jgamblin/Graph.ipynb Secret

## Graph.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1f5f8d46-ad03-4221-864e-46b8a14a5ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "28ecb8bb-ece3-4a3c-8abb-c5c11bfcf941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_df = pd.read_csv('https://gist.githubusercontent.com/jgamblin/c735af3fe21e5ba071288b7583d098a7/raw/e3aaff248747241c658ec9323b0bb34d873d6253/2022.csv')\n",
    "graph_df[['Published','AnalysisTime']] = graph_df[['Published','AnalysisTime']].apply(pd.to_datetime)\n",
    "graph_df['Published'] = pd.to_datetime(graph_df['Published']).dt.date\n",
    "graph_df['AnalysisTime'] = pd.to_datetime(graph_df['AnalysisTime']).dt.date\n",
    "graph_df['Difference'] = (graph_df['AnalysisTime'] - graph_df['Published'])\n",
    "graph_df['Difference'] = graph_df.Difference.apply(lambda x: x.days)\n",
    "count = pd.value_counts(graph_df['Difference'])\n",
    "count = count.to_frame().reset_index()\n",
    "count = count.rename(columns={\"index\": \"Days\", \"Difference\": \"Count\"})\n",
    "count = count.sort_values(by=['Days'])\n",
    "count.plot(x='Days', y='Count', kind=\"bar\", colormap='jet', figsize=(16, 8), title='CVE Analysis Time');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "de2cef2c-6ce7-440d-9df8-966955a77bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22619.000000\n",
       "mean         5.961890\n",
       "std          3.646976\n",
       "min          0.000000\n",
       "25%          3.000000\n",
       "50%          6.000000\n",
       "75%          8.000000\n",
       "max        213.000000\n",
       "Name: Difference, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_df['Difference'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eea810e-f42a-48a0-bb16-482c2837e963",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 70,
	"id": "1f5f8d46-ad03-4221-864e-46b8a14a5ae5",
	"metadata": {},
	"outputs": [],
	"source": [
	"import pandas as pd"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 72,
	"id": "28ecb8bb-ece3-4a3c-8abb-c5c11bfcf941",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 1152x576 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"graph_df = pd.read_csv('https://gist.githubusercontent.com/jgamblin/c735af3fe21e5ba071288b7583d098a7/raw/e3aaff248747241c658ec9323b0bb34d873d6253/2022.csv')\n",
	"graph_df[['Published','AnalysisTime']] = graph_df[['Published','AnalysisTime']].apply(pd.to_datetime)\n",
	"graph_df['Published'] = pd.to_datetime(graph_df['Published']).dt.date\n",
	"graph_df['AnalysisTime'] = pd.to_datetime(graph_df['AnalysisTime']).dt.date\n",
	"graph_df['Difference'] = (graph_df['AnalysisTime'] - graph_df['Published'])\n",
	"graph_df['Difference'] = graph_df.Difference.apply(lambda x: x.days)\n",
	"count = pd.value_counts(graph_df['Difference'])\n",
	"count = count.to_frame().reset_index()\n",
	"count = count.rename(columns={\"index\": \"Days\", \"Difference\": \"Count\"})\n",
	"count = count.sort_values(by=['Days'])\n",
	"count.plot(x='Days', y='Count', kind=\"bar\", colormap='jet', figsize=(16, 8), title='CVE Analysis Time');"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 73,
	"id": "de2cef2c-6ce7-440d-9df8-966955a77bba",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"count 22619.000000\n",
	"mean 5.961890\n",
	"std 3.646976\n",
	"min 0.000000\n",
	"25% 3.000000\n",
	"50% 6.000000\n",
	"75% 8.000000\n",
	"max 213.000000\n",
	"Name: Difference, dtype: float64"
	]
	},
	"execution_count": 73,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"graph_df['Difference'].describe()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"id": "4eea810e-f42a-48a0-bb16-482c2837e963",
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3 (ipykernel)",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.9.7"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 5
	}