8. Statistical Analysis_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import datashader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"pd.set_option('display.max_colwidth', -1)\\npd.set_option('max_info_columns', -1)\\npd.set_option('max_info_rows', -1)\\npd.set_option('display.max_columns', None)\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''pd.set_option('display.max_colwidth', -1)\n",
    "pd.set_option('max_info_columns', -1)\n",
    "pd.set_option('max_info_rows', -1)\n",
    "pd.set_option('display.max_columns', None)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(r\"/home/uia72388/Share_A/Analyse.csv\")\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Unnamed: 0' ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dry', 'Wet', 'Ice', 'Snow'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['annotations.framelabel.street_conditions.value'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['annotations.framelabel.weather.value'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.nunique() <7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['annotations.framelabel.light_conditions.value'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols= []\n",
    "# for i in df.columns:\n",
    "#     cols.append(i)\n",
    "# cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in cols:\n",
    "#     if (df[i].nunique() <7):\n",
    "#         print(i, df[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = df.drop(['_id' , 'vdy.wheelBase', 'vdy.overhangfront', 'vdy.axisLaodDisr', 'vdy._id', 'timestamp', 'annotations.framelabel.road_works.value'], axis=1)\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>vdy.yawRate</th>\n",
       "      <th>vdy.velocity</th>\n",
       "      <th>vdy.acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>186867.000000</td>\n",
       "      <td>186867.000000</td>\n",
       "      <td>186867.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000788</td>\n",
       "      <td>19.648680</td>\n",
       "      <td>-0.018870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.046133</td>\n",
       "      <td>11.984132</td>\n",
       "      <td>0.593297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.515058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.251752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.005812</td>\n",
       "      <td>10.860244</td>\n",
       "      <td>-0.203160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.000039</td>\n",
       "      <td>18.208334</td>\n",
       "      <td>-0.001999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.005875</td>\n",
       "      <td>30.378686</td>\n",
       "      <td>0.156006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.686965</td>\n",
       "      <td>53.281746</td>\n",
       "      <td>5.210007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         vdy.yawRate   vdy.velocity  vdy.acceleration\n",
       "count  186867.000000  186867.000000     186867.000000\n",
       "mean        0.000788      19.648680         -0.018870\n",
       "std         0.046133      11.984132          0.593297\n",
       "min        -0.515058       0.000000         -4.251752\n",
       "25%        -0.005812      10.860244         -0.203160\n",
       "50%        -0.000039      18.208334         -0.001999\n",
       "75%         0.005875      30.378686          0.156006\n",
       "max         0.686965      53.281746          5.210007"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[: , ['vdy.yawRate' , 'vdy.velocity', 'vdy.acceleration']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"sns.factorplot(x='vdy.yawRate', data=t ,kind='bar')\\n\\nplt.xticks(fontsize=20, rotation=90)\\nplt.yticks(fontsize=20)\\nplt.xlabel('vdy.yawRate', fontsize=20)\\nplt.ylabel('count', fontsize=20)\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sns.factorplot(x='vdy.yawRate', data=t ,kind='bar')\n",
    "\n",
    "plt.xticks(fontsize=20, rotation=90)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('vdy.yawRate', fontsize=20)\n",
    "plt.ylabel('count', fontsize=20)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/uia72388/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    186867.000000\n",
       "mean          0.000788\n",
       "std           0.046133\n",
       "min          -0.515058\n",
       "25%          -0.005812\n",
       "50%          -0.000039\n",
       "75%           0.005875\n",
       "max           0.686965\n",
       "Name: vdy.yawRate, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(data['vdy.yawRate'].dropna())\n",
    "\n",
    "data['vdy.yawRate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/uia72388/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    186867.000000\n",
       "mean         19.648680\n",
       "std          11.984132\n",
       "min           0.000000\n",
       "25%          10.860244\n",
       "50%          18.208334\n",
       "75%          30.378686\n",
       "max          53.281746\n",
       "Name: vdy.velocity, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(data['vdy.velocity'].dropna(), hist=True, kde=False)\n",
    "\n",
    "data['vdy.velocity'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8ce3c13c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['vdy.velocity'].dropna().hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'newdf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-8eee591bab02>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriticality\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Count(Target)'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#imbalanced\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'newdf' is not defined"
     ]
    }
   ],
   "source": [
    "newdf.criticality.value_counts().plot(kind='bar', title='Count(Target)')  #imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(data['vdy.acceleration'].dropna())\n",
    "\n",
    "data['vdy.acceleration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=[]\n",
    "for i in range(4):\n",
    "    cols.append('annotations.boxlabel.{0}.area'.format(i))\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(40,40))\n",
    "\n",
    "for i in range(4):\n",
    "    sns.distplot(df['annotations.boxlabel.{0}.area'.format(i)].dropna())\n",
    "plt.legend(['annotations.boxlabel.0.area',\n",
    " 'annotations.boxlabel.1.area',\n",
    " 'annotations.boxlabel.2.area',\n",
    " 'annotations.boxlabel.3.area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''plt.figure(figsize=(10,10))\n",
    "\n",
    "sns.distplot(train.avg_glucose_level)\n",
    "plt.title('Distribution of average glucose level overall')\n",
    "#plt.figure()\n",
    "\n",
    "sns.distplot(train[train.stroke==1].avg_glucose_level)\n",
    "plt.title('Distribution of average glucose level for stroke and no stroke')\n",
    "\n",
    "sns.distplot(train[train.stroke==0].avg_glucose_level)\n",
    "\n",
    "#plt.axvline(150,linestyle='--',color='r')\n",
    "plt.legend(['Critical level','Stroke','Nonstroke'])'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(data['vdy.acceleration'].dropna())\n",
    "data['vdy.acceleration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# sphinx_gallery_thumbnail_number = 2\n",
    "\n",
    "vegetables = [\"cucumber\", \"tomato\", \"lettuce\", \"asparagus\",\n",
    "              \"potato\", \"wheat\", \"barley\"]\n",
    "farmers = [\"Farmer Joe\", \"Upland Bros.\", \"Smith Gardening\",\n",
    "           \"Agrifun\", \"Organiculture\", \"BioGoods Ltd.\", \"Cornylee Corp.\"]\n",
    "\n",
    "harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],\n",
    "                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],\n",
    "                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],\n",
    "                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],\n",
    "                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],\n",
    "                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],\n",
    "                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(harvest)\n",
    "\n",
    "# We want to show all ticks...\n",
    "#ax.set_xticks(np.arange(len(farmers)))\n",
    "#ax.set_yticks(np.arange(len(vegetables)))\n",
    "# ... and label them with the respective list entries\n",
    "#ax.set_xticklabels(farmers)\n",
    "#ax.set_yticklabels(vegetables)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "\"\"\"for i in range(len(vegetables)):\n",
    "    for j in range(len(farmers)):\n",
    "        text = ax.text(j, i, harvest[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"w\")\"\"\"\n",
    "\n",
    "ax.set_title(\"Harvest of local farmers (in tons/year)\")\n",
    "fig.tight_layout()\n",
    "plt.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(tdss, xticklabels='auto', yticklabels='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "import numpy\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#datatype=[('index',numpy.float32), ('floati',numpy.float32),  ('floatq',numpy.float32)]\n",
    "       \n",
    "#filename='bigdata.bin'\n",
    "\n",
    "def main():\n",
    "    data = t\n",
    "    plt.plot(t, 'r,')\n",
    "    plt.grid(True)\n",
    "    plt.title(\"Signal-Diagram\")\n",
    "    plt.xlabel(\"Sample\")\n",
    "    plt.ylabel(\"In-Phase\")\n",
    "    plt.savefig('foo2.png')\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['vdy.yawRate' , 'vdy.velocity', 'vdy.acceleration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.factorplot(x = data, kind = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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