Practice_Project_02_Visual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring with Visuals\n",
    "Use histograms and scatterplots to explore `clean_08.csv` and `clean_18.csv` in the Jupyter notebook. Then, answer the quiz questions below the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the distributions of greenhouse gas score in 2008 and 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_08 = pd.read_csv('clean_08.csv')\n",
    "df_18 = pd.read_csv('clean_18.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rgLcCXx1xKYP2N8BfAT8fcR3DdD7wA+CfuumozyYZzD/aPAVU1X7gE8B3gQPA4ar6j9FWNVQTVXWgW/4+MLEYB13K4T62kiwHPg98qKqeG3U9g5LkD4FDVbVr1LUM2enA24BbquqtwAss0lv1U1E3x7ye2V9qbwCWJfmT0VY1GjV7bfqiXJ++lMN9LG9xkORVzAb7tqq6e9T1DNglwB8l2cvstNvvJfnn0ZY0FPuAfVV19F3ZXcyGfat+H3iqqn5QVf8L3A389ohrGqaDSc4F6B4PLcZBl3K4j90tDpKE2XnYJ6rqk6OuZ9Cq6sNVdV5VrWL2+/tfVdX8GV1VfR/4XpI3dk2XAo+PsKRB+y5wcZLXdK/xS2n4D8jHcQ+woVveAOxYjIOO7K6Q/RryLQ5OFZcAVwO7kzzStX2kqu4bXUkakD8DtnUnLt8B3jfiegamqr6a5C7gYWavCPsGjd6GIMkdwBRwTpJ9wEeBG4E7k1wLPA1csSh9efsBSWrPUp6WkSSdgOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGvR/0puIXJpP4GsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.greenhouse_gas_score.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kggx3SSrIcJekggx3SSro/wG5VpP3Lpuj/QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.greenhouse_gas_score.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "\n",
       "  smartway  \n",
       "0       no  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.cmb_mpg.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.cmb_mpg.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe the correlation between displacement and combined mpg.\n",
    "\n",
    "问题：因素之间的相关性\n",
    "\n",
    "解决：散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'the correlation between displacement and combined mpg'}, xlabel='cmb_mpg', ylabel='displ'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.plot(\n",
    "    y = 'displ',\n",
    "    x = 'cmb_mpg',\n",
    "    kind = 'scatter',\n",
    "    title = 'the correlation between displacement and combined mpg'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'the correlation between displacement and combined mpg'}, xlabel='cmb_mpg', ylabel='displ'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.plot(\n",
    "    y = 'displ',\n",
    "    x = 'cmb_mpg',\n",
    "    kind = 'scatter',\n",
    "    title = 'the correlation between displacement and combined mpg'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知是负相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe the correlation between greenhouse gas score and combined mpg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "\n",
       "  smartway  \n",
       "0       no  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'the correlation between displacement and combined mpg'}, xlabel='cmb_mpg', ylabel='greenhouse_gas_score'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.plot(\n",
    "    x = 'cmb_mpg',\n",
    "    y = 'greenhouse_gas_score',\n",
    "    kind = 'scatter',\n",
    "    title = 'the correlation between displacement and combined mpg'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知是正相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结论和视觉效果\n",
    "得出结论并创建视觉效果以在下面的 Jupyter 笔记本中传达结果！确保解决以下问题。\n",
    "\n",
    "* Q1：与 2008 年相比，2018 年使用替代燃料的独特车型是否更多？多少？\n",
    "* Q2：车辆等级的燃油经济性提高了多少（以 mpg 为单位增加）？\n",
    "* Q3：SmartWay车辆有什么特点？它们是否随着时间而改变？（mpg，温室气体）\n",
    "* Q4：哪些特征与更好的燃油经济性（mpg）相关？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load datas\n",
    "df_08 = pd.read_csv('clean_08.csv')\n",
    "df_18 = pd.read_csv('clean_18.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "\n",
       "  smartway  \n",
       "0       no  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1：与 2008 年相比，2018 年使用替代燃料的独特车型是否更多？多少？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python:\n",
    "| Method | fuc |\n",
    "| --- | ---|\n",
    "| count | 计数 |\n",
    "| value_counts | value_counts常用于数据表的计数及排序 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(987, 13)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "987"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.fuel.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline    984\n",
       "CNG           1\n",
       "ethanol       1\n",
       "gas           1\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每一个Value出现了多少次\n",
    "df_08.fuel.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline       749\n",
       "Ethanol         26\n",
       "Gas             26\n",
       "Diesel          19\n",
       "Electricity     12\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.fuel.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many unique models used alternative sources of fuel in 2008\n",
    "alt_08 = df_08.query('fuel in [\"CNG\", \"ethanol\"]').model.nunique()\n",
    "alt_08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt_18 = df_18.query(\"fuel in ['Ethanol', 'Electricity']\").model.nunique()\n",
    "alt_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "plt.bar(['2008','2018'], [alt_08, alt_18])\n",
    "plt.title('Number of Unique Models in 2018 and 2008')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Numbers')\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aa4b46fbcc5df3f614041fa3e70bf0c784c58d4e266be456a69a3548db5918d4"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}