Untitled2 .ipynb

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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014年</th>\n",
       "      <th>2015年</th>\n",
       "      <th>2016年</th>\n",
       "      <th>2017年</th>\n",
       "      <th>2018年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>普通本专科（万人）</th>\n",
       "      <td>721</td>\n",
       "      <td>738</td>\n",
       "      <td>749</td>\n",
       "      <td>761</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中等职业教育（万人）</th>\n",
       "      <td>620</td>\n",
       "      <td>601</td>\n",
       "      <td>593</td>\n",
       "      <td>582</td>\n",
       "      <td>557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普通高中（万人）</th>\n",
       "      <td>797</td>\n",
       "      <td>797</td>\n",
       "      <td>803</td>\n",
       "      <td>800</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2014年  2015年  2016年  2017年  2018年\n",
       "普通本专科（万人）     721    738    749    761    791\n",
       "中等职业教育（万人）    620    601    593    582    557\n",
       "普通高中（万人）      797    797    803    800    793"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "index_list = ['普通本专科（万人）','中等职业教育（万人）', '普通高中（万人）']\n",
    "columns_list = ['2014年','2015年','2016年','2017年','2018年']\n",
    "df = pd.DataFrame([[721, 738, 749, 761, 791],[620, 601, 593, 582, 557],[797, 797, 803, 800, 793]],index = index_list,columns = columns_list)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014年</th>\n",
       "      <th>2015年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>普通本专科（万人）</th>\n",
       "      <td>721</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中等职业教育（万人）</th>\n",
       "      <td>620</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普通高中（万人）</th>\n",
       "      <td>797</td>\n",
       "      <td>797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2014年  2015年\n",
       "普通本专科（万人）     721    738\n",
       "中等职业教育（万人）    620    601\n",
       "普通高中（万人）      797    797"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['2014年','2015年']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2015年</th>\n",
       "      <th>2016年</th>\n",
       "      <th>2017年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>普通本专科（万人）</th>\n",
       "      <td>738</td>\n",
       "      <td>749</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中等职业教育（万人）</th>\n",
       "      <td>601</td>\n",
       "      <td>593</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普通高中（万人）</th>\n",
       "      <td>797</td>\n",
       "      <td>803</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2015年  2016年  2017年\n",
       "普通本专科（万人）     738    749    761\n",
       "中等职业教育（万人）    601    593    582\n",
       "普通高中（万人）      797    803    800"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'2015':'2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2015年</th>\n",
       "      <th>2016年</th>\n",
       "      <th>2017年</th>\n",
       "      <th>2018年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>普通本专科（万人）</th>\n",
       "      <td>738</td>\n",
       "      <td>749</td>\n",
       "      <td>761</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中等职业教育（万人）</th>\n",
       "      <td>601</td>\n",
       "      <td>593</td>\n",
       "      <td>582</td>\n",
       "      <td>557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普通高中（万人）</th>\n",
       "      <td>797</td>\n",
       "      <td>803</td>\n",
       "      <td>800</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2015年  2016年  2017年  2018年\n",
       "普通本专科（万人）     738    749    761    791\n",
       "中等职业教育（万人）    601    593    582    557\n",
       "普通高中（万人）      797    803    800    793"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['2015年','2016年','2017年','2018年']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014年    721\n",
       "2015年    738\n",
       "2016年    749\n",
       "2017年    761\n",
       "2018年    791\n",
       "Name: 普通本专科（万人）, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "———————————————————————————————————————————————————————————————————————"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "———————————————————————————————————————————————————————————————————————"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>年份</th>\n",
       "      <th>GDP(亿元)</th>\n",
       "      <th>GDP增长率（%）</th>\n",
       "      <th>人均GDP(元/人)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2012</td>\n",
       "      <td>519322.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      年份   GDP(亿元)  GDP增长率（%）  人均GDP(元/人)\n",
       "34  2012  519322.0        7.8       40007"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.read_csv('1978_2020_GDP.csv',encoding='ansi')\n",
    "a = s[s['GDP(亿元)'] >= 500000 ]\n",
    "a.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "s = pd.read_csv('1978_2020_GDP.csv',encoding='ansi')\n",
    "gdp_y=s.iloc[22:44,1]\n",
    "gdp_x=s.iloc[22:44,0]\n",
    "gdp_data = np.array(gdp_y)\n",
    "gdp_index = np.array(gdp_x)\n",
    "gdp = pd.Series(gdp_data,gdp_index)\n",
    "gdp.plot(kind='bar',title='2000-2020年国内生产总值',color=['r','g','b','c','m','y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 21 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "s = pd.read_csv('1978_2020_GDP.csv',encoding='ansi')\n",
    "gdp_temp1 = s.iloc[22:44,1]\n",
    "gdp_temp2 = s.iloc[22:44,3]\n",
    "gdp_temp3 = s.iloc[22:44,0]\n",
    "#GDP\n",
    "gdp_data1 = np.array(gdp_temp1)\n",
    "#人均GDP\n",
    "gdp_data2 = np.array(gdp_temp2)\n",
    "gdp_index = np.array(gdp_temp3)\n",
    "fig,ax = plt.subplots()\n",
    "ax.bar(gdp_index,gdp_data1,label=gdp_index)\n",
    "ax.bar(gdp_index,gdp_data2,bottom=gdp_data1,label=gdp_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='年份'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "s = pd.read_csv('1978_2020_GDP.csv',encoding='ansi')\n",
    "gdp_temp = s.iloc[22:44,1:4:2]\n",
    "gdp_2000 = gdp_temp.iloc[0,:]\n",
    "gdp_2001 = gdp_temp.iloc[1,:]\n",
    "gdp_2002 = gdp_temp.iloc[2,:]\n",
    "gdp_2003 = gdp_temp.iloc[3,:]\n",
    "gdp_2004 = gdp_temp.iloc[4,:]\n",
    "gdp_2005 = gdp_temp.iloc[5,:]\n",
    "gdp_2006 = gdp_temp.iloc[6,:]\n",
    "gdp_2007 = gdp_temp.iloc[7,:]\n",
    "gdp_2008 = gdp_temp.iloc[8,:]\n",
    "gdp_2009 = gdp_temp.iloc[9,:]\n",
    "gdp_2010 = gdp_temp.iloc[10,:]\n",
    "gdp_2011 = gdp_temp.iloc[11,:]\n",
    "gdp_2012 = gdp_temp.iloc[12,:]\n",
    "gdp_2013 = gdp_temp.iloc[13,:]\n",
    "gdp_2014 = gdp_temp.iloc[14,:]\n",
    "gdp_2015 = gdp_temp.iloc[15,:]\n",
    "gdp_2016 = gdp_temp.iloc[16,:]\n",
    "gdp_2017 = gdp_temp.iloc[17,:]\n",
    "gdp_2018 = gdp_temp.iloc[18,:]\n",
    "gdp_2019 = gdp_temp.iloc[19,:]\n",
    "gdp_2020 = gdp_temp.iloc[20,:]\n",
    "gdp_index = s.iloc[22:44,0]\n",
    "gdp = pd.DataFrame([gdp_2000,gdp_2001,gdp_2002,gdp_2003,gdp_2004,gdp_2005,gdp_2006,gdp_2007,gdp_2008,gdp_2009,gdp_2010,gdp_2011,gdp_2012,gdp_2013,gdp_2014,gdp_2015,gdp_2016,gdp_2017,gdp_2018,gdp_2019,gdp_2020],columns=['GDP(亿元)','人均GDP(元/人)'],index = gdp_index)\n",
    "gdp.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= 0.10148441648026882 b= -403.6116172175295 c= 401296.10669696453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import leastsq\n",
    "s = pd.read_csv('1978_2020_GDP.csv',encoding = 'ansi')\n",
    "chinese = matplotlib.font_manager.FontProperties(fname = 'C:\\Windows\\Fonts\\simsun.ttc')\n",
    "Xi = s[\"年份\"]\n",
    "Yi = s[\"GDP(亿元)\"]\n",
    "def func(params, x):\n",
    "    a, b, c = params\n",
    "    return a * x * x + b * x + c # ax2+bx+c\n",
    "def error(params, x, y):\n",
    "    return func(params, x) - y\n",
    "p0 = [1,20,10]\n",
    "Para = leastsq(error,p0,args = (Xi,Yi))\n",
    "a,b,c = Para[0]\n",
    "print(\"a=\",a/10000,\"b=\",b/10000,\"c=\",c/10000)\n",
    "plt.figure(figsize = (8,6))\n",
    "plt.scatter(Xi,Yi,color = \"green\",label = \"样本数据\",linewidth = 2)\n",
    "x = np.linspace(1980,2020,100)\n",
    "y = a * x * x + b * x + c\n",
    "plt.plot(x,y,color = \"red\",label = \"拟合直线\",linewidth = 2)\n",
    "plt.legend(loc = 2,prop = chinese)\n",
    "plt.title(\"1978-2019国内生产总值(万亿元)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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>Date (LST)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Value</th>\n",
       "      <th>QC Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016 0:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>231</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2016 1:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2016 2:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>205</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2016 3:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>167</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2016 4:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>132</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8779</th>\n",
       "      <td>12/31/2016 19:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>409</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8780</th>\n",
       "      <td>12/31/2016 20:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>432</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8781</th>\n",
       "      <td>12/31/2016 21:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>482</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8782</th>\n",
       "      <td>12/31/2016 22:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>488</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>12/31/2016 23:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>507</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8784 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date (LST)  Year  Month  Day  Hour  Value QC Name\n",
       "0        1/1/2016 0:00  2016      1    1     0    231   Valid\n",
       "1        1/1/2016 1:00  2016      1    1     1    239   Valid\n",
       "2        1/1/2016 2:00  2016      1    1     2    205   Valid\n",
       "3        1/1/2016 3:00  2016      1    1     3    167   Valid\n",
       "4        1/1/2016 4:00  2016      1    1     4    132   Valid\n",
       "...                ...   ...    ...  ...   ...    ...     ...\n",
       "8779  12/31/2016 19:00  2016     12   31    19    409   Valid\n",
       "8780  12/31/2016 20:00  2016     12   31    20    432   Valid\n",
       "8781  12/31/2016 21:00  2016     12   31    21    482   Valid\n",
       "8782  12/31/2016 22:00  2016     12   31    22    488   Valid\n",
       "8783  12/31/2016 23:00  2016     12   31    23    507   Valid\n",
       "\n",
       "[8784 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('Beijing_2016_HourlyPM25_created20170201.csv',skiprows = 3,encoding = \"unicode_escape\")\n",
    "data.drop([\"Unit\",\"Duration\",\"Parameter\",\"Site\"],axis = 1,inplace = True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Date (LST)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Value</th>\n",
       "      <th>QC Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016 0:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>231</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2016 1:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2016 2:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>205</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2016 3:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>167</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2016 4:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>132</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8779</th>\n",
       "      <td>12/31/2016 19:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>409</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8780</th>\n",
       "      <td>12/31/2016 20:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>432</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8781</th>\n",
       "      <td>12/31/2016 21:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>482</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8782</th>\n",
       "      <td>12/31/2016 22:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>488</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>12/31/2016 23:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>507</td>\n",
       "      <td>Valid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8755 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date (LST)  Year  Month  Day  Hour  Value QC Name\n",
       "0        1/1/2016 0:00  2016      1    1     0    231   Valid\n",
       "1        1/1/2016 1:00  2016      1    1     1    239   Valid\n",
       "2        1/1/2016 2:00  2016      1    1     2    205   Valid\n",
       "3        1/1/2016 3:00  2016      1    1     3    167   Valid\n",
       "4        1/1/2016 4:00  2016      1    1     4    132   Valid\n",
       "...                ...   ...    ...  ...   ...    ...     ...\n",
       "8779  12/31/2016 19:00  2016     12   31    19    409   Valid\n",
       "8780  12/31/2016 20:00  2016     12   31    20    432   Valid\n",
       "8781  12/31/2016 21:00  2016     12   31    21    482   Valid\n",
       "8782  12/31/2016 22:00  2016     12   31    22    488   Valid\n",
       "8783  12/31/2016 23:00  2016     12   31    23    507   Valid\n",
       "\n",
       "[8755 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(data[data[\"QC Name\"] != \"Valid\"].index)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8755.0</td>\n",
       "      <td>8755.000000</td>\n",
       "      <td>8755.00000</td>\n",
       "      <td>8755.000000</td>\n",
       "      <td>8755.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.511479</td>\n",
       "      <td>15.74586</td>\n",
       "      <td>11.500628</td>\n",
       "      <td>72.723929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.451475</td>\n",
       "      <td>8.82042</td>\n",
       "      <td>6.928226</td>\n",
       "      <td>77.125116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.00000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>782.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year        Month         Day         Hour        Value\n",
       "count  8755.0  8755.000000  8755.00000  8755.000000  8755.000000\n",
       "mean   2016.0     6.511479    15.74586    11.500628    72.723929\n",
       "std       0.0     3.451475     8.82042     6.928226    77.125116\n",
       "min    2016.0     1.000000     1.00000     0.000000    -4.000000\n",
       "25%    2016.0     4.000000     8.00000     5.000000    18.000000\n",
       "50%    2016.0     7.000000    16.00000    12.000000    49.000000\n",
       "75%    2016.0    10.000000    23.00000    18.000000    95.000000\n",
       "max    2016.0    12.000000    31.00000    23.000000   782.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8739.0</td>\n",
       "      <td>8739.000000</td>\n",
       "      <td>8739.000000</td>\n",
       "      <td>8739.000000</td>\n",
       "      <td>8739.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.510928</td>\n",
       "      <td>15.739673</td>\n",
       "      <td>11.504978</td>\n",
       "      <td>72.859938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.454536</td>\n",
       "      <td>8.824558</td>\n",
       "      <td>6.926523</td>\n",
       "      <td>77.130089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>782.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year        Month          Day         Hour        Value\n",
       "count  8739.0  8739.000000  8739.000000  8739.000000  8739.000000\n",
       "mean   2016.0     6.510928    15.739673    11.504978    72.859938\n",
       "std       0.0     3.454536     8.824558     6.926523    77.130089\n",
       "min    2016.0     1.000000     1.000000     0.000000     0.000000\n",
       "25%    2016.0     4.000000     8.000000     5.500000    18.000000\n",
       "50%    2016.0     7.000000    16.000000    12.000000    49.000000\n",
       "75%    2016.0    10.000000    23.000000    18.000000    95.000000\n",
       "max    2016.0    12.000000    31.000000    23.000000   782.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(data[data[\"Value\"] < 0].index)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "8779    False\n",
       "8780    False\n",
       "8781    False\n",
       "8782    False\n",
       "8783    False\n",
       "Length: 8739, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date (LST)  Year   Month  Day    Hour   Value  QC Name\n",
       "False       False  False  False  False  False  False      8739\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('Beijing2016_PM25_After.csv',encoding = \"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "北京2016年出现PM2.5大于500的天数为: 18 天\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('Beijing2016_PM25_After.csv')\n",
    "PM25_500 = data.loc[data[\"Value\"] > 500]\n",
    "print(\"北京2016年出现PM2.5大于500的天数为:\",PM25_500.shape[0],\"天\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "PM25_avg = data.groupby([\"Month\"])[\"Value\"].mean()\n",
    "PM25_month = np.arange(1,13)\n",
    "PM25 = pd.Series(PM25_avg,PM25_month)\n",
    "PM25.plot(kind='bar',title=\"北京2016年全年12个月PM2.5平均值\")\n",
    "plt.xticks(rotation = 360)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "import random as rd\n",
    "data = pd.read_csv('Beijing2016_PM25_After.csv')\n",
    "\n",
    "m = rd.randint(1,12)\n",
    "if m == 2:\n",
    "    d = rd.randint(1,29)\n",
    "elif m in (1,3,5,7,8,10,12):\n",
    "    d = rd.randint(1,31)\n",
    "elif m in (4,6,9,11):\n",
    "    d = rd.randint(1,30)\n",
    "\n",
    "daydata = data.loc[((data[\"Month\"] == m)&(data[\"Day\"] == d)&(data[\"Hour\"] >= 0)&(data[\"Month\"] == m)&(data[\"Day\"] == d)&(data[\"Hour\"] <= 23))]\n",
    "dayValue = daydata[\"Value\"]\n",
    "dayValue.plot()\n",
    "plt.title(\"北京2016年{month}月{day}日各时间段PM2.5数值\".format(month = m, day = d))\n",
    "time = list(daydata.loc[:,[\"Hour\"]].index)\n",
    "plt.xticks(range(time[0],time[23]),range(0,23))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "———————————————————————————————————————————————————————————————————————"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "———————————————————————————————————————————————————————————————————————"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}