{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_change = np.random.standard_normal((8,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.05954431e+00, -1.72865576e+00, -7.21114127e-01,\n",
       "        -4.75577989e-01,  4.76113223e-01, -4.08004561e-01,\n",
       "        -1.45765503e-01,  1.05695904e+00,  1.04761823e+00,\n",
       "        -2.04295107e+00],\n",
       "       [-4.14064819e-01,  1.89499964e+00, -1.79501192e+00,\n",
       "         1.51309205e+00,  9.45206240e-01,  3.91295825e-01,\n",
       "        -2.09010121e-01, -7.20526276e-01,  1.55712724e+00,\n",
       "         1.05576943e+00],\n",
       "       [-9.95814577e-01, -1.84870623e+00,  1.15085632e+00,\n",
       "        -9.22600427e-02, -4.93442921e-01,  1.33326962e+00,\n",
       "        -4.63666886e-01,  9.45638784e-03, -5.12583791e-01,\n",
       "        -1.83298183e+00],\n",
       "       [-2.63810565e-01, -2.45596570e+00, -4.44054637e-01,\n",
       "        -7.55784996e-01,  8.00579541e-01, -6.02168364e-01,\n",
       "         5.61225069e-01,  6.67194813e-01, -1.95884968e-01,\n",
       "         1.39621786e+00],\n",
       "       [-1.47313467e+00,  1.56456798e+00, -5.47354244e-01,\n",
       "         2.31116830e-01,  6.59499640e-01,  2.28311180e-01,\n",
       "        -9.94858068e-01, -1.67086823e+00, -3.20666500e-01,\n",
       "        -2.40045810e+00],\n",
       "       [ 1.26742492e+00,  3.39317422e-01,  1.01116646e+00,\n",
       "         8.04772329e-01,  5.84381614e-01,  1.01816726e+00,\n",
       "        -4.18455899e-01, -4.79148767e-01, -1.78785774e+00,\n",
       "         6.48489427e-01],\n",
       "       [-7.89279176e-02, -9.85191500e-01,  2.00559681e-01,\n",
       "        -9.40303927e-01,  1.58372986e-01, -1.18875827e+00,\n",
       "         8.57952280e-01,  3.25502183e-01, -1.51167038e-03,\n",
       "        -8.20105948e-01],\n",
       "       [-4.38535434e-01,  5.40811696e-01,  1.93599764e-01,\n",
       "        -1.57053014e+00,  2.68923360e+00,  2.89640051e-01,\n",
       "        -2.48231544e-01, -1.95334953e+00, -1.10090318e+00,\n",
       "        -2.69112273e-01]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(stock_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-1.728656</td>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-0.475578</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>-0.408005</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.056959</td>\n",
       "      <td>1.047618</td>\n",
       "      <td>-2.042951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.414065</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>1.055769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-1.832982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.267425</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>0.648489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-0.820106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.438535</td>\n",
       "      <td>0.540812</td>\n",
       "      <td>0.193600</td>\n",
       "      <td>-1.570530</td>\n",
       "      <td>2.689234</td>\n",
       "      <td>0.289640</td>\n",
       "      <td>-0.248232</td>\n",
       "      <td>-1.953350</td>\n",
       "      <td>-1.100903</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  2.059544 -1.728656 -0.721114 -0.475578  0.476113 -0.408005 -0.145766   \n",
       "1 -0.414065  1.895000 -1.795012  1.513092  0.945206  0.391296 -0.209010   \n",
       "2 -0.995815 -1.848706  1.150856 -0.092260 -0.493443  1.333270 -0.463667   \n",
       "3 -0.263811 -2.455966 -0.444055 -0.755785  0.800580 -0.602168  0.561225   \n",
       "4 -1.473135  1.564568 -0.547354  0.231117  0.659500  0.228311 -0.994858   \n",
       "5  1.267425  0.339317  1.011166  0.804772  0.584382  1.018167 -0.418456   \n",
       "6 -0.078928 -0.985192  0.200560 -0.940304  0.158373 -1.188758  0.857952   \n",
       "7 -0.438535  0.540812  0.193600 -1.570530  2.689234  0.289640 -0.248232   \n",
       "\n",
       "          7         8         9  \n",
       "0  1.056959  1.047618 -2.042951  \n",
       "1 -0.720526  1.557127  1.055769  \n",
       "2  0.009456 -0.512584 -1.832982  \n",
       "3  0.667195 -0.195885  1.396218  \n",
       "4 -1.670868 -0.320666 -2.400458  \n",
       "5 -0.479149 -1.787858  0.648489  \n",
       "6  0.325502 -0.001512 -0.820106  \n",
       "7 -1.953350 -1.100903 -0.269112  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成股票名字列表\n",
    "codes = ['股票'+ str(i) for i in range(8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成日期数据\n",
    "date = pd.date_range('20190603', periods=10, freq='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(stock_change, index=codes, columns=date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019-06-03 00:00:00</th>\n",
       "      <th>2019-06-04 00:00:00</th>\n",
       "      <th>2019-06-05 00:00:00</th>\n",
       "      <th>2019-06-06 00:00:00</th>\n",
       "      <th>2019-06-07 00:00:00</th>\n",
       "      <th>2019-06-10 00:00:00</th>\n",
       "      <th>2019-06-11 00:00:00</th>\n",
       "      <th>2019-06-12 00:00:00</th>\n",
       "      <th>2019-06-13 00:00:00</th>\n",
       "      <th>2019-06-14 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票0</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-1.728656</td>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-0.475578</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>-0.408005</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.056959</td>\n",
       "      <td>1.047618</td>\n",
       "      <td>-2.042951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票1</th>\n",
       "      <td>-0.414065</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>1.055769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票2</th>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-1.832982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票5</th>\n",
       "      <td>1.267425</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>0.648489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票6</th>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-0.820106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票7</th>\n",
       "      <td>-0.438535</td>\n",
       "      <td>0.540812</td>\n",
       "      <td>0.193600</td>\n",
       "      <td>-1.570530</td>\n",
       "      <td>2.689234</td>\n",
       "      <td>0.289640</td>\n",
       "      <td>-0.248232</td>\n",
       "      <td>-1.953350</td>\n",
       "      <td>-1.100903</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2019-06-03  2019-06-04  2019-06-05  2019-06-06  2019-06-07  2019-06-10  \\\n",
       "股票0    2.059544   -1.728656   -0.721114   -0.475578    0.476113   -0.408005   \n",
       "股票1   -0.414065    1.895000   -1.795012    1.513092    0.945206    0.391296   \n",
       "股票2   -0.995815   -1.848706    1.150856   -0.092260   -0.493443    1.333270   \n",
       "股票3   -0.263811   -2.455966   -0.444055   -0.755785    0.800580   -0.602168   \n",
       "股票4   -1.473135    1.564568   -0.547354    0.231117    0.659500    0.228311   \n",
       "股票5    1.267425    0.339317    1.011166    0.804772    0.584382    1.018167   \n",
       "股票6   -0.078928   -0.985192    0.200560   -0.940304    0.158373   -1.188758   \n",
       "股票7   -0.438535    0.540812    0.193600   -1.570530    2.689234    0.289640   \n",
       "\n",
       "     2019-06-11  2019-06-12  2019-06-13  2019-06-14  \n",
       "股票0   -0.145766    1.056959    1.047618   -2.042951  \n",
       "股票1   -0.209010   -0.720526    1.557127    1.055769  \n",
       "股票2   -0.463667    0.009456   -0.512584   -1.832982  \n",
       "股票3    0.561225    0.667195   -0.195885    1.396218  \n",
       "股票4   -0.994858   -1.670868   -0.320666   -2.400458  \n",
       "股票5   -0.418456   -0.479149   -1.787858    0.648489  \n",
       "股票6    0.857952    0.325502   -0.001512   -0.820106  \n",
       "股票7   -0.248232   -1.953350   -1.100903   -0.269112  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame的常用属性和方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2019-06-03 00:00:00</th>\n",
       "      <th>2019-06-04 00:00:00</th>\n",
       "      <th>2019-06-05 00:00:00</th>\n",
       "      <th>2019-06-06 00:00:00</th>\n",
       "      <th>2019-06-07 00:00:00</th>\n",
       "      <th>2019-06-10 00:00:00</th>\n",
       "      <th>2019-06-11 00:00:00</th>\n",
       "      <th>2019-06-12 00:00:00</th>\n",
       "      <th>2019-06-13 00:00:00</th>\n",
       "      <th>2019-06-14 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票0</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-1.728656</td>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-0.475578</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>-0.408005</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.056959</td>\n",
       "      <td>1.047618</td>\n",
       "      <td>-2.042951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票1</th>\n",
       "      <td>-0.414065</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>1.055769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票2</th>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-1.832982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票5</th>\n",
       "      <td>1.267425</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>0.648489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票6</th>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-0.820106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票7</th>\n",
       "      <td>-0.438535</td>\n",
       "      <td>0.540812</td>\n",
       "      <td>0.193600</td>\n",
       "      <td>-1.570530</td>\n",
       "      <td>2.689234</td>\n",
       "      <td>0.289640</td>\n",
       "      <td>-0.248232</td>\n",
       "      <td>-1.953350</td>\n",
       "      <td>-1.100903</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2019-06-03  2019-06-04  2019-06-05  2019-06-06  2019-06-07  2019-06-10  \\\n",
       "股票0    2.059544   -1.728656   -0.721114   -0.475578    0.476113   -0.408005   \n",
       "股票1   -0.414065    1.895000   -1.795012    1.513092    0.945206    0.391296   \n",
       "股票2   -0.995815   -1.848706    1.150856   -0.092260   -0.493443    1.333270   \n",
       "股票3   -0.263811   -2.455966   -0.444055   -0.755785    0.800580   -0.602168   \n",
       "股票4   -1.473135    1.564568   -0.547354    0.231117    0.659500    0.228311   \n",
       "股票5    1.267425    0.339317    1.011166    0.804772    0.584382    1.018167   \n",
       "股票6   -0.078928   -0.985192    0.200560   -0.940304    0.158373   -1.188758   \n",
       "股票7   -0.438535    0.540812    0.193600   -1.570530    2.689234    0.289640   \n",
       "\n",
       "     2019-06-11  2019-06-12  2019-06-13  2019-06-14  \n",
       "股票0   -0.145766    1.056959    1.047618   -2.042951  \n",
       "股票1   -0.209010   -0.720526    1.557127    1.055769  \n",
       "股票2   -0.463667    0.009456   -0.512584   -1.832982  \n",
       "股票3    0.561225    0.667195   -0.195885    1.396218  \n",
       "股票4   -0.994858   -1.670868   -0.320666   -2.400458  \n",
       "股票5   -0.418456   -0.479149   -1.787858    0.648489  \n",
       "股票6    0.857952    0.325502   -0.001512   -0.820106  \n",
       "股票7   -0.248232   -1.953350   -1.100903   -0.269112  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.05954431e+00, -1.72865576e+00, -7.21114127e-01,\n",
       "        -4.75577989e-01,  4.76113223e-01, -4.08004561e-01,\n",
       "        -1.45765503e-01,  1.05695904e+00,  1.04761823e+00,\n",
       "        -2.04295107e+00],\n",
       "       [-4.14064819e-01,  1.89499964e+00, -1.79501192e+00,\n",
       "         1.51309205e+00,  9.45206240e-01,  3.91295825e-01,\n",
       "        -2.09010121e-01, -7.20526276e-01,  1.55712724e+00,\n",
       "         1.05576943e+00],\n",
       "       [-9.95814577e-01, -1.84870623e+00,  1.15085632e+00,\n",
       "        -9.22600427e-02, -4.93442921e-01,  1.33326962e+00,\n",
       "        -4.63666886e-01,  9.45638784e-03, -5.12583791e-01,\n",
       "        -1.83298183e+00],\n",
       "       [-2.63810565e-01, -2.45596570e+00, -4.44054637e-01,\n",
       "        -7.55784996e-01,  8.00579541e-01, -6.02168364e-01,\n",
       "         5.61225069e-01,  6.67194813e-01, -1.95884968e-01,\n",
       "         1.39621786e+00],\n",
       "       [-1.47313467e+00,  1.56456798e+00, -5.47354244e-01,\n",
       "         2.31116830e-01,  6.59499640e-01,  2.28311180e-01,\n",
       "        -9.94858068e-01, -1.67086823e+00, -3.20666500e-01,\n",
       "        -2.40045810e+00],\n",
       "       [ 1.26742492e+00,  3.39317422e-01,  1.01116646e+00,\n",
       "         8.04772329e-01,  5.84381614e-01,  1.01816726e+00,\n",
       "        -4.18455899e-01, -4.79148767e-01, -1.78785774e+00,\n",
       "         6.48489427e-01],\n",
       "       [-7.89279176e-02, -9.85191500e-01,  2.00559681e-01,\n",
       "        -9.40303927e-01,  1.58372986e-01, -1.18875827e+00,\n",
       "         8.57952280e-01,  3.25502183e-01, -1.51167038e-03,\n",
       "        -8.20105948e-01],\n",
       "       [-4.38535434e-01,  5.40811696e-01,  1.93599764e-01,\n",
       "        -1.57053014e+00,  2.68923360e+00,  2.89640051e-01,\n",
       "        -2.48231544e-01, -1.95334953e+00, -1.10090318e+00,\n",
       "        -2.69112273e-01]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取数据\n",
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['股票0', '股票1', '股票2', '股票3', '股票4', '股票5', '股票6', '股票7'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取行索引\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-06-03', '2019-06-04', '2019-06-05', '2019-06-06',\n",
       "               '2019-06-07', '2019-06-10', '2019-06-11', '2019-06-12',\n",
       "               '2019-06-13', '2019-06-14'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取列索引\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取形状\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
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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>股票0</th>\n",
       "      <th>股票1</th>\n",
       "      <th>股票2</th>\n",
       "      <th>股票3</th>\n",
       "      <th>股票4</th>\n",
       "      <th>股票5</th>\n",
       "      <th>股票6</th>\n",
       "      <th>股票7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-03</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-0.414065</td>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.267425</td>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.438535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>-1.728656</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.540812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-05</th>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>0.193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>-0.475578</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>-1.570530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07</th>\n",
       "      <td>0.476113</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>2.689234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10</th>\n",
       "      <td>-0.408005</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.289640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-11</th>\n",
       "      <td>-0.145766</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>-0.248232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-12</th>\n",
       "      <td>1.056959</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-1.953350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>1.047618</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-1.100903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-14</th>\n",
       "      <td>-2.042951</td>\n",
       "      <td>1.055769</td>\n",
       "      <td>-1.832982</td>\n",
       "      <td>1.396218</td>\n",
       "      <td>-2.400458</td>\n",
       "      <td>0.648489</td>\n",
       "      <td>-0.820106</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 股票0       股票1       股票2       股票3       股票4       股票5  \\\n",
       "2019-06-03  2.059544 -0.414065 -0.995815 -0.263811 -1.473135  1.267425   \n",
       "2019-06-04 -1.728656  1.895000 -1.848706 -2.455966  1.564568  0.339317   \n",
       "2019-06-05 -0.721114 -1.795012  1.150856 -0.444055 -0.547354  1.011166   \n",
       "2019-06-06 -0.475578  1.513092 -0.092260 -0.755785  0.231117  0.804772   \n",
       "2019-06-07  0.476113  0.945206 -0.493443  0.800580  0.659500  0.584382   \n",
       "2019-06-10 -0.408005  0.391296  1.333270 -0.602168  0.228311  1.018167   \n",
       "2019-06-11 -0.145766 -0.209010 -0.463667  0.561225 -0.994858 -0.418456   \n",
       "2019-06-12  1.056959 -0.720526  0.009456  0.667195 -1.670868 -0.479149   \n",
       "2019-06-13  1.047618  1.557127 -0.512584 -0.195885 -0.320666 -1.787858   \n",
       "2019-06-14 -2.042951  1.055769 -1.832982  1.396218 -2.400458  0.648489   \n",
       "\n",
       "                 股票6       股票7  \n",
       "2019-06-03 -0.078928 -0.438535  \n",
       "2019-06-04 -0.985192  0.540812  \n",
       "2019-06-05  0.200560  0.193600  \n",
       "2019-06-06 -0.940304 -1.570530  \n",
       "2019-06-07  0.158373  2.689234  \n",
       "2019-06-10 -1.188758  0.289640  \n",
       "2019-06-11  0.857952 -0.248232  \n",
       "2019-06-12  0.325502 -1.953350  \n",
       "2019-06-13 -0.001512 -1.100903  \n",
       "2019-06-14 -0.820106 -0.269112  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转置\n",
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019-06-03 00:00:00</th>\n",
       "      <th>2019-06-04 00:00:00</th>\n",
       "      <th>2019-06-05 00:00:00</th>\n",
       "      <th>2019-06-06 00:00:00</th>\n",
       "      <th>2019-06-07 00:00:00</th>\n",
       "      <th>2019-06-10 00:00:00</th>\n",
       "      <th>2019-06-11 00:00:00</th>\n",
       "      <th>2019-06-12 00:00:00</th>\n",
       "      <th>2019-06-13 00:00:00</th>\n",
       "      <th>2019-06-14 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票0</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-1.728656</td>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-0.475578</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>-0.408005</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.056959</td>\n",
       "      <td>1.047618</td>\n",
       "      <td>-2.042951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票1</th>\n",
       "      <td>-0.414065</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>1.055769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票2</th>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-1.832982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2019-06-03  2019-06-04  2019-06-05  2019-06-06  2019-06-07  2019-06-10  \\\n",
       "股票0    2.059544   -1.728656   -0.721114   -0.475578    0.476113   -0.408005   \n",
       "股票1   -0.414065    1.895000   -1.795012    1.513092    0.945206    0.391296   \n",
       "股票2   -0.995815   -1.848706    1.150856   -0.092260   -0.493443    1.333270   \n",
       "股票3   -0.263811   -2.455966   -0.444055   -0.755785    0.800580   -0.602168   \n",
       "股票4   -1.473135    1.564568   -0.547354    0.231117    0.659500    0.228311   \n",
       "\n",
       "     2019-06-11  2019-06-12  2019-06-13  2019-06-14  \n",
       "股票0   -0.145766    1.056959    1.047618   -2.042951  \n",
       "股票1   -0.209010   -0.720526    1.557127    1.055769  \n",
       "股票2   -0.463667    0.009456   -0.512584   -1.832982  \n",
       "股票3    0.561225    0.667195   -0.195885    1.396218  \n",
       "股票4   -0.994858   -1.670868   -0.320666   -2.400458  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前5行数据\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>2019-06-03 00:00:00</th>\n",
       "      <th>2019-06-04 00:00:00</th>\n",
       "      <th>2019-06-05 00:00:00</th>\n",
       "      <th>2019-06-06 00:00:00</th>\n",
       "      <th>2019-06-07 00:00:00</th>\n",
       "      <th>2019-06-10 00:00:00</th>\n",
       "      <th>2019-06-11 00:00:00</th>\n",
       "      <th>2019-06-12 00:00:00</th>\n",
       "      <th>2019-06-13 00:00:00</th>\n",
       "      <th>2019-06-14 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票5</th>\n",
       "      <td>1.267425</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>0.648489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票6</th>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-0.820106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票7</th>\n",
       "      <td>-0.438535</td>\n",
       "      <td>0.540812</td>\n",
       "      <td>0.193600</td>\n",
       "      <td>-1.570530</td>\n",
       "      <td>2.689234</td>\n",
       "      <td>0.289640</td>\n",
       "      <td>-0.248232</td>\n",
       "      <td>-1.953350</td>\n",
       "      <td>-1.100903</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2019-06-03  2019-06-04  2019-06-05  2019-06-06  2019-06-07  2019-06-10  \\\n",
       "股票3   -0.263811   -2.455966   -0.444055   -0.755785    0.800580   -0.602168   \n",
       "股票4   -1.473135    1.564568   -0.547354    0.231117    0.659500    0.228311   \n",
       "股票5    1.267425    0.339317    1.011166    0.804772    0.584382    1.018167   \n",
       "股票6   -0.078928   -0.985192    0.200560   -0.940304    0.158373   -1.188758   \n",
       "股票7   -0.438535    0.540812    0.193600   -1.570530    2.689234    0.289640   \n",
       "\n",
       "     2019-06-11  2019-06-12  2019-06-13  2019-06-14  \n",
       "股票3    0.561225    0.667195   -0.195885    1.396218  \n",
       "股票4   -0.994858   -1.670868   -0.320666   -2.400458  \n",
       "股票5   -0.418456   -0.479149   -1.787858    0.648489  \n",
       "股票6    0.857952    0.325502   -0.001512   -0.820106  \n",
       "股票7   -0.248232   -1.953350   -1.100903   -0.269112  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最后5行数据  日志数据\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引的设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019-06-03 00:00:00</th>\n",
       "      <th>2019-06-04 00:00:00</th>\n",
       "      <th>2019-06-05 00:00:00</th>\n",
       "      <th>2019-06-06 00:00:00</th>\n",
       "      <th>2019-06-07 00:00:00</th>\n",
       "      <th>2019-06-10 00:00:00</th>\n",
       "      <th>2019-06-11 00:00:00</th>\n",
       "      <th>2019-06-12 00:00:00</th>\n",
       "      <th>2019-06-13 00:00:00</th>\n",
       "      <th>2019-06-14 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票0</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-1.728656</td>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-0.475578</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>-0.408005</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.056959</td>\n",
       "      <td>1.047618</td>\n",
       "      <td>-2.042951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票1</th>\n",
       "      <td>-0.414065</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>1.055769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票2</th>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-1.832982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票5</th>\n",
       "      <td>1.267425</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>0.648489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票6</th>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-0.820106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票7</th>\n",
       "      <td>-0.438535</td>\n",
       "      <td>0.540812</td>\n",
       "      <td>0.193600</td>\n",
       "      <td>-1.570530</td>\n",
       "      <td>2.689234</td>\n",
       "      <td>0.289640</td>\n",
       "      <td>-0.248232</td>\n",
       "      <td>-1.953350</td>\n",
       "      <td>-1.100903</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2019-06-03  2019-06-04  2019-06-05  2019-06-06  2019-06-07  2019-06-10  \\\n",
       "股票0    2.059544   -1.728656   -0.721114   -0.475578    0.476113   -0.408005   \n",
       "股票1   -0.414065    1.895000   -1.795012    1.513092    0.945206    0.391296   \n",
       "股票2   -0.995815   -1.848706    1.150856   -0.092260   -0.493443    1.333270   \n",
       "股票3   -0.263811   -2.455966   -0.444055   -0.755785    0.800580   -0.602168   \n",
       "股票4   -1.473135    1.564568   -0.547354    0.231117    0.659500    0.228311   \n",
       "股票5    1.267425    0.339317    1.011166    0.804772    0.584382    1.018167   \n",
       "股票6   -0.078928   -0.985192    0.200560   -0.940304    0.158373   -1.188758   \n",
       "股票7   -0.438535    0.540812    0.193600   -1.570530    2.689234    0.289640   \n",
       "\n",
       "     2019-06-11  2019-06-12  2019-06-13  2019-06-14  \n",
       "股票0   -0.145766    1.056959    1.047618   -2.042951  \n",
       "股票1   -0.209010   -0.720526    1.557127    1.055769  \n",
       "股票2   -0.463667    0.009456   -0.512584   -1.832982  \n",
       "股票3    0.561225    0.667195   -0.195885    1.396218  \n",
       "股票4   -0.994858   -1.670868   -0.320666   -2.400458  \n",
       "股票5   -0.418456   -0.479149   -1.787858    0.648489  \n",
       "股票6    0.857952    0.325502   -0.001512   -0.820106  \n",
       "股票7   -0.248232   -1.953350   -1.100903   -0.269112  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成新的index列表\n",
    "new_index = ['股票_' + str(i) for i in range(8)]\n",
    "\n",
    "# 替换旧索引\n",
    "data.index = new_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019-06-03 00:00:00</th>\n",
       "      <th>2019-06-04 00:00:00</th>\n",
       "      <th>2019-06-05 00:00:00</th>\n",
       "      <th>2019-06-06 00:00:00</th>\n",
       "      <th>2019-06-07 00:00:00</th>\n",
       "      <th>2019-06-10 00:00:00</th>\n",
       "      <th>2019-06-11 00:00:00</th>\n",
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       "      <th>2019-06-13 00:00:00</th>\n",
       "      <th>2019-06-14 00:00:00</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.059544</td>\n",
       "      <td>-1.728656</td>\n",
       "      <td>-0.721114</td>\n",
       "      <td>-0.475578</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>-0.408005</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.056959</td>\n",
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       "      <td>-2.042951</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.414065</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>-1.795012</td>\n",
       "      <td>1.513092</td>\n",
       "      <td>0.945206</td>\n",
       "      <td>0.391296</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.720526</td>\n",
       "      <td>1.557127</td>\n",
       "      <td>1.055769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.995815</td>\n",
       "      <td>-1.848706</td>\n",
       "      <td>1.150856</td>\n",
       "      <td>-0.092260</td>\n",
       "      <td>-0.493443</td>\n",
       "      <td>1.333270</td>\n",
       "      <td>-0.463667</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.512584</td>\n",
       "      <td>-1.832982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.263811</td>\n",
       "      <td>-2.455966</td>\n",
       "      <td>-0.444055</td>\n",
       "      <td>-0.755785</td>\n",
       "      <td>0.800580</td>\n",
       "      <td>-0.602168</td>\n",
       "      <td>0.561225</td>\n",
       "      <td>0.667195</td>\n",
       "      <td>-0.195885</td>\n",
       "      <td>1.396218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.473135</td>\n",
       "      <td>1.564568</td>\n",
       "      <td>-0.547354</td>\n",
       "      <td>0.231117</td>\n",
       "      <td>0.659500</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>-0.994858</td>\n",
       "      <td>-1.670868</td>\n",
       "      <td>-0.320666</td>\n",
       "      <td>-2.400458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.267425</td>\n",
       "      <td>0.339317</td>\n",
       "      <td>1.011166</td>\n",
       "      <td>0.804772</td>\n",
       "      <td>0.584382</td>\n",
       "      <td>1.018167</td>\n",
       "      <td>-0.418456</td>\n",
       "      <td>-0.479149</td>\n",
       "      <td>-1.787858</td>\n",
       "      <td>0.648489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.078928</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>0.200560</td>\n",
       "      <td>-0.940304</td>\n",
       "      <td>0.158373</td>\n",
       "      <td>-1.188758</td>\n",
       "      <td>0.857952</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>-0.001512</td>\n",
       "      <td>-0.820106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.438535</td>\n",
       "      <td>0.540812</td>\n",
       "      <td>0.193600</td>\n",
       "      <td>-1.570530</td>\n",
       "      <td>2.689234</td>\n",
       "      <td>0.289640</td>\n",
       "      <td>-0.248232</td>\n",
       "      <td>-1.953350</td>\n",
       "      <td>-1.100903</td>\n",
       "      <td>-0.269112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2019-06-03  2019-06-04  2019-06-05  2019-06-06  2019-06-07  2019-06-10  \\\n",
       "0    2.059544   -1.728656   -0.721114   -0.475578    0.476113   -0.408005   \n",
       "1   -0.414065    1.895000   -1.795012    1.513092    0.945206    0.391296   \n",
       "2   -0.995815   -1.848706    1.150856   -0.092260   -0.493443    1.333270   \n",
       "3   -0.263811   -2.455966   -0.444055   -0.755785    0.800580   -0.602168   \n",
       "4   -1.473135    1.564568   -0.547354    0.231117    0.659500    0.228311   \n",
       "5    1.267425    0.339317    1.011166    0.804772    0.584382    1.018167   \n",
       "6   -0.078928   -0.985192    0.200560   -0.940304    0.158373   -1.188758   \n",
       "7   -0.438535    0.540812    0.193600   -1.570530    2.689234    0.289640   \n",
       "\n",
       "   2019-06-11  2019-06-12  2019-06-13  2019-06-14  \n",
       "0   -0.145766    1.056959    1.047618   -2.042951  \n",
       "1   -0.209010   -0.720526    1.557127    1.055769  \n",
       "2   -0.463667    0.009456   -0.512584   -1.832982  \n",
       "3    0.561225    0.667195   -0.195885    1.396218  \n",
       "4   -0.994858   -1.670868   -0.320666   -2.400458  \n",
       "5   -0.418456   -0.479149   -1.787858    0.648489  \n",
       "6    0.857952    0.325502   -0.001512   -0.820106  \n",
       "7   -0.248232   -1.953350   -1.100903   -0.269112  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重置索引\n",
    "data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把某一列设置为新索引\n",
    "df = pd.DataFrame({'month': [1, 4, 7, 10],\n",
    "                    'year': [2012, 2014, 2013, 2014],\n",
    "                    'sale':[55, 40, 84, 31]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>month</th>\n",
       "      <th>year</th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2014</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  year  sale\n",
       "0      1  2012    55\n",
       "1      4  2014    40\n",
       "2      7  2013    84\n",
       "3     10  2014    31"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>month</th>\n",
       "      <th>year</th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2014</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  year  sale\n",
       "month                   \n",
       "1          1  2012    55\n",
       "4          4  2014    40\n",
       "7          7  2013    84\n",
       "10        10  2014    31"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把某一列设置为新索引\n",
    "df.set_index('month', drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把某些列设置为新索引\n",
    "res = df.set_index(['month', 'year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[[1, 4, 7, 10], [2012, 2013, 2014]],\n",
       "           codes=[[0, 1, 2, 3], [0, 2, 1, 2]],\n",
       "           names=['month', 'year'])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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></th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2012</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2014</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>2013</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>2014</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sale\n",
       "month year      \n",
       "1     2012    55\n",
       "4     2014    40\n",
       "7     2013    84\n",
       "10    2014    31"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3296: FutureWarning: \n",
      "Panel is deprecated and will be removed in a future version.\n",
      "The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method\n",
      "Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.\n",
      "Pandas provides a `.to_xarray()` method to help automate this conversion.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "p = pd.Panel(np.arange(24).reshape(4,3,2),\n",
    "                 items=list('ABCD'),\n",
    "                 major_axis=pd.date_range('20130101', periods=3),\n",
    "                 minor_axis=['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)\n",
       "Items axis: A to D\n",
       "Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00\n",
       "Minor_axis axis: first to second"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>first</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            first  second\n",
       "2013-01-01      0       1\n",
       "2013-01-02      2       3\n",
       "2013-01-03      4       5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p['A',:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     6.7\n",
       "2     5.6\n",
       "3     3.0\n",
       "4    10.0\n",
       "5     2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([6.7,5.6,3,10,2], index=[1,2,3,4,5])   # pandas 一维数组容器   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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