zhusimaji/fenzu.ipynb

## fenzu.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  matplotlib.pyplot as plt\n",
    "import  seaborn as sns\n",
    "import  pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_one=pd.read_csv('/Users/leiyang/Desktop/cate_1.csv',sep=',')\n"
   ]
  },
  {
   "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>b.sec_node</th>\n",
       "      <th>b.saletime</th>\n",
       "      <th>b.salecount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>161</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>161</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>161</td>\n",
       "      <td>2018-01</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   b.sec_node b.saletime  b.salecount\n",
       "0         161    2017-09           10\n",
       "1         161    2017-10            9\n",
       "2         161    2017-11           55\n",
       "3         161    2017-12          179\n",
       "4         161    2018-01           64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_one.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt_one=data_one.groupby(['b.sec_node','b.saletime']).sum()\n",
    "plot_cat_one=tt_one['b.salecount']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b.sec_node  b.saletime\n",
       "5           2016-08       127632\n",
       "            2016-09       137168\n",
       "            2016-10       148912\n",
       "            2016-11       193824\n",
       "            2016-12       122256\n",
       "Name: b.salecount, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_cat_one.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "unstack_cat_one=plot_cat_one.unstack()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>b.saletime</th>\n",
       "      <th>2016-08</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b.sec_node</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>127632.0</td>\n",
       "      <td>137168.0</td>\n",
       "      <td>148912.0</td>\n",
       "      <td>193824.0</td>\n",
       "      <td>122256.0</td>\n",
       "      <td>160528.0</td>\n",
       "      <td>188576.0</td>\n",
       "      <td>431408.0</td>\n",
       "      <td>734448.0</td>\n",
       "      <td>1167328.0</td>\n",
       "      <td>...</td>\n",
       "      <td>640304.0</td>\n",
       "      <td>711008.0</td>\n",
       "      <td>545072.0</td>\n",
       "      <td>836160.0</td>\n",
       "      <td>674464.0</td>\n",
       "      <td>870240.0</td>\n",
       "      <td>875904.0</td>\n",
       "      <td>1004128.0</td>\n",
       "      <td>1280080.0</td>\n",
       "      <td>1117184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>135732.0</td>\n",
       "      <td>179388.0</td>\n",
       "      <td>311676.0</td>\n",
       "      <td>604896.0</td>\n",
       "      <td>335712.0</td>\n",
       "      <td>303612.0</td>\n",
       "      <td>261048.0</td>\n",
       "      <td>436308.0</td>\n",
       "      <td>467196.0</td>\n",
       "      <td>726324.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1345716.0</td>\n",
       "      <td>2462868.0</td>\n",
       "      <td>2396712.0</td>\n",
       "      <td>2833284.0</td>\n",
       "      <td>1750488.0</td>\n",
       "      <td>2257044.0</td>\n",
       "      <td>2269056.0</td>\n",
       "      <td>2411772.0</td>\n",
       "      <td>2832168.0</td>\n",
       "      <td>2135004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>42690.0</td>\n",
       "      <td>44490.0</td>\n",
       "      <td>41860.0</td>\n",
       "      <td>57370.0</td>\n",
       "      <td>38460.0</td>\n",
       "      <td>50650.0</td>\n",
       "      <td>50140.0</td>\n",
       "      <td>80510.0</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>270280.0</td>\n",
       "      <td>...</td>\n",
       "      <td>209940.0</td>\n",
       "      <td>268150.0</td>\n",
       "      <td>238390.0</td>\n",
       "      <td>430060.0</td>\n",
       "      <td>360200.0</td>\n",
       "      <td>516680.0</td>\n",
       "      <td>542660.0</td>\n",
       "      <td>639940.0</td>\n",
       "      <td>761610.0</td>\n",
       "      <td>581810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>46236.0</td>\n",
       "      <td>41532.0</td>\n",
       "      <td>30324.0</td>\n",
       "      <td>36576.0</td>\n",
       "      <td>27768.0</td>\n",
       "      <td>44376.0</td>\n",
       "      <td>30060.0</td>\n",
       "      <td>44496.0</td>\n",
       "      <td>46380.0</td>\n",
       "      <td>103668.0</td>\n",
       "      <td>...</td>\n",
       "      <td>282744.0</td>\n",
       "      <td>221400.0</td>\n",
       "      <td>157788.0</td>\n",
       "      <td>222120.0</td>\n",
       "      <td>129624.0</td>\n",
       "      <td>128772.0</td>\n",
       "      <td>113580.0</td>\n",
       "      <td>102024.0</td>\n",
       "      <td>117804.0</td>\n",
       "      <td>123048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>NaN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>189228.0</td>\n",
       "      <td>171327.0</td>\n",
       "      <td>180193.0</td>\n",
       "      <td>159861.0</td>\n",
       "      <td>113789.0</td>\n",
       "      <td>108680.0</td>\n",
       "      <td>109746.0</td>\n",
       "      <td>106041.0</td>\n",
       "      <td>166803.0</td>\n",
       "      <td>143351.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "b.saletime   2016-08   2016-09   2016-10   2016-11   2016-12   2017-01  \\\n",
       "b.sec_node                                                               \n",
       "5           127632.0  137168.0  148912.0  193824.0  122256.0  160528.0   \n",
       "6           135732.0  179388.0  311676.0  604896.0  335712.0  303612.0   \n",
       "88           42690.0   44490.0   41860.0   57370.0   38460.0   50650.0   \n",
       "90           46236.0   41532.0   30324.0   36576.0   27768.0   44376.0   \n",
       "96               NaN     260.0    1911.0    1196.0     611.0     403.0   \n",
       "\n",
       "b.saletime   2017-02   2017-03   2017-04    2017-05    ...        2017-10  \\\n",
       "b.sec_node                                             ...                  \n",
       "5           188576.0  431408.0  734448.0  1167328.0    ...       640304.0   \n",
       "6           261048.0  436308.0  467196.0   726324.0    ...      1345716.0   \n",
       "88           50140.0   80510.0  123900.0   270280.0    ...       209940.0   \n",
       "90           30060.0   44496.0   46380.0   103668.0    ...       282744.0   \n",
       "96             377.0     442.0     468.0      637.0    ...       189228.0   \n",
       "\n",
       "b.saletime    2017-11    2017-12    2018-01    2018-02    2018-03    2018-04  \\\n",
       "b.sec_node                                                                     \n",
       "5            711008.0   545072.0   836160.0   674464.0   870240.0   875904.0   \n",
       "6           2462868.0  2396712.0  2833284.0  1750488.0  2257044.0  2269056.0   \n",
       "88           268150.0   238390.0   430060.0   360200.0   516680.0   542660.0   \n",
       "90           221400.0   157788.0   222120.0   129624.0   128772.0   113580.0   \n",
       "96           171327.0   180193.0   159861.0   113789.0   108680.0   109746.0   \n",
       "\n",
       "b.saletime    2018-05    2018-06    2018-07  \n",
       "b.sec_node                                   \n",
       "5           1004128.0  1280080.0  1117184.0  \n",
       "6           2411772.0  2832168.0  2135004.0  \n",
       "88           639940.0   761610.0   581810.0  \n",
       "90           102024.0   117804.0   123048.0  \n",
       "96           106041.0   166803.0   143351.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstack_cat_one.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'2016-08'),\n",
       " Text(0,0,'2016-09'),\n",
       " Text(0,0,'2016-10'),\n",
       " Text(0,0,'2016-11'),\n",
       " Text(0,0,'2016-12'),\n",
       " Text(0,0,'2017-01'),\n",
       " Text(0,0,'2017-02'),\n",
       " Text(0,0,'2017-03'),\n",
       " Text(0,0,'2017-04'),\n",
       " Text(0,0,'2017-05'),\n",
       " Text(0,0,'2017-06'),\n",
       " Text(0,0,'2017-07'),\n",
       " Text(0,0,'2017-08'),\n",
       " Text(0,0,'2017-09'),\n",
       " Text(0,0,'2017-10'),\n",
       " Text(0,0,'2017-11'),\n",
       " Text(0,0,'2017-12'),\n",
       " Text(0,0,'2018-01'),\n",
       " Text(0,0,'2018-02'),\n",
       " Text(0,0,'2018-03'),\n",
       " Text(0,0,'2018-04'),\n",
       " Text(0,0,'2018-05'),\n",
       " Text(0,0,'2018-06'),\n",
       " Text(0,0,'2018-07')]"
      ]
     },
     "execution_count": 5,
     "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": [
    "pt_one=(unstack_cat_one.T).plot()\n",
    "pt_one.set_xticks(range(len(unstack_cat_one.columns.values)))\n",
    "pt_one.set_xticklabels(unstack_cat_one.columns.values,rotation=90)"
   ]
  }
 ],
 "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}