From bdeb57cc1a020d22a2dc0907d81442367c479720 Mon Sep 17 00:00:00 2001 From: Miguel Moraes Date: Mon, 20 Nov 2023 17:47:31 +0000 Subject: [PATCH] Solved lab --- your-code/continuous.ipynb | 1231 +++++++++++++++++++++++++++++++++++- your-code/discrete.ipynb | 171 ++++- 2 files changed, 1351 insertions(+), 51 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..73e2920 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,9 +35,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.46173903 2.09430617 2.3178207 2.06440534 2.16138454 2.00509481\n", + " 2.41288175 2.85508329 2.18864533 2.66190279]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +82,266 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12.82035269 12.99825591 12.50906517 11.14212653 12.73161502 12.57833139\n", + " 12.92668172 10.56542171 10.88522326 14.39815439 13.84360718 14.58705226\n", + " 13.46866902 12.14976002 13.83904206 10.73274696 10.30229483 13.43824838\n", + " 12.96109374 11.87464194 12.4142435 13.19123405 14.67253472 14.42492434\n", + " 12.2198553 10.88590506 13.12325379 13.36342642 14.14982127 12.78067035\n", + " 13.41107021 12.10550144 14.31944981 10.44601862 12.73267312 12.16121039\n", + " 14.61697575 10.26448439 13.01943985 13.9596426 14.8230622 11.82078889\n", + " 14.30328434 12.44999399 12.80032542 10.86743403 11.23598245 10.98503883\n", + " 14.89099365 13.0186811 13.74258049 13.34810235 12.78072798 13.94002127\n", + " 10.1574134 14.35049558 10.55976334 13.6445365 12.42416071 11.79662311\n", + " 13.9902898 10.62540591 11.42059627 11.24804146 13.44278279 13.90072857\n", + " 13.98564093 12.32590753 14.94780025 12.75764163 10.13921514 14.30067841\n", + " 11.63201722 14.95526637 12.13276976 10.52739488 14.4780262 12.56969611\n", + " 12.73573754 13.43632781 12.08775742 10.19557671 11.37004174 14.94831426\n", + " 10.36929844 12.05476748 13.73224488 10.98314676 12.56785653 13.33833044\n", + " 13.84180164 10.97750425 14.04140089 12.9424386 14.47290094 13.45053626\n", + " 14.53094777 13.726007 13.63169978 11.79423415]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def random_numbers(bottom, ceiling, count):\n", + " x = uniform.rvs(size=count)\n", + " randoms = bottom + (ceiling - bottom) * x\n", + " return randoms\n", + "\n", + "bottom_number = 10\n", + "ceiling_number = 15\n", + "count_number = 100\n", + "\n", + "result = random_numbers(bottom_number, ceiling_number, count_number)\n", + "print(result)\n", + "\n", + "plt.hist(result, bins=10)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[55.4771893 43.08821022 39.59233336 32.10713251 40.2549366 36.99497321\n", + " 43.9777805 21.32888819 52.04913806 11.95051908 45.94120479 17.60671349\n", + " 35.25401316 10.62156811 58.98935844 48.87359734 32.3357338 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "bottom_number = 10\n", + "ceiling_number = 60\n", + "count_number = 1000\n", + "\n", + "result = random_numbers(bottom_number, ceiling_number, count_number)\n", + "print(result)\n", + "\n", + "plt.hist(result, bins=10)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.show()" ] }, { @@ -93,7 +357,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here: The second distribution looks more even due to the fact that the universe is bigger.\n" ] }, { @@ -114,11 +378,494 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 9.2944638 8.99507575 9.71581463 9.36637368 10.60931549 10.53627657\n", + " 9.7350292 9.99253877 9.85665569 10.34816045 10.10474124 8.51555242\n", + " 8.63752162 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def random_numbers2(mean, std_dev, count):\n", + " return np.random.normal(mean, std_dev, count)\n", + "\n", + "mean = 10\n", + "std_dev = 1\n", + "count = 1000\n", + "\n", + "result = random_numbers2(mean, std_dev, count)\n", + "print(result)\n", + "\n", + "plt.hist(result)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3.17454092e+01 -1.72313974e+01 8.53358333e+01 5.77080761e+01\n", + " 2.36558086e+01 1.68873310e+01 2.74091935e+01 -4.06748888e+01\n", + " -3.30559938e+01 1.81987020e+01 7.52156167e+01 -2.22464394e+01\n", + " 4.33088612e+01 6.61575586e+01 -3.94362374e+00 -1.17049374e+01\n", + " -2.21696081e+01 -1.36774564e+01 4.81884212e+01 -6.68424493e+00\n", + " -6.19848736e+01 -2.38216380e+01 -3.85983604e+01 1.21289784e+01\n", + " 6.23486482e+01 1.51380423e+01 -5.13139131e+01 3.90151496e+01\n", + " 3.57468462e+01 -3.74272238e+00 1.15314786e+01 2.29993886e+01\n", + " -2.49502172e+01 -1.93936792e+01 4.86284539e+00 3.39927332e+01\n", + " 1.06895694e+02 -9.80008933e-01 2.59439574e+01 -6.09735696e+01\n", + " 5.53722806e+00 -6.11117696e+01 -1.65763138e+01 2.87839398e+01\n", + " 1.86881277e+01 4.40557412e+01 6.32071694e+01 -1.38278131e+01\n", + " 2.56160986e+01 8.63931447e+01 4.31861455e+01 5.81436842e+00\n", + " -3.10134681e+01 3.73946106e+01 7.64407700e+01 -1.33216009e+01\n", + " 2.14951608e+01 8.91828869e+00 5.60153789e+01 5.43606762e+01\n", + " 4.81686623e+01 6.86619227e+00 2.49486477e+01 -8.14687261e+01\n", + " 2.13395049e+01 -3.66588007e+01 8.83016308e+01 2.33207343e+01\n", + " -4.75806677e+01 5.95902095e+01 5.26711700e+01 4.36317936e+01\n", + " -5.99636172e+01 2.22993090e+01 2.01392284e+01 -2.45756747e+01\n", + " -5.60260492e+01 -7.11232951e+00 1.37620976e+01 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dVQAAoMGoVeB59tlntXLlylDXAgAAUCdq9ZLW5cuX9e6772rTpk367ne/q6ioqKD5+fPnh6Q4AACAUKhV4Nm/f7+6desmSTp48GDQHDcwAwCASFOrwLNly5ZQ1wEAAFBnanUPDwAAQENSqys8/fr1u+VLV5s3b651QQAAAKFWq8Bz/f6d665cuaIvvvhCBw8erPalogAAAOFWq8CzYMGCGsdnzZql8+fP31NBAAAAoRbSe3h++MMf8j1aAAAg4oQ08Hz++edq0aJFKA8JAABwz2r1ktaoUaOC9o0xKi4u1p49e/Taa6+FpDAAAIBQqVXg8Xg8QftNmjTRAw88oNmzZysrKyskhQEAAIRKrQLPsmXLQl0HAABAnalV4LmusLBQhw8flsvl0kMPPaS/+7u/C1VdAAAAIVOrwFNaWqonn3xSW7du1X333SdjjMrLy9WvXz+tWrVKbdu2DXWdAAAAtVard2nl5ubK7/fr0KFD+stf/qKysjIdPHhQfr9fL774YqhrBAAAuCe1usLz6aefatOmTerSpYsz9tBDD+ntt9/mpmUAABBxanWFp6qqSlFRUdXGo6KiVFVVdc9FAQAAhFKtAk///v310ksv6cyZM87Y6dOn9eMf/1gDBgwIWXEAAAChUKvAs2jRIlVUVKhjx4769re/re985ztKTU1VRUWFfvGLX4S6RgAAgHtSq3t4kpOTtXfvXuXn5+sPf/iDjDF66KGHNHDgwFDXBwAAcM/u6grP5s2b9dBDD8nv90uSBg0apNzcXL344ovq0aOHHn74Ye3YsaNOCgUAAKituwo8Cxcu1IQJExQXF1dtzuPxaOLEiZo/f37IigMAAAiFuwo8//u//6shQ4bcdD4rK0uFhYX3XBQAAEAo3VXg+frrr2t8O/p1zZo105///Od7LgoAACCU7irw3H///Tpw4MBN5/fv36+kpKR7LgoAACCU7irw/OM//qP+7d/+TZcvX642d+nSJc2cOVPDhg0LWXEAAAChcFdvS3/11Ve1Zs0ade7cWTk5OXrggQfkcrl0+PBhvf3227p27ZpmzJhRV7UCAADUyl0FnsTERBUUFOj555/X9OnTZYyRJLlcLg0ePFiLFy9WYmJinRQKAABQW3f9wYMpKSnasGGDysrKdPToURlj1KlTJ7Vu3bou6gMAALhntfqkZUlq3bq1evToEcpaAAAA6kStvksLAACgISHwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6BB4AAGA9Ag8AALAegQcAAFiPwAMAAKxH4AEAANYj8AAAAOsReAAAgPXCGni2b9+u4cOHy+fzyeVy6ZNPPgmaN8Zo1qxZ8vl8io6OVt++fXXo0KGgNYFAQLm5uUpISFBMTIxGjBihU6dO1WMXAAAg0oU18Fy4cEFdu3bVokWLapx/8803NX/+fC1atEi7d++W1+vVoEGDVFFR4azJy8vT2rVrtWrVKu3cuVPnz5/XsGHDdO3atfpqAwAARLhm4fzh2dnZys7OrnHOGKOFCxdqxowZGjVqlCRpxYoVSkxM1MqVKzVx4kSVl5dr6dKl+uCDDzRw4EBJ0ocffqjk5GRt2rRJgwcPrrdeAABA5IrYe3iKiopUUlKirKwsZ8ztdqtPnz4qKCiQJBUWFurKlStBa3w+n9LS0pw1NQkEAvL7/UEbAACwV8QGnpKSEklSYmJi0HhiYqIzV1JSoubNm6t169Y3XVOTuXPnyuPxOFtycnKIqwcAAJEkYgPPdS6XK2jfGFNt7Ea3WzN9+nSVl5c728mTJ0NSKwAAiEwRG3i8Xq8kVbtSU1pa6lz18Xq9qqysVFlZ2U3X1MTtdisuLi5oAwAA9orYwJOamiqv16v8/HxnrLKyUtu2bVNmZqYkKSMjQ1FRUUFriouLdfDgQWcNAABAWN+ldf78eR09etTZLyoq0hdffKH4+Hh16NBBeXl5mjNnjjp16qROnTppzpw5atmypZ5++mlJksfj0fjx4zVlyhS1adNG8fHxmjp1qtLT0513bQEAAIQ18OzZs0f9+vVz9idPnixJGjt2rJYvX66XX35Zly5d0gsvvKCysjL17NlTGzduVGxsrPOYBQsWqFmzZho9erQuXbqkAQMGaPny5WratGm99wMAACKTyxhjwl1EuPn9fnk8HpWXl3M/D+pVx2nrw10CEDLH5w0NdwloZO7m93fE3sMDAAAQKgQeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6BB4AAGA9Ag8AALAegQcAAFiPwAMAAKxH4AEAANYj8AAAAOsReAAAgPUIPAAAwHoEHgAAYD0CDwAAsB6BBwAAWI/AAwAArEfgAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6zcJdABAqHaetD3cJAIAIxRUeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6BB4AAGA9Ag8AALAegQcAAFiPwAMAAKxH4AEAANYj8AAAAOsReAAAgPUIPAAAwHoEHgAAYD0CDwAAsB6BBwAAWI/AAwAArEfgAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgvYgOPLNmzZLL5QravF6vM2+M0axZs+Tz+RQdHa2+ffvq0KFDYawYAABEoogOPJL08MMPq7i42NkOHDjgzL355puaP3++Fi1apN27d8vr9WrQoEGqqKgIY8UAACDSNAt3AbfTrFmzoKs61xljtHDhQs2YMUOjRo2SJK1YsUKJiYlauXKlJk6ceNNjBgIBBQIBZ9/v94e+cAAAEDEi/grPkSNH5PP5lJqaqieffFLHjh2TJBUVFamkpERZWVnOWrfbrT59+qigoOCWx5w7d648Ho+zJScn12kPAAAgvCI68PTs2VPvv/++fve73+mXv/ylSkpKlJmZqbNnz6qkpESSlJiYGPSYxMREZ+5mpk+frvLycmc7efJknfUAAADCL6Jf0srOznb+nJ6ert69e+vb3/62VqxYoV69ekmSXC5X0GOMMdXGbuR2u+V2u0NfMAAAiEgRfYXnRjExMUpPT9eRI0ec+3puvJpTWlpa7aoPAABo3BpU4AkEAjp8+LCSkpKUmpoqr9er/Px8Z76yslLbtm1TZmZmGKsEAACRJqJf0po6daqGDx+uDh06qLS0VD/5yU/k9/s1duxYuVwu5eXlac6cOerUqZM6deqkOXPmqGXLlnr66afDXToAAIggER14Tp06paeeekrffPON2rZtq169emnXrl1KSUmRJL388su6dOmSXnjhBZWVlalnz57auHGjYmNjw1w5ADQ+HaetD3cJd+34vKHhLgH1xGWMMeEuItz8fr88Ho/Ky8sVFxcX7nJQSw3xH1sA4UXgadju5vd3g7qHBwAAoDYIPAAAwHoEHgAAYD0CDwAAsB6BBwAAWI/AAwAArEfgAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6BB4AAGA9Ag8AALAegQcAAFiPwAMAAKxH4AEAANYj8AAAAOsReAAAgPUIPAAAwHoEHgAAYD0CDwAAsB6BBwAAWI/AAwAArEfgAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACs1yzcBSAydZy2PtwlAAAQMlzhAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6BB4AAGA9vi0dANBodZy2Ptwl3LXj84aGu4QGiSs8AADAegQeAABgPQIPAACwHvfw1LGG+PowAAC24QoPAACwnjWBZ/HixUpNTVWLFi2UkZGhHTt2hLskAAAQIax4SWv16tXKy8vT4sWL9Q//8A/6j//4D2VnZ+vLL79Uhw4dwl0eAAAh0xBvlYiEt9JbcYVn/vz5Gj9+vJ599ll16dJFCxcuVHJyst55551wlwYAACJAg7/CU1lZqcLCQk2bNi1oPCsrSwUFBTU+JhAIKBAIOPvl5eWSJL/fH/L6qgIXQ35MAAAakrr4/fq3xzXG3HZtgw8833zzja5du6bExMSg8cTERJWUlNT4mLlz5+r111+vNp6cnFwnNQIA0Jh5Ftbt8SsqKuTxeG65psEHnutcLlfQvjGm2th106dP1+TJk539qqoq/eUvf1GbNm1u+phw8fv9Sk5O1smTJxUXFxfucupdY+6/MfcuNe7+G3PvUuPuvzH3Lt19/8YYVVRUyOfz3XZtgw88CQkJatq0abWrOaWlpdWu+lzndrvldruDxu677766KjEk4uLiGuVf/usac/+NuXepcfffmHuXGnf/jbl36e76v92Vnesa/E3LzZs3V0ZGhvLz84PG8/PzlZmZGaaqAABAJGnwV3gkafLkyXrmmWf0yCOPqHfv3nr33Xd14sQJPffcc+EuDQAARAArAs+YMWN09uxZzZ49W8XFxUpLS9OGDRuUkpIS7tLumdvt1syZM6u9BNdYNOb+G3PvUuPuvzH3LjXu/htz71Ld9u8yd/JeLgAAgAaswd/DAwAAcDsEHgAAYD0CDwAAsB6BBwAAWI/AE0F++tOfKjMzUy1btrzpByG6XK5q25IlS4LWHDhwQH369FF0dLTuv/9+zZ49+46+ZySc7qT3EydOaPjw4YqJiVFCQoJefPFFVVZWBq1piL3XpGPHjtWe5xu/L+5OzkdDtXjxYqWmpqpFixbKyMjQjh07wl1SyM2aNavac+z1ep15Y4xmzZoln8+n6Oho9e3bV4cOHQpjxfdm+/btGj58uHw+n1wulz755JO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MakeModelYearEngine DisplacementCylindersTransmissionDrivetrainVehicle ClassFuel TypeFuel Barrels/YearCity MPGHighway MPGCombined MPGCO2 Emission Grams/MileFuel Cost/Year
0AM GeneralDJ Po Vehicle 2WD19842.54.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular19.388824181717522.7647061950
1AM GeneralFJ8c Post Office19844.26.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
2AM GeneralPost Office DJ5 2WD19852.54.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular20.600625161716555.4375002100
3AM GeneralPost Office DJ8 2WD19854.26.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
4ASC IncorporatedGNX19873.86.0Automatic 4-spdRear-Wheel DriveMidsize CarsPremium20.600625142116555.4375002550
................................................
35947smartfortwo coupe20131.03.0Auto(AM5)Rear-Wheel DriveTwo SeatersPremium9.155833343836244.0000001100
35948smartfortwo coupe20141.03.0Auto(AM5)Rear-Wheel DriveTwo SeatersPremium9.155833343836243.0000001100
35949smartfortwo coupe20151.03.0Auto(AM5)Rear-Wheel DriveTwo SeatersPremium9.155833343836244.0000001100
35950smartfortwo coupe20160.93.0Auto(AM6)Rear-Wheel DriveTwo SeatersPremium9.155833343936246.0000001100
35951smartfortwo coupe20160.93.0Manual 5-spdRear-Wheel DriveTwo SeatersPremium9.417429323935255.0000001150
\n", + "

35952 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement \\\n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 \n", + "1 AM General FJ8c Post Office 1984 4.2 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 \n", + "3 AM General Post Office DJ8 2WD 1985 4.2 \n", + "4 ASC Incorporated GNX 1987 3.8 \n", + "... ... ... ... ... \n", + "35947 smart fortwo coupe 2013 1.0 \n", + "35948 smart fortwo coupe 2014 1.0 \n", + "35949 smart fortwo coupe 2015 1.0 \n", + "35950 smart fortwo coupe 2016 0.9 \n", + "35951 smart fortwo coupe 2016 0.9 \n", + "\n", + " Cylinders Transmission Drivetrain \\\n", + "0 4.0 Automatic 3-spd 2-Wheel Drive \n", + "1 6.0 Automatic 3-spd 2-Wheel Drive \n", + "2 4.0 Automatic 3-spd Rear-Wheel Drive \n", + "3 6.0 Automatic 3-spd Rear-Wheel Drive \n", + "4 6.0 Automatic 4-spd Rear-Wheel Drive \n", + "... ... ... ... \n", + "35947 3.0 Auto(AM5) Rear-Wheel Drive \n", + "35948 3.0 Auto(AM5) Rear-Wheel Drive \n", + "35949 3.0 Auto(AM5) Rear-Wheel Drive \n", + "35950 3.0 Auto(AM6) Rear-Wheel Drive \n", + "35951 3.0 Manual 5-spd Rear-Wheel Drive \n", + "\n", + " Vehicle Class Fuel Type Fuel Barrels/Year City MPG \\\n", + "0 Special Purpose Vehicle 2WD Regular 19.388824 18 \n", + "1 Special Purpose Vehicle 2WD Regular 25.354615 13 \n", + "2 Special Purpose Vehicle 2WD Regular 20.600625 16 \n", + "3 Special Purpose Vehicle 2WD Regular 25.354615 13 \n", + "4 Midsize Cars Premium 20.600625 14 \n", + "... ... ... ... ... \n", + "35947 Two Seaters Premium 9.155833 34 \n", + "35948 Two Seaters Premium 9.155833 34 \n", + "35949 Two Seaters Premium 9.155833 34 \n", + "35950 Two Seaters Premium 9.155833 34 \n", + "35951 Two Seaters Premium 9.417429 32 \n", + "\n", + " Highway MPG Combined MPG CO2 Emission Grams/Mile Fuel Cost/Year \n", + "0 17 17 522.764706 1950 \n", + "1 13 13 683.615385 2550 \n", + "2 17 16 555.437500 2100 \n", + "3 13 13 683.615385 2550 \n", + "4 21 16 555.437500 2550 \n", + "... ... ... ... ... \n", + "35947 38 36 244.000000 1100 \n", + "35948 38 36 243.000000 1100 \n", + "35949 38 36 244.000000 1100 \n", + "35950 39 36 246.000000 1100 \n", + "35951 39 35 255.000000 1150 \n", + "\n", + "[35952 rows x 15 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "vehicles = pd.read_csv('vehicles.csv')\n", + "vehicles" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jvdZHRkYqODjYrDmdnJwc8zopu92uuLi4n3SMAADAfzWJ0FRTU6NbbrlFdXV1euKJJ85abxiGbDabufzDf/4pNaeaMWOG3G63+SopKTlrbwAAoGny+9BUU1Oj4cOHa8+ePcrPz/f6rdHhcKi6uloVFRVe25SXl5szRw6HQwcPHqy330OHDnnVnDqjVFFRoZqamnozUD8UEhKiiIgIrxcAAGie/Do0fR+YvvjiC7377ruKioryWp+UlKSgoCDl5+ebY6WlpSoqKlKPHj0kSSkpKXK73dqyZYtZs3nzZrndbq+aoqIilZaWmjWrV69WSEiIkpKSzuchAgCAJiLQl29eVVWlL7/80lzes2ePCgsL1apVKzmdTv3mN7/R9u3b9dZbb6m2ttacDWrVqpWCg4Nlt9s1evRoZWdnKyoqSq1atdLUqVOVmJio/v37S5I6deqkgQMHasyYMVqyZIkkaezYsUpPT1fHjh0lSampqercubNcLpfmzZunb7/9VlOnTtWYMWOYPQIAAJJ8HJq2bdumvn37mstTpkyRJI0cOVIzZ87UG2+8IUm69tprvbZbs2aN+vTpI0lauHChAgMDNXz4cB07dkz9+vXT0qVLFRAQYNYvX75ckyZNMu+yy8jI8Ho2VEBAgFauXKnx48erZ8+eCg0NVWZmph599NHzcdgAAKAJ8pvnNDUHPKcJvsBzmgDgp2mWz2kCAADwFUITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgQYNC0549exq7DwAAAL/WoNB0+eWXq2/fvnrxxRd1/Pjxxu4JAADA7zQoNH388cfq0qWLsrOz5XA4NG7cOG3ZsqWxewMAAPAbDQpNCQkJWrBggfbv36/nnntOZWVl6tWrl6666iotWLBAhw4dauw+AQAAfOonXQgeGBiom266Sf/4xz80Z84cffXVV5o6daratGmjO+64Q6WlpY3VJwAAgE/9pNC0bds2jR8/XpdeeqkWLFigqVOn6quvvtJ7772n/fv3a+jQoY3VJwAAgE8FNmSjBQsW6LnnntPu3bt144036oUXXtCNN96oiy46mcE6dOigJUuW6Morr2zUZgEAAHylQTNNixcvVmZmpoqLi/Xaa68pPT3dDEzfa9u2rZ599tkf3c+6des0ZMgQOZ1O2Ww2vfbaa17rDcPQzJkz5XQ6FRoaqj59+mjnzp1eNR6PRxMnTlR0dLTCwsKUkZGhffv2edVUVFTI5XLJbrfLbrfL5XLp8OHDXjXFxcUaMmSIwsLCFB0drUmTJqm6uvrcTgwAAGi2GhSavvjiC82YMUMOh+OMNcHBwRo5cuSP7ufo0aO65pprtGjRotOunzt3rhYsWKBFixZp69atcjgcGjBggI4cOWLWZGVlacWKFcrNzdWGDRtUVVWl9PR01dbWmjWZmZkqLCxUXl6e8vLyVFhYKJfLZa6vra3V4MGDdfToUW3YsEG5ubl65ZVXlJ2dbfWUAACAZs5mGIZxrhs999xzuvjii/Xb3/7Wa/yf//ynvvvuu7OGpdM2YrNpxYoVGjZsmKSTs0xOp1NZWVm67777JJ2cVYqNjdWcOXM0btw4ud1utW7dWsuWLdOIESMkSQcOHFBcXJxWrVqltLQ07dq1S507d9amTZuUnJwsSdq0aZNSUlL02WefqWPHjnr77beVnp6ukpISOZ1OSVJubq5GjRql8vJyRUREnLZnj8cjj8djLldWViouLk5ut/uM2wCNrf30lb5uoUG+nj3Y1y0AgKST3992u/2s398NmmmaPXu2oqOj643HxMRo1qxZDdllPXv27FFZWZlSU1PNsZCQEPXu3VsbN26UJBUUFKimpsarxul0KiEhwaz58MMPZbfbzcAkSd27d5fdbveqSUhIMAOTJKWlpcnj8aigoOCMPebk5Jg/+dntdsXFxTXKsQMAAP/ToNC0d+9edejQod54u3btVFxc/JObkqSysjJJUmxsrNd4bGysua6srEzBwcGKjIz80ZqYmJh6+4+JifGqOfV9IiMjFRwcbNaczowZM+R2u81XSUnJOR4lAABoKhp091xMTIw++eQTtW/f3mv8448/VlRUVGP0ZbLZbF7LhmHUGzvVqTWnq29IzalCQkIUEhLyo70AAIDmoUEzTbfccosmTZqkNWvWqLa2VrW1tXrvvfc0efJk3XLLLY3S2PcXmZ8601NeXm7OCjkcDlVXV6uiouJHaw4ePFhv/4cOHfKqOfV9KioqVFNTU28GCgAA/Dw1KDQ98sgjSk5OVr9+/RQaGqrQ0FClpqbqhhtuaLRrmjp06CCHw6H8/HxzrLq6WmvXrlWPHj0kSUlJSQoKCvKqKS0tVVFRkVmTkpIit9vt9bfxNm/eLLfb7VVTVFTk9QTz1atXKyQkRElJSY1yPAAAoGlr0M9zwcHBevnll/XnP/9ZH3/8sUJDQ5WYmKh27dqd036qqqr05Zdfmst79uxRYWGhWrVqpbZt2yorK0uzZs1SfHy84uPjNWvWLLVs2VKZmZmSJLvdrtGjRys7O1tRUVFq1aqVpk6dqsTERPXv31+S1KlTJw0cOFBjxozRkiVLJEljx45Venq6OnbsKElKTU1V586d5XK5NG/ePH377beaOnWqxowZw11wAABAUgND0/euuOIKXXHFFQ3eftu2berbt6+5PGXKFEnSyJEjtXTpUk2bNk3Hjh3T+PHjVVFRoeTkZK1evVrh4eHmNgsXLlRgYKCGDx+uY8eOqV+/flq6dKkCAgLMmuXLl2vSpEnmXXYZGRlez4YKCAjQypUrNX78ePXs2VOhoaHKzMzUo48+2uBjAwAAzUuDntNUW1urpUuX6t///rfKy8tVV1fntf69995rtAabEqvPeQAaE89pAoCfxur3d4NmmiZPnqylS5dq8ODBSkhIOOvdbAAAAE1dg0JTbm6u/vGPf+jGG29s7H4AAAD8UoPungsODtbll1/e2L0AAAD4rQaFpuzsbP31r39VAy6HAgAAaJIa9PPchg0btGbNGr399tu66qqrFBQU5LX+1VdfbZTmAAAA/EWDQtMll1yim266qbF7AQAA8FsNCk3PPfdcY/cBAADg1xp0TZMknThxQu+++66WLFmiI0eOSJIOHDigqqqqRmsOAADAXzRopmnv3r0aOHCgiouL5fF4NGDAAIWHh2vu3Lk6fvy4nnzyycbuEwAAwKcaNNM0efJkdevWTRUVFQoNDTXHb7rpJv373/9utOYAAAD8RYPvnvvggw8UHBzsNd6uXTvt37+/URoDAADwJw2aaaqrq1NtbW298X379nn9MV0AAIDmokGhacCAAXrsscfMZZvNpqqqKj300EP8aRUAANAs2YwGPNb7wIED6tu3rwICAvTFF1+oW7du+uKLLxQdHa1169YpJibmfPTq96z+lWSgMbWfvtLXLfxsfD17sK9bAHAeWP3+btA1TU6nU4WFhfr73/+u7du3q66uTqNHj9Ztt93mdWE4AABAc9Gg0CRJoaGhuvPOO3XnnXc2Zj8AAAB+qUGh6YUXXvjR9XfccUeDmgEAAPBXDQpNkydP9lquqanRd999p+DgYLVs2ZLQBAAAmp0G3T1XUVHh9aqqqtLu3bvVq1cv/f3vf2/sHgEAAHyuwX977lTx8fGaPXt2vVkoAACA5qDRQpMkBQQE6MCBA425SwAAAL/QoGua3njjDa9lwzBUWlqqRYsWqWfPno3SGAAAgD9pUGgaNmyY17LNZlPr1q11ww03aP78+Y3RFwAAgF9pUGiqq6tr7D4AAAD8WqNe0wQAANBcNWimacqUKZZrFyxY0JC3AAAA8CsNCk0fffSRtm/frhMnTqhjx46SpM8//1wBAQHq2rWrWWez2RqnSwAAAB9rUGgaMmSIwsPD9fzzzysyMlLSyQde/u53v9OvfvUrZWdnN2qTAAAAvtaga5rmz5+vnJwcMzBJUmRkpB555BHungMAAM1Sg0JTZWWlDh48WG+8vLxcR44c+clNAQAA+JsGhaabbrpJv/vd7/Svf/1L+/bt0759+/Svf/1Lo0eP1s0339xozZ04cUJ//OMf1aFDB4WGhuqyyy7Tww8/7PXIA8MwNHPmTDmdToWGhqpPnz7auXOn1348Ho8mTpyo6OhohYWFKSMjQ/v27fOqqaiokMvlkt1ul91ul8vl0uHDhxvtWAAAQNPWoND05JNPavDgwbr99tvVrl07tWvXTrfddpsGDRqkJ554otGamzNnjp588kktWrRIu3bt0ty5czVv3jz97W9/M2vmzp2rBQsWaNGiRdq6dascDocGDBjgNeOVlZWlFStWKDc3Vxs2bFBVVZXS09NVW1tr1mRmZqqwsFB5eXnKy8tTYWGhXC5Xox0LAABo2myGYRgN3fjo0aP66quvZBiGLr/8coWFhTVmb0pPT1dsbKyeffZZc+zXv/61WrZsqWXLlskwDDmdTmVlZem+++6TdHJWKTY2VnPmzNG4cePkdrvVunVrLVu2TCNGjJAkHThwQHFxcVq1apXS0tK0a9cude7cWZs2bVJycrIkadOmTUpJSdFnn31m3iF4Ko/HI4/HYy5XVlYqLi5ObrdbERERjXougDNpP32lr1v42fh69mBftwDgPKisrJTdbj/r9/dPerhlaWmpSktLdcUVVygsLEw/IX+dVq9evfTvf/9bn3/+uSTp448/1oYNG3TjjTdKkvbs2aOysjKlpqaa24SEhKh3797auHGjJKmgoEA1NTVeNU6nUwkJCWbNhx9+KLvdbgYmSerevbvsdrtZczo5OTnmz3l2u11xcXGNd/AAAMCvNCg0/fe//1W/fv10xRVX6MYbb1Rpaakk6fe//32jPm7gvvvu06233qorr7xSQUFB6tKli7KysnTrrbdKksrKyiRJsbGxXtvFxsaa68rKyhQcHOx1p9/pamJiYuq9f0xMjFlzOjNmzJDb7TZfJSUlDT9YAADg1xoUmu655x4FBQWpuLhYLVu2NMdHjBihvLy8Rmvu5Zdf1osvvqiXXnpJ27dv1/PPP69HH31Uzz//vFfdqQ/RNAzjrA/WPLXmdPVn209ISIgiIiK8XgAAoHlq0MMtV69erXfeeUdt2rTxGo+Pj9fevXsbpTFJuvfeezV9+nTdcsstkqTExETt3btXOTk5GjlypBwOh6STM0WXXnqpuV15ebk5++RwOFRdXa2Kigqv2aby8nL16NHDrDndIxQOHTpUbxYLAAD8PDVopuno0aNeM0zf++abbxQSEvKTm/red999p4su8m4xICDAfORAhw4d5HA4lJ+fb66vrq7W2rVrzUCUlJSkoKAgr5rS0lIVFRWZNSkpKXK73dqyZYtZs3nzZrndbrMGAAD8vDVopun666/XCy+8oD//+c+STv60VVdXp3nz5qlv376N1tyQIUP0l7/8RW3bttVVV12ljz76SAsWLNCdd95pvm9WVpZmzZql+Ph4xcfHa9asWWrZsqUyMzMlSXa7XaNHj1Z2draioqLUqlUrTZ06VYmJierfv78kqVOnTho4cKDGjBmjJUuWSJLGjh2r9PT0M945BwAAfl4aFJrmzZunPn36aNu2baqurta0adO0c+dOffvtt/rggw8arbm//e1veuCBBzR+/HiVl5fL6XRq3LhxevDBB82aadOm6dixYxo/frwqKiqUnJys1atXKzw83KxZuHChAgMDNXz4cB07dkz9+vXT0qVLFRAQYNYsX75ckyZNMu+yy8jI0KJFixrtWAAAQNPW4Oc0lZWVafHixSooKFBdXZ26du2qCRMmeF1b9HNj9TkPQGPiOU0XDs9pAponq9/f5zzT9P0zj5YsWaI//elPP6lJAACApuKcLwQPCgpSUVHRWW/pBwAAaE4adPfcHXfc4fWnTQAAAJq7Bl0IXl1drWeeeUb5+fnq1q1bvb85t2DBgkZpDgAAwF+cU2j6z3/+o/bt26uoqEhdu3aVJPPvwn2Pn+0AAEBzdE6hKT4+XqWlpVqzZo2kk3825fHHH+ep2QAAoNk7p2uaTn06wdtvv62jR482akMAAAD+qEEXgn+vgY94AgAAaHLOKTTZbLZ61yxxDRMAAPg5OKdrmgzD0KhRo8w/ynv8+HHddddd9e6ee/XVVxuvQwAAAD9wTqFp5MiRXsu33357ozYDAADgr84pND333HPnqw8AAAC/9pMuBAcAAPi5IDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABef0yAGguWs/faWvWwAA+ClmmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwwO9D0/79+3X77bcrKipKLVu21LXXXquCggJzvWEYmjlzppxOp0JDQ9WnTx/t3LnTax8ej0cTJ05UdHS0wsLClJGRoX379nnVVFRUyOVyyW63y263y+Vy6fDhwxfiEAEAQBPg16GpoqJCPXv2VFBQkN5++219+umnmj9/vi655BKzZu7cuVqwYIEWLVqkrVu3yuFwaMCAATpy5IhZk5WVpRUrVig3N1cbNmxQVVWV0tPTVVtba9ZkZmaqsLBQeXl5ysvLU2FhoVwu14U8XAAA4MdshmEYvm7iTKZPn64PPvhA69evP+16wzDkdDqVlZWl++67T9LJWaXY2FjNmTNH48aNk9vtVuvWrbVs2TKNGDFCknTgwAHFxcVp1apVSktL065du9S5c2dt2rRJycnJkqRNmzYpJSVFn332mTp27Gip38rKStntdrndbkVERDTCGcCF1n76Sl+3AD/29ezBvm4BwHlg9fvbr2ea3njjDXXr1k2//e1vFRMToy5duujpp5821+/Zs0dlZWVKTU01x0JCQtS7d29t3LhRklRQUKCamhqvGqfTqYSEBLPmww8/lN1uNwOTJHXv3l12u92sOR2Px6PKykqvFwAAaJ78OjT95z//0eLFixUfH6933nlHd911lyZNmqQXXnhBklRWViZJio2N9douNjbWXFdWVqbg4GBFRkb+aE1MTEy994+JiTFrTicnJ8e8BsputysuLq7hBwsAAPyaX4emuro6de3aVbNmzVKXLl00btw4jRkzRosXL/aqs9lsXsuGYdQbO9WpNaerP9t+ZsyYIbfbbb5KSkqsHBYAAGiC/Do0XXrppercubPXWKdOnVRcXCxJcjgcklRvNqi8vNycfXI4HKqurlZFRcWP1hw8eLDe+x86dKjeLNYPhYSEKCIiwusFAACaJ78OTT179tTu3bu9xj7//HO1a9dOktShQwc5HA7l5+eb66urq7V27Vr16NFDkpSUlKSgoCCvmtLSUhUVFZk1KSkpcrvd2rJli1mzefNmud1uswYAAPy8Bfq6gR9zzz33qEePHpo1a5aGDx+uLVu26KmnntJTTz0l6eRPallZWZo1a5bi4+MVHx+vWbNmqWXLlsrMzJQk2e12jR49WtnZ2YqKilKrVq00depUJSYmqn///pJOzl4NHDhQY8aM0ZIlSyRJY8eOVXp6uuU75wAAQPPm16Hpuuuu04oVKzRjxgw9/PDD6tChgx577DHddtttZs20adN07NgxjR8/XhUVFUpOTtbq1asVHh5u1ixcuFCBgYEaPny4jh07pn79+mnp0qUKCAgwa5YvX65JkyaZd9llZGRo0aJFF+5gAQCAX/Pr5zQ1NTynqenjOU34MTynCWiemsVzmgAAAPwFoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsaFKhKScnRzabTVlZWeaYYRiaOXOmnE6nQkND1adPH+3cudNrO4/Ho4kTJyo6OlphYWHKyMjQvn37vGoqKirkcrlkt9tlt9vlcrl0+PDhC3BUAACgKWgyoWnr1q166qmndPXVV3uNz507VwsWLNCiRYu0detWORwODRgwQEeOHDFrsrKytGLFCuXm5mrDhg2qqqpSenq6amtrzZrMzEwVFhYqLy9PeXl5KiwslMvlumDHBwAA/FuTCE1VVVW67bbb9PTTTysyMtIcNwxDjz32mO6//37dfPPNSkhI0PPPP6/vvvtOL730kiTJ7Xbr2Wef1fz589W/f3916dJFL774onbs2KF3331XkrRr1y7l5eXpmWeeUUpKilJSUvT000/rrbfe0u7du31yzAAAwL80idA0YcIEDR48WP379/ca37Nnj8rKypSammqOhYSEqHfv3tq4caMkqaCgQDU1NV41TqdTCQkJZs2HH34ou92u5ORks6Z79+6y2+1mzel4PB5VVlZ6vQAAQPMU6OsGziY3N1fbt2/X1q1b660rKyuTJMXGxnqNx8bGau/evWZNcHCw1wzV9zXfb19WVqaYmJh6+4+JiTFrTicnJ0d/+tOfzu2AAABAk+TXM00lJSWaPHmyXnzxRbVo0eKMdTabzWvZMIx6Y6c6teZ09Wfbz4wZM+R2u81XSUnJj74nAABouvw6NBUUFKi8vFxJSUkKDAxUYGCg1q5dq8cff1yBgYHmDNOps0Hl5eXmOofDoerqalVUVPxozcGDB+u9/6FDh+rNYv1QSEiIIiIivF4AAKB58uvQ1K9fP+3YsUOFhYXmq1u3brrttttUWFioyy67TA6HQ/n5+eY21dXVWrt2rXr06CFJSkpKUlBQkFdNaWmpioqKzJqUlBS53W5t2bLFrNm8ebPcbrdZAwAAft78+pqm8PBwJSQkeI2FhYUpKirKHM/KytKsWbMUHx+v+Ph4zZo1Sy1btlRmZqYkyW63a/To0crOzlZUVJRatWqlqVOnKjEx0bywvFOnTho4cKDGjBmjJUuWSJLGjh2r9PR0dezY8QIeMQAA8Fd+HZqsmDZtmo4dO6bx48eroqJCycnJWr16tcLDw82ahQsXKjAwUMOHD9exY8fUr18/LV26VAEBAWbN8uXLNWnSJPMuu4yMDC1atOiCHw8AAPBPNsMwDF830VxUVlbKbrfL7XZzfVMT1X76Sl+3AD/29ezBvm4BwHlg9fvbr69pAgAA8BeEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGCBX4emnJwcXXfddQoPD1dMTIyGDRum3bt3e9UYhqGZM2fK6XQqNDRUffr00c6dO71qPB6PJk6cqOjoaIWFhSkjI0P79u3zqqmoqJDL5ZLdbpfdbpfL5dLhw4fP9yECAIAmwmYYhuHrJs5k4MCBuuWWW3TdddfpxIkTuv/++7Vjxw59+umnCgsLkyTNmTNHf/nLX7R06VJdccUVeuSRR7Ru3Trt3r1b4eHhkqQ//OEPevPNN7V06VJFRUUpOztb3377rQoKChQQECBJGjRokPbt26ennnpKkjR27Fi1b99eb775puV+KysrZbfb5Xa7FRER0chnAxdC++krfd0C0Ki+nj3Y1y0Afs/q97dfh6ZTHTp0SDExMVq7dq2uv/56GYYhp9OprKws3XfffZJOzirFxsZqzpw5GjdunNxut1q3bq1ly5ZpxIgRkqQDBw4oLi5Oq1atUlpamnbt2qXOnTtr06ZNSk5OliRt2rRJKSkp+uyzz9SxY8fT9uPxeOTxeMzlyspKxcXFEZqaMEITmhtCE3B2VkOTX/88dyq32y1JatWqlSRpz549KisrU2pqqlkTEhKi3r17a+PGjZKkgoIC1dTUeNU4nU4lJCSYNR9++KHsdrsZmCSpe/fustvtZs3p5OTkmD/n2e12xcXFNd7BAgAAv9JkQpNhGJoyZYp69eqlhIQESVJZWZkkKTY21qs2NjbWXFdWVqbg4GBFRkb+aE1MTEy994yJiTFrTmfGjBlyu93mq6SkpOEHCAAA/Fqgrxuw6u6779Ynn3yiDRs21Ftns9m8lg3DqDd2qlNrTld/tv2EhIQoJCTkbK0DAIBmoEnMNE2cOFFvvPGG1qxZozZt2pjjDodDkurNBpWXl5uzTw6HQ9XV1aqoqPjRmoMHD9Z730OHDtWbxQIAAD9Pfh2aDMPQ3XffrVdffVXvvfeeOnTo4LW+Q4cOcjgcys/PN8eqq6u1du1a9ejRQ5KUlJSkoKAgr5rS0lIVFRWZNSkpKXK73dqyZYtZs3nzZrndbrMGAAD8vPn1z3MTJkzQSy+9pNdff13h4eHmjJLdbldoaKhsNpuysrI0a9YsxcfHKz4+XrNmzVLLli2VmZlp1o4ePVrZ2dmKiopSq1atNHXqVCUmJqp///6SpE6dOmngwIEaM2aMlixZIunkIwfS09PPeOccAAD4efHr0LR48WJJUp8+fbzGn3vuOY0aNUqSNG3aNB07dkzjx49XRUWFkpOTtXr1avMZTZK0cOFCBQYGavjw4Tp27Jj69eunpUuXms9okqTly5dr0qRJ5l12GRkZWrRo0fk9QAAA0GQ0qec0+Tsebtn08ZwmNDc8pwk4u2b5nCYAAABfITQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFgb5uAABw/rSfvtLXLZyzr2cP9nULwGkx0wQAAGABoQkAAMACQhMAAIAFXNOE86YpXksBAMCZMNMEAABgAaEJAADAAkITAACABYSmUzzxxBPq0KGDWrRooaSkJK1fv97XLQEAAD9AaPqBl19+WVlZWbr//vv10Ucf6Ve/+pUGDRqk4uJiX7cGAAB8zGYYhuHrJvxFcnKyunbtqsWLF5tjnTp10rBhw5STk3PW7SsrK2W32+V2uxUREdGovXEnGoCfC54IjgvN6vc3jxz4P9XV1SooKND06dO9xlNTU7Vx48bTbuPxeOTxeMxlt9st6eTJb2x1nu8afZ8A4I/a3vNPX7fws1D0pzRft+A3vv/ePts8EqHp/3zzzTeqra1VbGys13hsbKzKyspOu01OTo7+9Kc/1RuPi4s7Lz0CANBY7I/5ugP/c+TIEdnt9jOuJzSdwmazeS0bhlFv7HszZszQlClTzOW6ujp9++23ioqKOuM2DVFZWam4uDiVlJQ0+s9+ODvOv+9w7n2L8+9bnP8LxzAMHTlyRE6n80frCE3/Jzo6WgEBAfVmlcrLy+vNPn0vJCREISEhXmOXXHLJ+WpRERER/A/Hhzj/vsO59y3Ov29x/i+MH5th+h53z/2f4OBgJSUlKT8/32s8Pz9fPXr08FFXAADAXzDT9ANTpkyRy+VSt27dlJKSoqeeekrFxcW66667fN0aAADwMULTD4wYMUL//e9/9fDDD6u0tFQJCQlatWqV2rVr59O+QkJC9NBDD9X7KRAXBuffdzj3vsX59y3Ov//hOU0AAAAWcE0TAACABYQmAAAACwhNAAAAFhCaAAAALCA0+bknnnhCHTp0UIsWLZSUlKT169f7uqVmad26dRoyZIicTqdsNptee+01r/WGYWjmzJlyOp0KDQ1Vnz59tHPnTt802wzl5OTouuuuU3h4uGJiYjRs2DDt3r3bq4bP4PxYvHixrr76avMBiikpKXr77bfN9Zz3CysnJ0c2m01ZWVnmGJ+B/yA0+bGXX35ZWVlZuv/++/XRRx/pV7/6lQYNGqTi4mJft9bsHD16VNdcc40WLVp02vVz587VggULtGjRIm3dulUOh0MDBgzQkSNHLnCnzdPatWs1YcIEbdq0Sfn5+Tpx4oRSU1N19OhRs4bP4Pxo06aNZs+erW3btmnbtm264YYbNHToUPNLmfN+4WzdulVPPfWUrr76aq9xPgM/YsBv/fKXvzTuuusur7Err7zSmD59uo86+nmQZKxYscJcrqurMxwOhzF79mxz7Pjx44bdbjeefPJJH3TY/JWXlxuSjLVr1xqGwWdwoUVGRhrPPPMM5/0COnLkiBEfH2/k5+cbvXv3NiZPnmwYBv/u+xtmmvxUdXW1CgoKlJqa6jWempqqjRs3+qirn6c9e/aorKzM67MICQlR7969+SzOE7fbLUlq1aqVJD6DC6W2tla5ubk6evSoUlJSOO8X0IQJEzR48GD179/fa5zPwL/wRHA/9c0336i2trbeHwuOjY2t90eFcX59f75P91ns3bvXFy01a4ZhaMqUKerVq5cSEhIk8Rmcbzt27FBKSoqOHz+uiy++WCtWrFDnzp3NL2XO+/mVm5ur7du3a+vWrfXW8e++fyE0+Tmbzea1bBhGvTFcGHwWF8bdd9+tTz75RBs2bKi3js/g/OjYsaMKCwt1+PBhvfLKKxo5cqTWrl1rrue8nz8lJSWaPHmyVq9erRYtWpyxjs/AP/DznJ+Kjo5WQEBAvVml8vLyev/FgfPL4XBIEp/FBTBx4kS98cYbWrNmjdq0aWOO8xmcX8HBwbr88svVrVs35eTk6JprrtFf//pXzvsFUFBQoPLyciUlJSkwMFCBgYFau3atHn/8cQUGBprnmc/APxCa/FRwcLCSkpKUn5/vNZ6fn68ePXr4qKufpw4dOsjhcHh9FtXV1Vq7di2fRSMxDEN33323Xn31Vb333nvq0KGD13o+gwvLMAx5PB7O+wXQr18/7dixQ4WFhearW7duuu2221RYWKjLLruMz8CP8POcH5syZYpcLpe6deumlJQUPfXUUyouLtZdd93l69aanaqqKn355Zfm8p49e1RYWKhWrVqpbdu2ysrK0qxZsxQfH6/4+HjNmjVLLVu2VGZmpg+7bj4mTJigl156Sa+//rrCw8PN/6q22+0KDQ01n1vDZ9D4/ud//keDBg1SXFycjhw5otzcXL3//vvKy8vjvF8A4eHh5rV73wsLC1NUVJQ5zmfgR3x34x6s+N///V+jXbt2RnBwsNG1a1fzFmw0rjVr1hiS6r1GjhxpGMbJ234feughw+FwGCEhIcb1119v7Nixw7dNNyOnO/eSjOeee86s4TM4P+68807z/2Nat25t9OvXz1i9erW5nvN+4f3wkQOGwWfgT2yGYRg+ymsAAABNBtc0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAFoEt5//33ZbDYdPnzY162ck6baN4D6CE0AGsWoUaNks9nqvX74N/3Ot/bt25vvGxAQIKfTqdGjR6uiouKC9dAY9u7dq5CQEP3ud79TYmKiqqurvdavWrVKQUFB2rZtm486BH6eCE0AGs3AgQNVWlrq9erQocMF7eHhhx9WaWmpiouLtXz5cq1bt06TJk36Sfs8NbRIkmEYOnHixE/a75m8/vrr6tOnjx5//HEdOXJEDz30kLnu8OHDGjt2rO6//35169at0d+7pqam0fcJNBeEJgCNJiQkRA6Hw+sVEBCgUaNGadiwYV61WVlZ6tOnj7lsGIbmzp2ryy67TKGhobrmmmv0r3/965x7CA8Pl8Ph0C9+8Qv17dtXd9xxh7Zv326u/+9//6tbb71Vbdq0UcuWLZWYmKi///3vXvvo06eP7r77bk2ZMkXR0dEaMGCA+TPbO++8o27duikkJETr168/57737t2rIUOGKDIyUmFhYbrqqqu0atUqr5rXX39dGRkZCg8P19KlSzV//nxt3rzZPG+XXnqp/vjHP2r//v0aMWKEIiMjFRUVpaFDh+rrr78297N161YNGDBA0dHRstvt6t27t9e5kCSbzaYnn3xSQ4cOVVhYmB555JFzPufAzwWhCYBf+OMf/6jnnntOixcv1s6dO3XPPffo9ttv19q1axu8z/379+utt95ScnKyOXb8+HElJSXprbfeUlFRkcaOHSuXy2WGku89//zzCgwM1AcffKAlS5aY49OmTVNOTo527dqlq6+++pz7njBhgjwej9atW6cdO3Zozpw5uvjii831hw8f1vr165WRkSHpZIAbP368Ro4cqX/+85/6xz/+oRdeeEHV1dXq27evLr74Yq1bt04bNmzQxRdfrIEDB5ozY0eOHNHIkSO1fv16bdq0SfHx8brxxht15MgRr54eeughDR06VDt27NCdd97Z4PMNNHsGADSCkSNHGgEBAUZYWJj5+s1vfmOuGzp0qFf95MmTjd69exuGYRhVVVVGixYtjI0bN3rVjB492rj11lsNwzCMNWvWGJKMioqKM/bQrl07Izg42AgLCzNatGhhSDKSk5N/dBvDMIwbb7zRyM7ONpd79+5tXHvttV4137//a6+9Zo41pO/ExERj5syZZ+xl+fLlRteuXb3GvvvuO+PKK680LrroImPhwoWGYRjGs88+a3Ts2NGoq6sz6zwejxEaGmq88847p933iRMnjPDwcOPNN980xyQZWVlZZ+wHwP8v0KeJDUCz0rdvXy1evNhcDgsLs7Tdp59+quPHj2vAgAFe49XV1erSpcs59XDvvfdq1KhRMgxDJSUl+p//+R8NHjxY69atU0BAgGprazV79my9/PLL2r9/vzwejzweT71ez3S90A/HG9L3pEmT9Ic//EGrV69W//799etf/1pXX321uf77n+Z+KDQ0VNnZ2brnnns0efJkSVJBQYG+/PJLhYeHe9UeP35cX331lSSpvLxcDz74oN577z0dPHhQtbW1+u6771RcXGzpWAF4IzQBaDRhYWG6/PLL641fdNFFMgzDa+yHFxzX1dVJklauXKlf/OIXXnUhISHn1EN0dLTZQ3x8vB577DGlpKRozZo16t+/v+bPn6+FCxfqscceU2JiosLCwpSVlVXvYu8zBb4fjjek79///vdKS0vTypUrtXr1auXk5Gj+/PmaOHGiampqlJeXpxkzZtTbLjAwUAEBAbLZbOZ7JyUlafny5fVqW7duLenkHY2HDh3SY489pnbt2ikkJEQpKSmWjxWAN0ITgPOudevWKioq8horLCxUUFCQJKlz584KCQlRcXGxevfu3ajvHRAQIEk6duyYJGn9+vUaOnSobr/9dkknw8cXX3yhTp06nfO+G9p3XFyc7rrrLt11112aMWOGnn76aU2cOFFr1qzRJZdcomuvvfas++jatatefvllxcTEKCIi4rQ169ev1xNPPKEbb7xRklRSUqJvvvnGcp8AvBGaAJx3N9xwg+bNm6cXXnhBKSkpevHFF1VUVGT+hBUeHq6pU6fqnnvuUV1dnXr16qXKykpt3LhRF198sUaOHGn5vY4cOaKysjLz57lp06YpOjpaPXr0kCRdfvnleuWVV7Rx40ZFRkZqwYIFKisra1BoakjfWVlZGjRokK644gpVVFTovffeM9/7jTfeqPfT3JncdtttmjdvnoYOHaqHH35Ybdq0UXFxsV599VXde++9atOmjS6//HItW7ZM3bp1U2Vlpe69916Fhoae83ECOIm75wCcd2lpaXrggQc0bdo0XXfddTpy5IjuuOMOr5o///nPevDBB5WTk6NOnTopLS1Nb7755jk/5+nBBx/UpZdeKqfTqfT0dIWFhSk/P19RUVGSpAceeEBdu3ZVWlqa+vTpI4fDUe9xCOfiXPuura3VhAkT1KlTJw0cOFAdO3bUE088IelkaBo6dKil923ZsqXWrVuntm3b6uabb1anTp1055136tixY+bM0//7f/9PFRUV6tKli1wulyZNmqSYmJgGHyvwc2czTr3QAABwwW3fvl033HCDDh06ZP5sCcC/MNMEAH7gxIkT+tvf/kZgAvwYM00AAAAWMNMEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYMH/B7USh0lPKhZyAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles['Fuel Barrels/Year'])\n", + "plt.xlabel('Fuel Barrels/Year')\n", + "plt.ylabel('Frequency')\n", + "plt.show()" ] }, { @@ -174,11 +1250,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles['CO2 Emission Grams/Mile'])\n", + "plt.xlabel('CO2 Emission Grams/Mile')\n", + "plt.ylabel('Frequency')\n", + "plt.show()\n" ] }, { @@ -190,11 +1280,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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8fJSTk2OOFRUVaffu3erVq5ckKS4uTi6XS9u2bTNrtm7dKpfL5Vaze/duFRUVmTXr16+Xn5+fYmJiGvMUAQBAM9GkH8+Vl5fryy+/NLf37dun/Px8BQcHy+l06pe//KU++ugjvf3226qpqTFXg4KDg+Xr6yu73a4xY8YoLS1NISEhCg4OVnp6urp06aL+/ftLkjp16qSBAwdq7NixWrx4sSRp3LhxSkxMVMeOHSVJ8fHxioqKUkpKiubOnaujR48qPT1dY8eO5VtwAABAUhOHph07dqhv377m9tSpUyVJI0eO1MyZM/Xmm29Kkm644Qa3/TZs2KA+ffpIkhYsWCBvb28NGzZMJ06cUL9+/bRs2TJ5eXmZ9StWrNCUKVPMb9klJSW5PRvKy8tLa9as0YQJE3TLLbfI399fycnJevrppxvjtAEAQDPkMc9puhRYfc4DPBfPaQKAy88l+ZwmAACApkJoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCgSUPThx9+qMGDB8vpdMpms2n16tVu84ZhaObMmXI6nfL391efPn20Z88et5rKykpNnjxZoaGhCggIUFJSkg4ePOhWU1paqpSUFNntdtntdqWkpOjYsWNuNQcOHNDgwYMVEBCg0NBQTZkyRVVVVY1x2gAAoBlq0tBUUVGhbt26aeHChWednzNnjubPn6+FCxdq+/btcjgcGjBggI4fP27WpKamatWqVcrKytKmTZtUXl6uxMRE1dTUmDXJycnKz89Xdna2srOzlZ+fr5SUFHO+pqZGgwYNUkVFhTZt2qSsrCy98cYbSktLa7yTBwAAzYrNMAyjqZuQJJvNplWrVmnIkCGSvl9lcjqdSk1N1bRp0yR9v6oUHh6u2bNna/z48XK5XGrTpo2WL1+u4cOHS5IOHTqkiIgIrV27VgkJCSooKFBUVJRyc3MVGxsrScrNzVVcXJw+++wzdezYUevWrVNiYqIKCwvldDolSVlZWRo1apRKSkoUFBRk6RzKyspkt9vlcrks7wPPcs30NU3dwgX7Ztagpm4BAJo1qz+/Pfaepn379qm4uFjx8fHmmJ+fn3r37q3NmzdLkvLy8lRdXe1W43Q6FR0dbdZs2bJFdrvdDEyS1LNnT9ntdrea6OhoMzBJUkJCgiorK5WXl3fOHisrK1VWVub2AgAAlyaPDU3FxcWSpPDwcLfx8PBwc664uFi+vr5q3br1j9aEhYXVOX5YWJhbzZnv07p1a/n6+po1Z5OZmWneJ2W32xUREXGBZwkAAJoLjw1Np9lsNrdtwzDqjJ3pzJqz1den5kwzZsyQy+UyX4WFhT/aFwAAaL48NjQ5HA5JqrPSU1JSYq4KORwOVVVVqbS09EdrDh8+XOf4R44ccas5831KS0tVXV1dZwXqh/z8/BQUFOT2AgAAlyaPDU3t27eXw+FQTk6OOVZVVaWNGzeqV69ekqSYmBj5+Pi41RQVFWn37t1mTVxcnFwul7Zt22bWbN26VS6Xy61m9+7dKioqMmvWr18vPz8/xcTENOp5AgCA5sG7Kd+8vLxcX375pbm9b98+5efnKzg4WG3btlVqaqoyMjIUGRmpyMhIZWRkqGXLlkpOTpYk2e12jRkzRmlpaQoJCVFwcLDS09PVpUsX9e/fX5LUqVMnDRw4UGPHjtXixYslSePGjVNiYqI6duwoSYqPj1dUVJRSUlI0d+5cHT16VOnp6Ro7diyrRwAAQFITh6YdO3aob9++5vbUqVMlSSNHjtSyZcv08MMP68SJE5owYYJKS0sVGxur9evXKzAw0NxnwYIF8vb21rBhw3TixAn169dPy5Ytk5eXl1mzYsUKTZkyxfyWXVJSktuzoby8vLRmzRpNmDBBt9xyi/z9/ZWcnKynn366sS8BAABoJjzmOU2XAp7T1PzxnCYAuPw0++c0AQAAeBJCEwAAgAWEJgAAAAvqFZr27dvX0H0AAAB4tHqFpuuuu059+/bVyy+/rJMnTzZ0TwAAAB6nXqHp448/Vvfu3ZWWliaHw6Hx48e7PTwSAADgUlOv0BQdHa358+fr22+/1dKlS1VcXKxbb71VnTt31vz583XkyJGG7hMAAKBJ/aQbwb29vXX33Xfr73//u2bPnq2vvvpK6enpuvrqq3Xfffe5/VoSAACA5uwnhaYdO3ZowoQJuuqqqzR//nylp6frq6++0vvvv69vv/1Wd911V0P1CQAA0KTq9WtU5s+fr6VLl2rv3r2688479dJLL+nOO+/UFVd8n8Hat2+vxYsX6/rrr2/QZgEAAJpKvULTokWLNHr0aN1///1yOBxnrWnbtq2WLFnyk5oDAADwFPUKTV988cV5a3x9fTVy5Mj6HB4AAMDj1OuepqVLl+r111+vM/7666/rxRdf/MlNAQAAeJp6haZZs2YpNDS0znhYWJgyMjJ+clMAAACepl6haf/+/Wrfvn2d8Xbt2unAgQM/uSkAAABPU6/QFBYWpk8++aTO+Mcff6yQkJCf3BQAAICnqVdouueeezRlyhRt2LBBNTU1qqmp0fvvv68HH3xQ99xzT0P3CAAA0OTq9e25J598Uvv371e/fv3k7f39IWpra3XfffdxTxMAALgk1Ss0+fr66rXXXtP/+3//Tx9//LH8/f3VpUsXtWvXrqH7AwAA8Aj1Ck2ndejQQR06dGioXgAAADxWvUJTTU2Nli1bpvfee08lJSWqra11m3///fcbpDkAAABPUa/Q9OCDD2rZsmUaNGiQoqOjZbPZGrovAAAAj1Kv0JSVlaW///3vuvPOOxu6HwAAAI9Ur0cO+Pr66rrrrmvoXgAAADxWvUJTWlqa/vjHP8owjIbuBwAAwCPV6+O5TZs2acOGDVq3bp06d+4sHx8ft/mVK1c2SHMAAACeol6hqVWrVrr77rsbuhcAAACPVa/QtHTp0obuA5ega6avaeoWAABoMPW6p0mSTp06pXfffVeLFy/W8ePHJUmHDh1SeXl5gzUHAADgKeq10rR//34NHDhQBw4cUGVlpQYMGKDAwEDNmTNHJ0+e1HPPPdfQfQIAADSpeq00Pfjgg+rRo4dKS0vl7+9vjt9999167733Gqw5AAAAT1Hvb8/9+9//lq+vr9t4u3bt9O233zZIYwAAAJ6kXitNtbW1qqmpqTN+8OBBBQYG/uSmAAAAPE29QtOAAQP0zDPPmNs2m03l5eV67LHH+NUqAADgklSvj+cWLFigvn37KioqSidPnlRycrK++OILhYaG6tVXX23oHgEAAJpcvVaanE6n8vPzlZ6ervHjx6t79+6aNWuWdu7cqbCwsAZr7tSpU/qf//kftW/fXv7+/rr22mv1xBNPqLa21qwxDEMzZ86U0+mUv7+/+vTpoz179rgdp7KyUpMnT1ZoaKgCAgKUlJSkgwcPutWUlpYqJSVFdrtddrtdKSkpOnbsWIOdCwAAaN7qtdIkSf7+/ho9erRGjx7dkP24mT17tp577jm9+OKL6ty5s3bs2KH7779fdrtdDz74oCRpzpw5mj9/vpYtW6YOHTroySef1IABA7R3717z/qrU1FS99dZbysrKUkhIiNLS0pSYmKi8vDx5eXlJkpKTk3Xw4EFlZ2dLksaNG6eUlBS99dZbjXZ+AACg+bAZ9fituy+99NKPzt933331buiHEhMTFR4eriVLlphjv/jFL9SyZUstX75chmHI6XQqNTVV06ZNk/T9qlJ4eLhmz56t8ePHy+VyqU2bNlq+fLmGDx8u6fuHcEZERGjt2rVKSEhQQUGBoqKilJubq9jYWElSbm6u4uLi9Nlnn6ljx45n7a+yslKVlZXmdllZmSIiIuRyuRQUFNQg16A544ngF8c3swY1dQsA0KyVlZXJbref9+d3vVaaTq/ynFZdXa3vvvtOvr6+atmyZYOFpltvvVXPPfecPv/8c3Xo0EEff/yxNm3aZN6Evm/fPhUXFys+Pt7cx8/PT71799bmzZs1fvx45eXlqbq62q3G6XQqOjpamzdvVkJCgrZs2SK73W4GJknq2bOn7Ha7Nm/efM7QlJmZqccff7xBzhUAAHi2eoWm0tLSOmNffPGFfvvb3+qhhx76yU2dNm3aNLlcLl1//fXy8vJSTU2NnnrqKf3617+WJBUXF0uSwsPD3fYLDw/X/v37zRpfX1+1bt26Ts3p/YuLi896L1ZYWJhZczYzZszQ1KlTze3TK00AAODSU+97ms4UGRmpWbNm6d5779Vnn33WIMd87bXX9PLLL+uVV15R586dlZ+fr9TUVDmdTo0cOdKss9lsbvsZhlFn7Exn1pyt/nzH8fPzk5+fn9XTARpFc/0YlI8VATQ3DRaaJMnLy0uHDh1qsOM99NBDmj59uu655x5JUpcuXbR//35lZmZq5MiRcjgckr5fKbrqqqvM/UpKSszVJ4fDoaqqKpWWlrqtNpWUlKhXr15mzeHDh+u8/5EjR+qsYgEAgMtTvULTm2++6bZtGIaKioq0cOFC3XLLLQ3SmCR99913uuIK96cieHl5mY8caN++vRwOh3JyctS9e3dJUlVVlTZu3KjZs2dLkmJiYuTj46OcnBwNGzZMklRUVKTdu3drzpw5kqS4uDi5XC5t27ZNN998syRp69atcrlcZrACAACXt3qFpiFDhrht22w2tWnTRj//+c81b968huhLkjR48GA99dRTatu2rTp37qydO3dq/vz55mMObDabUlNTlZGRocjISEVGRiojI0MtW7ZUcnKyJMlut2vMmDFKS0tTSEiIgoODlZ6eri5duqh///6SpE6dOmngwIEaO3asFi9eLOn7Rw4kJiae8yZwAABwealXaPrhwyUb05///Gc9+uijmjBhgkpKSuR0OjV+/Hj94Q9/MGsefvhhnThxQhMmTFBpaaliY2O1fv16t9+Bt2DBAnl7e2vYsGE6ceKE+vXrp2XLlpnPaJKkFStWaMqUKea37JKSkrRw4cKLcp4AAMDz1es5TTg7q895uFw01xuUcXFwIzgAT9Goz2n64dfsz2f+/Pn1eQsAAACPUq/QtHPnTn300Uc6deqUec/P559/Li8vL914441m3fm+9g8AANBc1Cs0DR48WIGBgXrxxRfNr/GXlpbq/vvv12233aa0tLQGbRIAAKCpXXH+krrmzZunzMxMt+cetW7dWk8++WSDfnsOAADAU9QrNJWVlZ31YZAlJSU6fvz4T24KAADA09QrNN199926//779Y9//EMHDx7UwYMH9Y9//ENjxozR0KFDG7pHAACAJleve5qee+45paen695771V1dfX3B/L21pgxYzR37twGbRAAAMAT1Cs0tWzZUs8++6zmzp2rr776SoZh6LrrrlNAQEBD9wcAAOAR6vXx3GlFRUUqKipShw4dFBAQIJ6TCQAALlX1Ck3//e9/1a9fP3Xo0EF33nmnioqKJEm/+c1veNwAAAC4JNUrNP3ud7+Tj4+PDhw4oJYtW5rjw4cPV3Z2doM1BwAA4CnqdU/T+vXr9c477+jqq692G4+MjNT+/fsbpDEAAABPUq+VpoqKCrcVptP+85//yM/P7yc3BQAA4GnqFZpuv/12vfTSS+a2zWZTbW2t5s6dq759+zZYcwAAAJ6iXh/PzZ07V3369NGOHTtUVVWlhx9+WHv27NHRo0f173//u6F7BAAAaHL1WmmKiorSJ598optvvlkDBgxQRUWFhg4dqp07d+pnP/tZQ/cIAADQ5C54pam6ulrx8fFavHixHn/88cboCQAAwONc8EqTj4+Pdu/eLZvN1hj9AAAAeKR6fTx33333acmSJQ3dCwAAgMeq143gVVVV+utf/6qcnBz16NGjzu+cmz9/foM0BwAA4CkuKDR9/fXXuuaaa7R7927deOONkqTPP//crYaP7QAAwKXogkJTZGSkioqKtGHDBknf/9qUP/3pTwoPD2+U5gAAADzFBd3TZBiG2/a6detUUVHRoA0BAAB4onrdCH7amSEKAADgUnVBoclms9W5Z4l7mAAAwOXggu5pMgxDo0aNMn8p78mTJ/XAAw/U+fbcypUrG65DAAAAD3BBoWnkyJFu2/fee2+DNgMAAOCpLig0LV26tLH6AAAA8Gg/6UZwAACAywWhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACzw+ND07bff6t5771VISIhatmypG264QXl5eea8YRiaOXOmnE6n/P391adPH+3Zs8ftGJWVlZo8ebJCQ0MVEBCgpKQkHTx40K2mtLRUKSkpstvtstvtSklJ0bFjxy7GKQIAgGbAo0NTaWmpbrnlFvn4+GjdunX69NNPNW/ePLVq1cqsmTNnjubPn6+FCxdq+/btcjgcGjBggI4fP27WpKamatWqVcrKytKmTZtUXl6uxMRE1dTUmDXJycnKz89Xdna2srOzlZ+fr5SUlIt5ugAAwIPZDA/+rbvTp0/Xv//9b/3rX/8667xhGHI6nUpNTdW0adMkfb+qFB4ertmzZ2v8+PFyuVxq06aNli9fruHDh0uSDh06pIiICK1du1YJCQkqKChQVFSUcnNzFRsbK0nKzc1VXFycPvvsM3Xs2NFSv2VlZbLb7XK5XAoKCmqAK9C8XTN9TVO3AA/2zaxBTd0CAEiy/vPbo1ea3nzzTfXo0UO/+tWvFBYWpu7du+uFF14w5/ft26fi4mLFx8ebY35+furdu7c2b94sScrLy1N1dbVbjdPpVHR0tFmzZcsW2e12MzBJUs+ePWW3282as6msrFRZWZnbCwAAXJo8OjR9/fXXWrRokSIjI/XOO+/ogQce0JQpU/TSSy9JkoqLiyVJ4eHhbvuFh4ebc8XFxfL19VXr1q1/tCYsLKzO+4eFhZk1Z5OZmWneA2W32xUREVH/kwUAAB7No0NTbW2tbrzxRmVkZKh79+4aP368xo4dq0WLFrnV2Ww2t23DMOqMnenMmrPVn+84M2bMkMvlMl+FhYVWTgsAADRDHh2arrrqKkVFRbmNderUSQcOHJAkORwOSaqzGlRSUmKuPjkcDlVVVam0tPRHaw4fPlzn/Y8cOVJnFeuH/Pz8FBQU5PYCAACXJo8OTbfccov27t3rNvb555+rXbt2kqT27dvL4XAoJyfHnK+qqtLGjRvVq1cvSVJMTIx8fHzcaoqKirR7926zJi4uTi6XS9u2bTNrtm7dKpfLZdYAAIDLm3dTN/Bjfve736lXr17KyMjQsGHDtG3bNj3//PN6/vnnJX3/kVpqaqoyMjIUGRmpyMhIZWRkqGXLlkpOTpYk2e12jRkzRmlpaQoJCVFwcLDS09PVpUsX9e/fX9L3q1cDBw7U2LFjtXjxYknSuHHjlJiYaPmbcwAA4NLm0aHppptu0qpVqzRjxgw98cQTat++vZ555hmNGDHCrHn44Yd14sQJTZgwQaWlpYqNjdX69esVGBho1ixYsEDe3t4aNmyYTpw4oX79+mnZsmXy8vIya1asWKEpU6aY37JLSkrSwoULL97JAgAAj+bRz2lqbnhOkzue04Qfw3OaAHiKS+I5TQAAAJ6C0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgQbMKTZmZmbLZbEpNTTXHDMPQzJkz5XQ65e/vrz59+mjPnj1u+1VWVmry5MkKDQ1VQECAkpKSdPDgQbea0tJSpaSkyG63y263KyUlRceOHbsIZwUAAJqDZhOatm/frueff15du3Z1G58zZ47mz5+vhQsXavv27XI4HBowYICOHz9u1qSmpmrVqlXKysrSpk2bVF5ersTERNXU1Jg1ycnJys/PV3Z2trKzs5Wfn6+UlJSLdn4AAMCzNYvQVF5erhEjRuiFF15Q69atzXHDMPTMM8/okUce0dChQxUdHa0XX3xR3333nV555RVJksvl0pIlSzRv3jz1799f3bt318svv6xdu3bp3XfflSQVFBQoOztbf/3rXxUXF6e4uDi98MILevvtt7V3794mOWcAAOBZmkVomjhxogYNGqT+/fu7je/bt0/FxcWKj483x/z8/NS7d29t3rxZkpSXl6fq6mq3GqfTqejoaLNmy5Ytstvtio2NNWt69uwpu91u1pxNZWWlysrK3F4AAODS5N3UDZxPVlaWPvroI23fvr3OXHFxsSQpPDzcbTw8PFz79+83a3x9fd1WqE7XnN6/uLhYYWFhdY4fFhZm1pxNZmamHn/88Qs7IQAA0Cx59EpTYWGhHnzwQb388stq0aLFOetsNpvbtmEYdcbOdGbN2erPd5wZM2bI5XKZr8LCwh99TwAA0Hx5dGjKy8tTSUmJYmJi5O3tLW9vb23cuFF/+tOf5O3tba4wnbkaVFJSYs45HA5VVVWptLT0R2sOHz5c5/2PHDlSZxXrh/z8/BQUFOT2AgAAlyaPDk39+vXTrl27lJ+fb7569OihESNGKD8/X9dee60cDodycnLMfaqqqrRx40b16tVLkhQTEyMfHx+3mqKiIu3evdusiYuLk8vl0rZt28yarVu3yuVymTUAAODy5tH3NAUGBio6OtptLCAgQCEhIeZ4amqqMjIyFBkZqcjISGVkZKhly5ZKTk6WJNntdo0ZM0ZpaWkKCQlRcHCw0tPT1aVLF/PG8k6dOmngwIEaO3asFi9eLEkaN26cEhMT1bFjx4t4xgAAwFN5dGiy4uGHH9aJEyc0YcIElZaWKjY2VuvXr1dgYKBZs2DBAnl7e2vYsGE6ceKE+vXrp2XLlsnLy8usWbFihaZMmWJ+yy4pKUkLFy686OcDAAA8k80wDKOpm7hUlJWVyW63y+VycX+TpGumr2nqFuDBvpk1qKlbAABJ1n9+e/Q9TQAAAJ6C0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALvJu6AQCXp2umr2nqFi7YN7MGNXULAJoQK00AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABggUeHpszMTN10000KDAxUWFiYhgwZor1797rVGIahmTNnyul0yt/fX3369NGePXvcaiorKzV58mSFhoYqICBASUlJOnjwoFtNaWmpUlJSZLfbZbfblZKSomPHjjX2KQIAgGbCo0PTxo0bNXHiROXm5ionJ0enTp1SfHy8KioqzJo5c+Zo/vz5WrhwobZv3y6Hw6EBAwbo+PHjZk1qaqpWrVqlrKwsbdq0SeXl5UpMTFRNTY1Zk5ycrPz8fGVnZys7O1v5+flKSUm5qOcLAAA8l80wDKOpm7DqyJEjCgsL08aNG3X77bfLMAw5nU6lpqZq2rRpkr5fVQoPD9fs2bM1fvx4uVwutWnTRsuXL9fw4cMlSYcOHVJERITWrl2rhIQEFRQUKCoqSrm5uYqNjZUk5ebmKi4uTp999pk6dux41n4qKytVWVlpbpeVlSkiIkIul0tBQUGNfDU8X3N8Dg/wY3hOE3BpKisrk91uP+/Pb49eaTqTy+WSJAUHB0uS9u3bp+LiYsXHx5s1fn5+6t27tzZv3ixJysvLU3V1tVuN0+lUdHS0WbNlyxbZ7XYzMElSz549ZbfbzZqzyczMND/Os9vtioiIaLiTBQAAHqXZhCbDMDR16lTdeuutio6OliQVFxdLksLDw91qw8PDzbni4mL5+vqqdevWP1oTFhZW5z3DwsLMmrOZMWOGXC6X+SosLKz/CQIAAI/WbH6NyqRJk/TJJ59o06ZNdeZsNpvbtmEYdcbOdGbN2erPdxw/Pz/5+fmdr3UAAHAJaBYrTZMnT9abb76pDRs26OqrrzbHHQ6HJNVZDSopKTFXnxwOh6qqqlRaWvqjNYcPH67zvkeOHKmzigUAAC5PHh2aDMPQpEmTtHLlSr3//vtq376923z79u3lcDiUk5NjjlVVVWnjxo3q1auXJCkmJkY+Pj5uNUVFRdq9e7dZExcXJ5fLpW3btpk1W7dulcvlMmsAAMDlzaM/nps4caJeeeUV/fOf/1RgYKC5omS32+Xv7y+bzabU1FRlZGQoMjJSkZGRysjIUMuWLZWcnGzWjhkzRmlpaQoJCVFwcLDS09PVpUsX9e/fX5LUqVMnDRw4UGPHjtXixYslSePGjVNiYuI5vzkHAAAuLx4dmhYtWiRJ6tOnj9v40qVLNWrUKEnSww8/rBMnTmjChAkqLS1VbGys1q9fr8DAQLN+wYIF8vb21rBhw3TixAn169dPy5Ytk5eXl1mzYsUKTZkyxfyWXVJSkhYuXNi4JwgAAJqNZvWcJk9n9TkPlwue04RLDc9pAi5Nl+RzmgAAAJoKoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFjg3dQNwJprpq9p6haAy15z/Hv4zaxBTd0CcMlgpQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAp4IDgCXMJ5iDjQcVprO8Oyzz6p9+/Zq0aKFYmJi9K9//aupWwIAAB6A0PQDr732mlJTU/XII49o586duu2223THHXfowIEDTd0aAABoYjbDMIymbsJTxMbG6sYbb9SiRYvMsU6dOmnIkCHKzMw87/5lZWWy2+1yuVwKCgpq0N6a4xI7AFwu+EixebP685t7mv5XVVWV8vLyNH36dLfx+Ph4bd68+az7VFZWqrKy0tx2uVySvr/4Da228rsGPyYAoGG0/d3rTd3CZWH34wmNctzTP7fPt45EaPpf//nPf1RTU6Pw8HC38fDwcBUXF591n8zMTD3++ON1xiMiIhqlRwAALmf2Zxr3+MePH5fdbj/nPKHpDDabzW3bMIw6Y6fNmDFDU6dONbdra2t19OhRhYSEnHOf5qCsrEwREREqLCxs8I8ZURfX++Liel9cXO+Li+tdP4Zh6Pjx43I6nT9aR2j6X6GhofLy8qqzqlRSUlJn9ek0Pz8/+fn5uY21atWqsVq86IKCgvhLdxFxvS8urvfFxfW+uLjeF+7HVphO49tz/8vX11cxMTHKyclxG8/JyVGvXr2aqCsAAOApWGn6galTpyolJUU9evRQXFycnn/+eR04cEAPPPBAU7cGAACaGKHpB4YPH67//ve/euKJJ1RUVKTo6GitXbtW7dq1a+rWLio/Pz899thjdT56ROPgel9cXO+Li+t9cXG9GxfPaQIAALCAe5oAAAAsIDQBAABYQGgCAACwgNAEAABgAaHpMvXhhx9q8ODBcjqdstlsWr16tdu8YRiaOXOmnE6n/P391adPH+3Zs6dpmr0EZGZm6qabblJgYKDCwsI0ZMgQ7d27162Ga95wFi1apK5du5oP+IuLi9O6devMea5148rMzJTNZlNqaqo5xjVvODNnzpTNZnN7ORwOc55r3XgITZepiooKdevWTQsXLjzr/Jw5czR//nwtXLhQ27dvl8Ph0IABA3T8+PGL3OmlYePGjZo4caJyc3OVk5OjU6dOKT4+XhUVFWYN17zhXH311Zo1a5Z27NihHTt26Oc//7nuuusu8wcH17rxbN++Xc8//7y6du3qNs41b1idO3dWUVGR+dq1a5c5x7VuRAYue5KMVatWmdu1tbWGw+EwZs2aZY6dPHnSsNvtxnPPPdcEHV56SkpKDEnGxo0bDcPgml8MrVu3Nv76179yrRvR8ePHjcjISCMnJ8fo3bu38eCDDxqGwZ/vhvbYY48Z3bp1O+sc17pxsdKEOvbt26fi4mLFx8ebY35+furdu7c2b97chJ1dOlwulyQpODhYEte8MdXU1CgrK0sVFRWKi4vjWjeiiRMnatCgQerfv7/bONe84X3xxRdyOp1q37697rnnHn399deSuNaNjSeCo47Tv7T4zF9UHB4erv379zdFS5cUwzA0depU3XrrrYqOjpbENW8Mu3btUlxcnE6ePKkrr7xSq1atUlRUlPmDg2vdsLKysvTRRx9p+/btdeb4892wYmNj9dJLL6lDhw46fPiwnnzySfXq1Ut79uzhWjcyQhPOyWazuW0bhlFnDBdu0qRJ+uSTT7Rp06Y6c1zzhtOxY0fl5+fr2LFjeuONNzRy5Eht3LjRnOdaN5zCwkI9+OCDWr9+vVq0aHHOOq55w7jjjjvM/+7SpYvi4uL0s5/9TC+++KJ69uwpiWvdWPh4DnWc/hbG6X+xnFZSUlLnXy+4MJMnT9abb76pDRs26OqrrzbHueYNz9fXV9ddd5169OihzMxMdevWTX/84x+51o0gLy9PJSUliomJkbe3t7y9vbVx40b96U9/kre3t3ldueaNIyAgQF26dNEXX3zBn+9GRmhCHe3bt5fD4VBOTo45VlVVpY0bN6pXr15N2FnzZRiGJk2apJUrV+r9999X+/bt3ea55o3PMAxVVlZyrRtBv379tGvXLuXn55uvHj16aMSIEcrPz9e1117LNW9ElZWVKigo0FVXXcWf70bGx3OXqfLycn355Zfm9r59+5Sfn6/g4GC1bdtWqampysjIUGRkpCIjI5WRkaGWLVsqOTm5CbtuviZOnKhXXnlF//znPxUYGGj+K9But8vf3998pg3XvGH8/ve/1x133KGIiAgdP35cWVlZ+uCDD5Sdnc21bgSBgYHm/XmnBQQEKCQkxBznmjec9PR0DR48WG3btlVJSYmefPJJlZWVaeTIkfz5bmxN98U9NKUNGzYYkuq8Ro4caRjG919bfeyxxwyHw2H4+fkZt99+u7Fr166mbboZO9u1lmQsXbrUrOGaN5zRo0cb7dq1M3x9fY02bdoY/fr1M9avX2/Oc60b3w8fOWAYXPOGNHz4cOOqq64yfHx8DKfTaQwdOtTYs2ePOc+1bjw2wzCMJsprAAAAzQb3NAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBuGTZbDatXr36nPPffPONbDab8vPzm7wXAJ6P0ASgURUXF2vy5Mm69tpr5efnp4iICA0ePFjvvfdeU7emiIgIFRUV1fm9aU1h1KhRstlseuCBB+rMTZgwQTabTaNGjapTb7PZ5OPjo2uvvVbp6emqqKhw2/eNN97Qz3/+c7Vu3VotW7ZUx44dNXr0aO3cubOxTwm45BCaADSab775RjExMXr//fc1Z84c7dq1S9nZ2erbt68mTpzY1O3Jy8tLDodD3t6e8bvLIyIilJWVpRMnTphjJ0+e1Kuvvqq2bdvWqR84cKCKior09ddf68knn9Szzz6r9PR0c37atGkaPny4brjhBr355pvas2ePnn/+ef3sZz/T73//+4tyTsClhNAEoNGcXiHZtm2bfvnLX6pDhw7q3Lmzpk6dqtzcXLPuwIEDuuuuu3TllVcqKChIw4YN0+HDh835mTNn6oYbbtDf/vY3tW3bVldeeaV++9vfqqamRnPmzJHD4VBYWJieeuqpOj0UFRXpjjvukL+/v9q3b6/XX3/dnDvz47kPPvhANptN7733nnr06KGWLVuqV69e2rt3r9sx33rrLcXExKhFixa69tpr9fjjj+vUqVPm/BdffKHbb79dLVq0UFRUlHJycixdrxtvvFFt27bVypUrzbGVK1cqIiJC3bt3r1Pv5+cnh8OhiIgIJScna8SIEeZHgLm5uZozZ47mz5+v+fPn67bbblP79u3Vu3dvPfLII1q7dq2lngD8H0ITgEZx9OhRZWdna+LEiQoICKgz36pVK0mSYRgaMmSIjh49qo0bNyonJ0dfffWVhg8f7lb/1Vdfad26dcrOztarr76qv/3tbxo0aJAOHjyojRs3avbs2fqf//kftzAmSY8++qh+8Ytf6OOPP9a9996rX//61yooKPjR3h955BHNmzdPO3bskLe3t0aPHm3OvfPOO7r33ns1ZcoUffrpp1q8eLGWLVtmBrba2loNHTpUXl5eys3N1XPPPadp06ZZvm7333+/li5dam7/7W9/c3v/H+Pv76/q6mpJ0quvvqorr7xSEyZMOGutzWaz3BOA/2UAQCPYunWrIclYuXLlj9atX7/e8PLyMg4cOGCO7dmzx5BkbNu2zTAMw3jssceMli1bGmVlZWZNQkKCcc011xg1NTXmWMeOHY3MzExzW5LxwAMPuL1fbGys8dvf/tYwDMPYt2+fIcnYuXOnYRiGsWHDBkOS8e6775r1a9asMSQZJ06cMAzDMG677TYjIyPD7ZjLly83rrrqKsMwDOOdd94xvLy8jMLCQnN+3bp1hiRj1apV57wOI0eONO666y7jyJEjhp+fn7Fv3z7jm2++MVq0aGEcOXLEuOuuu4yRI0fWqT9t69atRkhIiDFs2DDDMAxj4MCBRteuXd3eY968eUZAQID5Onbs2Dn7AVCXZ3yQD+CSYxiGpPOvaBQUFCgiIkIRERHmWFRUlFq1aqWCggLddNNNkqRrrrlGgYGBZk14eLi8vLx0xRVXuI2VlJS4HT8uLq7O9vm+Lde1a1fzv6+66ipJUklJidq2bau8vDxt377d7aPAmpoanTx5Ut99950KCgrUtm1bXX311efs4ceEhoZq0KBBevHFF2UYhgYNGqTQ0NCz1r799tu68sorderUKVVXV+uuu+7Sn//8Z3P+zGs/evRoJSUlaevWrbr33nvN/0cArCE0AWgUkZGRstlsKigo0JAhQ85ZZxjGWYPVmeM+Pj5u86e/NXbmWG1t7Xl7O1+Q++FxT9eePm5tba0ef/xxDR06tM5+LVq0OGsQudCPwkaPHq1JkyZJkv7yl7+cs65v375atGiRfHx85HQ63fqOjIzUpk2bVF1dbY63atVKrVq10sGDBy+oHwDf454mAI0iODhYCQkJ+stf/lLna/CSdOzYMUnfryodOHBAhYWF5tynn34ql8ulTp06/eQ+zrzHKTc3V9dff329j3fjjTdq7969uu666+q8rrjiCvN8Dh06ZO6zZcuWC3qPgQMHqqqqSlVVVUpISDhnXUBAgK677jq1a9euToD89a9/rfLycj377LMXdoIAzomVJgCN5tlnn1WvXr10880364knnlDXrl116tQp5eTkaNGiRSooKFD//v3VtWtXjRgxQs8884xOnTqlCRMmqHfv3urRo8dP7uH1119Xjx49dOutt2rFihXatm2blixZUu/j/eEPf1BiYqIiIiL0q1/9SldccYU++eQT7dq1S08++aT69++vjh076r777tO8efNUVlamRx555ILew8vLy7xZ3cvLq159xsXFKS0tTWlpadq/f7+GDh1qPpdqyZIlstlsbh9tAjg//sYAaDTt27fXRx99pL59+yotLU3R0dEaMGCA3nvvPS1atEjS/z0pu3Xr1rr99tvVv39/XXvttXrttdcapIfHH39cWVlZ6tq1q1588UWtWLFCUVFR9T5eQkKC3n77beXk5Oimm25Sz549NX/+fLVr106SdMUVV2jVqlWqrKzUzTffrN/85jdnfRTC+QQFBSkoKKjefUrS008/rVdeeUU7d+5UYmKiIiMj9atf/Uq1tbXasmXLTz4+cLmxGdwJCAAAcF6sNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgwf8HBBDnj4TGSzkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles['Combined MPG'])\n", + "plt.xlabel('Combined MPG')\n", + "plt.ylabel('Frequency')\n", + "plt.show()\n" ] }, { @@ -237,11 +1341,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def func_expo(mean, size):\n", + " return np.random.exponential(scale=mean, size=size)\n", + "\n", + "\n", + "mean1 = func_expo(1, 1000)\n", + "mean100 = func_expo(100, 1000)\n", + "\n", + "plt.hist(mean1, bins=100)\n", + "plt.show()\n", + "\n", + "plt.hist(mean100, bins=100)\n", + "plt.show()" ] }, { @@ -257,7 +1403,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here: we have higher values with mean=100\n" ] }, { @@ -273,12 +1419,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.7534030360583935" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "from scipy.stats import expon\n", + "\n", + "lambda_inv = 10/1 \n", + "\n", + "expon_dist = expon(scale=lambda_inv)\n", + "\n", + "expon_dist.cdf(14)\n" ] }, { @@ -290,11 +1452,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.2231301601484298" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "1 - expon_dist.cdf(15)\n" ] } ], @@ -314,7 +1487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..240d2d0 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -42,8 +42,10 @@ "p = probability that the fruit is an apple \n", "q = probability that the fruit is an orange\n", "\"\"\"\n", + "from scipy.stats import bernoulli\n", "\n", - "# your code here\n" + "p = 0.6\n", + "q = 0.4\n" ] }, { @@ -61,11 +63,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.07775999999999998" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p**5\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.349416423424006e-08" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "(p**5)*(q**15)" ] }, { @@ -83,11 +116,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.675713479244708e-06\n" + ] + } + ], "source": [ - "# your code here\n" + "from scipy.stats import binom\n", + "\n", + "n = 20\n", + "\n", + "binomial_dist_apple = binom(n, p)\n", + "binomial_dist_orange = binom(n, q)\n", + "\n", + "prob_apples = binomial_dist_apple.pmf(5)\n", + "prob_oranges = binomial_dist_orange.pmf(15)\n", + "\n", + "prob_combined = prob_apples * prob_oranges\n", + "print(prob_combined)" ] }, { @@ -101,11 +153,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00031703112116863004\n" + ] + } + ], "source": [ - "# your code here\n" + "prob_apples = binomial_dist_apple.cdf(4)\n", + "print(prob_apples)" ] }, { @@ -119,12 +180,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "X = np.arange(0, n)\n", + "\n", + "plt.plot(X, binomial_dist_apple.pmf(X), \"o\")\n", + "plt.show()\n", + "\n", + "plt.plot(X, binomial_dist_orange.pmf(X), \"o\")\n", + "plt.show()\n", + "\n" ] }, { @@ -151,11 +242,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.053775025581946814" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "from scipy.stats import poisson\n", + "\n", + "mu = 2.3\n", + "\n", + "poisson_dist = poisson(mu)\n", + "\n", + "poisson_dist.pmf(5)\n" ] }, { @@ -167,12 +275,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "# Please label the axes and give a title to the plot \n", + "\n", + "X = np.arange(1, 11)\n", + "\n", + "plt.plot(X, poisson_dist.pmf(X), \"o\")\n", + "plt.xlabel('Goals')\n", + "plt.ylabel('Probability')\n", + "plt.title('Goals per game')\n", + "plt.show()\n" ] } ], @@ -192,7 +319,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.4" } }, "nbformat": 4,