diff --git a/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb index 453580a..4cd72e4 100644 --- a/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb +++ b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb @@ -3970,7 +3970,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -3982,7 +3982,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -4074,7 +4074,7 @@ "4 True " ] }, - "execution_count": 92, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -4086,7 +4086,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -4178,7 +4178,7 @@ "4 True " ] }, - "execution_count": 94, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -4191,7 +4191,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -4276,7 +4276,7 @@ "4 3377906280028510514 2017-03-01 2017-03-01 06:18:00 1.682314 True" ] }, - "execution_count": 95, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -4288,7 +4288,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -4419,7 +4419,7 @@ "7 False 1.671675 " ] }, - "execution_count": 98, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -4439,7 +4439,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -4454,7 +4454,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -4470,7 +4470,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -4481,7 +4481,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -4648,7 +4648,7 @@ "7 False 1.629241 1.629241 1.682314 1.676886 1.671675 " ] }, - "execution_count": 102, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -4659,7 +4659,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -4669,7 +4669,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -4680,7 +4680,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -4691,7 +4691,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -4844,7 +4844,7 @@ "4 3 2 144 " ] }, - "execution_count": 128, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4855,7 +4855,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -4869,7 +4869,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -5028,7 +5028,7 @@ "4 3 2 144 0.0 " ] }, - "execution_count": 130, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -5039,7 +5039,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -5056,7 +5056,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -5221,7 +5221,7 @@ "4 3 2 144 0.0 8.0 " ] }, - "execution_count": 133, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -5232,7 +5232,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -5245,7 +5245,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -5257,7 +5257,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -5267,7 +5267,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -5463,7 +5463,7 @@ "4 1.0 1.0 1.0,1.0 " ] }, - "execution_count": 139, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -5474,7 +5474,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -5483,7 +5483,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -5494,57 +5494,16 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "ename": "MemoryError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 688\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 689\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 690\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m_python_apply_general\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m 706\u001b[0m keys, values, mutated = self.grouper.apply(f, self._selected_obj,\n\u001b[1;32m--> 707\u001b[1;33m self.axis)\n\u001b[0m\u001b[0;32m 708\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m 165\u001b[0m \u001b[0mmutated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmutated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m \u001b[0msplitter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_splitter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 167\u001b[0m \u001b[0mgroup_keys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_group_keys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m_get_splitter\u001b[1;34m(self, data, axis)\u001b[0m\n\u001b[0;32m 148\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_splitter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m \u001b[0mcomp_ids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mngroups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 150\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mget_splitter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomp_ids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mngroups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36mgroup_info\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 251\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgroup_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 252\u001b[1;33m \u001b[0mcomp_ids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobs_group_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m_get_compressed_labels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36mlabels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 366\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 367\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 368\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m_make_labels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 391\u001b[0m labels, uniques = algorithms.factorize(\n\u001b[1;32m--> 392\u001b[1;33m self.grouper, sort=self.sort)\n\u001b[0m\u001b[0;32m 393\u001b[0m \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 189\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\algorithms.py\u001b[0m in \u001b[0;36mfactorize\u001b[1;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[0msize_hint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msize_hint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 613\u001b[1;33m na_value=na_value)\n\u001b[0m\u001b[0;32m 614\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\algorithms.py\u001b[0m in \u001b[0;36m_factorize_array\u001b[1;34m(values, na_sentinel, size_hint, na_value)\u001b[0m\n\u001b[0;32m 459\u001b[0m uniques, labels = table.factorize(values, na_sentinel=na_sentinel,\n\u001b[1;32m--> 460\u001b[1;33m na_value=na_value)\n\u001b[0m\u001b[0;32m 461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.StringHashTable.factorize\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.StringHashTable._unique\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mMemoryError\u001b[0m: ", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'link_ID'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmean_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - 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"\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m 1348\u001b[0m \u001b[0mnew_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1349\u001b[0m return self.reindex_indexer(new_axis=new_labels, indexer=indexer,\n\u001b[1;32m-> 1350\u001b[1;33m axis=axis, allow_dups=True)\n\u001b[0m\u001b[0;32m 1351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1352\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[0;32m 1229\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1230\u001b[0m new_blocks = self._slice_take_blocks_ax0(indexer,\n\u001b[1;32m-> 1231\u001b[1;33m fill_tuple=(fill_value,))\n\u001b[0m\u001b[0;32m 1232\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1233\u001b[0m new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_slice_take_blocks_ax0\u001b[1;34m(self, slice_or_indexer, fill_tuple)\u001b[0m\n\u001b[0;32m 1310\u001b[0m blocks.append(blk.take_nd(blklocs[mgr_locs.indexer],\n\u001b[0;32m 1311\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_mgr_locs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1312\u001b[1;33m fill_tuple=None))\n\u001b[0m\u001b[0;32m 1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1314\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mblocks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_tuple)\u001b[0m\n\u001b[0;32m 1232\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1233\u001b[0m new_values = algos.take_nd(values, indexer, axis=axis,\n\u001b[1;32m-> 1234\u001b[1;33m allow_fill=False, fill_value=fill_value)\n\u001b[0m\u001b[0;32m 1235\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1236\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfill_tuple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\algorithms.py\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, out, fill_value, mask_info, allow_fill)\u001b[0m\n\u001b[0;32m 1649\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'F'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1650\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1651\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1653\u001b[0m func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis,\n", - "\u001b[1;31mMemoryError\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "df2 = df2.groupby('link_ID').apply(mean_time)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -5554,21 +5513,235 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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