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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nts = pd.read_csv(\"/kaggle/input/features-cout-by-week/Features count.csv\")\nts.plot()\nplt.grid()\nplt.show()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":false,"execution":{"iopub.status.busy":"2023-04-29T06:53:21.492391Z","iopub.execute_input":"2023-04-29T06:53:21.493250Z","iopub.status.idle":"2023-04-29T06:53:21.908943Z","shell.execute_reply.started":"2023-04-29T06:53:21.493192Z","shell.execute_reply":"2023-04-29T06:53:21.907464Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABNY0lEQVR4nO3deVhU9f4H8PfMMAzrgKgwoKCYuIELLplaiinkklreXNOse383vZq5dNPMumIZpqXZzbJrt91LltcyMzcyxcpMRFFBxQ0RQcIFGfbZvr8/kLkhuIzOzJnl/Xoensc5czjz+cxhzrw9y/fIhBACRERERHYil7oAIiIici8MH0RERGRXDB9ERERkVwwfREREZFcMH0RERGRXDB9ERERkVwwfREREZFcMH0RERGRXHlIXcD2TyYSCggL4+/tDJpNJXQ4RERHdBiEESktLERYWBrn85vs2HC58FBQUIDw8XOoyiIiI6A7k5eWhefPmN53H4cKHv78/gJri1Wq1VZet1+uxfft2JCQkQKlUWnXZjoj9ujZ36xdwv57Zr2tztX61Wi3Cw8PN3+M343Dho/ZQi1qttkn48PHxgVqtdokVfSvs17W5W7+A+/XMfl2bq/Z7O6dM8IRTIiIisiuGDyIiIrIrhg8iIiKyK4c75+N2CCFgMBhgNBot+j29Xg8PDw9UVVVZ/LvOiP26Nmv2q1Ao4OHhwcvbicgunC586HQ6XLhwARUVFRb/rhACGo0GeXl5brGRZb+uzdr9+vj4IDQ0FJ6enlaojojoxpwqfJhMJuTk5EChUCAsLAyenp4WbXRNJhPKysrg5+d3ywFQXAH7dW3W6lcIAZ1Oh4sXLyInJwdRUVFu8f4RkXScKnzodDqYTCaEh4fDx8fH4t83mUzQ6XTw8vJyi40r+3Vt1uzX29sbSqUSubm55mUSEdmKU26h3eGLhcje+LkiInvh1oaIiIjsyuLwkZ+fjwkTJqBx48bw8fFBly5dkJ6ebn5eCIHExESEhYXB29sbcXFxyMrKsmrRRLZ09uxZyGQyZGRkSF3KXdm1axdkMhmuXr0qdSlERHVYFD6Ki4vRp08fKJVKbNmyBUePHsWyZcsQGBhonmfp0qVYvnw5Vq5cibS0NGg0GsTHx6O0tNTatTuVJ598EjKZrN7PqVOnrLL8Tz75pM56oIbJZDJs2LBB6jKIiNyaRSecLlmyBOHh4fj444/N01q2bGn+txACK1aswPz58zFy5EgAwKeffoqQkBAkJydj8uTJ1qnaSQ0aNKjOewcATZs2laiaG9Pr9S51nwEiInIsFu352LhxI7p3745Ro0YhODgYsbGx+OCDD8zP5+TkoLCwEAkJCeZpKpUK/fr1w549expcZnV1NbRabZ0foOYLsKEfIQRMJtMd/QghAOCulnE3r+3p6Yng4OA6PzKZDCaTCd9++y26desGLy8vtGrVComJieare0wmE5YtW4aOHTvC19cX4eHh+Nvf/gatVguTyYQff/wRTz31FEpKSsx7VBYsWAAhBBo1aoQNGzbUqSUwMBAfffQRTCYTzpw5A5lMhrVr1yIuLg5eXl747LPPYDKZ8OGHH6J9+/bw8vJCu3bt8O6775qXUVVVhWnTpiE0NBReXl5o2bIlkpKSbvoe/Pvf/0Z0dDRUKhVCQ0Mxbdo083Nnz57F8OHD4efnB7VajVGjRuHChQvm5ydNmoQRI0bUWd6MGTMQFxdnfty/f3/MnTsXc+bMQVBQEDQaDRYsWGB+vjYoP/roo5DJZGjZsuUNawWAo0ePonfv3vDy8kJ0dDR+/PFHmEwmGI1GtG7dGm+88Uad3zl8+DDkcjlOnjxZb3mHDh2CXC5HUVERTCYTLl++DLlcjscee8w8T1JSEnr16mV+nJmZicGDB8PPzw8hISGYMGGC+fdrf95++220bt0a3t7e6Ny5M7766qt6fdT+u7y8HEOGDMF9992HS5cu3fDv9EafPUf5udn2wRV/2K9r/9irX51Oh3OXSrHlcD7e3HoMC749YrN+bodFez7OnDmDVatWYfbs2XjxxRexb98+PPvss1CpVHjiiSdQWFgIAAgJCanzeyEhIcjNzW1wmYsXL8bChQvrTd++fXu9y2k9PDyg0WhQVlYGnU4HoCZIVOlNlrSBystXLZr/RryU8tseZ0Sv18NgMJjD1R/t2LEDTz31FJYsWYJevXohJycHM2fORHV1NebOnQug5jLjpKQkREREIDc3F3//+98xa9YsLFu2DDExMVi8eDGSkpKQlpYGAPD19TUf6qqsrKzzukIIVFVVQavVoqysDAAwd+5cLFq0CG+//TY8PT3xzjvv4PXXX8fSpUvRqVMnHD58GDNmzIBcLse4cePwzjvv4Ntvv8WHH36I5s2bIz8/H/n5+Q32BwAffvghXnrpJSxYsAADBw6EVqvFb7/9Bq1WCyEERowYAR8fH2zatAkGgwF///vfMWrUKGzatOmG759Op6szzWg04osvvsC0adOQkpKCtLQ0TJ06FV26dEH//v3xww8/ICoqCu+++y4GDBgAhULRYL2178nzzz+PxYsXo23btnjvvffwyCOPICMjA0FBQRg3bhw++ugj/PWvfzX/3r/+9S/06tULTZs2rbfciIgIBAUFYevWrRg+fDi2bduGoKAg7N692zzvjh070LNnT2i1WhQWFiIuLg5PPPEEFi5ciKqqKiQmJuKxxx7Dxo0bAQCvvvoqNm3ahDfeeAP33HMP9uzZgyeeeAK+vr7o06ePeSC+0tJSlJaWYuzYsfDy8sL69evh4eFRr0adTofKykrs3r0bBoOhwfXoKFJSUqQuwa7Yr2uzdr9CAFeqgbxyGfLKZThfBpwvl6HM8L/vK4VMoJPpDDyseNmJJYN/WhQ+TCYTunfvjqSkJABAbGwssrKysGrVKjzxxBPm+a7/QhZC3PBLet68eZg9e7b5sVarRXh4OBISEqBWq+
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