project001_pm2.5/pm2.5_for_five_Chinese_cities_data_analysis.ipynb

{
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   "source": [
    "现在，你有一份中国五个城市的 pm2.5 数据集，接下来该怎么办呢？\n",
    "\n",
    "一个可供参考的数据分析框架：\n",
    "\n",
    "1. 提出问题：请提出你感兴趣的问题，这个问题需要能够由现有的数据进行回答。\n",
    "\n",
    "2. 筛选数据：写出合适的数据条件，并根据这些条件筛选出合适的数据。\n",
    "\n",
    "3. 结论和可视化：能够使用正确的可视化回答提出的问题。\n",
    "\n",
    "4. 拓展思考：提出一个你感兴趣的场景，并进行思考。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、观察数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集包括 5 份 csv 文件，是中国五个城市（北京、上海、成都、广州、沈阳）2010 年 1 月 1 日至 2015 年 12 月 31 日的空气和气象数据，数据中有大量缺失值 NaN, 数据列名包括：\n",
    "\n",
    "- No: 行号\n",
    "- year: 年份\n",
    "- month: 月份\n",
    "- day: 日期\n",
    "- hour: 小时\n",
    "- season: 季节\n",
    "- PM_: pm2.5的浓度（ug/m^3）\n",
    "- DEWP: 露点（摄氏温度）指在固定气压之下，空气中所含的气态水达到饱和而凝结成液态水所需将至的温度。\n",
    "- TEMP: Temperature（摄氏温度）\n",
    "- HUMI: 湿度（%）\n",
    "- PRES: 气压（hPa）\n",
    "- cbwd: 组合风向\n",
    "- lws: 累计风速（m/s）\n",
    "- precipitation: 降水量/时（mm）\n",
    "- iprec: 累计降水量（mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上海数据为例，使用 pandas 的 read_csv 函数导入，并使用 head 查看前五行、tail 查看后五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "shanghai_data = pd.read_csv('ShanghaiPM20100101_20151231.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
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       "      <th>DEWP</th>\n",
       "      <th>HUMI</th>\n",
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       "      <td>63.94</td>\n",
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       "      <td>SE</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   No  year  month  day  hour  season  PM_Jingan  PM_US Post  PM_Xuhui  DEWP  \\\n",
       "0   1  2010      1    1     0       4        NaN         NaN       NaN  -6.0   \n",
       "1   2  2010      1    1     1       4        NaN         NaN       NaN  -6.0   \n",
       "2   3  2010      1    1     2       4        NaN         NaN       NaN  -7.0   \n",
       "3   4  2010      1    1     3       4        NaN         NaN       NaN  -6.0   \n",
       "4   5  2010      1    1     4       4        NaN         NaN       NaN  -6.0   \n",
       "\n",
       "    HUMI    PRES  TEMP cbwd  Iws  precipitation  Iprec  \n",
       "0  59.48  1026.1   1.0   cv  1.0            0.0    0.0  \n",
       "1  59.48  1025.1   1.0   SE  2.0            0.0    0.0  \n",
       "2  59.21  1025.1   0.0   SE  4.0            0.0    0.0  \n",
       "3  63.94  1024.0   0.0   SE  5.0            0.0    0.0  \n",
       "4  63.94  1023.0   0.0   SE  8.0            0.0    0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shanghai_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>season</th>\n",
       "      <th>PM_Jingan</th>\n",
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       "      <th>52579</th>\n",
       "      <td>52580</td>\n",
       "      <td>2015</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>52580</th>\n",
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       "      <td>2015</td>\n",
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       "      <td>4</td>\n",
       "      <td>83.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.27</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>19.0</td>\n",
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       "      <td>85.0</td>\n",
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       "      <td>70.27</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>52582</th>\n",
       "      <td>52583</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>83.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.06</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52583</th>\n",
       "      <td>52584</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.06</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "          No  year  month  day  hour  season  PM_Jingan  PM_US Post  PM_Xuhui  \\\n",
       "52579  52580  2015     12   31    19       4       86.0        82.0      90.0   \n",
       "52580  52581  2015     12   31    20       4       83.0        93.0      86.0   \n",
       "52581  52582  2015     12   31    21       4       85.0        90.0      95.0   \n",
       "52582  52583  2015     12   31    22       4       83.0        86.0      92.0   \n",
       "52583  52584  2015     12   31    23       4        NaN        77.0       NaN   \n",
       "\n",
       "       DEWP   HUMI    PRES  TEMP cbwd   Iws  precipitation  Iprec  \n",
       "52579  -1.0  65.13  1035.0   5.0   NE  15.0            0.0    0.0  \n",
       "52580   1.0  70.27  1035.0   6.0   NE  19.0            0.0    0.0  \n",
       "52581   1.0  70.27  1036.0   6.0   NE  22.0            0.0    0.0  \n",
       "52582   0.0  70.06  1035.0   5.0   NE  25.0            0.0    0.0  \n",
       "52583   0.0  70.06  1035.0   5.0   NE  27.0            0.0    0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shanghai_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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