "Two Days, One Night (2014)                                4.40       3.70   \n",
       "Gett: The Trial of Viviane Amsalem (2015)                 3.65       3.90   \n",
       "Kumiko, The Treasure Hunter (2015)                        3.20       3.35   \n",
       "\n",
       "                                           RT_norm_round  RT_user_norm_round  \\\n",
       "FILM                                                                           \n",
       "Avengers: Age of Ultron (2015)                       3.5                 4.5   \n",
       "Cinderella (2015)                                    4.5                 4.0   \n",
       "Ant-Man (2015)                                       4.0                 4.5   \n",
       "Do You Believe? (2015)                               1.0                 4.0   \n",
       "Hot Tub Time Machine 2 (2015)                        0.5                 1.5   \n",
       "...                                                  ...                 ...   \n",
       "Mr. Holmes (2015)                                    4.5                 4.0   \n",
       "'71 (2015)                                           5.0                 4.0   \n",
       "Two Days, One Night (2014)                           5.0                 4.0   \n",
       "Gett: The Trial of Viviane Amsalem (2015)            5.0                 4.0   \n",
       "Kumiko, The Treasure Hunter (2015)                   4.5                 3.0   \n",
       "\n",
       "                                           Metacritic_norm_round  \\\n",
       "FILM                                                               \n",
       "Avengers: Age of Ultron (2015)                               3.5   \n",
       "Cinderella (2015)                                            3.5   \n",
       "Ant-Man (2015)                                               3.0   \n",
       "Do You Believe? (2015)                                       1.0   \n",
       "Hot Tub Time Machine 2 (2015)                                1.5   \n",
       "...                                                          ...   \n",
       "Mr. Holmes (2015)                                            3.5   \n",
       "'71 (2015)                                                   4.0   \n",
       "Two Days, One Night (2014)                                   4.5   \n",
       "Gett: The Trial of Viviane Amsalem (2015)                    4.5   \n",
       "Kumiko, The Treasure Hunter (2015)                           3.5   \n",
       "\n",
       "                                           Metacritic_user_norm_round  \\\n",
       "FILM                                                                    \n",
       "Avengers: Age of Ultron (2015)                                    3.5   \n",
       "Cinderella (2015)                                                 4.0   \n",
       "Ant-Man (2015)                                                    4.0   \n",
       "Do You Believe? (2015)                                            2.5   \n",
       "Hot Tub Time Machine 2 (2015)                                     1.5   \n",
       "...                                                               ...   \n",
       "Mr. Holmes (2015)                                                 4.0   \n",
       "'71 (2015)                                                        4.0   \n",
       "Two Days, One Night (2014)                                        4.5   \n",
       "Gett: The Trial of Viviane Amsalem (2015)                         3.5   \n",
       "Kumiko, The Treasure Hunter (2015)                                3.0   \n",
       "\n",
       "                                           IMDB_norm_round  \\\n",
       "FILM                                                         \n",
       "Avengers: Age of Ultron (2015)                         4.0   \n",
       "Cinderella (2015)                                      3.5   \n",
       "Ant-Man (2015)                                         4.0   \n",
       "Do You Believe? (2015)                                 2.5   \n",
       "Hot Tub Time Machine 2 (2015)                          2.5   \n",
       "...                                                    ...   \n",
       "Mr. Holmes (2015)                                      3.5   \n",
       "'71 (2015)                                             3.5   \n",
       "Two Days, One Night (2014)                             3.5   \n",
       "Gett: The Trial of Viviane Amsalem (2015)              4.0   \n",
       "Kumiko, The Treasure Hunter (2015)                     3.5   \n",
       "\n",
       "                                           Fandango_Difference  \n",
       "FILM                                                            \n",
       "Avengers: Age of Ultron (2015)                             0.5  \n",
       "Cinderella (2015)                                          0.5  \n",
       "Ant-Man (2015)                                             0.5  \n",
       "Do You Believe? (2015)                                     0.5  \n",
       "Hot Tub Time Machine 2 (2015)                              0.5  \n",
       "...                                                        ...  \n",
       "Mr. Holmes (2015)                                          0.0  \n",
       "'71 (2015)                                                 0.0  \n",
       "Two Days, One Night (2014)                                 0.0  \n",
       "Gett: The Trial of Viviane Amsalem (2015)                  0.0  \n",
       "Kumiko, The Treasure Hunter (2015)                         0.0  \n",
       "\n",
       "[146 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types = fandango_films.dtypes\n",
    "print(types)\n",
    "float_columns = types[types.values == 'float64'].index  # filtrer les nombres à virgules et recuperer le nom des colonnes\n",
    "float_columns\n",
    "float_df = fandango_films[float_columns]\n",
    "float_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fonction anonyme lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def carre(x):\n",
    "    res = x**2\n",
    "    return res\n",
    "carre(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x:x**2)(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exo_df.insert(1,\"Metacritic_User_2\" , value = exo_df[\"Metacritic_User\"]*2)\n",
    "exo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>Metacritic_norm</th>\n",
       "      <th>Metacritic_user_nom</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron (2015)</th>\n",
       "      <td>3.55</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.850</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.650</td>\n",
       "      <td>1.775</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinderella (2015)</th>\n",
       "      <td>3.75</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.675</td>\n",
       "      <td>1.875</td>\n",
       "      <td>1.775</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ant-Man (2015)</th>\n",
       "      <td>4.05</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.600</td>\n",
       "      <td>2.025</td>\n",
       "      <td>1.950</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Metacritic_User  IMDB  Fandango_Stars  \\\n",
       "FILM                                                                    \n",
       "Avengers: Age of Ultron (2015)             3.55  3.90             2.5   \n",
       "Cinderella (2015)                          3.75  3.55             2.5   \n",
       "Ant-Man (2015)                             4.05  3.90             2.5   \n",
       "\n",
       "                                Fandango_Ratingvalue  RT_norm  RT_user_norm  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                  2.25    1.850          2.15   \n",
       "Cinderella (2015)                               2.25    2.125          2.00   \n",
       "Ant-Man (2015)                                  2.25    2.000          2.25   \n",
       "\n",
       "                                Metacritic_norm  Metacritic_user_nom  \\\n",
       "FILM                                                                   \n",
       "Avengers: Age of Ultron (2015)            1.650                1.775   \n",
       "Cinderella (2015)                         1.675                1.875   \n",
       "Ant-Man (2015)                            1.600                2.025   \n",
       "\n",
       "                                IMDB_norm  RT_norm_round  RT_user_norm_round  \\\n",
       "FILM                                                                           \n",
       "Avengers: Age of Ultron (2015)      1.950           1.75                2.25   \n",
       "Cinderella (2015)                   1.775           2.25                2.00   \n",
       "Ant-Man (2015)                      1.950           2.00                2.25   \n",
       "\n",
       "                                Metacritic_norm_round  \\\n",
       "FILM                                                    \n",
       "Avengers: Age of Ultron (2015)                   1.75   \n",
       "Cinderella (2015)                                1.75   \n",
       "Ant-Man (2015)                                   1.50   \n",
       "\n",
       "                                Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                        1.75             2.00   \n",
       "Cinderella (2015)                                     2.00             1.75   \n",
       "Ant-Man (2015)                                        2.00             2.00   \n",
       "\n",
       "                                Fandango_Difference  \n",
       "FILM                                                 \n",
       "Avengers: Age of Ultron (2015)                 0.25  \n",
       "Cinderella (2015)                              0.25  \n",
       "Ant-Man (2015)                                 0.25  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exo_df = float_df.apply(lambda x: x/2)  # diviser une variable par 2\n",
    "exo_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>Metacritic_norm</th>\n",
       "      <th>Metacritic_user_nom</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "      <th>nouvelle_colonne</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron (2015)</th>\n",
       "      <td>3.55</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.850</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.650</td>\n",
       "      <td>1.775</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinderella (2015)</th>\n",
       "      <td>3.75</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.675</td>\n",
       "      <td>1.875</td>\n",
       "      <td>1.775</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ant-Man (2015)</th>\n",
       "      <td>4.05</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.600</td>\n",
       "      <td>2.025</td>\n",
       "      <td>1.950</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Metacritic_User  IMDB  Fandango_Stars  \\\n",
       "FILM                                                                    \n",
       "Avengers: Age of Ultron (2015)             3.55  3.90             2.5   \n",
       "Cinderella (2015)                          3.75  3.55             2.5   \n",
       "Ant-Man (2015)                             4.05  3.90             2.5   \n",
       "\n",
       "                                Fandango_Ratingvalue  RT_norm  RT_user_norm  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                  2.25    1.850          2.15   \n",
       "Cinderella (2015)                               2.25    2.125          2.00   \n",
       "Ant-Man (2015)                                  2.25    2.000          2.25   \n",
       "\n",
       "                                Metacritic_norm  Metacritic_user_nom  \\\n",
       "FILM                                                                   \n",
       "Avengers: Age of Ultron (2015)            1.650                1.775   \n",
       "Cinderella (2015)                         1.675                1.875   \n",
       "Ant-Man (2015)                            1.600                2.025   \n",
       "\n",
       "                                IMDB_norm  RT_norm_round  RT_user_norm_round  \\\n",
       "FILM                                                                           \n",
       "Avengers: Age of Ultron (2015)      1.950           1.75                2.25   \n",
       "Cinderella (2015)                   1.775           2.25                2.00   \n",
       "Ant-Man (2015)                      1.950           2.00                2.25   \n",
       "\n",
       "                                Metacritic_norm_round  \\\n",
       "FILM                                                    \n",
       "Avengers: Age of Ultron (2015)                   1.75   \n",
       "Cinderella (2015)                                1.75   \n",
       "Ant-Man (2015)                                   1.50   \n",
       "\n",
       "                                Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                        1.75             2.00   \n",
       "Cinderella (2015)                                     2.00             1.75   \n",
       "Ant-Man (2015)                                        2.00             2.00   \n",
       "\n",
       "                                Fandango_Difference  nouvelle_colonne  \n",
       "FILM                                                                   \n",
       "Avengers: Age of Ultron (2015)                 0.25             10.65  \n",
       "Cinderella (2015)                              0.25             11.25  \n",
       "Ant-Man (2015)                                 0.25             12.15  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>Metacritic_norm</th>\n",
       "      <th>Metacritic_user_nom</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron (2015)</th>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinderella (2015)</th>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ant-Man (2015)</th>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.20</td>\n",
       "      <td>4.05</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Metacritic_User  IMDB  Fandango_Stars  \\\n",
       "FILM                                                                    \n",
       "Avengers: Age of Ultron (2015)              7.1   7.8             5.0   \n",
       "Cinderella (2015)                           7.5   7.1             5.0   \n",
       "Ant-Man (2015)                              8.1   7.8             5.0   \n",
       "\n",
       "                                Fandango_Ratingvalue  RT_norm  RT_user_norm  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                   4.5     3.70           4.3   \n",
       "Cinderella (2015)                                4.5     4.25           4.0   \n",
       "Ant-Man (2015)                                   4.5     4.00           4.5   \n",
       "\n",
       "                                Metacritic_norm  Metacritic_user_nom  \\\n",
       "FILM                                                                   \n",
       "Avengers: Age of Ultron (2015)             3.30                 3.55   \n",
       "Cinderella (2015)                          3.35                 3.75   \n",
       "Ant-Man (2015)                             3.20                 4.05   \n",
       "\n",
       "                                IMDB_norm  RT_norm_round  RT_user_norm_round  \\\n",
       "FILM                                                                           \n",
       "Avengers: Age of Ultron (2015)       3.90            3.5                 4.5   \n",
       "Cinderella (2015)                    3.55            4.5                 4.0   \n",
       "Ant-Man (2015)                       3.90            4.0                 4.5   \n",
       "\n",
       "                                Metacritic_norm_round  \\\n",
       "FILM                                                    \n",
       "Avengers: Age of Ultron (2015)                    3.5   \n",
       "Cinderella (2015)                                 3.5   \n",
       "Ant-Man (2015)                                    3.0   \n",
       "\n",
       "                                Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                         3.5              4.0   \n",
       "Cinderella (2015)                                      4.0              3.5   \n",
       "Ant-Man (2015)                                         4.0              4.0   \n",
       "\n",
       "                                Fandango_Difference  \n",
       "FILM                                                 \n",
       "Avengers: Age of Ultron (2015)                  0.5  \n",
       "Cinderella (2015)                               0.5  \n",
       "Ant-Man (2015)                                  0.5  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Méthode Apply() sur les lignes d'un DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Utiliser la méthode apply() pour calculer la moyenne (méthode mean() de la librairie Numpy) des valeurs de chaque film pour 'RT_user_norm' et 'metacritic_user_nom' puis assigner le résultat à la variable rt_mt_means.\n",
    "- Afficher les 5 premières valeurs de rt_mt_means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FILM\n",
       "Avengers: Age of Ultron (2015)               3.925\n",
       "Cinderella (2015)                            3.875\n",
       "Ant-Man (2015)                               4.275\n",
       "Do You Believe? (2015)                       3.275\n",
       "Hot Tub Time Machine 2 (2015)                1.550\n",
       "                                             ...  \n",
       "Mr. Holmes (2015)                            3.925\n",
       "'71 (2015)                                   3.925\n",
       "Two Days, One Night (2014)                   4.150\n",
       "Gett: The Trial of Viviane Amsalem (2015)    3.850\n",
       "Kumiko, The Treasure Hunter (2015)           3.175\n",
       "Length: 146, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "rt_mt_user = float_df[['RT_user_norm', 'Metacritic_user_nom']]\n",
    "rt_mt_user\n",
    "rt_mt_means = rt_mt_user.apply(lambda x: np.mean(x), axis=1)\n",
    "rt_mt_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FILM\n",
      "Avengers: Age of Ultron (2015)    3.925\n",
      "Cinderella (2015)                 3.875\n",
      "Ant-Man (2015)                    4.275\n",
      "Do You Believe? (2015)            3.275\n",
      "Hot Tub Time Machine 2 (2015)     1.550\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(rt_mt_means[0:5])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}