Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "d4c68c4e-57b6-4b0e-b5ad-655994f9f847",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "0f3b2209-16be-4bfa-8d4f-b61b91e4e446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Name</th>\n",
       "      <th>Size [h]</th>\n",
       "      <th>Size [GB]</th>\n",
       "      <th>Frames</th>\n",
       "      <th>N° Scenes</th>\n",
       "      <th>Sampling Rate [Hz]</th>\n",
       "      <th>Scene Length [s]</th>\n",
       "      <th>Sensortypes</th>\n",
       "      <th>Publish Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cityscapes 3D</td>\n",
       "      <td></td>\n",
       "      <td>63.141</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>17</td>\n",
       "      <td>1.8</td>\n",
       "      <td>camera, gps, thermometer</td>\n",
       "      <td>2016.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ApolloScape</td>\n",
       "      <td>100</td>\n",
       "      <td></td>\n",
       "      <td>143,906</td>\n",
       "      <td></td>\n",
       "      <td>30</td>\n",
       "      <td></td>\n",
       "      <td>camera, lidar, imu/gnss</td>\n",
       "      <td>2018.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name Size [h] Size [GB]   Frames N° Scenes Sampling Rate [Hz]  \\\n",
       "0  Cityscapes 3D             63.141                                    17   \n",
       "1    ApolloScape      100            143,906                           30   \n",
       "\n",
       "  Scene Length [s]               Sensortypes Publish Date  \n",
       "0              1.8  camera, gps, thermometer      2016.02  \n",
       "1                    camera, lidar, imu/gnss      2018.03  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"adDatasets.csv\")\n",
    "data=data[0:40]\n",
    "data=data.drop(columns=['Sensors - Details', 'Benchmark', 'Annotations', 'Licensing', 'Related Datasets', 'Related Paper', 'Last Update'])\n",
    "data=data.fillna(\"\")\n",
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "9822e641-9985-4355-b328-a37a213f7f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Publish Date'] = pd.to_datetime(data['Publish Date']).dt.strftime('%Y/%m')\n",
    "data\n",
    "\n",
    "for col in ['Size [h]','Size [GB]', 'Frames', 'Sampling Rate [Hz]', 'Scene Length [s]', 'N° Scenes']:\n",
    "    data[col] = pd.to_numeric(data[col].apply(lambda x: re.sub(',', '', str(x))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "85233ca6-9c31-47ca-a8a2-e21279a31ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   object\n",
       "Size [h]              float64\n",
       "Size [GB]             float64\n",
       "Frames                float64\n",
       "N° Scenes             float64\n",
       "Sampling Rate [Hz]    float64\n",
       "Scene Length [s]      float64\n",
       "Sensortypes            object\n",
       "Publish Date           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "cedb9e92-a593-4b85-87a4-ba735960ee31",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.scatter(data['Publish Date'], data['Size [h]'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0331727a-5c6f-4d92-b29a-c8f8dbe36b95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12bd5d3c-7613-4729-b3b6-906eecd5c09b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AISS_env",
   "language": "python",
   "name": "aiss_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}