Задание 5.ipynb

{"cells":[{"metadata":{"scrolled":false,"trusted":true},"cell_type":"code","source":"# Загрузка зависимостей\nimport numpy\nimport pandas\nimport matplotlib.pyplot\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import MinMaxScaler","execution_count":102,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# Загрузка и анализ набора данных\nraw_dataset = pandas.read_csv('machine.data.csv', header=None) # Убедиться в правильности пути к файлу!\nraw_dataset.head(10) # Вывод первых 10 строк","execution_count":103,"outputs":[{"output_type":"execute_result","execution_count":103,"data":{"text/plain":"         0         1    2      3      4    5   6    7     8     9\n0  adviser     32/60  125    256   6000  256  16  128   198   199\n1   amdahl    470v/7   29   8000  32000   32   8   32   269   253\n2   amdahl   470v/7a   29   8000  32000   32   8   32   220   253\n3   amdahl   470v/7b   29   8000  32000   32   8   32   172   253\n4   amdahl   470v/7c   29   8000  16000   32   8   16   132   132\n5   amdahl    470v/b   26   8000  32000   64   8   32   318   290\n6   amdahl  580-5840   23  16000  32000   64  16   32   367   381\n7   amdahl  580-5850   23  16000  32000   64  16   32   489   381\n8   amdahl  580-5860   23  16000  64000   64  16   32   636   749\n9   amdahl  580-5880   23  32000  64000  128  32   64  1144  1238","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>adviser</td>\n      <td>32/60</td>\n      <td>125</td>\n      <td>256</td>\n      <td>6000</td>\n      <td>256</td>\n      <td>16</td>\n      <td>128</td>\n      <td>198</td>\n      <td>199</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>amdahl</td>\n      <td>470v/7</td>\n      <td>29</td>\n      <td>8000</td>\n      <td>32000</td>\n      <td>32</td>\n      <td>8</td>\n      <td>32</td>\n      <td>269</td>\n      <td>253</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>amdahl</td>\n      <td>470v/7a</td>\n      <td>29</td>\n      <td>8000</td>\n      <td>32000</td>\n      <td>32</td>\n      <td>8</td>\n      <td>32</td>\n      <td>220</td>\n      <td>253</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>amdahl</td>\n      <td>470v/7b</td>\n      <td>29</td>\n      <td>8000</td>\n      <td>32000</td>\n      <td>32</td>\n      <td>8</td>\n      <td>32</td>\n      <td>172</td>\n      <td>253</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>amdahl</td>\n      <td>470v/7c</td>\n      <td>29</td>\n      <td>8000</td>\n      <td>16000</td>\n      <td>32</td>\n      <td>8</td>\n      <td>16</td>\n      <td>132</td>\n      <td>132</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>amdahl</td>\n      <td>470v/b</td>\n      <td>26</td>\n      <td>8000</td>\n      <td>32000</td>\n      <td>64</td>\n      <td>8</td>\n      <td>32</td>\n      <td>318</td>\n      <td>290</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>amdahl</td>\n      <td>580-5840</td>\n      <td>23</td>\n      <td>16000</td>\n      <td>32000</td>\n      <td>64</td>\n      <td>16</td>\n      <td>32</td>\n      <td>367</td>\n      <td>381</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>amdahl</td>\n      <td>580-5850</td>\n      <td>23</td>\n      <td>16000</td>\n      <td>32000</td>\n      <td>64</td>\n      <td>16</td>\n      <td>32</td>\n      <td>489</td>\n      <td>381</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>amdahl</td>\n      <td>580-5860</td>\n      <td>23</td>\n      <td>16000</td>\n      <td>64000</td>\n      <td>64</td>\n      <td>16</td>\n      <td>32</td>\n      <td>636</td>\n      <td>749</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>amdahl</td>\n      <td>580-5880</td>\n      <td>23</td>\n      <td>32000</td>\n      <td>64000</td>\n      <td>128</td>\n      <td>32</td>\n      <td>64</td>\n      <td>1144</td>\n      <td>1238</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Размер набора данных\nprint(raw_dataset.shape)","execution_count":104,"outputs":[{"output_type":"stream","text":"(209, 10)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Создаем набор данных, в котором будут храниться обработанные данные\ndataset = pandas.DataFrame()","execution_count":105,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №3 (MMIN: minimum main memory in kilobytes (integer))\n\n# Загружаем данные\ndata = raw_dataset[3]\n\n# Анализируем распределение, используя гистограмму. Параметр bins отвечает за число столбцов в гистрограмме.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":106,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"scrolled":false,"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №4 - выброс в районе 32000. Применяем отсечение с разрешенным интервалом от 0 до 16000.\ndata = numpy.clip(data, 0, 16000)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":107,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXEAAAD4CAYAAAAaT9YAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAPeUlEQVR4nO3dbYylZX3H8e+vuyI+VcAdt1sWO2tFG3wh2ClCtI3iE4IRmhgDMWatmE3rQ7Sa2kWTpjZ9AdrUh7Sp3ajttkWFIhYCaS3dYh+SZnFRQR5lWZeWDbCrlWr7ohX998W5FmbXM8yZM+fMzAXfT3Iy933d95n7v/855zf3XPc5Z1NVSJL69FOrXYAkaXyGuCR1zBCXpI4Z4pLUMUNckjq2fiUPtmHDhpqdnV3JQ0pS92666abvVNXMsG0rGuKzs7Ps2bNnJQ8pSd1Lcu9C25xOkaSOGeKS1DFDXJI6ZohLUscMcUnqmCEuSR0zxCWpY4a4JHXMEJekjq3oOzYfb2a3Xzd0fP8l565wJZKeqDwTl6SOGeKS1DFDXJI6ZohLUscMcUnqmCEuSR0zxCWpY4a4JHXMEJekjhniktQxQ1ySOjbSZ6ck2Q/8APgR8HBVzSU5AbgcmAX2A2+qqu9Np0xJ0jBLORN/RVWdWlVzbX07sKuqTgZ2tXVJ0gpaznTKecDOtrwTOH/55UiSlmLUEC/g75PclGRbG9tYVfe35QeAjcPumGRbkj1J9hw6dGiZ5UqS5hv188RfVlUHkjwbuD7JnfM3VlUlqWF3rKodwA6Aubm5oftIksYz0pl4VR1oXw8CXwJOBx5MsgmgfT04rSIlScMtGuJJnpbkGYeXgdcAtwLXAFvbbluBq6dVpCRpuFGmUzYCX0pyeP/PVdXfJfkqcEWSi4B7gTdNr0xJ0jCLhnhV7QNeNGT8u8Arp1GUJGk0vmNTkjpmiEtSx0Z9iWF3ZrdfN3R8/yXnrnAlkjQ9nolLUscMcUnqmCEuSR0zxCWpY4a4JHXMEJekjhniktQxQ1ySOmaIS1LHDHFJ6pghLkkdM8QlqWOGuCR1zBCXpI4Z4pLUMUNckjpmiEtSxwxxSeqYIS5JHTPEJaljhrgkdcwQl6SOGeKS1DFDXJI6ZohLUscMcUnq2MghnmRdkq8nubatb0myO8neJJcnOWZ6ZUqShlnKmfh7gDvmrV8KfKyqngd8D7hokoVJkhY3Uogn2QycC3y6rQc4C7iy7bITOH8aBUqSFjbqmfjHgQ8AP27rzwIeqqqH2/p9wInD7phkW5I9SfYcOnRoWcVKko60aIgneT1wsKpuGucAVbWjquaqam5mZmacbyFJWsD6EfZ5KfCGJOcAxwI/DXwCOC7J+nY2vhk4ML0yJUnDLHomXlUXV9XmqpoFLgD+sareDNwAvLHtthW4empVSpKGWs7rxH8beF+SvQzmyD8zmZIkSaMaZTrlEVX1FeArbXkfcPrkS5Ikjcp3bEpSxwxxSeqYIS5JHTPEJaljhrgkdcwQl6SOGeKS1DFDXJI6ZohLUscMcUnqmCEuSR0zxCWpY4a4JHXMEJekjhniktQxQ1ySOmaIS1LHDHFJ6pghLkkdM8QlqWOGuCR1zBCXpI4Z4pLUMUNckjpmiEtSxwxxSeqYIS5JHTPEJaljhrgkdcwQl6SOLRriSY5NcmOSm5PcluTDbXxLkt1J9ia5PMkx0y9XkjTfKGfi/wucVVUvAk4Fzk5yBnAp8LGqeh7wPeCi6ZUpSRpm0RCvgf9uq09qtwLOAq5s4zuB86dSoSRpQSPNiSdZl+QbwEHgeuAe4KGqerjtch9w4gL33ZZkT5I9hw4dmkTNkqRmpBCvqh9V1anAZuB04BdGPUBV7aiquaqam5mZGbNMSdIwS3p1SlU9BNwAnAkcl2R927QZODDh2iRJixjl1SkzSY5ry08BXg3cwSDM39h22wpcPa0iJUnDrV98FzYBO5OsYxD6V1TVtUluB76Q5PeBrwOfmWKdkqQhFg3xqroFOG3I+D4G8+OSpFXiOzYlqWOGuCR1zBCXpI4Z4pLUMUNckjpmiEtSxwxxSeqYIS5JHTPEJaljo7ztfk2b3X7dapcgSavGM3FJ6pghLkkdM8QlqWOGuCR1zBCXpI4Z4pLUMUNckjpmiEtSxwxxSeqYIS5JHTPEJaljhrgkdcwQl6SOGeKS1LHuP4pWj1roY3n3X3LuClciaaV4Ji5JHTPEJaljhrgkdcwQl6SOLRriSU5KckOS25PcluQ9bfyEJNcnubt9PX765UqS5hvlTPxh4P1VdQpwBvDOJKcA24FdVXUysKutS5JW0KIhXlX3V9XX2vIPgDuAE4HzgJ1tt53A+dMqUpI03JJeJ55kFjgN2A1srKr726YHgI0L3GcbsA3gOc95zrh1ShPha+n1eDPyhc0kTwe+CLy3qr4/f1tVFVDD7ldVO6pqrqrmZmZmllWsJOlII4V4kicxCPDLquqqNvxgkk1t+ybg4HRKlCQtZJRXpwT4DHBHVf3hvE3XAFvb8lbg6smXJ0l6LKPMib8UeAvwzSTfaGMfBC4BrkhyEXAv8KbplDiw0FymJD2RLRriVfWvQBbY/MrJliNJWgrfsSlJHTPEJaljhrgkdcwQl6SOGeKS1DFDXJI6ZohLUscMcUnqmCEuSR0zxCWpY4a4JHXMEJekjhniktQxQ1ySOmaIS1LHDHFJ6pghLkkdM8QlqWOGuCR1zBCXpI4Z4pLUMUNckjpmiEtSxwxxSeqYIS5JHTPEJaljhrgkdcwQl6SOGeKS1LFFQzzJZ5McTHLrvLETklyf5O729fjplilJGmaUM/E/B84+amw7sKuqTgZ2tXVJ0gpbNMSr6p+B/zxq+DxgZ1veCZw/4bokSSMYd058Y1Xd35YfADYutGOSbUn2JNlz6NChMQ8nSRpm2Rc2q6qAeoztO6pqrqrmZmZmlns4SdI844b4g0k2AbSvBydXkiRpVOOG+DXA1ra8Fbh6MuVIkpZilJcYfh74N+AFSe5LchFwCfDqJHcDr2rrkqQVtn6xHarqwgU2vXLCtUiSlsh3bEpSxwxxSerYotMpkqTRzG6/buj4/kvOndoxPROXpI4Z4pLUMUNckjpmiEtSxwxxSeqYIS5JHTPEJaljhrgkdcwQl6SOGeKS1DFDXJI6ZohLUscMcUnqmCEuSR0zxCWpY4a4JHXMEJekjhniktQx/3u2NWA1/ksnSY8PnolLUscMcUnqmCEuSR17ws2Jr+b880LH7sVj1e/8vbQ6PBOXpI4Z4pLUMUNckjr2hJsTX8gk58ofz3PfktaWZZ2JJzk7yV1J9ibZPqmiJEmjGTvEk6wD/hh4HXAKcGGSUyZVmCRpccs5Ez8d2FtV+6rq/4AvAOdNpixJ0ihSVePdMXkjcHZVvb2tvwV4SVW966j9tgHb2uoLgLvGONwG4DtjFTpd1rU0a7UuWLu1WdfSPF7r+rmqmhm2YeoXNqtqB7BjOd8jyZ6qmptQSRNjXUuzVuuCtVubdS3NE7Gu5UynHABOmre+uY1JklbIckL8q8DJSbYkOQa4ALhmMmVJkkYx9nRKVT2c5F3Al4F1wGer6raJVXakZU3HTJF1Lc1arQvWbm3WtTRPuLrGvrApSVp9vu1ekjpmiEtSx9Z0iK/02/qTnJTkhiS3J7ktyXva+AlJrk9yd/t6fBtPkk+2+m5J8uJ532tr2//uJFsnVN+6JF9Pcm1b35Jkdzv+5e0CM0me3Nb3tu2z877HxW38riSvnVBdxyW5MsmdSe5IcuZa6FmS32w/x1uTfD7JsavRsySfTXIwya3zxibWnyS/mOSb7T6fTJJl1PXR9nO8JcmXkhy3WB8Wep4u1Otxa5u37f1JKsmGtdCzNv7u1rfbknxkRXtWVWvyxuBi6T3Ac4FjgJuBU6Z8zE3Ai9vyM4BvMfhIgY8A29v4duDStnwO8LdAgDOA3W38BGBf+3p8Wz5+AvW9D/gccG1bvwK4oC1/CviNtvwO4FNt+QLg8rZ8Suvjk4Etrb/rJlDXTuDtbfkY4LjV7hlwIvBt4CnzevXW1egZ8CvAi4Fb541NrD/AjW3ftPu+bhl1vQZY35YvnVfX0D7wGM/ThXo9bm1t/CQGL6a4F9iwRnr2CuAfgCe39WevZM+mFojLvQFnAl+et34xcPEK13A18GoG7zLd1MY2AXe15T8FLpy3/11t+4XAn84bP2K/MWvZDOwCzgKubQ++78x7wj3Sr/YgP7Mtr2/75egezt9vGXU9k0FY5qjxVe0ZgxD/j/YEXt969trV6hkwe9QTfyL9advunDd+xH5Lreuobb8KXNaWh/aBBZ6nj/X4XE5twJXAi4D9PBriq9ozBsH7qiH7rUjP1vJ0yuEn4WH3tbEV0f6cPg3YDWysqvvbpgeAjW15oRqnUfvHgQ8AP27rzwIeqqqHhxzjkeO37f/V9p9GXVuAQ8CfZTDV8+kkT2OVe1ZVB4A/AP4duJ9BD25ibfQMJtefE9vypOsDeBuDs9Rx6nqsx+dYkpwHHKiqm4/atNo9ez7wy20a5J+S/NKYdY3Vs7Uc4qsmydOBLwLvrarvz99Wg1+RK/q6zCSvBw5W1U0redwRrWfw5+WfVNVpwP8wmB54xCr17HgGH8i2BfhZ4GnA2StZw6hWoz+LSfIh4GHgstWuBSDJU4EPAr+z2rUMsZ7BX3xnAL8FXDHqHPskrOUQX5W39Sd5EoMAv6yqrmrDDybZ1LZvAg4uUuOka38p8IYk+xl8WuRZwCeA45IcfsPW/GM8cvy2/ZnAd6dQFwzOFu6rqt1t/UoGob7aPXsV8O2qOlRVPwSuYtDHtdAzmFx/DrTlidWX5K3A64E3t18w49T1XRbu9Th+nsEv5Jvb82Az8LUkPzNGbZPu2X3AVTVwI4O/ljeMUdd4PVvq3N5K3Rj8dtvH4Ad3ePL/hVM+ZoC/AD5+1PhHOfIi1Efa8rkceUHlxjZ+AoN54uPb7dvACROq8eU8emHzrznyIsg72vI7OfIi3RVt+YUceaFlH5O5sPkvwAva8u+2fq1qz4CXALcBT23H2gm8e7V6xk/Oo06sP/zkRbpzllHX2cDtwMxR+w3tA4/xPF2o1+PWdtS2/Tw6J77aPft14Pfa8vMZTJVkpXo2tUCcxI3BVedvMbiS+6EVON7LGPxZewvwjXY7h8Fc1S7gbgZXoQ8/EMLgP8a4B/gmMDfve70N2NtuvzbBGl/OoyH+3PZg3Nt++Ievjh/b1ve27c+dd/8PtXrvYsQr8iPUdCqwp/Xtb9oTZtV7BnwYuBO4FfjL9mRa8Z4Bn2cwL/9DBmdtF02yP8Bc+zfeA/wRR11kXmJdexmE0OHH/6cW6wMLPE8X6vW4tR21fT+Phvhq9+wY4K/a9/sacNZK9sy33UtSx9bynLgkaRGGuCR1zBCXpI4Z4pLUMUNckjpmiEtSxwxxSerY/wNalDlMdNvYXwAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение. Попробуем применить к данным логарифм и извлечение квадратного корня.\nmatplotlib.pyplot.hist(numpy.log(data), bins = 50)\nmatplotlib.pyplot.show()\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()","execution_count":108,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Логарифм даёт более равномерно распределенные данные, используем его\ndata = numpy.log(data)","execution_count":109,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":110,"outputs":[{"output_type":"stream","text":"4.1588830833596715\n9.680344001221918\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":111,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":112,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# Можем взглянуть на сами данные\nmatplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":113,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":114,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Конвертируем в одномерный массив\ndata = data.flatten()","execution_count":115,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['MMIN'] = data","execution_count":116,"outputs":[]},{"metadata":{"scrolled":false,"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №7 (CHMAX: maximum channels in units (integer))\n\n# Загружаем данные\ndata = raw_dataset[7]\n\n# Анализируем распределение, используя гистограмму. Параметр bins отвечает за число столбцов в гистрограмме.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":117,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №4 - выбросы значений в интервале (100, 175). Применяем отсечение с разрешенным интервалом от 0 до 70.\ndata = numpy.clip(data, 0, 70)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":118,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение. Применять логарифм нельзя, т.к. среди значений есть нули.\n# Применим извлечение квадратного корня.\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()\n","execution_count":119,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = data ** 0.5","execution_count":120,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":121,"outputs":[{"output_type":"stream","text":"0.0\n8.366600265340756\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":122,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":123,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Можем взглянуть на сами данные\nmatplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":124,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":125,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Конвертируем в одномерный массив\ndata = data.flatten()","execution_count":126,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['CHMAX'] = data","execution_count":127,"outputs":[]},{"metadata":{"trusted":true,"scrolled":true},"cell_type":"code","source":"# Обработка данных в столбце №2 \n\n# Загружаем данные\ndata = raw_dataset[2]\n\n# Анализируем распределение\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":128,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №4 - выбросы значений в интервале (1000, 1500). Применяем отсечение с разрешенным интервалом от 0 до 1000.\ndata = numpy.clip(data, 0, 1000)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":129,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение.  логарифм  извлечение квадратного корня.\nmatplotlib.pyplot.hist(numpy.log(data), bins = 50)\nmatplotlib.pyplot.show()\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()","execution_count":130,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Логарифм даёт более равномерно распределенные данные, используем его\ndata = numpy.log(data)","execution_count":131,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":132,"outputs":[{"output_type":"stream","text":"2.833213344056216\n6.907755278982137\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":133,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":134,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Можем взглянуть на сами данные\nmatplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":135,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO29ebgld13n//7Ucra739v7crsT0lk6nYQkbRIWgQhkQTDiBARxwI2Mz4A/HRmRGRQVUVFxZOYRwTgGxNEgogMZEwRFNCSGJB2zkM7a6aT3Ti/33r77Oaeqvr8/qr61ndpOnao6y/2+nqefe8851ed8b52qT33q/dmIMQaBQCAQ9D9StxcgEAgEgmwQBl0gEAgGBGHQBQKBYEAQBl0gEAgGBGHQBQKBYEBQuvXB69atYzt37uzWxwsEAkFf8sgjj5xhjK0Peq1rBn3nzp3Yt29ftz5eIBAI+hIiOhT2mpBcBAKBYEAQBl0gEAgGBGHQBQKBYEAQBl0gEAgGBGHQBQKBYECINehEdAcRnSKiJ0NeJyL6X0R0gIieIKKrsl+mQCAQCOJI4qF/AcBNEa/fDGCX9e82AJ/tfFkCgUAgaJfYPHTG2L1EtDNik1sAfJGZfXi/S0TjRLSZMXYiozUKBLmi6Qa+8G8vYX6lidfuWo9rzpss5HO//r0TePrEPLZP1vCOvdsL+UzBYJNFYdFWAEdcj49az7UYdCK6DaYXj+np6Qw+WiDonKdPLOATdz8NALjvwBn83X9+TSGf+5G/+x7OrTQBADfs3oSxmlrI5woGl0KDooyx2xljexlje9evD6xcFQgKp6EbAIDhsmIb2CLQdAPbJ6sAgEMzS4V9rmBwycKgHwPgvl/cZj0nEPQFumFO7RqrqlhY1Qr7XIMBO6eGAACHZ5YL+1zB4JKFQb8LwHutbJfrAJwT+rmgn+AGfWKoaIPOsGOqBgA4dFYYdEHnxGroRHQngDcAWEdERwH8GgAVABhjnwNwD4C3ADgAYBnAT+a1WIEgD7hBH6+WsNKcR1M3oMqOr/Po4Vk8c3Kh5f/JEuHGDrRvxoDhsop1w2UcFgZdkAFJslzeHfM6A/CBzFYkEBSMbg1KH7cM8+Kqhomhkv36z935KI7OrgT+33NvaeL9rzs/1ecajEEiYHqyKiQXQSZ0rX2uQNAr6IYZFOUGfcFn0BdWNbzj6m340A0X2c8ZjOHVn/xnLDf01J/LAEhE2DE1hIdenEn9PgIBRxh0wZpH0y0NvWYa8flVb6bLalPH1HAZm8YqnuclAppWhkwaHA+9hq8+dgx1TUdZkVO/n0AgerkI1jyGLbmYBt0dGDUMhrpmoKK2niqqLKFppDPojDEwBhARpidrYAw4FiLrCARJEQa9z3n8yBy+/ewpHJsTxiAtGs9ysSUXx0Ova6bBrqqtnrMqS2hqLNVnWtcQS3KxMl2Eji7oECG59DEnzq3gls/cDwB45fZxfPUD7VU4MsbwibufxtHZVkNSVWX8ylt3Y91wOZO19jJ22mKAh77SNDXySqBBJ2gpPXR+V0AETFsGXWS6CDpFGPQ+ZtEyPBIB8ykqHOdXNfzZfS9iw0gZk64g4GpTx0tnl3HzZZtx46WbMltvr2IXFlkeultD5wY9yENXZCm1hm7YHjqwbsi8aM4uN1K9l0DAEQa9j+FSwVBJwWqz/WwLbox+7gcuwH981U77+WdOzuOmT3/HNnSDjpOH7mS5cPh+LQdo6CVZQiOl5OJ46ARJIvO5NbK/BfkhNPQ+hmdn1MqyrfW2Azfo7iIaAFAsA6OtEQPDDXpFlVFRJY+GvtKI8tDTSy5uDR0wi5R4PrxAkBZh0PsYbkyGyko6g255l60G3XyspzRW/Qa/cCkSYaTiLf+va1EaeieSi/mZ1rUTEjkyjECQFmHQ+xjuWQ6X00kuvMugIpPneZl76PrasDC2cZUIIxXFGxRtWFkupTCDnjLLxfrJPXSJSEgugo4RBr2P4cakVpKhGQyaZaAZY3j+5QU8cXTOlgyC/7+5fcnvoVsGfq1o6PzCxT10d1CUXygrAQU/qkwde+iWPTcllzWyvwX5IYKifYzbQwdMj1uRJew7NIt3fO4BAMA7927D7916ReD/D9PQ5TWmobs99FG/h86zXErBhUVp72KYdR2wNXQSGrqgc4SH3se4NXQAWG2aj88u1u1tZpbC0xltg66Eaehrw8C4NfTRiuoJitpZLgEeuiKRLVu1S4uGLgnJRdA5wqD3Mdzg1kqmQecBvIblNVZVOTKw2bCDosEaeid9SvoJvh8latXQV20PvdWglxTJlrnaxX1XYH62CIoKOkcY9D6Ga+jDZdPY1C0PvWllvHBtPfz/h2jo0trS0HVPlovfoJv7KCjLRZEodVCU71p+KRVpi4IsEAa9j/F76KuWh84NdUWVI73s0Dx0eW1p6Hw/ylZQdKWp2/vGLv1XQppzpfTQmauwCBBZLoJsEAa9j+EaOg+K2h66zrV1OTJoF15YtLY0dN0w29iSJbkATrXoalOHKhMUOVuD7pT+uwqL1sj+FuSHMOh9DDcAPCjKi4tsDb2koBlhJPh2JcWrofNA3Vrx0DWD2RexkYq34+JKUw+UWwCetthZ6b9TWERCQxd0jDDofQz3vocsDZ0H8BpcQ48JinKt3e+hExEUidZMpajBGCx7HuChGxEGPYOgKJdcJOc5gSAtwqD3Me7mXIDjoXMZoFZKJ7kApgSwVipFNd3x0MesBl3nrO6Vq009sI8LYHZbbKTNQ+dBUV5YREJyEXSOMOh9jO7LQ6+7gqJEZofANEFRwMzgWCuSCx8FBzg90Xkr29WmHjitCABKGTbnkkSWiyADhEHvY5otkgvX0A2osgRFkiKNsq2hBxl0WVozHqNmGHbQk08tml12NPQoD72Zoika4M5DNx/LIstFkAHCoPcxYYVFTY2hJEtme9ckkosvKApwD31taOi64RRT8bmic0umh77S0FGO0NA7D4q60haFhy7oEGHQ+xjN18vFnbZYUiSokhRplMOCosDaSqPTDQOyZVhLioShkmx76KuaEeqhqzKhaRh2Tnk7+HetJBHWSGGuIEeEQe9jHA3dklw0J8vFzJ1O5qHzylA3yloKihrM9tAB00uf4xp6I1xDV2UJjKXL12c+D10WWS6CDBAGvY9x2ue2euimhh7d3rWhm9IMr1Z0I8trx0M3fAZ9Ykh1gqJauIbO72zSBI9bCotElosgA4RB72N4haMsEUqy5CosMiwNPTooahr+VmMOmNWiUUVJg4RZWOQy6LWSExRtRBcWAUjVcTGw26Lw0AUdIgx6H6MZzM7OKKuSXVhke+gJJBd/61yOvOYKi7wGfc6TthjjoaeQpoygXi7CoAs6RBj0PkbTDduzLCuyq7CIJQuKWoY/iDWloet+D111gqIRlaK8iVmafi5OHrr5U0gugiwQBr2PcQfzyorkKSziQVGDITS/uWGlNwahDLCG/tVHj+GXv/KE/dgsLPIGRedXm2hoBhp6VJaLue86M+iu0v+1cUO0ZvnaY8dw7W//E675rX/C3z5yNJfPEAa9j9ENZhuViirZQdG65gRFAaAZYik0I1xDl2OKkvqZB188i3/Yf9J+bEpXXg+dMeDUwioARGS5cA89veRiFxaJStGB59HDc5hdbuKNl2zA1olqLp+RyKAT0U1E9CwRHSCijwS8Pk1E3yaiR4noCSJ6S/ZLFfjxeuiyx0MvKZKtr4dJJ3GSy6B66IbhNDIDzAujN8vFLC46cc406EHTioDOPHShoa89GrqBsaqK3/mRy3Hd+VO5fEasQSciGcBnANwMYDeAdxPRbt9mvwLgy4yxKwG8C8AfZ71QQSseDV2VPM253B56mEFvaCzUoMsxKY/9jM4Y6ppTEKQbzC4sApxqUW7QKwHzRAGnb3w6g27+5J8qBlwMPk3NgBpQ85ElSTz0awAcYIwdZIw1AHwJwC2+bRiAUev3MQDHs1uiIAy3Z1lRZCfLRWNQZXLlSQcbnKgsl8H20M2/i18A/YVFvJ/LibkVAEAlxEPnfeTTSC6thUVCchl0os63rEjy7lsBHHE9Pmo95+bXAfw4ER0FcA+Anwt6IyK6jYj2EdG+06dPp1iuwI3m0tD9HnpJkWNHyTV1A6WwPPSYHPZ+hksb/ALYUljU4qGHV4oCSNUT3V9YJJEo/R90mkb4HXFWZPXu7wbwBcbYNgBvAfAXRNTy3oyx2xljexlje9evX5/RR69dNMPwZrl4ui2SExQNsRRrVUPnDjXvTtla+m956OdMDz1MQ+eSSxaFRbIUno0kGAyamhHYZiNLkhj0YwC2ux5vs55z89MAvgwAjLEHAFQArMtigYJw3PnTFVX29HIpWe1z+XZBNPRoDX3NeOjMa9CHywoUiRwPPSRtkUsuorBIkASerJAnSd79YQC7iOg8IirBDHre5dvmMIA3AgARXQLToPecpmIYDAurTTsbpN/RXel2bg/dXSkKRGjoWpyHPpgaAPeE+QXQX1hERBivlXB8zspyCSss6iAoCl9hkRhwMfg0IxyorFDiNmCMaUT0QQDfACADuIMxtp+IPg5gH2PsLgAfAvCnRPRfYB6qP8HS9BTNmfd/cR++9cwpDJcV3PfL19vZDP2KKRVYGronbdE8cOKaR5keQ1ge+uB66FxK4pKL30MHgHXDJTxzcgGAMxHKT2dpi+ZP3nJADLgYfKJ6J2VFrEEHAMbYPTCDne7nPub6/SkAr8l2adlzaGYZALBY13B2qTEABt1wSS6Sd2KRQrFpi2u19N+wNXTLQzdaDfpv/8hl+PdDsxivlbBzqhb4PpkUFtkauvDQB52mboQ6B1mR77v3GIbVndBI2cO619D01sIixhiauoGyS3IJD4pGaeiDO4LOr6HrrjsdzlXTE7hqeiLyfTIvLBpMhUtgUYTksqZK/w3G7KDEIHifZum/o6EbDFbBDOyZokC45NKI8NDVDgYg9zp+ycUsLGr/fdQOjqWWXi4kBlwMOkVILmvKoOvMuUIOgvfp1tB5Jsb8qtklUFWSeOjheeiDPIKOG04ecwjy0JPAq/46SVvke3+Q97fAJErizIo1ZdANw/RkgfDMj37CraGXrQZSC6saAHiComGGIi7LZVCDosGSS/vvk2lhkRhwMfAIySVjdMNpF+s2cl999BheOrPUrWWlxp1uxy9Ui5ZBL8lJgqIsYsCFBH0AZKkg/JKLltJDVzIIivIWMqIf+uAjJJeM8WjorpPnw195An/10OFuLSs17jx0Lrks1i2DrkiRedKMsUgNXbEm2g8i/iwXg7FUFXx2UDTFfvL3cuHBesHgIiSXjDECNHRu2LhU0U9489C55GJp6J7ColZLwZ9bkxq630PXjZa0xSTYBl3rYEi0xH+KPPRBR0guGaMbrR46P4e4Z9tPeDR0q8WrV0MPD4ry50KzXArU0F86s4SPfe1JNKzmYowxfOxrT+K9dzyET379mcw/j+d780pRgyGVQZclgkTZTCySSeShDzo9U1g0KBgMtkHnZe08OLpoebZtvZ/B8NSJ+dBWAhIR9mwdy+2qrLs19ICgKJdcgjxt7lVG5aEza3ydlMLYtcO/PncaX3zgEK6/aAOuv3gD9h+fxxcfOIRaScZ3XziLj9x8caaf11pYlM5DB8yulGkkl8DCIuGhDzRFSC5ry6C72lfyQBY/idJ46P/6/Gn85OcfjtzmN37oUrzv1Tvbfu8kaAEaOk9bLCnkSC4BQTueahfaD90l15RyNuh8339j/0lcf/EGfHP/SUgE3Hr1NnzxgUMtE4U6xS+5dPL+JVlKKbn4CoskgnDQBxez4K8HerkMEjpjttbMDTmXFdJo6DOLDQDAH7zjCqwfKXteMxjDT3z+YZxbad/zT4q7ZH20Yn6VM0vmmtweepAHyWWCKA0dKCZff8ky6P/41Mv4rbczfGP/y/i+nZPYMm7OXVxt6pmWTPO/qe5OW6R0Bj1tAVZQYZGQXAYXbmeE5JIh7rRFvoN5at5So32Dzr3c11ywDpvGKp7X3OPN8sIcQWf+PaMVs4f3mcU6ANNzjPLQ4zR0O+XRMGD2ZGsfxhhOL9RDX6+WZIxUVNugn11q4K8ePIRnX17Ax96625YjsjbohktDZ4yl1tABS3LJorBIpC0ONHHnW1asKYPOAjV0S3JJ4aFzDy+oxzER5V7OrRuOhj5a5Qbd8tAVCWpE2iJ/TgnV0NP3+ub8ztefwe33Hgx9XZUJ9374eiw1dIzXVKw0dPzq1/YDAG64dCPue/4MAGBVyzZ90iksMmwjmnbwQEmWUuahmz/dhUVAMTELQfHExayyYk0ZdD2gl4tbQ2eM2ZpmEriHHta0Pu9AV9NgkF0aekmRcDbAQw9aQ0OLTlt0PPT063/xzBI2j1XwwR+4oOW1F04t4Y77X8Sx2RUs1TWsGy7jEz+8By+cXsSW8Sq2TdTsuAAPXmaFU1ik239fWiOqyOmGaQcVFgHmMSpBGPRBIy5mlRVry6AHVIpy/bOpm5Pgw6bTBMHT7MohX5KUcyqa20MHgLGq6njoMXnosZJLBj1vFlab2DZRxXuu3dHy2qOHZ3HH/S9iYVXDYl3DUFnBdedP4brzp+xtKlbmTtYGnX8lq03dNqxpPXQ1peRiFxZJPg9d6OgDCbczas53X2smD52fQC156K5zsd1Ml4ZmgCjcGMg5FoswxiyD7nyFoxXFDsKaM0XjJZeoEXRAZz1vFusahkO07xFL859fbWK5oWO43HohLdseeraSi+6SXPhxkFpDl6hDyYX/5JJLqmUIepyiJJc1Y9B1w7tD/R464GRbJKVuze4Mk2nkHCe5B2m/XEcHzAuXLBGIYtIWY4KinXjoi6uabbj98Kyc+VUNS3UNQ6VWw89Hv9Uz9tDdQVEeFE+dtqik9dDNn3ZhkfU1iEyXwaQoyWXtGHTrRCn7PHS3wWo3dbGuRQ99zbODnu1Zyl7JhcOlJVUKLnzhXmXUCDr356RhYVXDcCXaQ19YbdqSix9bQ894BizfHfWmYR8XaQ26KksdDok2H3PDLjJdBpO4NOGsWDMGndvVsCwXIIXkoht2yX0QeQZFtSAP3eUNc89bkSmwa2JTi/PQOx8EslDXMBIiuVRUCYpEWOAeeoDk4mjoGUsurqConoHkkqYfur85F//8HhzFK8gAfh4pKbp6tsOaMeh+ySXIQ283dbGhGaEBUSDfoKgecIC4PXR+axfW1zxvDb2u6WhoBkZCPHQiwkhFwcJqE0uN4DzzipJPlou7H3rHaYuKlE0/dOGhDzQiyyVjdF9QlBvEjjz0GMlFlpBbUJTLKIrs1tCdr5NXpIUVvsRp6GpEymMSluqmEQ4LigKm7DK71ERDMzAcoKFXcgqKOhq6k4cupawUTR8U9fZy4VkuQkNvxTAYHj86Zx8Hk0MlXLRppMurag/HgRKVopnADWtLpajLA23XoNc13X6/IPKs/guSCjweuuTy0AMrRb37w0+nGjpv4xsWFDVfU3ByfhUAUAvU0PNJW9Rdd2e8sZqS8kRLm7bIdyvB6bYIiCyXIPw9k4iAh/77m1rabfQyRVWKrhnJhZ9AskQebdtt7LL20CUpP8nF7g3hSVs0jacqk+3xqSHdAO0DLCQoGtWpMQk8wBwWFOXrPXnONOhBaYu5BUVdfxK/k0jroXeah07W1yeyXMJ5/uUFAMDnf/L78KE3XwjGgFMLq6HbL6w2cd/zZ/DgwbOp5LA84HZGVIpmhHNrbRr1TDR0PVpDzzMPPSjdjqctug8aRQ6+S0isoacMivKLY1hQFDA99JctDz1IQ+f7NnPJxWBQZVMq4T180gar+Pu0vQZfUJRsD10YdD+HZ5YxVlVx/UUb7LhKVNO7T33jWfz5A4cAAJ/78atw057NhawzikZBkssa8tCdyjxThsggyyVOQydCXmM5gzT0sSCDHiK5NOKyXDrU0LmHHi25qPb+DzLoRISyImWeh64zhpql2XMPPa3jpMidBkXNn7bkIjz0Fg6dXcaOqRoA5xg/txxu0E+cW7WD8TNL+XU7bQcnbVFILpnATxSZKNRDzyUPvUAN3ZFc3AY9WBJIqqGnnSu6WDdPpCjJxZ0BE1RYBJiySx5ZLkMl09Nbtjz0NEOiAXNfr2oGjs2tBKYcrjSC1+730ItsV9xvHJ5ZxvSkZdBrlkGP8NBnlxvY6mq93AvENcPLijVj0N3ZDIpbQ3edQO1WijasStEwZErXKzsJQXmt3HtxFy8ocnDaohZzC2hXiqaVXLiGHiG5jLoNeoCGDpiB0ewlF7N1L+DclaVNWxwuy5hZauA1n/xn3PO9k57Xnjo+jz2//g0cOLXY8v+47Sdflovw0L1ouoFjsyu2QR+vJjHoTWy22llnHX9JS9PW0IXkkgncrkoSQZakliyXWklOJbmUI5p5SVLRpf+mgXTnukalLRKFF9TYhUUpPcZ5W3KJTlvkhBn+iipnflLqjNkSzzIPiqY06D/7+lfg0z/6SigS4akT5zyvPXnsHHSD4dR8awCvpbDIzkNPtYyB5cS5VWgGsyWXWkmGIlGkQZ9bbmDjqGXQQ+6QikZILhnjlHibV0l/peh4VcVCyl4uYchSfh4Xl0Lcpf/cQLrXpIZUq640dFRVObQPTaca+mJdgypTZNDYI7mEGXQlH8mllpGHPjVcxg9fuRVbJ6o4dHbZ89qhmSUAwZkrrYVFztoEDodnzH263fLQiQhjVRVzIQadMYa55SYmhkrm3V3GvfTTEleZnRVrxqC7NcsgDX20qrY9KDpOQy8iD92dtihLhJGy4jlo5JCg6HJTt41aEJ1Wii6ump0Wo/rLuz30cA09W8mFMQbGYAdFuYaeNm2RMz1Zw5EZr0E/PLMCIPguJ7SwSGjoHvhFcsfUkP3cWFUN9dAX6ho0g2GipuYSf0kLl1zS1jskJZFBJ6KbiOhZIjpARB8J2eadRPQUEe0nor/KdpmdY4Rp6NaOHq+pKSQXPbr0P8/mXCFdAkerqkdyCctDX2noto4cRKfdFhfr4Z0WnbWaRlUip4jITznjk5L/PY6H3llhEWd6soZDfoN+1vTQgwLjdmGRT3IRHrqXQzNLKMkSNo06Ix7HairmQwz6nJXVMl4roarKoUHpouHnYNfz0IlIBvAZAG8GcBTAw0R0F2PsKdc2uwD8NwCvYYzNEtGGvBacFndXvSAPfbxawlJ9vq33bOjd89C1gLRFwDToLUFRneHxI3PYOFqxZ58u1TXU1PCvv9M89IXVZmRAFHA89KEIT76iypkO2uZfx5Cdtpidhz633MS5laYdnOYGPshDN6djOY9FlovDgVOLuO/50wCAB144i20T1ZaK6LPWIBc/s8vm8xO1khV/6RXJpXcKi64BcIAxdhAAiOhLAG4B8JRrm/cD+AxjbBYAGGOnsl5op9hBUTIHP/h7uawbKWGxrsV6rhzGWHxzrgK6Lfo99N2bRz16ME9b/Jkv7sONl27EJ374MgDASjPOQ+8sKBrVOpfDNfQow19RJJzK0EPnHvCI3Y/dvFik1dA5PGh3ZGYZY1vHcG6liTkrVzrYQ2eei4iT5dLRMgaC3/2HZ/CPT71sP/6RK7d6Xh+rqjh4einw/zoGXUVZkXpIcjFsZzJPkhj0rQCOuB4fBXCtb5sLAYCI7oc5Iv7XGWP/4H8jIroNwG0AMD09nWa9qXFrll4P3bT0F200m/0cOLWIy7aNxb6fZpjT4ruVtsgvSKovf/oP3nmF57EqExqagTOLdcy6ijGWG9EauhMUTZuHrtmpY2Fwoxq1jqx1UHfMBIBtdDs90aYnTY330Nll7Nk65tHTgzV0Rz8HRFDUzWpTx2Vbx/DFn7oGgLdHEWAmMITdtfHvc7xWQrXUSxq60bHTkISs/H8FwC4AbwDwbgB/SkTj/o0YY7czxvYyxvauX78+o49OhrsQRwnIcrlk8ygA4Dmrb0QcvNIyutti8R560BpOL9TBmDfP3jTo4dfzTodEL6yGj5/j8EKoSA8946AoN5g8/Y1rsR0bdMtD55kt7oyXICNt+AaSO2mLwqBrOkNFlTAxVMLEUKklpXSsqmJ+tRl45+P20PPIkEpLU2e5pywCyTz0YwC2ux5vs55zcxTAg4yxJoAXieg5mAb+YfQI7tL/IA39FeuHUZIlPHcqO4NuNufqZNXhhGnoflRZstMxec41AKw0tERZLp0EReMkl7IiQZUpNGURyD4P3S29DbtmsHbqPQ2XFUwNlfDY4Tn81t1PeaqOA+MQfg+dSy7CoEMzjMgL7GhVBWOm08ArRzn8LnSsqqKiSjiz2F6iQ140dSP3XuhAMoP+MIBdRHQeTEP+LgA/5tvmqzA9888T0TqYEszBLBfaKe60xaBK0bIq4fz1Q3j+5daqviB4s53IiUWU3wmadNKO21Attnjo+WnoUfNEOeaQCzXeoGcpubikt6GS0xwsbWGRm+2TNXzTpf3an5lAQ7cvoEJygWYwu9NmEGOualG/QZ9bbmC0okCRJVRLMlYK8NCPzCxj/3FvQsX6kTKu3jFhPy5Kcok16IwxjYg+COAbMPXxOxhj+4no4wD2Mcbusl67gYieAqAD+CXG2Nk8F94uvAIvrJeLLBEu2DCMx4/OJXq/erPLkkuIhu7H3TuCdxYETIMeFRTtxEOvazoauhEruQBmdsi2iWro6xXFlFyYT6JIi+HKdhqpKDg211pxm5ZLNo/gxTNL+Pgtl+JT33wWqizh4Oml0MIiT1CUnOfXOprOIr+P8VoJQHD5/6xVVATkU5QWxH/568ew79Cs5zkiYN9H34SpYbNne0M3cs9wARK2z2WM3QPgHt9zH3P9zgD8ovWvJ7F7uUim98mv3O6eKBduHMHfP3ECyw0tUl8GgIZu/v9IyYXyHBLdWikahLt3BNfQGWPW3xifh56m1/dCgrJ/zl/+zLWRshFvrVDXjEivLSn8jomIPBecTtMWAeDX3nYpPnLzJRirqrhh9yacnF/F9Z/6l9DCIvdH8s8XkgvPCAk/r8Yi+rnMLTdsg18pyZn3AQpisa7hVedP4VffuhsA8G8vnMEn7n4apxbqtkHXdBZpK7JizfRD570zWj108wuXCLhw4zAAM9Pl8lRcGsYAACAASURBVG0tMV0PdS2+N0PRQ6LD1sDhkktdM2C4qiWDkCQCUToPnRdzVBMY4Ci5BXCGXNSbGRl068+RJa92n0UFX0WV7TVWS7JteIKMNPN56CIP3UG3+tWHwffr3EprLvrscgPrLCNalIfe1A1MDpWwe4uZWMEvNDNLDc82eTfmAtZQ6b/uCor6+6ErEoGIsMtKXXwugY7Og6Kxeeh5DYlOaNDdt3mrTQOabiQ2uKqriVk78JMoCwNsj6HLKDCquy7s7qBtHvnBUWP8wjx0oaFb52SEoxTloc8uNTHJPXS1mDx03WCe42dq2Pz8sz6DnnaISjusHYPuKv13e846c76MHZM1lBUJT5+IrxhNYtBlyq8felD73CD8Bn+poWPZOsjDWtZy0t5h8NvcTAy6wgdFZ3NiOpILPIOp5QwkFz/8uEpSWMS3DeqpvtaICyCOR/REd0suVVWGZrBUsmE7mBcgZ72TloY/6zLoDZ31TJbLQMBct9ruHuG6KwCjyBIu2TyK/cfPhb2NTT1B2qKS60zRZBq639NZqmtYsYKj1Zg4Qdi0ozi4Nx3Wn6Ud7LmiGWmh7qCo20PPw3uKyuVvLSwS7XM5uhEdFK2oMkqKhL9//ISngIsx02GZsAy+c+zouQYk/UHc8aoKIq+HrumGpyVHXqwZg+6dKSp50hbdt0uXbhnFXY8dh2GwyFS2pHnoeU1xT6qhq5IzEUc3mGnQLW+3FuNBy64CrHbIRXLJyEN3ZzV5gqI5nO92oDPgou7P2hFDoh2aerTkAgBvvHgDHjk0i2897e0ysnW8iqt3mumCzrFjYCS6aLkjTBvinUEwVlUxs1S3n2v2UpbLIKC78tBVySnJ13163Z6tY/jLBw/jyOyyp2WnH56H3rX2uXoyg849+K3jVRyeWcZiXbO93agsF/7ezQ4klyRB0TjcXlYWcOPqz3LJ1UMPuMsxjGAPXWS5mIkKccf1Z3/86tj3yfrYCUM3WgOek0MlzLrmmTZ0hmpJaOiZYbg8M1kiT3Mut4e+Z4vZx+XJY9E6uqOhx0wsym3ARbLCIp6nzptHLTd0rDS55JJAQ08juTSzlFx4UDQrycX86Q+K5uKhRxQLMYQUFmVg0P/1udM4tdA6Jalf0HSWWdYRkL9B99sQAJgaKuGs20PXipFc1o5BD9PQfd7AhZuGoUiEJ2N09LoWn4cuS3lWiprl0XHFNvzE4DMZF+salq0sl7hceyVllguXdKIudknh75FVX2tHcvGmTOaVgWBWJbdejFoLi7Lph84Yw/v/fB/uuO+ljt6nmzSNbOSJrOMvYQQVQk0OlTxpi1pGf1Mca1By8WZv+K+uZUXGhRtH8MihWRyZWca2iSqICKfmV+2eKEMlxdHQY7ot5hcUjQ4ccbicxA36kjXRBUgguaTU0OuZaui8sChbg05EGHFl+eRVlR02V7YlbVHKxqA3dYaGbuDo7HL8xj2KPw0wLVzyy7v8X/dp6IBp0B9xVY82dSYMepZ4JxZJntJ/v2G8YvsY7nzoCL7/976Nz77nKlyyeRRv+NS/eLZ5x9XbACRozpWjhp7EoPOgKJdcluqafbcSZ9DdBVjt4KQtZie5nDi3mshIbR6rRhoDFiC5JLnTSUuYh95SWJRRlguP7Zw415+SC2PMNH4ZGPSsA+phaGEa+nLTTq5oaEbu4+eAtWTQfROLwjx0APivN1yE686fws9/6TE89/Ki7eX+0o0XoaxI+MTdT+NJqxlP1/LQE3ox3Gi9Yr1ZBbvo6rgYL7mkzUPPzkMfKZspaJ/8+jP45Nefid3+va/agY/fsif0dffkKj61KM+hA2ZgvPX5Vg/du7608DvHE3MrHb1Pt7AL5jKVXPIz6IY1F8F/DE0OlaEbDPOrTYzXSmjq0QPls2LNGHTd46G7slx01qKfTg2Xccsrt+I3//5pnDi3gokh06i8Y+82VFUZn7j7aRyy5kXGlv7nmIee5KC/YfcmfPk/vQoXbBiGRKaHTmQW1sR50HLaSlFNhyxRJreYYzUVX/ypa3ByPt7j/J//9DyOxxgyR3Jxes3kUVTECUv99GvofA2dFhZxg/7yQj0z6aJIkvb5T0KlAMklLH14asipFh2vlaAZQnLJFH8/9CgPnbN1vILj51YxVlNRkiWsGypDsrr0LaxqUGWKzFXPIw+dMYZPffNZ3H/gbCLJpaRIuOa8SQBmEHCxrkGWCFVVjg+ouloktMNq00Alw6q4112YbBjKlx46bBd8hcHcHroVFM2zrWlYHCW09L/DOzpu0HWD4dTCKjaPhXey7EW4gcyi7wl3WOo5BkXD7igm3NWi680slyIklzWT5eJun6v4mnOF7ejNY1Ucn1vBiblVbBqr2MZ7i3WSxN1C5REUPbvUwGe+/QLmlht4w0XtTX0aLitYsrJc4vRzoBMNXc9EbmmXcoJmTHaWC5Hd+iCLXuhhhLVPYCEzRTs26Lrz9x+f6z8dnTsQUd0Wk2JLLhkOSPHT5INmIjx0wBooX4CHvmYMunemqATGTP1LM1ho69TN4xWcmFvB8bkVz3zMzePm73HtMPMIinIP9CM3X4zfu/WKmK29DJUVLDXM0v8kg7BVOd36V7pk0CuqFOuh667CorJilpDn6qGHHAP+wiK770uHDoD77z9xrv909Cw9dDvLJaOU1yB4nUarhm4adJ66WFSlaN8Z9G8/cwq/+NeP2beWSXFLLtwj1wwW2Tdi63gVSw0dz728gK3jzq3rFuv3uDxrrotmGRitd5DjPVSSsVjXsdzQ7YBgFGk9dLPVbfGHVpLpRu6ePoB515JrUDRkH7YUFmWV5eI26H3poSdrOpeEIvLQtRDJxW3QdStwKgx6AM+fWsDfPXrMTs9KivtW22lrakRq6Fx/nF/VbK8cALaMJfPQ+feXdoxbEM7ou/a/uqGygmWrl0sSD72syPYFpB26J7nED5R29/QBijHowd0W4Ylh8F879dDdBv14H3rovDNiFndNZmCecpVcwtpYV1QZQyUZZxbr9iDyuBm7WdB3Bt2eddmmQefnFO+HDrg89JDbuy1uIx7goSeRXMzPztJDtwx6Cg+YB0WTaujDVvC3XVa1bkkucmwBkrsvPmDuk6546IwFSy4da+j97aE7QcZsvpOKKucquTRtzb91vVsnqjg6u4Kjs+aFNWrUYlb0nUHnPYWbbfYYMQy3hm7d3uqspVOaG48Rd2ULbG4jKApkO4WmnqCHTBjDloa+VNdQVeO9hdGKivk0Br1LkksSD909uQoARvI26CFjCMPSFrPKQ5+oqX2qoVseekbyRJKLfCdEDZrZPlGzDLpZECcMegC8gqzdpvXughKvhx7e2W3dcNl+zSO5WL/Hecl5THLnB2c6yUXGUl3HSjOZhz5aUbCw2jpEII7Vpm4PpiiSRB66ddhwYzpcKcBDD+q2GFb6n1Ha4o6pIbxwegm/8f/291UbgGbCLqJJMacWta+ha7qBt/zP7+BbT78cvV1EIdS2iSqOziw7Hvp4re11tEvfGXS+49odvOBkuZD9HrrBoOnhGrosETZZernbW+fPxXnoebREtSWXNEHRNiWXkYqCuma07eF0TUNXzaHAUcU5TnDcfPzaC9bhdbvaS/9sB1kK99D9dQBZFKJxyeUHLt6AsiLh8/e/hL995FhH75mEs4v1TDo8Jh2tmJRqSsllqaHjqRPzeOp4dNdVLSRtEQC2T9awUNew//g5jJQVjFbz19D7rrCIpzM126zYcfdycQdF46ajbBmrYm65idGKaj9XVmSsGy4nCIrmKLmkkDSGraZi51aaiYKiI9bfvLCqoTyc3ECvNo1U6+sUftdS18IHSrvbKAPAT732vFzXpCTU0AFTDuz0UOHHx49ctRX/3xt34brf/hYOz+Tvof/KV5/E7HIDX7rtVR29D7/zziojpKLKqYKiPEYX93+1kLRFANg2YXrk3z04g61Wk7+86UODzjX0drNczJ9uyUW3gqJRt9yvnB4PNNxXTY97vPYgovphp6UTyeW89ebAjoZm4Lx14cM7OLw0fmFVsyepJ6FbHrrTmTHcoLsHnRRBWC2Cf6YoX1NWkgs/Zqenajg8s9TReyZhdrmBl850fuHQjXADmYZKgmKzIPhFOGnWVFDe/PZJ0z6cnF/Fnq2jba8hDX1n0KOmwEThb58LmF9aXBva//6WSwKfv/29e2M/08lDb2upkXQSFH3r5Vvw/bvWgzFmD9KNwvHQW3V0xhhePLNkH9BTw2U797ZbGrrtoTd1oKoGbmNnOxVk0MManPkLi4D0Q7nd2INXZHP/T0/W8J3nT3f0nklo6gynFzvvH2Nr6FlluZRknJpfxfMvLwAwZZAkzgZ3GOP6wNizfQMSK7iH7v89T/rOoKf10PktLlntcwG3h56PPJDHnEieFx4n94QxFmLognB76H7+5pGj+PBXnvBs++ivvhmKLGFV615hEYDIalG/5JI3UsgYQsM3UxTIplWEfzTijskavjJfz/2uqamb8uWZxTo2jqYf4MkNZFaSy1hVxb3Pncab//BeAMANuzcmcsb4hSXOu9cigrhjVRWjFQXzq1ohGS5AHwZF+RfdbrGO7irxl11evpZgfmFacgmK2h56/l+dY9BbPfR9L81gvKbij37sSrz7mu1YWNUws9SwT+ws5om2C98nUSehv7Aob5SQ9gksYA2SROj02h8kuQDIXUfnBvBkh33Ys+y2CAC/8oOX4I9+7Er80Y9diUu3jOLlBF07AUdDj2vsFRfE3W4NltkaI89mRd956PxWrNlm6b/OmK1pt2joOXVByyMo2ijQoPNAcFAu+pPH5nH5tnG89fItkIlw50NHcHqxbgdbu6mhR+meRtEaeojXzRiD5LszlCj+WLn93hfw/x4/YT9+36t34lZr2ApgHh+85z/gTKo6fHYZF24cSf13xMHvmE/Or6K9DkNeuMerZnTXvHG0grdevgUAcPcTJ3Dg1GKi/5fUQ+ezfcMkou0TNew/Pl+Y5NKHHjrPcmnPSDLmaNqy3cvFSDzKLQ355KEb1lzU4gy6X3Kpa2Z/mz1bzEDPuhEzYHp2sZHptKJ2sdulRmQmuAedFEGohs5aLypJ0hb//okTOHFuBetHynjxzBK+uf+k53V/V78dU2bw+1DuHrr5vZ9K6AGHwT3jPFrNVkuyPU83dh1GsiwXPUJDB5zAaFGSS9956LbkkqKXCz+H28ly6YR8JBe9EO8ccHpP8F4UnOdOLkIzGPZsHQMAOwPmzGLd9mjKXWqfC0R76P7CorwJ7bboKyzia4o7VpbqGq45bxJ//J6r8fY/vr/FQDU0wxNfmaipGC4rOJK3QdccD70TwgZGZEGtJCceduF46NF2JkpDB4Bbr96OkYqK8Vry2FUn9J1B5wHN9tMWHcnFneUSl4feCXl56EUZdHNMm9zioT95/BwAYM8WbtDN7Ba3Qe9W+1wgmYeeUxy8hXCDHlJYFGPQVxq63bahVpKx3Gi9e3IbdCLC9GTNnrCVFw1bQ6939D5Zl/67qZWUlv0Vug6e5RLj0cf1nrlo0wgu2pSf1OWn7wy6LbmkqBSVbQ3dVSmaY5ZLVlNo3NSbRqqUxbSMVNSWoOiTx85hpKLYt5PDZQVlRcIZt+RS0EXHTRIP3ZZcuuyhBxcWUWxh0bKrbUNVVXB2seF5va61DlLYMVXDvc+dxjv/5AEAwOsvXI8PXH9Bm39JNNzBShp0DH+f/Dz0qlVJnOSu3PbQYySXZo53FGlIdNYR0U1E9CwRHSCij0Rs9x+IiBFRfF5QSuzS/3YrRVlrlgvPyMjbQ882D10vtApzJKDj4pPH57Fny5jtYRIR1g2XcWahbp8A3fTQk2W5FGXQpeSFRVJ8Z05324YgCaERcAd369XbcPm2cUgEHJlZxufvfzHNnxKJOyjaCVl3W3TD91sS2YVXosdnuWQ3YSkLYldBRDKAzwC4GcBuAO8mot0B240A+HkAD2a9SDe2h661m7bonMTu4qQ8NfRc8tALlFwAy6DXHQ/dMBieO7mASzZ7K9/WDZdwuuuSS4I8dFcb5SKQKTjFNrCwKCRnnaMbDA3NQM0aTjJUbg3y+TV0AHjjJRtx523X4Uu3vQo/8eqdOLPYwHyKpmtR2B56p2mLdj/0PCQX8/hIIrtoGeShd4Mke+0aAAcYYwcZYw0AXwJwS8B2vwngdwHk2oTZLixK0cuFG1huwBsZNtMPIhfJJeCEzRNTcnFOgGNzK1hp6rhw47Bnu3XDZa/k0sVeLlEnobuNchFEeeh+DV2KyXLhhsgtufg13oYefXzwlg8vnclOU2eMoakzlBUJC3WzPXNacpVcrAthkmZddi+X2ErR/O4o0pDkrNsK4Ijr8VHrORsiugrAdsbY3VFvRES3EdE+Itp3+nS6cuS0pf8GY7Zuync+D57lnYee6YALTS9YQ/dKLs9ZJdS7Ag2646F3o7AoyVBgveC0RVkKv6AHeehRnSK5Iaq6JJflhub5P40ADd0NN+gvZmjQuVHjRTSd6OhFSC5JUhe5Nr7S1CO/k6wLoTqlYzeKiCQA/wPAh+K2ZYzdzhjbyxjbu359upalzoCL9guLyCe5cH0sNw09Bw89SCPNk9GqNyj6vFWYccEGb+R+3UgJM0sN2+h0Q3LhhixK9yy6sEiWpECvO6w5V9SxsmTtW9tDL8kwmFdiCpJc3ExP1UAEHDydnUHn5yLPtX55Pn2mSzPj0n831TYMOvfQDRadgKHz7pD9oqEDOAZgu+vxNus5zgiAPQD+hYheAnAdgLvyCoyqdtpimx660Zrlwk+E3LJcMhpa4KYbGvr8itdD3zhabukJs264DN1gOGFpqN1onytJhJIiRXroRuFB0eALelBhkdmZMfy9/JJLkMcZJ7mUFRnbJqqZeug8nrXBKjCbW25EbR5JVDvaTqlZTkYSycXtMEYdT7aH3keSy8MAdhHReURUAvAuAHfxFxlj5xhj6xhjOxljOwF8F8APMcb25bFgfiuWZqYoP0j4Ty659FUeesFpi6MVFQ3dsKWUA6cWsWtDa14tLy7i03G64aEDpo4e5aG72ygXgRKpoXufk2OyXBzJxclDB7xBvjjJBQDOWzecqUHnsagNI2ZTrtnl9AHXfAuLzP2WJCjqdhijdPQ815uGWIPOGNMAfBDANwA8DeDLjLH9RPRxIvqhvBfoh++4dkv/ddcJZGvozfABr1mQT1C0+LRFwCz/NwyGA6cWccGG4ZbtHINujtvqRvtcIH4MnSO5FLOe8Dz0gMKiGMlluUVyaQ3yxUkuAHD+uiG8dGYpUhtuB+7Nbhg1j4HZjjx0s1leHsMgqm2kLbodxmgHgRv03pBcEhUWMcbuAXCP77mPhWz7hs6XFQ4RQZWpbQ3dMJygqOOh56yh5xIULV5yAcyOi6tNHcsNPbDJ0/oRs1r06NwyJApu+F8EcTMkuWdcxPQYILr033/YUchAaY7foA8FSC5JsqB2Tpmj0c4sNrB+JPngkjD4uThcVlBV5Y4klzzTiNsJirpTTSM99B5LW+y7SlHAvBqm6eXSqqFbWS65B0Wze0/ToBeY5VI2tfL/+GcP2c/5M1wAr4deVeXCDKafshLvoRdVJQrEGfTW0v9IyaXJNXTztA0K8jX0+Av+eevN7+/g6cVMDboqS5ioqR1JLk2d5RIQBdrMcnFJLlEevWYYICquriGO/jToMqUo/UdLpajtofdR+9x6s7jmXADwfTsn8c692+yT4PqL1+PybWMt241VVXzg+lfg0NllXLa19fWiiPPQdaPYky9saIVhoFVDj8tyqXs9dG7YuaEHkmnoO60e6YdmlnHt+VPxf0QM/FxUZQnjtVJnQVHDyO18tCWXRIVFrqBoxPGUZ7fWNPSlQS/JUrrSf+s496ct5jexKCfJpUANfaym4vduje9wTUT4pRsvLmBF0STx0Is8/7iHznyFRCys9D/isA7KQwd8HnoCyWXLeBWyRKEdGD/8lcfx3YMz2DxWwf/5mWtjPWbuoZcUwsRQZx56ngayJEuQJUroobsNenQriV7Rz4E+7IcOWB5626X/QRp63lkuzmdngaab/dtLcncCjv1ArIZuFC+5AGhpuhU0sShOcrE1dJVXiraftgiYnvSW8QoOnQ026Pd87yROnlvFgy/OYGYp3ttuusr1x2ulDIKi+ZglIkJNNdslPPzSDJ49uRC6bTOhht7U85t4lob+NOiS1H7pf8DEIicPPed+6Bl56Dw9rBs53v1C3JR39+SqInBaNXuP19DCoiiD3tRQkiW7QZ3dbMoy6Ly/f5IL/o7JocChF6tNHYt1DTvXmbJM0DxZPw3NkVwmaipmE1wEwtB0lmsZfbUkY6Wh45e/8gR+8++filiHOw89OsulV3LQgT416KpMqUr/QzX0vPPQM/LQixw/16+UVSl2SHRRRUVAeMfNoH7ocQMuVho6amXHWA+VTcV0ydKE/fNEo9g+WQuUXLhHPj1ptghI0pfFI7nUSji30kxdTJe3Jl0ryVhu6jhxbhXPnJwP3a6dPHQhuXSIKkupBlzwW20ic+Yi/6Ly9tDbHWgdhjMgWkguYcR56O4CsyJQQjz0oH7ocSPolhu6LbcA5oWdyPHQ2zHoO6ZqmFlqtPS65wZ9hxU4bceg86CowZC6m6MZFM3PLFVLCk7Nr2KlqePMYgNnFoPbFLi/r+i0RSG5dIwiSylK/71TamSJXB56zkHRCIO+2EZnOh7EFR56OHEeuh4gdeSJM4bQ+3xg6T9Fl/6vNHQ7IAp4NWEAqOvmzyQG3R4e7fPSz9oeuvl6kuPTn7YIpK8Wber5e+gvuaY3henoms4wbN0BxXnovdKYC+hTg67K1NGAC8D0nHhQNK87prjS/yePncMVv/FN7LdGusXB1ys09HDKcR66UWyWiyKHa+itM0URWb251NDsVEVOtaTYBt2W5BJ4uLZB9wVGZ5bqnteXEqT4NXS3hm4WmKUNjOpGvhp6rSR7moc9E2LQG7qBoXKSGbWsa0V0QfSlZVBlqW0NXWfeK6kskavbYr4j6MI89O88fwa6wfDU8XAtz42QXOIpq9G9XAxWrEdlt3/wGeogLT9upuiyz0MHrKlFKTT06akQD90aabfd9tCTl8mXZMkehpw2F72ZY5YL4G3rLBHwbIiOrukMFVWG4pJmw7YTHnqHKBLZGR9J8Z9AiktyyW9iUXRQ9JFDswDMoRFJsD10IbmEUlFkNHQj9CLqnlxVBErIMWD2cvFuGzfgYsU1fo5j9kS3PHQ9uUEfraiYqKl46eySmQ5r/d+ZpQYUibB5zGy0tZggy8VOW5TJ8dCXUmroBUgunMu3jYdLLoYBVZZQseaQhqEZ+V6A2qUvC4tUWUo8vZvjD4YpsmSfCLn3Qw84Rxlj+PfDlkGfTWjQhYYei3sMnd+bBbwFZkUghRj0wNL/mCyX5YaG7aWq57mqa66o7aEnDCrumBrCnQ8dwZ0PmfNrfvq152FxVcPkUAm1kgyJkgVF3ZLLkCUJ9arkwhuaDZcVXDU9gTsfOozvHT2HCzcNe+58uZZfUeXogSk5r7dd+tIyKDK1nTmi+7TTreNVO8KdW5aLtXeDTtKXzi7bGQVHkxr0Nm6p1yr8YhdWLVp0L5cwD90Minq3jctyMT10rw82FKChJz0+fu1tu/GhN1+ID735Qly4cRj3HziDs0sNTA6VQEQYKivJgqKuC8lIRYFEwFzaoKjlGecF99A3jJaxZ+soVpo63vZH9+H3/+FZz3aazj10CasRlaWi9D8DVFmyD96k+D2iXRuG8diROQAF9HIJOEn/3ZJbLto4kkJyERp6GNxD//Q/PY/3XDuNXb7OkHqX8tBbJBe0roMouvR/udkquVRLsu2YtGvQr5yewJXTEwDMbJbP3/8SyqqMqWFTNhkuK+2lLSoESaKOqkXz1qT5/ts4UsHbrtiCdcNlfOhvHm9JX9QszzvOQ+81Db1PDXr7Hro/GObuGNiNIdGPHJ7FSFnBGy5ajzvuf9HU+GPWYQdFRZZLKBduHMZwWcEX/u0lNHQDv/32yzyvG12qFA3y0IP6oUeW/tdDgqJNnraY/g5u18YRNHQDTx+fx417NgEwC5eSZLm40xYBYLym4tvPnMJ//stHArffMTWED994UWBHzrwLdfj+2zhahipLeN2F6zFRU1t08oZmQJWk2FYSvaah985K2iBt+1yvh+54brk35wow6N89eBZX75zA9skamjrDqYX4OYx1USkay96dk3jyN27Ejqla4Kgxw0CxvVxCslxCC4tCHBVNN9DQDdRUrw/mDoryGEtSDd3NLmtoSUM3MDVkeuim5BKf5dLw9QR/6+VbMFRW8PzLiy3/Hn5pFp/9lxewEOL5m1JHjh66yg16xX6uqsotLXJtDz2ulUSPaeh96qGnKCxi3rapRXjoYSfz8bkVHDy9hB+7ZhpbrcG6R2eXsWms0vIebkTaYnIqihxo0PWA/O88sXu56EEaur/bYriHvtz0ts7lVFXFqRTV01/w3VOoJoe45CJjMUHFZ1M3W/Zyj/sX33whfvHNFwZu+5VHjuK//s3jmFtqYrSitrye54ALwGk5vMFl0Mtqq9HWdAPDZQUSUWQCRlNnqKi9Y9D70tVLM7HILP13Hm8Zq9onR35B0WAP/b4DZwAAr921DtvGTYOeREevN0VhUVIqpVavC/AOCy+CsBbKgTNFiVq6MnL8rXM5poeugTHWtobuZqisYKt1LHo19GR56Em91Mkh04jPhGjseQdF3ZKL/VyAQTcHbZga+kpMYVEvBUX70jKkyXLxa6eSRLZXkucXEpS5cP+BM1g3XMZFG0dcHnoCgy4kl8RUVSnYoBdcWOR0W3SOAcZY4ExRicJrFrisMlT2GfSyDIOZx0YnBh1w7lq9kkuygcpJjbCTpx5s0PPOQx+rmheULeNO+meQTs618Yoq2Y5UEKY00zvnZEu/VwAAFCBJREFUYx9LLp0VFgGmjv7E0XO5nuCyqz9HUzdw8PQS7j9wFq+9YMrsxVFSMDlUwlMn5iP7MwPAiXOm0U+jka41qqps9yVxowcY0jwJiqPw67v/sJMk884z6Dg4eHoRgCmxuOGa8JPHzuHorFn1mfb42LVhGP/y7GlMDpne63DCoGhDT+5VczknrM+6lrMm/ZoL1uF/v3cvrtw+bj8XlMnC2/hWVBkLdc3znVRUCTumhqzteqs515ox6HpA/vGlW0Zx1+Nkp7rlgSQ5t9u/dffT+MK/vQQA+P5d6+1tdk7VcPcTJ3D3Eydi32+konRtXmc/US3JWJkNkVy6oaG7DDo/HvwOBs8pv/HT94a+Hy+t50xYBvLWzz1gf54/Vz0pl24xRwfyKtGhpGmLmoFSwp06HtPrJc8BF4C5f960e6PnuaraGm/h0s9YVcXphXrLd3Ln+6/Dq14xlbvm3y59adAVKU0/9NZZku+5bhrXnj9p95XOA/ecyBPnVrBlrIKP37IHr7/IMeif/tEr8WTCBl28aZIgmkpA5gLQBckloJ+PEeKhv/9152PP1tFQHb1WkvF9Oyc9z920ZxP+93v32gHRTWOVwArZJLztii04f/2Q3cdluKygqTPUNb0lEH92sW4XIDV1A2pCmWe0okCWKNxDz3nARRCVIA1dMzX0n/uBC7B3xwT4V3J2sY5f/dp+HLdiXpqR31DrNPSnQZclaAFzGqMI6rJXVmTbK8kLyZWKttzQsX600uIhTE/V7GZJgmwI68GhG8mPmSxwui26JBfLPPjXMVZVcdOezW29f1mRW46ntMgS4fJtjhQxZF0YFlc1lIcdg35msY5Xf/Kf8ekffSXectnmtjR0IrPfS1h73W5UXgYdK7wv+3ithJsvc76TmaUGfvVr++0+8r3moffOpaUNSgEnSRz+botF4Z4TueIbUCDIj6DMBcDUr4vMQw/qtuho6L1jCIIYttIK/ZkuL5xaREMz7C6hjTZ15Mmh8DF1eQ+4CKKiSmjohicg3dQZ1IC/aaRi+sB8NJ+YKZoB/AtvR0cvuuSb45ZclgO65QnygReL+PuLF31h53qwrgdp6IUtIxXDVkaNP9PliJWRdcQKwjYTDKZ2M1ErBaYtMsZyH3ARRNVu6OZcuMxUzNa/ifd34YVRwkPPAP6Ft1NcxAIKOYrAXSyy0mwt3RbkQ7UkQzdYyzFiSi7FrYPH99weutEnHrp/ZimHzyLlP7U2JBfAzHQJ8tD5fim6lJ4nRbgDo82IbJuRimpLLr2moffOStqAewPtlP+bV9K8VhSO10PX7PaignyxT1Kf7MK65aEHZLn0uD23DXqrh24acl470WizXH9iKLh5l7uvepFUrEK9VVfDP003e7kEMVJRMG9JLppuCA+9U/hJ0o6HrhfclIlj9ucwfw+aOCPIB/sk9Rn0omeKcifCExS1jodeTz/lMzX9qYtHZ0xDfmqhjtWmbma5tOOhW0FRfwU130fdCIoCjoeuGwwGQ+jfZHrolkEXlaKdw6/g7WjorOA+2Byeh84YC5w4I8gHrou2GPSCJxbxxm9GgIfeQ3YgENtD900tOjyzbF8wj86u2L1ckjIxVIJuMNsocnicofigqPdYibtTGK0oniyXXmrO1ZcGXU2T5dLloGhDN6AZTBj0gqhGSi7FrYM7EUkKi3qN4QDJpa7peHlh1c6HPzK7bOVst6OhB/dzaRq8DW93g6L8uwpbx0hFwcKq2T9HM1hu3VrTkGglRHQTET1LRAeI6CMBr/8iET1FRE8Q0beIaEf2S3VQ28xyYYwFFhYVAZ8T6TRXEhp6EVRKrYEuoAsDLuTkhUW9Bs9Df/TwnGdcImPAq14xBQA4OrNspu61o6HXgsv/ebFg0Zq0I7kY1jqih8ePlFXMrzTtuEhfSS5EJAP4DICbAewG8G4i2u3b7FEAexljlwP4CoDfy3qhbhwNPalBN392Q3LhcyJ5cyXhoRdDmIdedCwlyENndlC0dwxBEIosYeNoGXd/7wR+9E8ewGJds1MWr56eQEmRcGR2BY02JRfez8Wf6aJxD73wLBdvvKWpJ/PQbc2/zySXawAcYIwdZIw1AHwJwC3uDRhj32aMLVsPvwtgW7bL9GJLLgmDonoXNUs+tEAY9GIJ09CLLiwKGkPIf+t1yQUAvvkLr8fv33o5mjrD40fm7FTF6akatk1UccTy0NuRXGwPfTnYQy/aQPov/vzCEqblj1RUrDR1e6BIL3noSe7/twI44np8FMC1Edv/NICvB71ARLcBuA0ApqenEy6xFX7waFEDGF3wW6OuSC7WWDFbchGVooXgv43m+IeF541t0F13k/0SFAWAsZqKGy7dhA//7RN45NAsFusaSrKEjSMVbJuo4ejsipmHrrRTKWoa9E9+/Rn88bcP2M83uyy52B66xj30YIM+WjXNJk+97CUNPVNBl4h+HMBeAK8Pep0xdjuA2wFg79697XXXcsGv4A0t2VvwE6hbpf+mh24GlvJsBCZwCJVcEsxuzRLHQ3ee65fCIs5YVcWFG0aw79Asjs4s48rpcUgSYftEFU8cnYPeZnHNUFnBL7xpF148s9Ty2muVdbY+XxS2Qbfy0OOCsyNWSwRu0IsO4kaRxLocA7Dd9Xib9ZwHInoTgI8CeD1jLH5AZge066EbXdTQJckcxsFHiIk89GKolILz0ItOX+W347rrWOUB0j6x5wCAq3ZM4Mv7jkA3GH729a8AAGyfrGFuuQmJ2u/B/gtvCh5R1w1sDd26i7aln4jCIgCYsxqM9Vth0cMAdhHReURUAvAuAHe5NyCiKwH8CYAfYoydyn6ZXvhJklhD7+IJJBM8kovQ0IshNA+98MIibtCd57ic3utBUTdX75iAbjCUFQk3X7YJALB9wuwQarDeCgy2S7t56Nygcw+9lzT0WIPOGNMAfBDANwA8DeDLjLH9RPRxIvoha7PfBzAM4G+I6DEiuivk7TKBe+iNhFku3CPqruRiGXRVSC5FENSfA7AKi7ohuRj9qaFzrt4xAQC44dJNtuSwfdIZ49ZL/UzaRZUlKBK5gqLRWS58uDVPu+w7DZ0xdg+Ae3zPfcz1+5syXlcktuSS0EPvpoYuEcEwYGvoQnIpBlWWoMrUM4VFbt+jXwqL3OycquGXbrwIN+3ZZD/HPXSgvw064O2JzvPQw0v/vR56v2noPYczNCBhlksX835liVDXDJG22AUqSuvUoqIlF0kiEPk9dPNnH9lzEBE+cP0FnufGayqGSjKWGnrfz7l1zxVtxBUW2UHR/tTQe46SXSma0EO3zqWuFBb5JBeRtlgclVLrkItutICQiXwDLvrPQw+CiOxxdb3kpaahokotQdGowiLAKYzqKw29F2m3OZduSy65LSkUPrFopaGhokpdyYVfqwQN/2WseI9KtjKd7DVYP/vdoAPANkt2STpTtFdxe+hxhUV8yIUTFO2dv713VtIGfAcm7YfupIl1rznXckMXvdALphowK7LowiLAuqj3YbfFJPDAaL9r6O6Lf9NOWwz/gkYqqpO22EN3J335LbQtuXAPvUt56Lphpi2KgGixVEqtGrrRhb74fg+dS4D9lLYYBg+MDoTkYgdFoytFAVN2mRGSSza0Lbl0M23RKv0X80SLp6JIwQa9aA19oD10btD70pTYBEkuURepkYoqJJesUNrsh26XWnczD72pi9a5BVMNCYoWfaem+DX0Piv9j+KSzSMoKZKtpfcrFZfk0tCi0xYB4LypGpq6Kd+tHykXssYk9KWF4e0173zoML7z/Gn7+SunJ/DLN13csn03PSJzSDSwXNdQExkuhRIUFO1GX3zeoM1ZQ/+V/oexbaKGp37jxsKnDGVNRZVRtwx5kra4n3rHFfjlmy9GVZUxbnWP7AX68luQJMJ7rp3GlvEqDGaepC/P1/G5f30hcJq4Lbl0JSgKOygqJJdiqapeDZ3LHkVf2BWJPEVw/VhYFEW/G3MAqKqSffGPG3ABmH/z5rFqTxlzoE89dAD4rbdf5nn8yKFZ/IfP/hvuO3AGb7tii+c1+wTq1sQig2GlKYKiRWPmoTtxFr1LwXE+tYrTj4VFg45bQ48bcNHL9P+l1eKKbWMYrSgeCYbDswq6NVPUDIpqwkMvGDNt0eWhd+nCrlgXdc6gFBYNEu5jJS4PvZfpvxWHoMgSXnPBOtz73Bn7hOF0u7DIkVz69oaoL6moZpYLPx66dWGXfAa93/qhrwXKVs0CY6yvPfSBsjCvu3A9vv7kSRw4tYhdG0fs5+2JRV3KQ+ftc4WHXixVVYZuMNx+70G7pw5Q/IU93EMvdh2CcHhP9D+59yAefHEGQPGzTbNgoAz69+9aBwC478AZj0FntofeHcml3jSgGUwY9II5b90wAOB3vv6M5/ntBafYSRTsoQ9CYdGgcP66IQDmWDwA2DZR7cs2HQNl0LdN1LB1vIoHD87gJ19znv18Nz10WSIs2q1zB2p39zw/ePlmXH/xjR5jKktUuPSlyIRnTi7gM98+gJ99/SuEh96D3LRnM576uHOsVPo0xbj/7iliuPb8STz00oxHR9e7GITavWUUNVXGaEXBJZtH4v+DIFNqJQUjFdX+1404xuXbxnFyfhW//41n8fSJ+a4WugnCcR8r/Vr5OnAu43XnTeHv/v0Ynj+1iAst2YXb9m5ILu/cux3v3Ls9fkPBwPLbb78MP3LlVtz6uQdwdqkBfhQKcy7Imv68DEVw7fmTAIAHD561n9O7VFAiEHAmhswClNmlhqtSVByQgmwZOA99erKGTaMV/PW+IzizaFaNHjq7BEDc4gq6x6RVUTiz1MBY1Zx4Iw5HQdYMnEEnIrztis340++8iCePzdvPj5QVbB6rdHFlgrXMaFWFROYcyh3MzLIReeiCrBk4gw4AH/3B3fjoD+7u9jIEAhtZIozXSphZaojCIkFuDJyGLhD0KpNDJcwuN+wMLGHPBVkjDLpAUBCTwkMX5Iww6AJBQUwMqZhdajqFReLsE2SMOKQEgoKYHCphZll46IL8EAZdICiIiVoJs0sNu3JZmHNB1giDLhAUxORQCZrBML/SBCAKiwTZIwy6QFAQE1Zx0Vmr4E0UFgmyRhh0gaAgJq3y/7NLdQBCQxdkjzDoAkFB8H4ujocuDLogW4RBFwgKgvdz4R66sOeCrElk0InoJiJ6logOENFHAl4vE9FfW68/SEQ7s16oQNDvTAyZTblsD12I6IKMiTXoRCQD+AyAmwHsBvBuIvI3SvlpALOMsQsA/CGA3816oQJBvzNcVqDKhLNLIigqyIckHvo1AA4wxg4yxhoAvgTgFt82twD4c+v3rwB4I4mcLIHAAxFhwir/BwASmeiCjEnSbXErgCOux0cBXBu2DWNMI6JzAKYAnHFvRES3AbgNAKanp1MuWSDoXz5w/QV48MWz2DBSwYaRcreXIxgwCm2fyxi7HcDtALB3714Ws7lAMHC879U78b5X7+z2MgQDShLJ5RgA91DMbdZzgdsQkQJgDMBZCAQCgaAwkhj0hwHsIqLziKgE4F0A7vJtcxeA91m/3wrgnxlvKScQCASCQoiVXCxN/IMAvgFABnAHY2w/EX0cwD7G2F0A/gzAXxDRAQAzMI2+QCAQCAokkYbOGLsHwD2+5z7m+n0VwDuyXZpAIBAI2kFUigoEAsGAIAy6QCAQDAjCoAsEAsGAIAy6QCAQDAjUrexCIjoN4FDK/74OvipUQQtiHyVD7Kd4xD6Kp8h9tIMxtj7oha4Z9E4gon2Msb3dXkcvI/ZRMsR+ikfso3h6ZR8JyUUgEAgGBGHQBQKBYEDoV4N+e7cX0AeIfZQMsZ/iEfsonp7YR32poQsEAoGglX710AUCgUDgQxh0gUAgGBD6zqDHDaxeqxDRS0T0PSJ6jIj2Wc9NEtE/EtHz1s+Jbq+zSIjoDiI6RURPup4L3Cdk8r+s4+oJIrqqeysvjpB99OtEdMw6lh4jore4Xvtv1j56lohu7M6qi4WIthPRt4noKSLaT0Q/bz3fc8dSXxn0hAOr1zLXM8Ze6cqH/QiAbzHGdgH4lvV4LfEFADf5ngvbJzcD2GX9uw3AZwtaY7f5Alr3EQD8oXUsvdLqtgrrXHsXgEut//PH1jk56GgAPsQY2w3gOgAfsPZFzx1LfWXQkWxgtcDBPbz7zwH8cBfXUjiMsXth9ud3E7ZPbgHwRWbyXQDjRLS5mJV2j5B9FMYtAL7EGKszxl4EcADmOTnQMMZOMMb+3fp9AcDTMOco99yx1G8GPWhg9dYuraXXYAC+SUSPWMO4AWAjY+yE9ftJABu7s7SeImyfiGPLywctueAOl1S35vcREe0EcCWAB9GDx1K/GXRBOK9ljF0F83bvA0T0OveL1khAkaPqQuyTUD4L4BUAXgngBIA/6O5yegMiGgbwtwB+gTE2736tV46lfjPoSQZWr0kYY8esn6cA/F+Yt8Iv81s96+ep7q2wZwjbJ+LYsmCMvcwY0xljBoA/hSOrrNl9REQqTGP+l4yxv7Oe7rljqd8MepKB1WsOIhoiohH+O4AbADwJ7/Du9wH4WndW2FOE7ZO7ALzXylC4DsA51+30msKn974d5rEEmPvoXURUJqLzYAb9Hip6fUVDRARzbvLTjLH/4Xqp944lxlhf/QPwFgDPAXgBwEe7vZ5e+AfgfACPW//28/0CYApm9P15AP8EYLLbay14v9wJUzJowtQxfzpsnwAgmBlULwD4HoC93V5/F/fRX1j74AmYxmmza/uPWvvoWQA3d3v9Be2j18KUU54A8Jj17y29eCyJ0n+BQCAYEPpNchEIBAJBCMKgCwQCwYAgDLpAIBAMCMKgCwQCwYAgDLpAIBAMCMKgCwQCwYAgDLpAIBAMCP8/s8os5D8u7ocAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":136,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = data.flatten()","execution_count":137,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['2'] = data","execution_count":138,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №4\n\n# Загружаем данные\ndata = raw_dataset[4]\n\n# Анализируем распределение, используя гистограмму.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()\n","execution_count":139,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"# проблема №4 - выброс в районе 60000. Применяем отсечение с разрешенным интервалом от 0 до 32000.\ndata = numpy.clip(data, 0, 32000)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":140,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение. Попробуем применить к данным логарифм и извлечение квадратного корня.\nmatplotlib.pyplot.hist(numpy.log(data), bins = 50)\nmatplotlib.pyplot.show()\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()","execution_count":141,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Логарифм даёт более равномерно распределенные данные, используем его\ndata = numpy.log(data)","execution_count":142,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":143,"outputs":[{"output_type":"stream","text":"4.1588830833596715\n10.373491181781864\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":144,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":145,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Можем взглянуть на сами данные\nmatplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":146,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":147,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Конвертируем в одномерный массив\ndata = data.flatten()","execution_count":148,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['4'] = data","execution_count":149,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №5 \n\n# Загружаем данные\ndata = raw_dataset[5]\n\n# Анализируем распределение, используя гистограмму. Параметр bins отвечает за число столбцов в гистрограмме.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":150,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №4 - выбросы значений в интервале (150, 280). Применяем отсечение с разрешенным интервалом от 0 до 170.\ndata = numpy.clip(data, 0, 170)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":151,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение. Применять логарифм нельзя, т.к. среди значений есть нули.\n# Извлечение квадратного корня.\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()","execution_count":152,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = data ** 0.5","execution_count":153,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":154,"outputs":[{"output_type":"stream","text":"0.0\n13.038404810405298\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":155,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":156,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"matplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":157,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":158,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Конвертируем в одномерный массив\ndata = data.flatten()","execution_count":159,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['5'] = data","execution_count":160,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №6 \n\n# Загружаем данные\ndata = raw_dataset[6]\n\n# Анализируем распределение, используя гистограмму. Параметр bins отвечает за число столбцов в гистрограмме.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":161,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №4 - выбросы значений в интервале (20, 60). Применяем отсечение с разрешенным интервалом от 0 до 20.\ndata = numpy.clip(data, 0, 20)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":162,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение. Применять логарифм нельзя\n# Применим извлечение квадратного корня.\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()","execution_count":163,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAALQ0lEQVR4nO3dX4hc93mH8edbycb5Q2snXowrma4gxkUEEpfFdRH0wm5BjULsCxNcWiOKim6S1mkCidKb0DsHSv5clIKw0gpq0hjHYBNDi3EUSqGoWdluE0sJEa6dyMjRhsZN2oumat5e7EmllVbZ8e7Mnn2l5wNi55w5o3k5SA8/ndmzSlUhSernF8YeQJK0PgZckpoy4JLUlAGXpKYMuCQ1tX0z3+zmm2+u+fn5zXxLSWrvxIkTP6iquUv3b2rA5+fnWVxc3My3lKT2kry62n4voUhSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTm3onpqZj/tAzq+5/5ZF9mzyJpDG5Apekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDU1UcCT/EmSl5J8M8kXk9yQZFeS40lOJ/lSkutnPawk6YI1A55kB/DHwEJVvRvYBjwIfBr4bFW9C/ghcGCWg0qSVpr0Esp24C1JtgNvBc4C9wBPDM8fBe6f/niSpCtZM+BV9Rrw58B3WQ73fwAngDeq6vxw2Blgx6yGlCRdbpJLKDcB9wG7gF8G3gbsnfQNkhxMsphkcWlpad2DSpJWmuQSym8B/1ZVS1X1P8CTwB7gxuGSCsBO4LXVXlxVh6tqoaoW5ubmpjK0JGmygH8XuDvJW5MEuBc4CRwDHhiO2Q88NZsRJUmrmeQa+HGWP6x8HvjG8JrDwCeAjyY5DbwTODLDOSVJl9i+9iFQVZ8CPnXJ7peBu6Y+kSRpIt6JKUlNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMTBTzJjUmeSPKtJKeS/EaSdyR5Nsl3hq83zXpYSdIFk67APw/8XVX9KvAe4BRwCHiuqm4Hnhu2JUmbZM2AJ/kl4DeBIwBV9ZOqegO4Dzg6HHYUuH9WQ0qSLjfJCnwXsAT8VZIXkjya5G3ALVV1djjmdeCWWQ0pSbrcJAHfDvwa8JdVdSfwX1xyuaSqCqjVXpzkYJLFJItLS0sbnVeSNJgk4GeAM1V1fNh+guWgfz/JrQDD13OrvbiqDlfVQlUtzM3NTWNmSRITBLyqXge+l+SOYde9wEngaWD/sG8/8NRMJpQkrWr7hMf9EfBYkuuBl4E/YDn+jyc5ALwKfHA2I0qSVjNRwKvqRWBhlafune44kqRJeSemJDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWpq4oAn2ZbkhSRfGbZ3JTme5HSSLyW5fnZjSpIu9WZW4A8Dpy7a/jTw2ap6F/BD4MA0B5Mk/XwTBTzJTmAf8OiwHeAe4InhkKPA/bMYUJK0uklX4J8DPg78dNh+J/BGVZ0fts8AO1Z7YZKDSRaTLC4tLW1oWEnSBWsGPMn7gXNVdWI9b1BVh6tqoaoW5ubm1vNbSJJWsX2CY/YAH0jyPuAG4BeBzwM3Jtk+rMJ3Aq/NbkxJ0qXWXIFX1SeramdVzQMPAl+tqt8DjgEPDIftB56a2ZSSpMts5PvAPwF8NMlplq+JH5nOSJKkSUxyCeX/VdXXgK8Nj18G7pr+SJKkSXgnpiQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrqTf2nxtK0zB96ZtX9rzyyb5MnkfpyBS5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekptYMeJLbkhxLcjLJS0keHva/I8mzSb4zfL1p9uNKkn5mkhX4eeBjVbUbuBv4UJLdwCHguaq6HXhu2JYkbZI1A15VZ6vq+eHxj4FTwA7gPuDocNhR4P5ZDSlJutybugaeZB64EzgO3FJVZ4enXgduucJrDiZZTLK4tLS0gVElSRebOOBJ3g58GfhIVf3o4ueqqoBa7XVVdbiqFqpqYW5ubkPDSpIumCjgSa5jOd6PVdWTw+7vJ7l1eP5W4NxsRpQkrWaS70IJcAQ4VVWfueipp4H9w+P9wFPTH0+SdCWT/K/0e4CHgG8keXHY96fAI8DjSQ4ArwIfnM2I0rVp/tAzq+5/5ZF9mzyJtqo1A15V/wjkCk/fO91xJEmT8k5MSWrKgEtSUwZckpqa5ENMSZfwA0ZtBa7AJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNeSu9pA3xxwqMxxW4JDXlClzSVeda+VeBK3BJasqAS1JTBlySmvIauFq7Vq51SqtxBS5JTbkCl3Alr55cgUtSUwZckpryEoou4+UEqQdX4JLUVJsVuKtCSVvdZnfKFbgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLU1IYCnmRvkm8nOZ3k0LSGkiStbd038iTZBvwF8NvAGeDrSZ6uqpPTGk66mngzmqZtIyvwu4DTVfVyVf0E+FvgvumMJUlaS6pqfS9MHgD2VtUfDtsPAb9eVR++5LiDwMFh8w7g2+uc9WbgB+t87dXI87GS52Mlz8dK3c/Hr1TV3KU7Z/6zUKrqMHB4o79PksWqWpjCSFcFz8dKno+VPB8rXa3nYyOXUF4Dbrtoe+ewT5K0CTYS8K8DtyfZleR64EHg6emMJUlay7ovoVTV+SQfBv4e2AZ8oapemtpkl9vwZZirjOdjJc/HSp6Pla7K87HuDzElSePyTkxJasqAS1JTLQLuLfsXJPlCknNJvjn2LFtBktuSHEtyMslLSR4ee6YxJbkhyT8n+ZfhfPzZ2DNtBUm2JXkhyVfGnmWatnzAL7pl/3eA3cDvJtk97lSj+mtg79hDbCHngY9V1W7gbuBD1/ifj/8G7qmq9wDvBfYmuXvkmbaCh4FTYw8xbVs+4HjL/gpV9Q/Av489x1ZRVWer6vnh8Y9Z/ku6Y9ypxlPL/nPYvG74dU1/p0KSncA+4NGxZ5m2DgHfAXzvou0zXMN/QXVlSeaBO4Hj404yruFywYvAOeDZqrqmzwfwOeDjwE/HHmTaOgRcWlOStwNfBj5SVT8ae54xVdX/VtV7Wb47+q4k7x57prEkeT9wrqpOjD3LLHQIuLfs6+dKch3L8X6sqp4ce56toqreAI5xbX9msgf4QJJXWL78ek+Svxl3pOnpEHBv2dcVJQlwBDhVVZ8Ze56xJZlLcuPw+C0s/7z+b4071Xiq6pNVtbOq5llux1er6vdHHmtqtnzAq+o88LNb9k8Bj8/4lv0tLckXgX8C7khyJsmBsWca2R7gIZZXVi8Ov9439lAjuhU4luRfWV78PFtVV9W3zukCb6WXpKa2/ApckrQ6Ay5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKb+D2soar8pmFfIAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = data ** 0.5","execution_count":164,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":165,"outputs":[{"output_type":"stream","text":"0.0\n4.47213595499958\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":166,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":167,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Можем взглянуть на сами данные\nmatplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":168,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":169,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Конвертируем в одномерный массив\ndata = data.flatten()","execution_count":170,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['6'] = data","execution_count":171,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №8\n\n# Загружаем данные\ndata = raw_dataset[8]\n\n# Анализируем распределение, используя гистограмму. Параметр bins отвечает за число столбцов в гистрограмме.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":172,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXsAAAD4CAYAAAANbUbJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOk0lEQVR4nO3df6xkZX3H8fdHVsSqdUFuN1tWetdIbPhHMDcUomksVEQwQhNCIMauLWaTtiZam9il/tGY9A9oG380MdWN2G4aVChqIZjW0hXT9J/V3arIDykLLgoBdrWitk1aV7/9Y56L42bv3nvnx94ZnvcruZlznnNm5vvsM/u5Z55zZm6qCknSc9vzNroASdL0GfaS1AHDXpI6YNhLUgcMe0nqwKaT+WRnnnlmLS4unsynlKS5d+DAge9W1cI4j3FSw35xcZH9+/efzKeUpLmX5LFxH8NpHEnqgGEvSR0w7CWpA4a9JHXAsJekDhj2ktQBw16SOmDYS1IHDHtJ6sBJ/QTtNCzu+vxx2w/deMVJrkSSZpdH9pLUAcNekjowN9M4K03XSJJW55G9JHXAsJekDhj2ktQBw16SOmDYS1IHDHtJ6oBhL0kdMOwlqQOGvSR1wLCXpA4Y9pLUAcNekjpg2EtSBwx7SeqAYS9JHVjT99knOQT8CPgJcLSqlpKcAdwKLAKHgGuq6vvTKVOSNI71HNn/RlWdV1VLbX0XsLeqzgH2tnVJ0gwaZxrnSmBPW94DXDV+OZKkaVhr2Bfwz0kOJNnZ2rZU1ZNt+Slgy/HumGRnkv1J9h85cmTMciVJo1jr36B9XVU9keSXgLuTfHN4Y1VVkjreHatqN7AbYGlp6bj7SJKma01H9lX1RLs9DHwOuAB4OslWgHZ7eFpFSpLGs2rYJ3lRkpcsLwOXAvcBdwI72m47gDumVaQkaTxrmcbZAnwuyfL+n6yqf0ryFeC2JNcDjwHXTK9MSdI4Vg37qnoUePVx2r8HXDKNoiRJk+UnaCWpA4a9JHXAsJekDhj2ktQBw16SOmDYS1IHDHtJ6oBhL0kdMOwlqQOGvSR1wLCXpA4Y9pLUAcNekjpg2EtSBwx7SeqAYS9JHTDsJakDhr0kdcCwl6QOGPaS1AHDXpI6YNhLUgcMe0nqgGEvSR0w7CWpA4a9JHXAsJekDhj2ktSBNYd9klOSfDXJXW19e5J9SQ4muTXJqdMrU5I0jvUc2b8LeHBo/Sbgg1X1SuD7wPWTLEySNDlrCvsk24ArgI+39QAXA7e3XfYAV02jQEnS+NZ6ZP8h4L3AT9v6y4BnqupoW38cOOt4d0yyM8n+JPuPHDkyVrGSpNGsGvZJ3gwcrqoDozxBVe2uqqWqWlpYWBjlISRJY9q0hn1eC7wlyeXAacAvAh8GNifZ1I7utwFPTK9MSdI4Vj2yr6obqmpbVS0C1wJfrKq3AvcAV7fddgB3TK1KSdJYxrnO/o+B9yQ5yGAO/+bJlCRJmrS1TOM8q6q+BHypLT8KXDD5kiRJk+YnaCWpA4a9JHXAsJekDhj2ktQBw16SOmDYS1IHDHtJ6oBhL0kdMOwlqQOGvSR1wLCXpA4Y9pLUAcNekjpg2EtSBwx7SeqAYS9JHTDsJakDhr0kdcCwl6QOGPaS1AHDXpI6YNhLUgcMe0nqgGEvSR0w7CWpA4a9JHXAsJekDhj2ktSBVcM+yWlJvpzk60nuT/L+1r49yb4kB5PcmuTU6ZcrSRrFWo7s/xe4uKpeDZwHXJbkQuAm4INV9Urg+8D10ytTkjSOVcO+Bv6rrT6//RRwMXB7a98DXDWVCiVJY9u0lp2SnAIcAF4JfAR4BHimqo62XR4HzlrhvjuBnQBnn332uPWu2eKuzx+3/dCNV5y0GiRpVqzpBG1V/aSqzgO2ARcAv7rWJ6iq3VW1VFVLCwsLI5YpSRrHuq7GqapngHuAi4DNSZbfGWwDnphwbZKkCVnL1TgLSTa35RcCbwAeZBD6V7fddgB3TKtISdJ41jJnvxXY0+btnwfcVlV3JXkA+HSSPwO+Ctw8xTolSWNYNeyr6l7g/OO0P8pg/l6SNOP8BK0kdcCwl6QOGPaS1AHDXpI6YNhLUgcMe0nqgGEvSR0w7CWpA4a9JHXAsJekDhj2ktQBw16SOmDYS1IHDHtJ6oBhL0kdMOwlqQOGvSR1wLCXpA4Y9pLUAcNekjpg2EtSBwx7SeqAYS9JHTDsJakDhr0kdcCwl6QOGPaS1AHDXpI6sGrYJ3l5knuSPJDk/iTvau1nJLk7ycPt9vTplytJGsVajuyPAn9UVecCFwJ/kORcYBewt6rOAfa2dUnSDFo17Kvqyar697b8I+BB4CzgSmBP220PcNW0ipQkjWddc/ZJFoHzgX3Alqp6sm16Ctiywn12JtmfZP+RI0fGKFWSNKo1h32SFwOfAd5dVT8c3lZVBdTx7ldVu6tqqaqWFhYWxipWkjSaNYV9kuczCPpbquqzrfnpJFvb9q3A4emUKEka11quxglwM/BgVX1gaNOdwI62vAO4Y/LlSZImYdMa9nkt8DbgG0m+1tr+BLgRuC3J9cBjwDXTKVGSNK5Vw76q/g3ICpsvmWw5kqRp8BO0ktQBw16SOmDYS1IHDHtJ6oBhL0kdMOwlqQOGvSR1wLCXpA4Y9pLUAcNekjpg2EtSBwx7SeqAYS9JHTDsJakDhr0kdcCwl6QOGPaS1AHDXpI6YNhLUgcMe0nqgGEvSR0w7CWpA4a9JHXAsJekDhj2ktQBw16SOmDYS1IHNm10AbNucdfnV9x26MYrTmIlkjS6VY/sk3wiyeEk9w21nZHk7iQPt9vTp1umJGkca5nG+VvgsmPadgF7q+ocYG9blyTNqFXDvqr+FfjPY5qvBPa05T3AVROuS5I0QaPO2W+pqifb8lPAlpV2TLIT2Alw9tlnj/h003eiuXlJmndjX41TVQXUCbbvrqqlqlpaWFgY9+kkSSMYNeyfTrIVoN0enlxJkqRJGzXs7wR2tOUdwB2TKUeSNA2rztkn+RTweuDMJI8DfwrcCNyW5HrgMeCaaRY5Sc7NS+rRqmFfVdetsOmSCdciSZoSvy5Bkjpg2EtSBwx7SeqAYS9JHTDsJakDhr0kdcCwl6QOGPaS1AHDXpI6YNhLUgcMe0nqgGEvSR0Y9S9V6QRW+mbNQzdeMRePL+m5xyN7SeqAYS9JHTDsJakDztnPAOfgJU2bR/aS1AHDXpI6YNhLUgecsz+JVpqb36jnXemcgOcQpOcej+wlqQOGvSR1wGmcMUx7Wmajpn1GMampH6ecNM9m+fXokb0kdcCwl6QOGPaS1AHn7DUXZu38xSzPzc6KE42Z/04n31hH9kkuS/JQkoNJdk2qKEnSZI0c9klOAT4CvAk4F7guybmTKkySNDnjHNlfABysqker6v+ATwNXTqYsSdIkpapGu2NyNXBZVb2jrb8N+LWqeucx++0EdrbVVwEPrfOpzgS+O1KRs+252C/7NB/s0/xY7tevVNXCOA809RO0VbUb2D3q/ZPsr6qlCZY0E56L/bJP88E+zY9J9mucaZwngJcPrW9rbZKkGTNO2H8FOCfJ9iSnAtcCd06mLEnSJI08jVNVR5O8E/gCcArwiaq6f2KV/czIU0Az7rnYL/s0H+zT/JhYv0Y+QStJmh9+XYIkdcCwl6QOzHTYz+vXMSR5eZJ7kjyQ5P4k72rtZyS5O8nD7fb01p4kf9X6eW+S12xsD1aW5JQkX01yV1vfnmRfq/3WdrKeJC9o6wfb9sWNrHslSTYnuT3JN5M8mOSi58g4/WF77d2X5FNJTpu3sUryiSSHk9w31LbusUmyo+3/cJIdG9GXoVqO16e/aK+/e5N8LsnmoW03tD49lOSNQ+3rz8aqmskfBid9HwFeAZwKfB04d6PrWmPtW4HXtOWXAP/B4Csl/hzY1dp3ATe15cuBfwQCXAjs2+g+nKBv7wE+CdzV1m8Drm3LHwV+ry3/PvDRtnwtcOtG175Cf/YA72jLpwKb532cgLOAbwEvHBqjt8/bWAG/DrwGuG+obV1jA5wBPNpuT2/Lp89Yny4FNrXlm4b6dG7LvRcA21senjJqNm74gJ7gH+Ui4AtD6zcAN2x0XSP25Q7gDQw+Pby1tW0FHmrLHwOuG9r/2f1m6YfBZyn2AhcDd7X/WN8deqE+O2YMrtK6qC1vavtlo/twTH9e2kIxx7TP+zidBXynBdymNlZvnMexAhaPCcZ1jQ1wHfCxofaf228W+nTMtt8CbmnLP5d5y+M0ajbO8jTO8gt22eOtba60t8TnA/uALVX1ZNv0FLClLc9LXz8EvBf4aVt/GfBMVR1t68N1P9untv0Hbf9Zsh04AvxNm5r6eJIXMefjVFVPAH8JfBt4ksG//QHme6yWrXds5mLMhvwug3coMOE+zXLYz70kLwY+A7y7qn44vK0Gv5Ln5rrXJG8GDlfVgY2uZYI2MXhL/ddVdT7w3wymBp41b+ME0Oaxr2Twy+yXgRcBl21oUVMwj2NzIkneBxwFbpnG489y2M/11zEkeT6DoL+lqj7bmp9OsrVt3wocbu3z0NfXAm9JcojBN5xeDHwY2Jxk+cN5w3U/26e2/aXA905mwWvwOPB4Ve1r67czCP95HieA3wS+VVVHqurHwGcZjN88j9Wy9Y7NXIxZkrcDbwbe2n6JwYT7NMthP7dfx5AkwM3Ag1X1gaFNdwLLVwPsYDCXv9z+2+2KgguBHwy9VZ0JVXVDVW2rqkUGY/HFqnorcA9wddvt2D4t9/Xqtv9MHYVV1VPAd5K8qjVdAjzAHI9T823gwiS/0F6Ly/2a27East6x+QJwaZLT2zueS1vbzEhyGYPp0bdU1f8MbboTuLZdLbUdOAf4MqNm40afgFnlRMblDK5keQR430bXs466X8fg7eW9wNfaz+UM5kH3Ag8D/wKc0fYPgz8E8wjwDWBpo/uwSv9ez8+uxnlFewEeBP4eeEFrP62tH2zbX7HRda/Ql/OA/W2s/oHBFRtzP07A+4FvAvcBf8fgio65GivgUwzOOfyYwbuw60cZGwbz4Afbz+/MYJ8OMpiDX86Kjw7t/77Wp4eANw21rzsb/boESerALE/jSJImxLCXpA4Y9pLUAcNekjpg2EtSBwx7SeqAYS9JHfh/6LPbU6Hft58AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №4 - выбросы значений в интервале (600, 1200). Применяем отсечение с разрешенным интервалом от 0 до 600.\ndata = numpy.clip(data, 0, 600)\n\n# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":173,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Наблюдаем проблему №3 - очень неравномерное распределение. Попробуем применить к данным логарифм и извлечение квадратного корня.\nmatplotlib.pyplot.hist(numpy.log(data), bins = 50)\nmatplotlib.pyplot.show()\n\nmatplotlib.pyplot.hist(data ** 0.5, bins = 50)\nmatplotlib.pyplot.show()","execution_count":174,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Логарифм даёт более равномерно распределенные данные, используем его\ndata = numpy.log(data)\n# Теперь данные имеют следующую область значений\nprint(numpy.min(data))\nprint(numpy.max(data))","execution_count":175,"outputs":[{"output_type":"stream","text":"1.791759469228055\n6.396929655216146\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":" #Приводим значения к интервалу (0, 1), считая, что они ближе к равномерному распределению\nscaler = MinMaxScaler()\ndata = numpy.array(data).reshape(-1,1)\ndata = scaler.fit_transform(data)","execution_count":176,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":177,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Можем взглянуть на сами данные\nmatplotlib.pyplot.plot(data)\nmatplotlib.pyplot.show()","execution_count":178,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy9abAl11Um+u3MM995qrlUVSqVJsuDbFmeoLEBg43BZm7z4EHDa8zjAdEdBl4bTNAMQRAN0TwacL9uMzSj2zY8TIhGeMAYY9lIlmRbU8mqKpVqvLfufOYhp/1+ZK6dO/Nk5skzn3tvfhGK0r33DPvkyVz57W99ay3GOUeCBAkSJNj7UMa9gAQJEiRIMBgkAT1BggQJ9gmSgJ4gQYIE+wRJQE+QIEGCfYIkoCdIkCDBPkFqXG+8vLzMT58+Pa63T5AgQYI9iSeffHKLc74S9LexBfTTp0/jiSeeGNfbJ0iQIMGeBGPsatjfEsklQYIECfYJkoCeIEGCBPsESUBPkCBBgn2CJKAnSJAgwT5BEtATJEiQYJ+gY0BnjP0RY2yDMfZsyN8ZY+x3GGOXGGNPM8ZePfhlJkiQIEGCTojD0P8YwNsi/v52AOec/94D4P/tf1kJEiRIkKBbdPShc87/mTF2OuIh7wLwp9zuw/soY2yeMXaUc742oDX2hcev7OBzFzYxk0vjh990Gik1UZkSJEjQH568uovPvrCBqWwKP/ymM8ikJiOuDKKw6DiA69LPN5zftQV0xth7YLN43HbbbQN46874pYeew3OrZQDA625fxCtOzI/kfRMkSLB/8Zuf+CoevbwDAHjNqQU8cHpxzCuyMdLbCuf8g5zzBzjnD6ysBFauDhSmxXFpo4q7j8wAAHTTGvp7JkiQYP9jq6phvpAGABjW5AwJGkRAvwngpPTzCed3Y8f1nTpahoW7nICexPMECRIMAjs1DcvTWQCAtc8C+kMAftBxu7weQGlS9PML6xUAwN1HZgHYjD1BggQJ+oFpcezWNSxPZwAAkxRWOmrojLH/CeDNAJYZYzcA/EcAaQDgnP83AA8D+BYAlwDUAfzwsBbbLS5uVAFASC5WMj81QYIEfaJY18A5XIY+QXEljsvl+zr8nQP4iYGtaIC4uF7BsbkcZvP2x5wkrStBggR7Ezs1DQCwMjN5AX0yvDZDwoX1Ks4dnoHCGIDJ0roSJNiLeGmrhk8/vy5+tiyOv3jsKpq6OcZVjRbbSUAfPUyL48XNKs4dmkZKUcTvEiRI0Dv++2dfxHs/+pT4+dnVEt7/sWfxuYtbY1zVaEEM3U2KjnM1XuzbgE4OlzsPz8CJ5zAn6E6aIMFexGqpibpmiJ+3q3ZwMw6QhWy72gIArEyghr5vA/rVnToA4MzKFFQlkVwSJBgE1ktN6CYXNR3EVg/SpUWSy9JedLnsVdzcbQAAjs/nUWvZjCJh6AcLN4sN/I9HXoLFge9//W04uzI97iXtedwqNwEATd1EWlWkgH5wrq2dmoa5fFqU+0/SZ9+3AX212ICqMByayeKqk7BJNPSDhb99ahV/8MhLAIB0iuHn3n7PmFe0t9HUTZQaOgCgoZuYyaWxUz94AX27pmFpKuOaLSbos+/bgH6z2MCR2RxSqgLVOfBJQD9YqDYNKAxYms6iVNfHvZw9j1ulpvj/pmZLLrsHkaFXNSxNZ+AouRMluexbDf1msYHj83kAEBp6EtAPFqotA1OZFObyacEsE/SONSmgN5xdr5BcDk5OFDs1DYtTGTCHKPIJupnt34C+28DxBW9AP0gsIgFQaxmYyiYBfVBYL7cH9N0DKrksTmXFzn+SPvu+DOimxXGr3MSx+RwAmaGPc1UJRo2aZmAqqyYBfUC4JQd0zcvQJyimDRWW08dF1tAnKa7sy4C+UWnCtDiOOZKLOPAH5axLAACotkxMOwy93EwCer/waOiCodvHdZJYar+4tFHBn3zhSuDfSg0dpsUdycX+3SR99n0Z0GXLIiAx9Em6lSYYOjySS5IU7Rvr5aZIBDZ0E6bFUazvPx/6Xz55A//xoecCtXHZg64oiYY+Etws+gK6YOhjW1KCMYAC+mw+jUrLSArL+sStchMnFgoAbMml3NBFIJ8kltov6OYf1MyPdimHZ3OJy2VUoIBOkouqJpWiBxHVliEkF86BStPo/KQEobhVauL08hQAm6GTBx2YLJbaL4oU0AMY4DWnAv3UUmEi7dD7MqCvFhuYL6QxlbVt9uoEaOh//aUbYnuaYDSwGbqK2Zx9HiSJ0d5hWhwblRbOLNkMvambwoMOTBZL7RfFhv259AAv5rWdOjKqgsMzucS2OCq8uFHDbYsF8bNozjWms+76Th3v/ehTePiZW2N5/4OKWssUGjqQBPR+sFVtwbQ4zjgMva6ZwuECTBZL7Relhr2TC2Lo13fqOLGQh6KwRHIZBXTTwpev7+LVty2I36lj7od+w0nSJkOqRwfNsKCZFqYzSUAfBOgcPrU0hbTK0NBN4UEH9puGHt5B8tpOHScdsjiJpf/7LqA/e7OEpm7hwTOL4nfkchnXxKJVR9NPJiaNDtSQbSqbwlwhCej9YlXKS+XSKhqaiZ2aezwnKKb1jaJznugB1+u1nbrY/SsTWIG+7wL641d2AAAPnHYZOmMMjI3vTrpWsi+GJCk7OlSdgD4tSS6JF713uAE9h3xatTX0ujaRXux+0DJM1J2iKT9DL9V1lBq6G9Cdzz5JH33fBfQvvrSL00sFHJrJeX6fUtjY7qQ3i7bVKWHoo0NNkxh6Irn0jdViA7O5FGZyaeQzqu1yqWlYKExeT/B+IJ8juk9Dv75rO1wSyWVEsCyOJ67u4LWnF9v+pjA2NpcLMXTzIHUwGjNqLZtlTWVV5NMq0ipLAnofuFlsChtwXkguGpbFkIfJCWr9oCydI4bveiXL4m1tAX1Ei4uBfRXQbxYbKNZ1vPrUQtvfVIWNTfKg7WqSEx0dapLkwhjDbC7p59IP5O6lubTL0Jem7DFsk2Td6wdFqaLY73KhgH5y0Wkp4kTPSbqZ7auAToUjC04STIbK2NgC6qojuSQMfXSQk6IAkgZdfWK12PAwdNLQJ3EMWz+QA7rflXZtp46FQhozOTu+KGN2zwVhXwV0aumZS6ttf1MUNpY7abmpiwRd0hxsdJCTogAwm097ttMJ4qPaMlBq6G5AlzT05QkclNwPih7JxfuZbpWaODqXFz8nksuQQR3g8gEBXVVYmyY2CpDcAiRJ0VEiYeiDwxr1RlpwGXqlaaDSNLA4tb8Yujcp6o0X29UWlmey4mdlAh0++yqgU4/mfCY4oI9DcpED+iRtzfY7apqbFAXsgJ4w9N7gNruznWP5jCqmFy1M2aPY9ouGXpKKpfwa+lZVw7JzAwOQlP4PG40ohs7GkxQl/RxIGPooUW0ZSKsM2ZQb0BOG3hv8ze7yaRWaYbOjxYI96GGSimv6QTHE5cI5x5aPoQMOUZyggL6vhkQ3IzT0cR341WIDqsJQyKj75qTfC6DWuQQK6JbFRYVfgni4udtASmGitkPeAS9MpaEwtm8kF29S1P1QNc1Ey7CwJDF0wJZdJumz70mGblkcX/sb/4gPPXbN83uhoQdILooyHsmjrtlTczKqkgT0EYIGRBMWpzKweFJc1AuuO/N5qYWGTJhocs8kyQ79oNjQkUvbYVGWXLYqLQAQSWACY+MxW4RhTwb0rVoL13ca+MKLW57fd5JcxsHQDcuCqjBHw5+cL74XtAwTn3xub3SMrDm90Alkr9uutca1pD2Lazt1nFxwu5fmfQFdmbCg1g9KDV0EbVlyofOGziOCnT8Y3fo6YU8GdJoacnG96vl9Q7O/gDDb4jg0bNPi+yagf/K5dbznz57E1e3auJfSEXbrXPc8oIt0u5r0pO8W16UOgwCQT7thY6GQsYv29vapLVCqu1ZMWXLZcs4bP0NXJyx/ECugM8bexhh7gTF2iTH2voC/38YY+wxj7MuMsacZY98y+KW6oETj5a2qx1rU0E1kUorYGsoYV1LUtDhS+ySgU7vUpj75BVJVn4ZO9rrtWhLQu0G1ZWCnpnnmC5CkOZNLIa0qY218N2gUZYYuxZatarDkMmm7k44BnTGmAvgAgLcDuBfA9zHG7vU97BcAfJRzfj+AdwP4r4NeqIxbJeovznFly2WLTd0MlFsAjC2gGjJDn6AvvhdQJe5euDGFSi7VRHLpBtd9/UsAdwdMN0mFsYmSHXqFYVooNXSsOE4WuX0u7ewWfUlRtgcllwcBXOKcX+acawA+DOBdvsdwALPO/88BWB3cEtuxVnatgBck2aWhRQf0cdxJZcllr9sWqf3sJDGSMPhdLtQVMGHo3cHfkApwNXQ6pso+Yeg7NQ2cA8fmbDePn6HP5dPIpLwhc1wV6GGIE9CPA7gu/XzD+Z2MXwLwA4yxGwAeBvBTQS/EGHsPY+wJxtgTm5ubPSzXxlqxicOzWSgMeGG9In7f0M1AhwswfoaeUhjMgJFWewl7iaFXfQw9rSqYL6QTDb1LXPc1pAJcyUVm6JMU1HrFhuNkIb+97HLZrmptCVHAkXIn6LMPKin6fQD+mHN+AsC3APgzxljba3POP8g5f4Bz/sDKykrPb3ar1MTppSmcWprCRV9Az6aCP5LdPrfnt+wZlqOhj7N976BAAX3Sdxqcc1RbBmZy3jKLpalM4nLpEtd26pjJuT3lgXaGzvaJD33TCehHnYpYeUj0VrXVpp8D9mefpC6qcQL6TQAnpZ9POL+T8X8A+CgAcM7/BUAOwPIgFhiE1VIDR+dyOHdo2sPQmx0Z+uiPvM3QFaTUvZ8UrewRyaWmmbA4AgJ6NmHoXeK6M3KNytwBWUOnroP7o60FBfRjc+0M3Q7o7Qx90toexAnojwM4xxg7wxjLwE56PuR7zDUA3wAAjLF7YAf03jWVCFgWx3q5iaPzeZw7PI2r23XhdInU0MdkL7I19MmzN/WCvSK50I2H2pwSlqYziYbeJeQZmgQiTQv7THLZdBLmRwI09G2p97uMSfvsHQM659wA8JMAPgHgedhulucYY7/CGHun87CfBvCjjLGnAPxPAP+GD+m2tVVrQTc5js7lcGpxCqbFsebYGBsdXC7jaEdODH0/2BarTkCfdDZGN542hj6dwU4S0LvCarEpBlsQFgsZpFXmma05iFOi1jLwQ3/0RY9zbZTYKDcxm0shl1aRUphwueimhWJdD5RcJq30P1YvF875w7CTnfLvflH6//MA3jTYpQWDioqOzOYEA7u2U8dtSwU0dBO5CMlFG4PY5Wro7cz2+k4d3/FfP4//78ffiFNLUyNfW7cg5jvpuYAwhr44lcVuXYNhWkipe7KmbqQwTAsN3cRs3nscF6Yy+MzPvFlIE4Mqf39hvYLPXtjEUzeKOL08+uths9rCoVmbnadUJhg6kYDFIMlljJPQgrDnzmoqKjo2n8epJZshkLWqpVuhDF0Zm8slvPT/0kYVW1VNrH/SsVeSouUQhr48nQHnwG496ecSB9SCuBBAkk4sFESTM1UZjA99x8lvjGsnu1lpYcVh4WlFEZWi1HZ5Pt8+CW3PSS6TBioqOjKXw+HZHDKqgqs79hYtUnIZk1eWKkVTitI2YIMm0+8FKcayOKraHpNcst6ATja7RHaJh7rmHRIShkH50Ol7GRdh2Ky0RFFRSnUH4lRo+lWu/ThMmuSy5wL62UPT+L4HT2Jpyu4hcWIhL7yyDS3a5eJvWD8KkA9dUdptkzRVZy8E9KpmCBY26esNTYpOUT+XxLoYB/6pT2EYVPtcSliPlaGLgK6IeEEEYTYwoCcMvS987bkV/Pp3vkLYqE4uFnBtpw7Oua2hh0kuYzrwllxY5GPo1Za9pZ10CQNwT2pg8myLV7Zq+LbffQS7TkAIS4qS7WwrYeixUHPOz6kQkkQYVC+XHadGYNTXwx8+8hI+/uwt1DQTh2ZIcmFCciEzwHQ2QHKZsErRPT/g4rbFAr58bRctZ4JKmOQyLh+44UguQHsBwl5i6MR6AUxUIQUAfPGlHTxzs4SrO3UsTGVQaepiqIgMIbkkDD0WSBIsZDoz9EGY2gRDH/EJ9jufvijW72HoJLmIHV+I5DJB18OeD+inlgooNw3hfpFbe8oYV6Um9XJhaGfoeymgVyWGPo5h21GgEWl0fKtNu+xfLoYBgKxzsx+H22kvou4w9Ok4kssADuk4NHTL4ig3dSEnejR0n+QSrKEnDH2goD7NVDEapaGPI5lneAK69/2reyigT7LkQoO45QswiE2pToDfA4d7IiAYeraz5DIIsjSOgF5pGh6HzsqM7HJxk6KMAdMBO5VJa3uw5zR0P6i44YVbdkAP09DHNbHIjCgsIoa+FzT08gRLLjSBno5vuWm0JUQBO/DIj0sQDVdDH5HkMgbbIo0kPDKbg8LsfwFyuRBB0DGdSQXOolWVySr9338MPcqHPgaXC9kWGdoDNyVFJ90GCPgY+oStlxi6Ll2AgQzduSAn6QKcZNRjMnRFGcyuRzD0EV6nFNB//h334MhsDvNOw7GU6jL0atMIlFuA8Um5YdjzDH06m8LSVAYXbnWQXMasoQdVlO0lhi4H9Ek6gTnnbRp6pWmEWszsx41ufXsZ3TD0fmW4hmaKmcCjbKJHAf3wTBYPnlkUv08rrKOEBySSy1BwcrGAl5z+D6GSizqeNpeGZUFltm3RH7jdwqLJjzCyy2WSbkA7NU04nMQF2NIDE3m0Y56kG9Iko6YZyKWDRzrKGIQPXW5rPMrziwL6XMEr0cmuOH9vfRl7sdvixOPUUkGcBFHdFsc2sUhlgX7V6h5l6JMkuZB+DrjaayVUQ2fOyLDJWf8ko9YyOrJzYDBBTa7eHYeGPucr60+riuiHbkt47ecTMHkul30R0IPmHfoxrm6Hbul/AEPfQy6XasuVMSZpvSS3APaNkXMeuUXeD22MR4W6ZnasEgUGE9TktsbjYOjzeW/jrZQsubTCNfRJO5/2RUA/GTCR3A/bKzvG0v+AL540ykk6IcJQaeoiYTRJjGRVCuimZVcLmxbvwKhGtbq9jVrLCGzM5ccgfOg71fEx9IyqIOerX5GTomE5GYCqZIe+zNjY8y4XIHiArR+qMh5pgxg6594TlXMuNPRJllw453jmZgmrxabYlk7Seld9DD2s7J9gOzImZ/2TjJpmxGLogyj9J8klo7Y3sRsmSg0Ns/l0WxFaWrItUqFaEBTGJqrQbl8w9DgB3W6ONR6GrigMquqVXBq6uSeaXX3pWhHv/L3P4/xaGUedSS6TtN7VYlOwK9OyIsu0ASeXMkHrn0T8xIe+hD/7lyuotczYDL3fS2u7piGtMswV0iNn6HP59nMlpSgwTAu60xM+dMc3IMvmoLAvAjq10QUQOiQ66EKutgx84dLWUNdGAy78708JUWC4AbKpm/jshd6nARbrNnP6te+4D7/+nS8HMFlJ0Y1K053SbnHRC302QnJJXC7hMEwLn3j2Fj57YQt1LZyZylCU/p1DO7UWFqcyHrvgKGAH9PZzJaXazbkozxXF0CeJ4OyLgK4qDCcW88illcBqLsBOcvhPur984jp+4A8f81jyBgnOuTsk2pcUJf0cGG5A/8Rzt/BDf/RFjzTRDUhHfPVtC2LK+7gDomZYaDqe5WrLFIMHTIuLnjPhkstghjGMGqWGjh//8yeH3vp3rdSEYXHcKjcchj6apOhGpYVDMznHXjz+gJ525hd0lPAGVCU7KOyLgA7YskuY3AK4F7J88G+Vm7C4HSCGATovU05hEeCy21pLbnY1vBOi7kydkd+vG5DHO5NS2j7DuPBLf/scfvRPnwBgVzPSiDTDlDX0MIY+fsno/GoZ7/nTJ8TNMg6evlHE3z97C8+uloe4Mnf6161S09HQO0sugyiuWS+3cHg2i5SiiIrfUSCKoXvPp3Af+gQR9P0T0P/VuRW89vRi6N9VUSXoHn3RO2JId1hKllA/dPt3brECYZhJOpqL2GuHQeoJTZJWkP1y1Li52xBDTeqaq2+aFpdavkY0aRszo/rMCxv45Pl1bHXBtsuN0RShXd22j+tWVUOlGS8pOggf+ka5iUOzOcdePLzPuFZq4Pv/4FEhJZbqIQzdcbmEDUshJD70IeFHvuYMPviDD4T+XfEFVMDNrA/r/KHXVWWGzgMY+hA1QwrIve5CiEWmnYA+ruSyDN20RB+cuuSPNywujmU6ZAj0oAYa94Mbu9SqIP46qDnasPVleb6tafGOwy2A/ov2NMPCdk3D4Zmcx/89DDx1vYTPX9rG+bUyLIuj0jIwV2gf/kzEpdpJQ1cmywa7L2yLcaD6AirgjiIblu2IXjclfelBDH2YjITWoPd4kbgB3RkIPAEuEc2wUNcMcM5R12WGbrnHXA3OpagD6t3dD9zeM/GPIxXADFsuuubM5yXE0dBZn8d007kOD89mh14A2NDt667cMETr3GDJRYkvuUxQRN83DL0TAiWXGAz9xm4d53vULem9VIVBVRxrnUkM3U2KDlPC6Jeha5KGDlDF7WDW1it000LdaebEuXuxGRYXnzethA06GX9SV/Rv74ahOwF92HLXtZ06Tizkxc9xNPR+h0RvlO32DYdnc0OX9BqaffKWmzqKDfv6D5ZcGHTLEgOiE8llwiAYuhSM4mjov/XJC3jvR7/S03vSiWnbFuF5L5JcChl1yBq6/drdJOBkaH7JZUDzI/sBJWq3Kvb3N51NiWQn5QzCGHpQ18tRgnOOm47k0s06RsHQOee4ul33dB2MW/rfzymxXrYZ+srM8Bk6tQSuNI3QPi6A7UPn3L2RRrlcxn09yDhwAZ0Cal0zYrXrrLYMjzzSDejEtAuL7ENNkgC95mwuPVTNkN6v1auGbviSoiOu5AsC3Zw2qzazK2RUuxDE4u5NNCygj/kCLNZ1cd51xdCbw68qLjV0VJoG7j06KwJYrOZcPVbf/tWTN/D9f/Ao1j0MfbjnV8NxfZUbenRAd86fHafgKay+xW72NqTF9oADo6FTUpKC7Land0T48zSnWqwXmBJDp5egc5X6ZKRTw2Uket8M3fTYLu1CioEtr8c1OQHdYeiFTEowO/qcqRDJRVUYxjDnROCmr/dMXLgMfXgHnxwuty0WcHQuh0qzGqtStNdE81eu7+Lzl7axOGUz86WpjFPQM7zPWHdupuVmdECnnNFuTQucT0tQJ8AkIOPgMHSfhr4ds12nblo968+uhq5ItkX7tWqaYQciNlzNUNgWe3a5cI9jRFXGnwSiXQMl0wpZFSnFDgSmxcEYQnt4j1syIocL0KXLZQQa+pVtOyF621IBR+dsHT1+t8Xu34/07E8/v45DM1l7J9unhn55sxp5rrsM3cCuEwMWpoIlFwDYqmmiKV0Qxr3j8+PgBHTnk9LddEdqqB91YWmG1bNDRNbQFZ+GX22ZmM6qQ7/D0xp6ZuiGJdgKML7JTzJchu4E9LQqKgx1k4cmRIHxdd0k+JuJxYUI6EPcXjy/VkFaZbh9eVr07YnrQ+8lqJHjpK6ZOESzPPvQ0KstA2/77c/hb75yM/w9NZehE6lbCAjYdM5vVVqBDJ7A2PBsz73gwAR0Gj9GF/OWLLlEnIyayXsuyqHtseIpLHIYessu2kgpylBnnep9FxZZwuECjD+pCAC64Q3o9nFkjg/dCtXPgfEXFsmSSzfrED70IR7751ZLOHdoBpmUgiMU0OO2z+3hmFIVM2CPgAPs3WyvN63dmgbNtMTNL/A9Hcml0tSxU9Mwl08H1iyknN9tVVuYL4QH9Ekr/T8wGjpd5HT3jzshRTNsyYVzHqqjhYFiaEphMMXv7PcqOyXHu3V9yJJL/7bFjHTCT0KlaMs5sFRpmc84Ox2Tw1C5uHkGgY05B3BTklziBi7O+dA1dM45zq+W8Q33HAIAfOM9h3Flq4al6WzH5/bKUhtyQB8AQyfPeNTzG5rrQ9+ualiaCpZT6BzadoJ+GCZhxyojFkNnjL2NMfYCY+wSY+x9IY/5XsbYecbYc4yxDw12mf1DDAjmlBSNJ7kQw+0liMml/36XTamhYzaXdoqOhsjQrf4ZetrH0Md5AnPuJj4FQ8+khMtFN63QKlHAlt7GyahWSw3Rcyhu4Grqruw3rJvpermF7ZqGlx2bAwDcd3wOv/3u+zvOEwXsoNbLMW3obnvew7MOQ3f8372AyvSjzs+6R3JpYWk6OKDTOWRaXDR/C8Kktc/tyNAZYyqADwB4K4AbAB5njD3EOT8vPeYcgJ8D8CbO+S5j7NCwFtwrXB96d0lRYradAkUQZJcLgVhZuWkz9Fvl5kgYOiUSu0VbUnTMGrQ9Zs7+fwrogqFbFgyTRUou426fe6vUxPGFPC5tVGOvoyRJCMOS5569WQIAvOzYbNfP7T0pauLBM4toaCbecHYJwGAYetT5WZdsi9tpFbevTAU+Tr6RBbUGILAJk1ziRKgHAVzinF/mnGsAPgzgXb7H/CiAD3DOdwGAc74x2GX2D3K5UPCUbYtRDFnvwyVC76U4/dDl9yo1dMwV0s4JPPzSf800OzwyGC2f5DKu2awEObkrXC4Z1e6OZ3HolhVqWQTGO4KOc47duibYaNzvvSy1dx7Wzf+51TIYA+452kNA79GHXtdMLE5l8JEfewNec8ouZlL76OVSFTN6wx/jtl02sF3TsDgVLCnJRoBIhr4Huy0eB3Bd+vmG8zsZdwK4kzH2ecbYo4yxtwW9EGPsPYyxJxhjT2xu9j50oRf4feg7Nc3T1CkM/SQVLYmhq6p7Q2kZJpq6hbl8GsqQmxG5PvReGbpPchmzTUu+sZK+n1YVwexMi3dg6OOzXZabBnST49CMrRfH/d49DH1oAb2EM8tTsVwtfvTaPrehm20tr/tj6PElF4vbMWA5RHKRSUFUUrTfxmSDxqBcLikA5wC8GcD3Afh9xti8/0Gc8w9yzh/gnD+wsrIyoLeOBz9D3qlpWHEy61EXeEtILr1o6ORDlxi65Sa4ZnOpoWvo/fvQLWSkAJlSx5sU9d9YC06vEZUqRc3opOg4dxiUiBfnXczvXXZtDOvYv7Be6YmdA723z21o7SPu6HvsBeWYkovMvhfDkqIyQ48I6GwPTiy6CeCk9PMJ53cybgB4iHOucztGyd0AACAASURBVM5fAnABdoCfGKg+l0u1ZYjsdZykaC8B0dXQ5cIiLi7Q2Xy670KKTqDX7ispqnoZ+jhPYP/3UHAYHjG7TrmOce4wqPbhkBPQ437vXoY+eHmuZZi4vlPH2ZXpnp7fyzG1LG4zdF9rgX4kSCG5RKyloRlihwSEB3T5HJrLRxcWTRBBjxXQHwdwjjF2hjGWAfBuAA/5HvM3sNk5GGPLsCWYywNcZ9/wM/SGZmKa2q5G+dClpGi38DB0SfIpOcMK5vLpvraYcdDPDYmeJ/vQx+3j9u+UCo5EQDdGo6PkMr71U96GGHrc731YDJ2GIF/fqcPiwO3LwQnCTuglL0E7X7/k0g/BIckljKFTu2XKYQDAcogtU97lRfvQx9+sTkZHwYxzbjDGfhLAJwCoAP6Ic/4cY+xXADzBOX/I+ds3McbOAzAB/CznfHuYC+8WbkC1T2TNtDDjBIOwC8u0uDhRewmIlhTQOXcDelnqIdFPEigO+u+22O5yGeQNyLQ4FIbYHn//90CFL8TsOGeRSVFVYdB6yw/3Db/kEjugSz25B3ns/91HvgLdsPDdrzkBADjTY0BnPeQl6iGTpfqqFO3gQ28ZFjiHKJoCoiQXmaFH2RYnS0OPlQHhnD8M4GHf735R+n8O4L3OfxMJ4UN3tnqAO4Uk7ASQg0c/DD3lC+gln+QyXB96f4VFtoY+PJfL//b7j+L+2xbwvrffHXs9MvIZ0tDtG6PCvA4FP9gYZ4qSVbYXyaWQUZFLqz0nt4PwzI0SNistvPKkne463RdD7zag29dg3h/Q1d41dFFYFLIWKmSSJZewwiL5HIoM6BMwMEXGgakUlSUPCuiU0Q87GWXduTcN3S0ssgICui259H4CxwElRfvp5eKXXFrG4Cjule0almc6VyMSaKueTSloGZZo75pSGVq6BYvxjgx9XL7hnZqGQkZ1z7suJBc33zOY6GFaHKvFBgyL4++eXsPydCYycEWhF+se2QcH63KJTopS2b/M0BdCK0Xtc2g6m+qQk5ksyeXA9HKRm3PRnXqabIshrMfL0Ht3uaQkDd3wBfRhuy7os/XcD930NudSBtx+tqGZojdL3PUAbkMll6FTpWhnDX1chUU7NQ0LhYznXIiD3brmynMDOlfWpYK282tl3L7cW0IU6I+ht7tc7Ouhl5tupUNSlMr+SUMP6+MCuAy9001u3DZePw5MQJebcxFDFxp6yBcis9puGG6lqePpG0XPgAu641vc1tALGRVpVRl6QKcy6l4Zut81og7Yx93Qza6CFN1kKVElGLrsQ4+wLY5zi7xd07A0nQkchxgGzjm+fK2Ie47ODjSBLrfxBXrXzwH7/O42pjUiGDrQmywmfOgh3y/dRGayaUxl1FC5BXA19KiEKLA3C4v2BSigmhYXXywx9LAAJTN0P8P9wGcu4SOPXwt83oceu4bv/m//gpbuDommmEgMfdZx2IyKoQ/S5TKo9eqm3aOkm5tNOENnopdLaoxb5GrLwL/9k8dxzRkWIWO3pmFxKuM5FzvhwnoV2zUNbzi7NFCGfmPXXt+9jvf8TEgJfBz0ckwbIRq6XIDXLTpJLg1pVzCbT4f2cQHcG0vHgO5rKTJuHJiArkiSS1PzJkXDTp4ohv7HX7iCh5+5Ffi8UkOHZlioOVs8z5Boy7LL/p2tnN29cIil/0JD7+2E04aYFCUdtZuA3sbQs16Xi2HxyKTosJPQX10r4x+e38DfPr3a9rcdJ6D7ayKi8C8vbgEA3nD70kBbLV/fsRn6t73yGIA+GXpfkku7Dx3oPqBzzjv60ElDz2VUHJvP47bF8M9Mu9L5CA86IO38J0R2OXBJUUtm6F0kReWgU27q2Ky0oK0EB6Km7vY8B+zdgeUEbdOCJ6ArPQTID3/xGm6Vm3jFiTl8/d2HIx/bv8vFP7FocBo0MaZubJv0ndAUGQoI5HKxeHRSdNiFUeRkeeylHfzEW/x/a2FpKtPWVygKX3hxGycX8zi5WBg4Qz80k8W7XnUMj17exoOnFzs/KQS9lP6HSS6C+HR542ropvhe4zD03//BByJv/JSHme2oodv/TghBPzgMXb6IhG0xF9+2KP//5U17VFeY24N+X3NOIEWRK1Vthj4rMXT5/X/poefwF49dDf0c17breN9fP4Pf/oeLeP/Hng19HCHM5fKp8+v4sT97IvK5pEn7K0UHtb2k70Hv4vXoe1hwGHpB8qHHSooqw23ORV7zJ6/siGMP2L7rpm6J+ZlAZ1ZnWRyPvbSDN9zudCJUB9fI7cZuAycXCzg2n8ef/MiDoW6POKCg1k0ikxKUbbZF3yCYuCC5BYhg6BTQ0yksTmUwkwsP1jT1qpPkwiaMoR+YgK5IF1FDi+dDl2UKOSC+uFEF4LLFjzx+DY9dduuoSG+n90kpipQIs0++2by3wpHwqfPreOTiVujnOL9mtzl95Yk5IVlEIczl8jdfuYlPPLceeRHSZ27T0AfF0HVi6N1o6PZ7LwiG7rpcTIvDsKzIEXTqkDV0Cug1zcT5tbL4PVWJLk1JLpcOLPTSZhWlho7XnbED+kAZerGOEwv5gbyWKzvEfw5990EuF6D7pKgnoIcy9OCbSBByGQXT2RROLRYiHxf35jwqHJiArvZZWKRJF9/lrarn7//Ppy7iLx5zE6QUaEly8Zb+t2vo8vs3dDPSYnh+tQyF2QMI4ujiYS6XZ27YN4aoAEE3rLaZogMKKsSYutPQ7efMC4buulwMy7Kbc3WyLQ5Tcqlq4rv+4ks74vcU6BenMoLRdroxXnKIw11HZgDYrHEQazdMC2vF5gADuv1vN2uj7z4X4nLp9sZVkVoMhwXXsJtIELIpFf/0s28WVbRh6EZy2aq28JXrxc4P7AMHJ6BLd36/hh7HtigH9xc3SHKxnH9Nz3xE+j0lRVOKO1O0ZViexmCKj3U1NDOSeZ9fK+PsyjRmculYDbeEy0V6bLGu4dpO3fP3IJA/fFgul2YPGnooQ3eGRBs+iciPXix23WC3ruHoXA6nlwrBAX06A8ZYrCZUtBOkIQydGDrnPNZuh4aqnFiIZp9x0Yvs0NBMZFNK20SkXhk6JUSjnht2EwnD8nQ20jEFdJcU/b1/vIT//Q8fi/XeveLABXSLuwydWq+GJWBaIaX/L256GXrLsERvCvl5tZYp3pskn926fWHLDJ00ac45mkZ0QH9+rYJ7j80iozIx6zQMnHMRAOTinWdvulJA1LgvCp7duFyu79QjJSMZrobeBUN3vofXnFrA97zmBF5zagGAq6EbptXBhz7c0v/tmj2n8vaVaayWXK/3erkJwC3799/Ig3B5q4Zjczl3F6JGH/uPffkmXvtr/9Cxkpc86IOWXLq5Ucrj52SkerQtkuRin58h7xlyE+kHdDPjMU7hFzerqDQNT6wYNA5cQDctW0vLp1WhtXZTWGSYFq46HmNvQHcvIgrIpKGrEkPfqVEvdPKhK85YNS6aB4VJLsW6hpvFBu49OitYc9SJL/9NZuhP33S3fVHsmD6fJykaYft7+kYR7/y9R/ATH/pS6GvKqPficpFsi7/5Pa8UbhcaEq1bXCSggzBs2+JOrYXFqQxyaUW4nQBgtdQEY95hyJ2Syy9uVnH2kFvBaTt5wiPH5y9tY7euo9zwBgw/c3cD+mAYuis7dCe5+B0ugFwv0m1S1L6u5vPp0HXUA/qv94tuPvt1Z1csT0sbNA5MQHebc1lOH2a1Y1FAkMvlxm7D7tSYS6FlWDBMy5Fx2hk6bQNV5mropYb9Zc7kXO0XsDU4uhGEMXRKst1zdFYEWb/+/N6PfAU/+5dPAXADZSalQDfdcmrSz+3HhF84QkOXJZcQDdowLfyb//E4duu6Z5p7FBq9+NCdVgT+7oxpVREMPSopyoZcqr1T1bAwlUEupXq+x9ViA4dncuJ7iyOfXN6seVrapjo853nn/PAf///y6Yv42t/4DC5tVADYlkXGgGPzubbX6AW9JAbpGvSjdw3daUldSEdKLn7fe7/wD38Pg2Fa4ka6JQ2oHzQOTED3a+j5tNfuFgSPhu78/3Wnwu7OwzPQDKtNXgGAlnMh1zUDCnNmijrvT+xpWurjTetqiIAeHODOrwYEdN/w5+dWy0ISIimDWszSZ3j6RgkUD6Msg8LlIidFQySXStPATs3uOaI5N7lOcAuLutDQfTNO5XXppgWLIzIpqg5xpijnXEgu2bTq2WmtlRo4KgXQTtLVRqWFastoY+hRCfyLTsCu616GfnG9irVSE//6vz+Ka9t13Ni1by7Z1GDYKuvF5aIFB/S4DiA/KKDP5sIZejPkJtIP4uYP1kpu75yEoQ8AwuXCvV+sEmHDC5Jc6MQ5NJOFZloiKAVq6JoptpD0/tSYayogoFMgD5Ncvny9iKNzOazMZIXk0vINf96pa+J16KIgVkIsfbXUwLE5Wz+NKuDQukiK0m6EyqnjFDK5kkuXDD3VftrKN+ZO3fGGpaHXNduhtDiVRTaleBj6WrEpjjmtN2oddFOWm2ZFdeZ8cbMqbowyuQDshOzJxTy2axr+/tk1XN8ZnGUR6M2HXtcMFNLtbDnVRRWtjFJDx0w2hXREnqHuSK2DhPvZox9HJgTALjAbFg5OQFe9laLCHRFRKEOBdSqjCiZMTfQpcFEgq2kBDL1liJYDisLAmDvBnQK6XEhBW+VWgOTCOcfjL+3gtU5FX0ZILtzzmN2ahqbhDZT0WTXDQlO3dXoqbIpOirZr6GE+dBHQnQKVOC126fN2U1jk788ur4sQmRQdooZOTpalqQxyaVX08uGc42ax4ZE4OtknX3SK184eciWXKIZOuzegXXLZrWu458gsjs3lcH6tjBu7jQEH9F586FYIQ++cGwpCsa5hfiodeVzrIbuCfhDX5SIH9K2EofcPl6HbhUW5WJKLw3CzKSFXUEBenLLdCsTYNUdPB9wbQV03PWXoKYWJaUUkuQQN3mgGBMNrO3VsVFp47Rk7oKdT9vNk90qlZcCwuAgkFCgpoOum5Xaa7NA6GJB96P5K0fbHkud+UQT0zqy7l14uLSN4ZqgcxKObcw2u0tUP2WueTSnQTAuWxbFb19EyLBztgqFf3qyikFFxZDbneU5YBeXzUhGT30VBPWTuPTaLZ26UcKvcHFhCFOgtKdoIYcuC4HTZHXS3rovWxGHrqDQNYUYYFOJ2zry6XUdaZcin1URyGQREcy4ncBYkyaVTUnRaCujERBedwpayVNBQ9yU1OfcyR4UxMU5MNJVSZcnF1ZT9Jwh5mqnnBgU12b2y6wQUfwVmXmLodLHP5kiGiUiKBkouwQkgcVycG10rJA8goyEdp7hbbN3kyAZILqp04+zUnGtY/dBlrzkRhpZhYbVoJ8OOzbsBXe1gQXxxs4bbV6Y8yV9y8gTh/FpZfKey44pzjt26nai99+gsLm/VYFocJxcHx9B78aGHOU569aEX6xrmnYAe9txyUxfHaFBgMSUXW+YqYGUmm0gug4CnUlRKikZd4LppQVUYsilFMOFq08BURhVBUi45rjvapcxOUz4pwBSsOUhDl4uTvCz98Ss7mMuncc5JkpHsIGvVFFD8yUbqGS5r/sRUora2wT50JXAAAem2y9OdJZfrO3WslRqewBOXpetxGHqH5lzDSopueyQXew1N3cRayfagy5KLyqIdK5c3q21DJ1Jq8HM453h+rYxXO558+bhWWwZ0k2OxYDN0wmAZOsmZ8Z8TlqDs1eViM3RHcgl5qt1yY7AMPa7kcnWnhpOLBSxNZxKGPgi0uVykPtphsYRax2ac7TNgXyDTuZRgrfJE9ppmwDAtz8noYejO/+fTqvi9fAI3pIDud7o8fmUXrz29KF6DEoNyIKSipaZuOkVFQQzdL7l0qaGH6KU1n4Ye5tQBgP/zz5/EL3zsWc/njXsBa6a3P7tYV1wNnQ2vd/WOw7xsyaWdocuSSxSRaOombhYbokLU85yAtd8qN7Fb1/GACOguydh16h5shj4nfj+MpOggfOi9MvTdujsNKuj75Zyj0tTFeT8oEHfotNxr23WcWixgaSqb2BYHAcbspKTFbSYsGDoLL8HWDNvznFYV1+XSMjCTSyOjBjN0v3YcFGgoIQr4NHQtmKHXWgZe2qrh/tvmxe+CkqJUtGRx+/eGj6Hrppt4pU5zUZbB4OZcEOuVQeO/FqezbeuXUdcMPL9Wxs1iQ5T+A4g9hs4/Eo8gWxUjbYtDTIpu1zSkVYbpbMrD0FdLDWRSimdCTpR88tJWDZwDZ1d8DD3E5UL6OTF0+Tyim/ziVBonFvKYzqbAmPfm0i+67QnOOQ+vFO0hKWqYFipNAwuFTGhStKaZsDgGH9BjaOjXtusoNw3ccWgay9MZsZMbBg5MP3TAlTzqfskliqGnVGRURbhcKk0D01mXocsBvaYZkQGd/n86657IQRo64GW45H2/Ter8Ru8vSy670onSNEwRkGWG3pYUjdgn02fxzxQF2i9eP0P3H4c/+NxllBo6vvbcCixuBz/qxwLEL/9vGTEYekRSlHp3c87bipP6xU7VTj4yxjwa+lqxiaNzOXHs7PWGWxCpPXNchk4Ol5cfn0NaZahpJv7phQ089NSqGGCxUMhAURjuOTqDG7uNwGPYK+LqyIRnbpbAOXAyoJOh3MTOj1/9X+fxmRc2sDydxZ/+yIPiGBcbtAtJQ1WCbyy0kx50UtRtexD+4f/54iYA4GvOLWOj0sRuTYNlcc/5MCgcqIBOd2+ZHUQxNruIhSGdUlB3Toiqs22jxJwnKaoZbcw0FRDQZYYu27TkIC4Hd5ouI18AQZWixMYAu/EVXfyUgNUkhk5aYmRzLlFY1C65+ANLrWUgk1LEZ5OPA+ccH/znyyg2dHEMdmoa6nPuzTBuIYluWsIhJEM+zumIC0UVF6AbiAYFu6jI3qHQ+WFr6A0cnfNWZaYizrsgDzpAGnp7oHt+rYLbFguYyaVRyKTQ0Az80wub+Osv3RQj5ujm+VNff27gSbluK0UffuYWUgrDW+9tH84S1cvl48/ewkalicubNdzYreOOQ3YXSiIyUUlRIl5RPdB7QRzL5j9f2MTx+TxuX57C0lQWhsVRbuqibcUgcaACuqow0QEx55tFGQQqYsmozE2Ktgwcns1JDF0O6GabduyVXOznyAFdHorb8CRFJYa+087QiTVrYQFdt1zbJSVFAxh6VDJSNwI09JBS52rLwEzWvdHJLpdLG1VsVOwg8kePvCQ+LyULO61DhmZYyE5Fu1w6zRSl9SsYbETfrraw7DTfIvbY1E2UG0Yb245qznV5s4rj8/nA4Q9BN77za2Xcc9QOboWMirrmDt5+8uouAIgBFv/qzpVeP14ouvGhc87x8DNreOMdy4EBLUpDr2kGjs/ncWW7Lgr0ADshCkBKigYwdOc6pTkEg0Kn/IFuWvjCi9v4tlceA2NM1K9sVbWhBPQDo6EDNjujwqCCc8FFJcmoiEXW0KsRkoutoXsZujcpav87HaaheyQXiaHv1jGVUcWUHiDa5QLYkguxOdeH7rYOpq1nlPanBWroznrNdoY+lU15pAbCI5e2xOcuNw3xehuVlrgg4pb/2xp6Jx96dGERMJyBBFtVTbh8SENvObNlg2Znhp13ZFn0Q27kRqi1DFzZromEZ94J6BTwnri6C1VhA7fryWAdgpqM51bLuLZTxztefiTw79SHJ+jGVWsZwvrpDej2eR+VFCXiNWiG3smy+eVrRVRbBr7uzmUAEDu47SElRg9UQFcUhmrLO208uj+G3VtbdrlQUjRIcqlpRpv/2sMcOzD0ZlhA37HHhcmabybI5VLTPc93S//lSlEfQ49hW4zL0Kckhi6v/5GLWzi9VMC7XmXruW88uyT+NiPsk/EZeicNPao5l99it11t4WaxEfr4uOCcY6vawvI0SS4uQ7dvdu2TeYI+s92Uq9qWEAW8jdwIX71VAecQDH0qk0JdM0TA26y0sFDIDDxfICOOjkz426dWoSoMb703OKCHDdBuGSZ0kwcG9GKdJJe0bXII1NC99ReDgjurOPjv//D8OlIKwxvvcAK6c8MfVmL0QAV0VWGotuwTIS984OHJKbLIpVXbh25ZXNgWKXBVZYYuDaeg60dmjvS/clJUFZqh5fOheyUXv284SEPfqWuiz7otuRBDd+WVus/lEtltMSgpKgJie0Cfzqqu5OI8VzctPHp5G2+6Yxnffv9xMAa84+VHxfNoCxxfQw8eYBGXodNTiVH92sPP4//68ydjvXcUqi07Ie5n6E3DQi2gy19Y18rtmoaaZuL0UnjCUL4RUAfFu4/YWrmfoQO2w2WYiCu5GKaFj335Jt5y1yFRUexHmA+dajxEQK8HSS4Zp1Cw/XWHxdCjJBfxee8+JHbEIqAPiaEfPA2dGLpwuUToX4YruWgmR103wTkwk0212RYZs7eEFMhmc2mUpCQgIDF06eL2FzwR3GpTjuu7dbzxDpfVAlKlqM/lcnQuh1JDR0N3ddSCz+WSVpkIOJ2SohlVaatWBNoZeq1lYmna7jIIuEnR59fKqGkm3nh2Ga89vYgn3v+NntebyaYBNGJNX7Jft18funf9m5WWJ/j1CndmqJeh11oGNMMSHS/FGlWGltF+7IltBg1tlndzhFslOzAcnrPft5CxS8vlz7QwBK1WRlwf+ucubWGj0ooc6xbmcqFKZEoul6Se77t1DRlVQSGjht4oyyIpOhzbYtBn/9zFLWz6Pu/SVBb/66e+JtDhM5D1DOVVJxQqY8JeJzfnCpVcHIaeTdkaOrHx6VwK2bQruTBm68PUbQ9wJxIFFRZFJUVJXyfpZqemoa6ZnoQoINkWnYBs9wzRxAnf1M02hk4ul3xadRl+hNRBPnzPMQxJWtVadm7BnxR94ZbNIEkSWJrOYj6fFq8Tp6eMjLDmXF4fehzJxbWhdtO+NwxULEJJUTo/KK8x5XPmhPmlKRDPBVQ0qgHsdaPS9BQy2UlRw8fQhxvQGQs+J/z4qydvYKGQxtfffSj0MaEM3SE7c/k0prMpr+RS0zFfSIMxFto9tdzUkUkpscfPxYWbP2j/2189eQOLUxm85S7386oKw33H5wK/30HgYAV0hYkCmFy6s4ZORSxpZ9wbyTXT2ZQIKuWmgWxKcQK64ZbW572l/YBcWCRJLh4N3RLDj4nhXnea4p/0SS4Zn+RSbuqwOHDU2ZKGaejUh9ptghTN0P2tasNsi1UnoKcUBoW5ksuF9QoyKQWnltwkn6Iw4Vd37ZP9aujeJmhhcC127roH0U5XBHQhudjH3A3oAY6VLgO6uPlL39l6uSXG2gH2zbtY16EZlkisBrH9QYIOdxRBNy2Of3x+A9/y8qORHnh/P/S1UgPr5aZg6IWMirl8ui0puiAmVwWbHMqNwTfmktcbxNA/e2ET3/yyIwP1/HfCgQroiuJq3oVYSVHbUUEul4q0bZMLe7IpFYWMiloAQ+/kQ5d9tw3dFCcm2R+p7aZ/i0bMmayFFDiOOQy9pVtCa6VgQt0WC5mUYLGRzbkC9Oowhk5JUSqqoRvShfUq7liZbpvjuOQkD+ki69RCt9oysF5uhleKyknROLZF5/2qTSN2QjYIdc3AzWJDtER1k6Jeht6moYecd5EMXW2votysNHFI6shYyKgi4Xb/SbtydHHokktn59DN3QYauomXH58LfQzQXin67z/8Ffz8Xz8jdtbT2RRmfQG9WNcFEQpLilaG0JgLCM8pmU6+Tb7ZjgKxAjpj7G2MsRcYY5cYY++LeNx3McY4Y+yBwS1xcFAZE9ZAOgHCfKuAK7lkUopTDOAGdLnjX9YpqKm33MKiIMnFrRQNsC06LQloXcT0L9yqQGFo646XUhUozLUWUmLoiFPS3XRcAYCbAKZeLrm0Grq19Xz+gOlAQbY/6jFPNyp7uIPL0O86MtP22sRkhdumQ+n/b3z8q3jX730ehsVF/kJGt7ZFcmRQ86pe8f6PPYt3/u4jwmdP8oade3DdDEEul8CAXo/B0C0vQz8sBQ3Zu/6aUwvIpZU2uW7QiNPP5NKmLb3dcajdvSPDr6Ff2a7hVrkp+tMUMinM5VOeHkoyQ1dCWiqUmwZmhiBzhEkutN6gIrhhouO7McZUAB8A8FYANwA8zhh7iHN+3ve4GQD/DsBjw1joIEAX8/c+cEL0skipLLSRlCYlRQG3+dJ0Ni0CqsVtvTSfthk6vRYxz0CGnpE1dPu1TdNOih6fz0NVmGD6n3juFh48sxg4C9FO1tIIPPsEWnEubppwDrgJYN3ptljIqO7OoENzLv920ZVc3N/VxMlrv082ZTP0clPHWqmJc4fbL+JlwdDdFgTkwsmn1bb3vbxpX9iA2wtehhzE49gWTc5hWRw1zfB8H93gVqmJv31qFYZlDx9ZKKTFucIYQy6liipG/3uENeeiZF9QV0C/y8WyODarLRyadQO6/D4nF/P4p595i7h5DgtxGPqlDbv6tVNAl4mGblrYqLSQVhVhN57OpjCXT+OlrZp4zm5dx8LUeBm637JJmn8hO1jNvuN6YjzmQQCXOOeXOecagA8DeFfA434VwH8C0Az420Qgl7IHBrz/HfeK30VNOKGAlhEB3dHQnRODgk42pdoMXevA0Fm75CInupqGHcxyzviySxtVXNyo4u33uTY/GXKPmaBuh8S+UypDJqWgZVpiDBcFvU7NucKSorJMURMXm/2Zs2kFLcPCxXX7Ir7rcDhDF5OTTI5v+91H8Mpf/iTe9J/+se1GQ8GcPrcf3krRCA1dstiRa6mbARsy/uzRK+IYP35lR8hIhGxaCU2Khg24KDV0TGXUSGsmPW+7psG0OA77JBfCXD6NI3O5yCTxIBDHh35xvYrl6WzH6kia7GVaHJuVFji3dy3EeKeyXg3dMC0UPRp6iMuloQ9VQw+qywBGz9DjfNPHAVyXfr7h/E6AMfZqACc5538X9UKMsfcwxp5gjD2xubnZqkHmXQAAIABJREFU9WL7xa9/58vx5//2dZ7tbGQvF0dDpqBGbIu+JAos2ZRtmaq3TOHumA0I6BRopgM0dNu2aCGXUZFNq2gaJj7+7BoA4JtfFlJVl1KgOTNFa1IFaFplHsklrSgi+Dd0C7m0CsVJXkYlBAMZekAhBZ28U4KhK2jpFi6s29vsOwMDuldDNywLL23VkFYZNistz0g/AFiX2gQEJZniSi5ii2xxkU/ptvc2YB+3Dz12Dd94z2FMZ1MwLN7GhHMpVSRL/Z0FlZAy/lJDD3VA+F0u685N7tCMG9DzvoA+CsTxoV/arIpe/p1ACWNqDVFpGUJimXIYOgX0y1s1GBYXu8CwEYOVpjFwyyIgWza9vyeC1evur+f19PsCjDEFwG8B+OlOj+Wcf5Bz/gDn/IGVlcH3lOiEV56cb9vyhfXHAFxHRcaxhG37Ajp5rrMpxanQM9E0TGF1tF/fO74N8Oqpfg09l7IZeku38PHnbuH+2+ZxxNfYiRDE0AtZFbmU6rhc7KhLDF0zTTQ0QwSXlKpE2haDxr2J9rm+8nPPcXEklwvrFeTTKo7Pt7dqJTZLF1lLt5wBy7TD8LYPrrQMcfF0nina2UVhcS5cS0EDOzqhWNewW9fxNXcs4b7jdlHPcgBDF3NpAxh6UOApNfTQIQz0XdD5uuno9mGSy+gCuv1vWCsDzjkubVQ7yi0EYtm3pJv4zWJTDJuZy6fR1C20DBPP3CgBgEi2hvvQw49rPwgr/add6yRKLjcBnJR+PuH8jjAD4D4A/8QYuwLg9QAemtTEqB/2BBt7iO/Dz6x5/qaZloeh79RamMq4wylchq6ikFVF6X82pYhgH2RbnA70odsOlHzG9srWdRNfXavgdWe8BUUy0ikm5IKalITJZVS7UpQkF4VhJpdCqWHY7+GsLR1xMwOCx4QF9X92Gbr9uXJOIFsrNnFiIR/YJvRrzy3jO199XEzRodegrbNcZEVyy9ecs0lAX71cpPXLfXi6ZenCjVJI45Un7T71/oCeS7nHzn8cw5rClRuuY8MPv9xFDF2WXGSGPuiqyDCwDgx9o9JCxekHHgdpRYFhcqyV3JYMq8UGChkVjDFxoyo1dDxzs4RCRsWZZZmhe+UfGo4+MwT5I0xu8pOcUSFOQH8cwDnG2BnGWAbAuwE8RH/knJc458uc89Oc89MAHgXwTs75E0NZ8YBBbOAjX7yGn/zQlwSr5Zw7SVEmtvi7NV3o54BrTcumFdHlrmXYLpKc87cgl0uQht7ULZgWFwnBtWIDhsXb2q7KSKu2Lg7YJ5DC7DXl0gpaDkNPKQyMMRyezWG91BQ+dMBm6FFJ0WJdw3zeKyME+W79eqHN0C0UG1pocDo8m8Nvfe+rxHPIQUQMXW5URkztex84gXOHpgNdM7F7uUg+dFo3EL+wiSDbC191ggK6T3JJu+uIa1ssNrRQZu3X0MlZszIt+9Dt73Yml2qzig4Lrg89+BhSQjSu5GLPW7U8DH212BDnCjHtckPHszdLuPforPisQRO1KqLT4uBvcO7uxPv7muTKGSU6BnTOuQHgJwF8AsDzAD7KOX+OMfYrjLF3DnuBwwZdWA3dnmhC2i2xJzkpul1ree64blJUQSGTgmlxlBtGKEOn/5dHbxGbJD03l1aRS6u4um37z2X25UdGdWed1lqm6wNPqaL0n17/yGwOt8pNZxfgMHSVRfq/Sw0dc76A7C/8sN/bH9DtpG6xrmMuH50Eo4QdaaQLEQH9Zcfm8Kn3fh3uC/Ayp7pOinJPH55uveh0A5rLp/HqUwvIqIpgiQSq3pRHDsrrDUuKdqOhL05lPDkFCuijklsA700yCJe3aGBHlxp62RvQ/Z9tp6bjudWy53wImqhVGVLZP9DeSoJQk1w5o0Ssd+OcPwzgYd/vfjHksW/uf1mjA9nHqCdKpWlfUG5jKte2uFFpeU7KrOxycU62rWrLDuhCQ/cG9KmM6p1c45wQ1ZYc0BWh14fp54B9Q9Elhj4tJA9VlP4TWz0yl8OtZ5vQTS5aB6eUcIbOOUepoWPeFxjUAM2w5pNcSDuutwzMH48OLCRn0UUnXDoBksuRiJubKpf+R/Zysf81LS6qhoHeGfpsLo3Dszk88r63YHmqXUMH2j3o9jrCXS7hDF0Rawfs89FfuEKMcKQBnY5pCEPfKDehMNdS2wlEstZLTUFEaprbFoM+21eu77YVKwXVSZSH1JgL6Cy5BH33w8SBas4VBEqiyEOgAe88TSp/rzQNPHh6QTxXZuhUyXlhvYJj83lPawHxXgprS47R3+kEyDsMnRAVxOwKVicpKiU7c2m7sMcwXYZ+aCYrPqMruYRr6HXNdsn4A4O/UvS3PnVBuHH8SdFiRHAiUJCibTFJLvLk+vVyE7O5VOCUePd1mPg3qlWsfMHLDN2fHP7QY9fwmRc2sFjI4Fe//b42Z42/olN2mhDoewzadqfU9oDeMuw6ho4M3fnONsreKlH7vUbP0Dv1BF8vN7E8nY0tAdHs1LVSE3cfnRE3dP/N6nMX7T77HoYekOMh6+gwuk6GFVVNrOSy30EN8cmNQBe5zNBlV8W33+86NmlLnU0rwpq3W9c9DF0+id9672G8+8HbPO9PAY1uJPmM24JWYe26rIyMqoh11lqml6E7Ay5I0pCZviu5KKGSC81p9GvgFBBNzlFrGfidT19EqaHjHa84KjTjbEpBtWmgrpltDN8PP0MP09CjdiqAe5yj5BbA2w+9JjF0f3D9w0cu41Pn1/GRJ67jynYNfogZlRGfj77HoGHIQQw9quwf8FpcOee4ulNvcxDlxyG5dPChb1S8xU+doCp276T1ctNTwzCVbQ/ox+ZyOCsNAwnyhfs7YQ4SYUVVtZYRKLUNGwlDd/Q6Ibk4F/l62U442X1b7C/l3KFpMaMR8BYWHZ/Pi8RoNqUGBvRvfcWxtvenO3w1gKEvT2cji0LSKQUNJwjUWu5UnFxaxVZVs330iquhE+QB2WGSC7Vx9WvgYsiCxcXsy19+58vwNqn4KZdWRSuCsKQogTGGlMLEtli4XHQvQ4/KJcjrikqIAt5+6FFJ0bpmYqGQxq7T6MqPckN3EtDhuwb6W9gMVL9u3+kmIbtctmsainUdd/qqcKfGILmo0k0yCBvlVscbsoyUwrBebsKwOE4s5DGdTYl++4D3+Pz7b7zTc40E9VYRDH0IFbNBri/AzsWNWm4BEoYuChFcDd2+yD/+3BpUheFr7lgWvUPsAQ1ugJYLixSFiSx+Lu1e6FF6rv13L0PPphVhd+t0EWRU2bZoSrZB14dOJ/vhgICeUlhopWgYW6QT2LC4qASlYb0Euc/NXIzGUCmVtTF02Ye+5mipURAuhw4MnUlJLFlD91eLVluGSNDKayFE+cUJgqEHBHQ1wF7XkaFLchcd+3O+Y59Pq2AMbcnsYaLTCLogrT8KqsJww+kyeng2J44HHce0anc3Pbsyhe989fG25wLeALtVayGjKkOyLdr/+j86jWQcNQ48Q6cSbKGhNw1wzvF3T6/hjWeXsDSdxVw+jf/wtrvx/a/3yiXZtMvQAbsi8qkbJR9D78QY2zV0et1OrDTtkVxcBkOtA3TJ5RLkVU6rStsgAUIphGGrEkO/uFFFWmU45Zuu4wnoMZhiWlHaGTq5jUwLW9XODI9ujFFFRYDLJrlPQ5cDAHfkpHuOzOLyZs0zPYoQlbwk0E3dP9wC8AYe+o46BXTZ5UKTivx9chSF4Te/+5V4rZTrGTaiKkUN08J2rfuATmMBzx6axnwhjZuSbREAfvXbX4Zzh2badrBKgOSyU9WwODWcMXxRksuo9XMgCehQmFdyqbZsK9SV7Tp+7OvOArCtdT/+5rNtzxUM3QnA5I/Opl3bYmeGbv+9WHdLmykQdGKlfpdLIYChkwSRSSlYmspgu6a5DF0NnzwfpqHLGuWljQpuX55uK/TJSjJEJw0dsKWjkrAt2o8nyWWj0oLFO+9WaF1BrXVluFtkrw9d3qm0DAsWd3cL/sHfQNyAThp6MEMH4FhL3dcEohi663K5uFHFTC4VGCijJgINA3QPDdLQt2saOAdWOpzLMugGl0+rOL005TJ06cb4HfcHf8Yg+We7ponRb4NG2M3MzmklksvIQUlRTUqKfuK5W1AVFtpDhSC7XADgnJPAofJ9AIFVkjLo75TJX5nOiud2CmJ+l8u0VKnpd7kALkunAJNWlNDGVHSD8RcWyZrhxY0q7gjopCgz9E4aOuBtVFXI2K2JKaDT1ts/UzXoNYAYSVFZQw/xoYuqVQroAd044wR02rkFXdjuwGdJcolonQt4bwIX1+3eKMMc/hwXUQx9w8lFdcfQ7S/p7qMzUBUmzqE4nu6g1hTbNW1oU5vE+dSmoY9HcjnwAT1FPnQx+cfA1e06TizkO54EclIUcLsKdsPQ6TGVpoGUYpc103M7XQTUPtcw7dJmSojlHZeLLbm4X/Fhx2mQzzjyRIRtsdTQkVEVT7Wj/HnqmolrO/XA6r+uJRdpjfm0inxGFT70m0W7wOrEQns/GBnUpa9TUlROmlVahthlyTsVGki8JBh6QFK02QVDD9HQ/e8b1ToXkFrLmhYublTb9PNxwW1Q1X4ubVSogVj8gE6fkwwIlJiPI2EEJUW3q622tgyDQpTkMurGXEAS0O2G+B7JxcBWtSUu5igI22KKNO8szixP4bbFAvJpFYdmsh0DEeBe3MvTWShOAyKgM0PPpmwNve6wWdHtMK2Cc6DeMoTLRX49GniRirAtlhoa5pw5jUFrvbheBeftSTl6f8BOlsUp5iBWrSr2uL98WnUZ+o7N0IMafLW9jsI6MnR5BF215fZNkW9sbQw9SHKpd+6vHUdDlwPPbl3DTDYVOnGJnrNVbWGr2grsMz8ORPnQN0QDse5cLgBEnx/6juK4RoKSojvDZOgRkss4XC4HXkMXhUWS5LJd1doSfUEQDD3tDjX4h/d+HRRm//+jP/cNiLMjTikMLQDLM955lJ009LTjcvFXatLzqy3DI3mQ5OJtzhUuuQTp3yQRvbBeBtCelAPcG9xsLo04PlwKYLZDgwK6va6bxQaWp7OxhvuqCuuYFJVLtatNAyszWWxUWp7jQL235d7yMojdd5Zc4mnohNViA0fnowvJAOCra/Gm/4wKUQMuSHJZ6YIh0035HsHQu5FcvEnRhmairplD1NDtf5Ok6ISA7GMtH0N/9anOLoGsT3Kh1yN00s/9j6OT/uvuXMEPveEUzixPRT1NzDoVrTqlSlHAtmDKW81vfcVRVJsGFpwgHyW52H1YAqbmOBevq223M2c6HnH0c8BlZBS0c2lVuFxu7DZwPMYuB7Dlls5JUftfy5n5SHkPeadCDD0sKVppGuC8c7MnwdADmFoQk1wrNcUkrSDQc24U4+9aRoGwBlWALbksFNJdDUpWFXt8392OyWBeJEW7l1y2nSljcXbcvYBJrikC59yT0xolDrzk4nY7tC/aYkPDTl3DSow7etaXFO0VKUlyAeyB0L/8rvs6TpqhpKi/2yH52KstwyNB3HFoBr/wrfeKkzCqH3oxpI0rEeDNSgu5tBJ4kdGOJW5xi2DoGfpXFd/HzWIjlmwF2B70TjsC+fvWTS6ChRkwgWkxJCnayY1CoPMiKDkWNB90rdTAsQiGTs/ZdoZmdJr+MypEMvRKK7AtQhQKaRV3rEyLc4t6wCzEKN33M/RhVol63k/6Hpu67ZJKfOhjAH0hpNle266Dc7SNEwsCsY5u2EfwGuznL3c5IVy09a17x5wRM6w09UgJImq4R7mhe6pi3efYr1fXTBwL0fgpkMUP6PZ3UEjb6y9kVNRaBiyL4+ZuA9/0ssOxXsfW0ONJLiWfLVO2LVIfjrl82jPflVCO2Y41KzT09svMX2HY1E1sVbVohu4cJ2rcNspq0Ci4g7fb/9Zt2T8A/Py33OPZFb35rkP44x9+Le4+0n4+tq3Fd1yHWSVqv5/9r6yh17TxNOYCEobeFtCpLWqcrLhcKdrfGux/u9EZ5fcnuxsFjiNz9utYHFiZCT+Ro7otFuvBvcxVKSkQdpG4kku8i4iCcC4jSS66XVCkmRZOxJQWKKkaBbrgqTKV1hjWDjibUtokl7gM/cR8HgoLlqXEkG4nElCL4Kj+93LNwlSmfZD2uBCmI3POcW27FvmZgnDbUkFIYYD9vb75rkOxnusfkUg3P38nzEEhKCE8rvFzQMLQpcpB7+/jJFH8laK9ItUjQxezTgVDt9fxmlOLeOQ/vAVN3cSppXAdPh1SWKQZFmqaGRiwZMK/EBKwScOPU1Qkf4582k2ONnUT12N60AkpRemcFHX+TJIO5R1kH7o8sd0O6L1JLucOz+DpX/rmQC1VlYqEAGDVmc5zLOLmJctJkyK3AOFOjwvrVezWdTxwenFka/H70EmeGjZDl+OHf8buKHHgA7qcuKTG+kA8hn7X4VmcWZ7qOzml+pKicUGzTnelKlNCnCAYVinqlyOC1gqEJ5roBhdXEqAgTO6bvJMUvbEbz4Mur60TQ6cbeNPwBXSfbTGtMqeFg9rWyyVuQAfCnRn+Nq9rxTgMvTt//6gQ1svlCy/a7W3feDZ8jOKgESS52DN/hxNcg7o7EiFICovGALnwZ0EKUFFtawn3HpvFZ37mzX03QqI1RMkjQaDgVfRp6PHfN7hSNCpgKSz4eMkgCSquy8VNiqri34Zuin4ecV0uqRi2RdoikxWRPPmGJykqd64MZ+j9jDTzJ9PWYjB0Od8b99iOAmHtc//lxW2cXMzH3mENAv4RiVtVDUtD6uMCBCeEiaGPw7Z44AO6EsA4qWJz1GtYme5Oa3STonaAKcTwastIh9gWxTY1IGCnYjD0Q7NZvPHsEh48E2+rTTcmj21RN7FabGC+kI59YXzDPYfwpnPLkY+hC550cTpm8k5F7i2fTaltLpdbpSamMmpfrM8f0FdL9ji5KL89tRoGJjOgy5s90+J49PI23nh79PcxaPh3PqWGNlR5igVILvUxjZ8DEsnFm+RzAtTS9PDu6EFIKQwZVcFsvruvg5KixbqGgm+0Xaz3VZXAWZrUVyZo+6+G7GhkZFMqPvSjr+9qHYBXctEMC6vFzm1zZbz/Hfd2fIzaxtDbJRe79ak7vMSfFL2wXsG5wzN9nSOijN85/mvFRqzkIfXv7zSrdZQISoo+v1ZGuWngDSOUWwBpIpUT0OWb81DeL6DVwLjGzwEJQ4fsciOr4rD6PoSvgWG5h5sISRW7da0nvS7t9EP3b5XJcRHUvpcxJljJoIo1qD2BCOiOH/3KVq2rwQhxQGv3J0Vl6cke5+cOvPZLLhfWK22DJbqFXxroVFREmESGznysGACeulEEALwmRoHeIOHXtGuagcIQA6vYEUjX0PXdOhQWvMMdNpKALmmuFKBGHdBTCuva4QJAzDolG1vX76u6TgvOOf7yieuoNHXcKtuSQlgfFjqJFwdkBWvT0J3Afm2n3rXlrRPcwiI7SFPgNn0Myzsf1Q3o29UWtqqaGDnY7zpoZ7BajC4q8j8vroNoFAhyelzaqKKQUWMntAe3Fu/NpTrkQRNuQtj93ZevFXH3kdmk9H8ckBm6LLmMEq86OS+Sc92AtOe1UhMP9MCEZC/0tZ06fvavnkZdMzvO8KRt/6CG7qYCNHRaV6chH92CLniSUfIhGjrd1LMpBcWGJv52wZkUNKiAblocV7drKDcN3N6h1QPg3oQniaEHJQYvbVRxdmX07X39O596yxyawwVwd6y0yzUtjq9cL+Lb728fNzkKHPiALrs2KJB3ax/sF7/8rvt6eh65SUyL4633xqumlEGtZnXTEjNUX9ys4la5c0AHhsDQ067LhdCNhh4Hfh96PkRy8QzclpKiF51JQTTMpFfI0sDfP3sLAPAN93T+DklymSQNXe5gSbi0UcXrbx+tfg7ISVH751GMglMYEzeQixsVVFsG7j85WqlJrGUs7zpBkG1uh2ZymMmlcHZlMrrYdYLcZrXTMI4gyNt+6lt9ebOG9VITR2YjClwcVjIoJ5AoLPJJLkDnFsLdQvElRXNpBQprl1ymsrKG7iZFX7hVwWzIpKBuIHdb/Ptnb+Hlx+dwcjFG7cBEauj2vxTUqi0Da6XmWLpB0uVMMuIommQpzL2ZffmanTuI09xvGDjwDF2WXKazKTzyf389pjv0uZ4UUEC/7/hsrGDQ/nw3qGw6fasvblSwXdVE+4AgKArDQiETqzVuHJCMUMgEMPRBa+g+ySWjKo4fX+7lYopEWjateGyLF9eruOtIfw4XwA3MN3YbeOp6ET/7zXfFW786eQHd70N/cYOGh48+oMuSCzXJGraWrTAmXC5furqLhUIap2O03x7KWsbyrhMEWXLJpBTMFeL18J4EEJN9+31He3o+BVLDskRAXy+3YFg8UupQFSZa8A4C5HLJpdsZ+tGInUIv8DP0tKo4bYTtn3XTgmZYmM4EJ0UvblRwxwAmBdE6PvmcLbfE3WHRjtI/GnCc8PvQL44zoEtJUbcL6XDtg7Lk8pXrRbzq5PzYRgMe+IAut5ftt8nWqHFqqYDf+K5X4IfeeLqn56ckyYUCOuFIh77cg2xH2uZDl/q6d+vN7wS/hp5OKZ6WD1QUUgiQXHTTwm5dH4jzhs67K9s1ZFIKzq50TogCkstlohi6/S8FtUsbVaRVhlM97Br7XovE0Ouad/DL0N7TkVxMi+PKdg139plf6WstY3vnCYGfoe8lMMbwva892bNGSJKNblrYqLQ8rxPJ0BmL1Zu623X4NfQjs7mBMx2XobuSS1oqsKpqXlaXTdsMnXOOckSPm25BN9ONcgvLXZSmp5wRhXEmOI0KzMfQL21UcWZ5qmMr42EgiKGPRHLhHGulBnST49RivJvzUNYytneeEMjySmYMJ+A4kfJp6K8+tSASXFHa9etuX8Qbzw6upNvttugL6APWzwHJh26Q5GIPxSA/uH+cXzalgHNAMy0Uu2jK1QmufdLqqhOgqrCJYucERbLuXdup4XREl89hQraDUpOsoSdFFVtDv7ZjN5OLM75yWNgb2b8hwhPQ9xhD7xcpyba4WW3hgdMLODaXx0alGVkF+l/eff+A1+HzoWfizVTtBRRINcNCWmVgjDkM3RfQpUpRwA68xfrgAnrKU9DWxbxNhU2Ufk5QnNm8gJ2Hed2Z0VsWAa/kIhj60DV0e3dybdsO6LeNQWoSa4nzIMbY2xhjLzDGLjHG3hfw9/cyxs4zxp5mjH2aMXZq8EsdDtQ9LLn0C2LGTd3ETk3DoZkczh6axqGZXNd9YfoBTfYp+CWXGKXw3UL+WLQjk5OiVT9Dd9bS0i2UnAKjQTR7kptCdtNCIa0qE8rQ7dm8Td1EqaH3bevsFbIPveYbzTgskORydaeOlMIGXt3cDTp+UsaYCuADAN4K4AaAxxljD3HOz0sP+zKABzjndcbYjwP4DQD/ehgLHjQONEN3Atqa07tlZSaLn37rnWKw7qjwtpcdgWlxcSGkVQX/+XteidfdPvjBCPL3Ta0TVIWJIdG0baYyfJehm131Qe8ED0PvQnL56W+6y5PInxRQtSQl17sdOzcoCB865yLBPeykKHNuZtd26jixkB9L7oAQ55M+COAS5/wyADDGPgzgXQBEQOecf0Z6/KMAfmCQixwmDrKGTnZBGq5waCaLV56cH/k6FqYy+IHXezd13/WaE0N5Lzn5SMnYtKLAdDT0i+t2D5Jjzu4gSHIZRB8V+bzrpuJ21N0L40JVbJa6QQG9y8HQA1uH1P2wapB8NlzJRVXs97u2Xf//2zu7GLuqKgB/65yZTkun9HdoastQsKPhJ9qWUSEBFUGEPlBM0JRE5IGkD4LR6AuEhBh8MkRJSJRYIwGJAf9jH2pQ0cQnkaoVqAQZFIQGO5Ui/cv83Vk+nH3uPXNmLnNhzj373LPXl9zMveeemVlnn33XXXuttddi2FPsIKUTDbYZeDXz+jV3rB23Ab9a6A0R2SsiB0Xk4LFjxzqXsouErNBTSyJtJDHkaZlcNuk9n+NycVkuY+On2HbOYNPltDzjckkV+lIaW+RlgPJrB3WD1OVy7GRrteeDuUHRcrNcXnnjtJdUzTmyFPnHRORzwChw30Lvq+o+VR1V1dGhoaEi//W7Jg2S9cdSqt+4CqSTP+2W42uZXDbpbU5dbH2ujDC0FHpKaqFPOJfLquV9hWw866SVXy8hwlwL3ZvLJRsUbbAsjrruSo1EePPMNCcmZrwGRKEzl8sR4NzM6y3u2BxE5BrgbuBjqlquE3YJpP7I0KxzmFutEd5ZtkUvk3yJa/P6++KIxqxywpUOHsnsBE37oyZB0enCApJzOj+VXAyuG0QiqCZ59ZH4m0vZPPRso5JuIpJsEAMY9piyCJ1Z6E8DIyJyvogsA/YA+7MniMgO4LvADao6XryY3SOdAKEFRKEVmPv38TOsX7ksmDForcqyFvosY27L+kjWQu9vBUX/d2aqsJTBulnoUdNCn2DD4IC38hnZKpanp7pfaRGS+ZSmLPrKv2/KstgJqjoD3AE8ATwP/FhVD4vIvSJygzvtPmAQ+ImIHBKR/W3+XOVIl2ihKLMsrSbT01y8ebVnacojzt3zxIeujLla5yMb57tcJmcSC72oCpP19KEnLhefrrtsS7jTkzPN/QTdJI6EqcYsK/rjjks4dIuOrlZVDwAHcsfuyTy/pmC5SqMvYIWeTa/aOVx+dosv0kSXloUeMTPb4MXxkwz0RXO61DeDojPJTtFNa4rJjU9Xhsv7Iy+dbYomTd0bPzHZlR2+ndIKisKZqUZpLheAD2xZ7TVlEWzrf/MbPUQfetaPu2PYT/1mH+SzXPrdxqKx8VNcMDQ4x3puWujTDd46M11Y67coSmrK1yVuEUliFY+fnGSjVws9+dlwO0XLcrmAvxroWcLTYjlay+/qFDsqi2yDjO0e8s99EWcym4BmLZfjZ6bnpdulQdHAnniEAAAI90lEQVQJZ6EX5XIB10u2Bu4WSJTaVGOWN05PMuQpBx2SlUL65VKWyyX9EtlRgc9Q8Ao9bJdLcu3bzhksVFFVHZG8Dz1ienaWUxPTrMpZdGlQ9PipKRqzWui2+0jES2f4bhAJHDs5iSretv2nxJEkQdHJRnAWeu8775ZIGhQdCNDlkvYUDcl/Dq0uVa2doklhqYnpxry6H6nL5ajbMFNkYay+SGqRsgjJ5+joidaOY6+yuA5CSZZL91fekQjD685qNhb3SfAKPeS0xZUDMZdfsJ4bPvh2G3/rRz5uEkcRMw3l1MTMvPaDy+IIERh3yqqIXaIpV1+4kStHiitD7JNIpLmpaEMVLPTZpJZLGRb6lSMbCp0XS8EUehyuQu+LIx7be5lvMUonyn2J98fC5EyD01MNVuUUukjSUCJVVkW6XB64udgyxD6JhGZphCHPlmoswsRMg6nGbNfruADctevCrv+PTglPi+WIA85yCZUo53Lpi6VZSXGhUqsDfXHTnVDF0rVVINv5y3dcIIqEkxPltJ+rGsFrsfwmE6P+xPN2ikbNWi55Cx0SP/rRE4mFHlLw+J2Q6vMkr95vxlgcSbNdYBlZLlUirKtdAFPo4dHc+t/narlk8s4HB+Yr7C9+YhtPv/wmm1Yv70oXpTqQjun6lQPeOt5nZWmuuBb4gq4zYV3tAoQcFA2VfGZTdnffQgrglsu3csvlW0uRrVdpKvQK5NXHEU2FvtCKq84Er8WiyHzooTHf5ZK10MNSAEWRGuW+/eeQuNDeLiZSZ0yLkXygzUIPh2Ytl0xxrpTQLLqiyLpcfBPNsdDDinnY7AW+cNU2Pv7+ajTcMLrP/FouGZdLYBZdUaSZQ5VwuYi8bZC7zoR1tW34yiff51sEo0RaQdEFXC6BKYCiiJsWun+FHgXsQjM/gxEcrbhJqzhXSmhpbkWRZrZUwYeefrlEgvcUyrIxhW4ER6q/+3Mul8GBYvqFhkg6bJVwuThhBgf6vKdQlo0pdCM48qmqaVA0tOV5kVQqKOpkCS0gCqbQjQBZqKcomP98KURVcrlEqUIP736aQjeCI83IWJbZ+g9moS8FqZDLJYrCXXGZQjeCI19tMXW5hGjRFUUkwor+uBL9UdNtBSHeT1PoRnCkS/J+s9ALI4qqYZ1DJihqPnTDqD8tH7orzmVB0SWzaqCfzWtW+BYDaN3fEO9neFdsBM/8tEULii6Vr994CY1Z9S0G0LLQzw7wfoZ3xUbwpB/4tF9o7Fwu+QbRRucMeW47lyW2oKhhhIPk0hb7LW2xVjRdLgHeT1PoRnDE+VouTrGHuBGljrTy0MO7n6bQjeDI56GHvESvIyEHRU2hG8HRzEN3Cn3L2hWsPaufkY2DPsUyCiKthmxBUcMIgHxP0Y1nL+ev91zrUySjQOKAYyJmoRvBkd9YZNQLc7kYRkBEIojMbWxh1AcLii6CiFwnIi+IyJiI3LnA+wMi8iP3/lMisrVoQQ2jKCJJrPPQamWHQizh1uZZVKGLSAx8G7geuAi4WUQuyp12G/Cmqm4D7ge+UbSghlEUcSTNgKhRP6JI6IukuXEsJDq54g8DY6r6T1WdAh4HdufO2Q084p7/FLhazPwxKkoUSbPSolE/YhEGl4fXrQg6y3LZDLyaef0a8JF256jqjIi8BawH/ps9SUT2AnsBhoeH36XIhrE0brp0C5e8Z7VvMYwu8dkPbWH78BrfYnihVCeTqu4D9gGMjo5Wo5KPERw7h9eyc3itbzGMLnHpeeu49Lx1vsXwQifrziPAuZnXW9yxBc8RkT5gNfBGEQIahmEYndGJQn8aGBGR80VkGbAH2J87Zz9wq3t+E/A7VTUL3DAMo0QWdbk4n/gdwBNADDykqodF5F7goKruB74PPCoiY8BxEqVvGIZhlEhHPnRVPQAcyB27J/N8AvhMsaIZhmEY7wTL3TIMw6gJptANwzBqgil0wzCMmmAK3TAMoyaIr+xCETkGvPIuf30DuV2oxjxsjDrDxmlxbIwWp8wxOk9VhxZ6w5tCXwoiclBVR33LUWVsjDrDxmlxbIwWpypjZC4XwzCMmmAK3TAMoyb0qkLf51uAHsDGqDNsnBbHxmhxKjFGPelDNwzDMObTqxa6YRiGkcMUumEYRk3oOYW+WMPqUBGRl0XkWRE5JCIH3bF1IvIbEXnR/Qyqq4OIPCQi4yLyXObYgmMiCQ+4efWMiOz0J3l5tBmjr4nIETeXDonIrsx7d7kxekFEPuVH6nIRkXNF5Pci8ncROSwiX3LHKzeXekqhd9iwOmSuUtXtmXzYO4EnVXUEeNK9DomHgetyx9qNyfXAiHvsBR4sSUbfPMz8MQK4382l7a7aKu6ztge42P3Od9xnsu7MAF9V1YuAy4Db3VhUbi71lEKns4bVRots8+5HgBs9ylI6qvoHkvr8WdqNyW7gB5rwR2CNiGwqR1J/tBmjduwGHlfVSVX9FzBG8pmsNar6uqr+xT0/CTxP0ke5cnOp1xT6Qg2rN3uSpWoo8GsR+bNrxg2wUVVfd8//A2z0I1qlaDcmNrfmcodzFzyUcdUFP0YishXYATxFBedSryl0oz1XqOpOkuXe7SLy0eybriWg5ahmsDFpy4PAe4HtwOvAN/2KUw1EZBD4GfBlVT2Rfa8qc6nXFHonDauDRFWPuJ/jwC9IlsJH06We+znuT8LK0G5MbG45VPWoqjZUdRb4Hi23SrBjJCL9JMr8h6r6c3e4cnOp1xR6Jw2rg0NEVorIqvQ5cC3wHHObd98K/NKPhJWi3ZjsBz7vMhQuA97KLKeDIufv/TTJXIJkjPaIyICInE8S9PtT2fKVjYgISd/k51X1W5m3qjeXVLWnHsAu4B/AS8DdvuWpwgO4APibexxOxwVYTxJ9fxH4LbDOt6wlj8tjJC6DaRI/5m3txgQQkgyql4BngVHf8nsco0fdGDxDopw2Zc6/243RC8D1vuUvaYyuIHGnPAMcco9dVZxLtvXfMAyjJvSay8UwDMNogyl0wzCMmmAK3TAMoyaYQjcMw6gJptANwzBqgil0wzCMmmAK3TAMoyb8H5Vrz7ytNbBFAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Проверяем и убеждаемся, что в процессе трансформации данные получили \"лишнее\" измерение\nprint(data.ndim)","execution_count":179,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Конвертируем в одномерный массив\ndata = data.flatten()","execution_count":180,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Сохраняем в итоговом наборе данных\ndataset['8'] = data","execution_count":181,"outputs":[]},{"metadata":{"scrolled":false,"trusted":true},"cell_type":"code","source":"# Обработка данных в столбце №0\n\n# Загружаем данные\ndata = raw_dataset[0]\n\n# Анализируем распределение, используя гистограмму. Параметр bins отвечает за число столбцов в гистрограмме.\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":182,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#проблема №6\ndata = pandas.get_dummies(data)","execution_count":183,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Результат\nmatplotlib.pyplot.hist(data, bins = 50)\nmatplotlib.pyplot.show()","execution_count":184,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"dataset = dataset.join(data)","execution_count":185,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"print(dataset)","execution_count":186,"outputs":[{"output_type":"stream","text":"         MMIN     CHMAX         2         4         5         6         8  \\\n0    0.251074  1.000000  0.489650  0.730638  1.000000  0.894427  0.759257   \n1    0.874463  0.676123  0.131078  1.000000  0.433861  0.632456  0.825801   \n2    0.874463  0.676123  0.131078  1.000000  0.433861  0.632456  0.782136   \n3    0.874463  0.676123  0.131078  1.000000  0.433861  0.632456  0.728689   \n4    0.874463  0.478091  0.131078  0.888465  0.433861  0.632456  0.671211   \n..        ...       ...       ...       ...       ...       ...       ...   \n204  0.497852  0.338062  0.487679  0.776930  0.000000  0.223607  0.422549   \n205  0.497852  0.338062  0.429927  0.776930  0.433861  0.316228  0.442303   \n206  0.623389  0.447214  0.489650  0.776930  0.000000  0.316228  0.468926   \n207  0.376611  0.000000  0.819865  0.776930  0.433861  0.000000  0.523962   \n208  0.497852  0.000000  0.819865  0.665395  0.000000  0.000000  0.437531   \n\n     adviser  amdahl  apollo  ...  microdata  nas  ncr  nixdorf  perkin-elmer  \\\n0          1       0       0  ...          0    0    0        0             0   \n1          0       1       0  ...          0    0    0        0             0   \n2          0       1       0  ...          0    0    0        0             0   \n3          0       1       0  ...          0    0    0        0             0   \n4          0       1       0  ...          0    0    0        0             0   \n..       ...     ...     ...  ...        ...  ...  ...      ...           ...   \n204        0       0       0  ...          0    0    0        0             0   \n205        0       0       0  ...          0    0    0        0             0   \n206        0       0       0  ...          0    0    0        0             0   \n207        0       0       0  ...          0    0    0        0             0   \n208        0       0       0  ...          0    0    0        0             0   \n\n     prime  siemens  sperry  sratus  wang  \n0        0        0       0       0     0  \n1        0        0       0       0     0  \n2        0        0       0       0     0  \n3        0        0       0       0     0  \n4        0        0       0       0     0  \n..     ...      ...     ...     ...   ...  \n204      0        0       1       0     0  \n205      0        0       1       0     0  \n206      0        0       0       1     0  \n207      0        0       0       0     1  \n208      0        0       0       0     1  \n\n[209 rows x 37 columns]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"dataset.to_csv('prepared_data.csv')","execution_count":187,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}