{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-13T05:56:31.464118Z",
     "start_time": "2025-09-13T05:56:31.460802Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import talib as ta  # Make sure TA-Lib is installed: pip install TA-Lib\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import warnings\n",
    "\n",
    "# 忽略所有警告\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# --- 0. Configure your file path ---\n",
    "# Please replace 'your_futures_data.csv' with the actual path to your CSV file\n",
    "file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv'\n",
    "\n",
    "sns.set(style='whitegrid')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n"
   ],
   "outputs": [],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-13T05:56:31.544397Z",
     "start_time": "2025-09-13T05:56:31.499612Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# --- 1. Data Loading and Preprocessing ---\n",
    "def load_and_preprocess_data(file_path):\n",
    "    \"\"\"\n",
    "    Loads historical futures data and performs basic preprocessing.\n",
    "    Assumes data contains 'datetime', 'open', 'high', 'low', 'close', 'volume' columns.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(file_path, parse_dates=['datetime'], index_col='datetime')\n",
    "        # Ensure data is sorted by time\n",
    "        df = df.sort_index()\n",
    "        # Check and handle missing values\n",
    "        initial_rows = len(df)\n",
    "        df.dropna(inplace=True)\n",
    "        if len(df) < initial_rows:\n",
    "            print(f\"Warning: Missing values found in data, deleted {initial_rows - len(df)} rows.\")\n",
    "\n",
    "        # Check if necessary columns exist\n",
    "        required_columns = ['open', 'high', 'low', 'close', 'volume']\n",
    "        if not all(col in df.columns for col in required_columns):\n",
    "            raise ValueError(f\"CSV file is missing required columns. Please ensure it contains: {required_columns}\")\n",
    "\n",
    "        print(f\"Successfully loaded {len(df)} rows of data.\")\n",
    "        print(\"First 5 rows of data:\")\n",
    "        print(df.head())\n",
    "        return df\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: File '{file_path}' not found. Please check the path.\")\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"Error during data loading or preprocessing: {e}\")\n",
    "        return None\n",
    "\n",
    "\n",
    "df_raw = load_and_preprocess_data(file_path)\n",
    "df_raw = df_raw[df_raw.index >= '2024-01-01']"
   ],
   "id": "548c68daa68af8c1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded 25662 rows of data.\n",
      "First 5 rows of data:\n",
      "                       open    high     low   close   volume   open_oi  \\\n",
      "datetime                                                                 \n",
      "2020-12-31 14:45:00  1607.0  1611.0  1603.0  1611.0  19480.0  148833.0   \n",
      "2021-01-04 09:00:00  1610.0  1636.0  1601.0  1620.0  55486.0  146448.0   \n",
      "2021-01-04 09:15:00  1620.0  1620.0  1601.0  1604.0  30314.0  153373.0   \n",
      "2021-01-04 09:30:00  1604.0  1606.0  1590.0  1595.0  30803.0  157091.0   \n",
      "2021-01-04 09:45:00  1595.0  1601.0  1594.0  1600.0  10031.0  158730.0   \n",
      "\n",
      "                     close_oi underlying_symbol  \n",
      "datetime                                         \n",
      "2020-12-31 14:45:00  146448.0        CZCE.SA105  \n",
      "2021-01-04 09:00:00  153373.0        CZCE.SA105  \n",
      "2021-01-04 09:15:00  157091.0        CZCE.SA105  \n",
      "2021-01-04 09:30:00  158730.0        CZCE.SA105  \n",
      "2021-01-04 09:45:00  160031.0        CZCE.SA105  \n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-13T05:56:31.825173Z",
     "start_time": "2025-09-13T05:56:31.581283Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = df_raw\n",
    "\n",
    "\n",
    "# --- 2. 因子计算 ---\n",
    "print(\"--- 正在计算因子 ---\")\n",
    "df['volume_safe'] = df['volume'].replace(0, 1e-6)\n",
    "df['price_impact_factor'] = np.abs(df['close'].pct_change()) / df['volume_safe']\n",
    "df['directional_impact_factor'] = (df['close'] - df['open']) / df['volume_safe']\n",
    "\n",
    "\n",
    "# --- 3. 定义未来收益率 (我们要预测的目标) ---\n",
    "print(\"--- 正在计算未来收益率 ---\")\n",
    "forward_periods = [1, 5, 10]\n",
    "for n in forward_periods:\n",
    "    df[f'fwd_return_{n}d'] = df['close'].pct_change(periods=n).shift(-n)\n",
    "\n",
    "# 清理数据，删除因计算位移产生的NaN值\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "\n",
    "# --- 4. 核心：执行分位数分析 ---\n",
    "print(\"--- 正在执行分位数分析 ---\")\n",
    "num_quantiles = 5 # 分为5组\n",
    "\n",
    "# 4.1 分析 价格冲击因子\n",
    "# 使用 pd.qcut 将因子值分为 N 组\n",
    "df['pi_quantile'] = pd.qcut(df['price_impact_factor'], q=num_quantiles, labels=False, duplicates='drop')\n",
    "pi_analysis = df.groupby('pi_quantile')[[f'fwd_return_{n}d' for n in forward_periods]].mean() * 100\n",
    "\n",
    "# 4.2 分析 定向冲击因子\n",
    "df['di_quantile'] = pd.qcut(df['directional_impact_factor'], q=num_quantiles, labels=False, duplicates='drop')\n",
    "di_analysis = df.groupby('di_quantile')[[f'fwd_return_{n}d' for n in forward_periods]].mean() * 100\n",
    "\n",
    "\n",
    "# --- 5. 结果可视化与解读 ---\n",
    "print(\"\\n--- 因子有效性分析结果 ---\")\n",
    "fig, axes = plt.subplots(1, 2, figsize=(18, 7))\n",
    "\n",
    "# 价格冲击因子 结果\n",
    "print(\"\\n价格冲击因子 (Price Impact Factor) 分位数分析:\")\n",
    "print(pi_analysis)\n",
    "pi_analysis.plot(kind='bar', ax=axes[0], title='价格冲击因子 vs 未来收益率\\n(Price Impact Factor vs. Forward Returns)')\n",
    "axes[0].set_xlabel('因子分位数 (Q1: 最低, Q5: 最高)')\n",
    "axes[0].set_ylabel('平均未来收益率 (%)')\n",
    "axes[0].set_xticklabels([f'Q{i+1}' for i in range(len(pi_analysis))], rotation=0)\n",
    "\n",
    "# 定向冲击因子 结果\n",
    "print(\"\\n定向冲击因子 (Directional Impact Factor) 分位数分析:\")\n",
    "print(di_analysis)\n",
    "di_analysis.plot(kind='bar', ax=axes[1], title='定向冲击因子 vs 未来收益率\\n(Directional Impact Factor vs. Forward Returns)')\n",
    "axes[1].set_xlabel('因子分位数 (Q1: 最低, Q5: 最高)')\n",
    "axes[1].set_ylabel('平均未来收益率 (%)')\n",
    "axes[1].set_xticklabels([f'Q{i+1}' for i in range(len(di_analysis))], rotation=0)\n",
    "\n",
    "plt.suptitle('因子有效性分位数分析', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()\n",
    "\n",
    "# --- 6. 进阶分析: 在特定市场状态下的表现 ---\n",
    "print(\"\\n--- 进阶分析：因子在不同市场状态下的表现 ---\")\n",
    "# 定义市场状态：使用ATR的移动平均值来衡量波动性\n",
    "high_low = df['high'] - df['low']\n",
    "high_close = np.abs(df['high'] - df['close'].shift())\n",
    "low_close = np.abs(df['low'] - df['close'].shift())\n",
    "tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)\n",
    "df['atr_pct'] = (tr / df['close']).ewm(span=20, adjust=False).mean()\n",
    "\n",
    "# 定义高波动状态为ATR百分比位于最高的30%的时间\n",
    "high_vol_threshold = df['atr_pct'].quantile(0.7)\n",
    "df_high_vol = df[df['atr_pct'] > high_vol_threshold]\n",
    "\n",
    "# 在高波动状态下，重新分析定向冲击因子\n",
    "di_analysis_high_vol = df_high_vol.groupby('di_quantile')[[f'fwd_return_{n}d' for n in forward_periods]].mean() * 100\n",
    "\n",
    "print(\"\\n定向冲击因子在高波动市场下的表现:\")\n",
    "print(di_analysis_high_vol)\n",
    "\n",
    "# 可视化高波动状态下的结果\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "di_analysis_high_vol.plot(kind='bar', ax=ax, title='定向冲击因子在高波动市场状态下的表现\\n(Directional Impact Factor in HIGH VOLATILITY Regime)')\n",
    "ax.set_xlabel('因子分位数 (Q1: 最低, Q5: 最高)')\n",
    "ax.set_ylabel('平均未来收益率 (%)')\n",
    "ax.set_xticklabels([f'Q{i+1}' for i in range(len(di_analysis_high_vol))], rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "e9248ae41d9a1b1d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 正在计算因子 ---\n",
      "--- 正在计算未来收益率 ---\n",
      "--- 正在执行分位数分析 ---\n",
      "\n",
      "--- 因子有效性分析结果 ---\n",
      "\n",
      "价格冲击因子 (Price Impact Factor) 分位数分析:\n",
      "             fwd_return_1d  fwd_return_5d  fwd_return_10d\n",
      "pi_quantile                                              \n",
      "0                -0.010825      -0.055032       -0.042790\n",
      "1                 0.001791      -0.008909       -0.010649\n",
      "2                -0.001939      -0.008222       -0.060071\n",
      "3                 0.004476      -0.001045       -0.046483\n",
      "4                -0.011277      -0.015913       -0.018583\n",
      "\n",
      "定向冲击因子 (Directional Impact Factor) 分位数分析:\n",
      "             fwd_return_1d  fwd_return_5d  fwd_return_10d\n",
      "di_quantile                                              \n",
      "0                -0.010517      -0.023939       -0.079702\n",
      "1                -0.002474       0.011427       -0.013968\n",
      "2                -0.004617      -0.048772       -0.039226\n",
      "3                 0.004874      -0.008277       -0.024593\n",
      "4                -0.005044      -0.019583       -0.021074\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ],
      "image/png": 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RBQYGymAwSJJcXFyUmZmp999/XxEREQoNDVVycrJmzpxp7nPmzBktXry4YqIGAAAAAJQbuR4AAAAAOKdSC3wmk0mPP/64rr/+eo0ePVoGg0EGg0ErVqzQxYsX9cILL0iSDAaD+vfvb+537Ngxkj4AAAAAqKLI9QAAAADAeVlV4Bs3bpzWrVunp556yrz8008/Vd++fdW4cWNlZGSY212WkZFRMREDAAAAAMqNXA8AAAAAnFepBT4XFxfdc889uueee5SWlqbbb79dkvTRRx8VS+yaN29u/ve5c+e0Z88eO4cLAAAAALAHcj0AAAAAcF5WPYPvsvr168tkMkmSatWqpVq1apnXGQwGDR8+3Pz62LFjWrt2rZ3CBAAAAABUFHI9AAAAAHAuNhX4/iozM1Nnz55VQECApEvTuyxfvtycFJ49e9Y+EQIAAAAAKg25HgAAAABUfVYV+FJSUjR37lw1a9ZMBoNBubm5Gjp0qBo0aKBFixaZ2x04cMD876ysLPtHCwAAAACwG3I9AAAAAHBOpRb4CgoK9Nhjj6lOnTrq0aOHJMnDw0N33nmnFi5cqHXr1qlHjx4yGAyaM2eOud+xY8f08MMPV1zkAAAAAIAyI9cDAAAAAOdVaoHP1dVVb7/9toKDg+Xq6qqXXnpJRUVFioqK0unTp/Xqq68qMDBQJpNJa9euNU/bkp6eXuHBAwAAAADKhlwPAAAAAJyXVVN0tmvXTpJUVFQkg8GgwsJCSdKkSZP0448/6qWXXlJgYKBWr14tFxcXSdLFixcrKGQAAAAAgD2Q6wEAAACAc7KqwHdZfn6+TCaTcnNzJUnu7u6aPn26HnzwQb300ksaMmSIue3Ro0c1cOBAuwYLAAAAALA/cj0AAAAAcC42FfiMRqNeffVVeXt7m5cFBwdr0qRJuueeeyza5ufnKy8vzy5BAgAAAAAqDrkeAAAAADgXmwp8rq6ueuihh4otHzRoULFl119/vVauXFn2yAAAAAAAlYJcDwAAAACci4stjQsKCrRu3TplZWWV2jY1NVVBQUFlDgwAAAAAUDnI9QAAAADAudhc4Bs3bpzOnTtXatu4uDg99dRTZQ4MAAAAAFA5yPUAAAAAwLnYVOBzd3eXyWTSiRMn9Oeff5bY7sKFC1q3bp1uuummcgcIAAAAAKhY5HoAAAAA4Fxsegafi8uleuATTzwhSapdu7batWunzp07q0+fPmratKkkaeLEifL29tbw4cPtHC4AAAAAwN7I9QAAAADAudhU4Lts8+bNMplMSk5O1qFDh/TNN9/ojTfe0G233aaaNWtq165dWrFihby8vOwdLwAAAACggpDrAQAAAIBzsKrAt337dt1xxx3m1zVq1JCvr68CAgJ0xx136B//+Idmz56tDz74QAaDQWPHjtWNN95YYUEDAAAAAMqPXA8AAAAAnFOpBb4DBw5oxIgRatasmf7xj3/IYDBIkn799Vft2LFDu3fv1v/+9z/VrVtXTz/9tFJTUzV37lyFhoaqffv2FX4AAAAAAADbkesBAAAAgPMqtcDXrFkzzZgxQzt27NDrr78uSVq0aJHS0tJ08OBB3XHHHXrrrbfUuXNn83MbLl68qGeffVbr16+Xt7d3hR4AAAAAAMB25HoAAAAA4LxKLfDVqVNH9957r+69915lZGRo3bp1Wr58udLT0zV8+HA99dRTcnd3t+gzbdo09e3bV6+88ormzJlTYcEDAAAAAMqGXA8AAAAAnJeLLY1r166tyMhIbd68WRMnTtQHH3ygDRs2FGvn5eWlMWPG6ODBg7p48aLdggUAAAAA2B+5HgAAAAA4l1Lv4MvOztYPP/wgNzc3i+UBAQGKjY2Vl5eX4uPji/WrW7euHn30UXl6etovWgAAAACAXZDrAQAAAIDzKrXAl5qaqmeeeUaenp7mh65fyZ9//qk6deqYXxcVFSkvL09PPPGEfSIFAAAAANgNuR4AAAAAOK9SC3zNmzfX/v37S91Q3759NWvWLLVq1cougQEAAAAAKg65HgAAAAA4L5uewXc1N954o3bu3GmvzQEAAAAAqgByPQAAAACoekq9g++v/vWvf2nnzp1yd3cvtu7ChQs6fvy4nnrqKbsFBwAAAACoeOR6AAAAAOBcbCrw9evXTzfddFOxh7BLUmJios6ePavCwkIZjUa7BQgAAAAAqFjkegAAAADgXGwq8HXp0qXEdf379y93MAAAAACAykeuBwAAAADOpdzP4Dt79qzWrl1rsWzr1q3KyMgo76Yrxe+//669e/fqwoULjg4FAAAAAKoMZ8/1AAAAAKA6s6nAl5GRoU8//VS5ubnmZadOndLkyZMt2r3zzjsaMmSIPeLT9u3bFRERoZtuukkxMTEW+y5NSkqKIiMjFRoaqsGDB+vkyZPmdSaTSePGjdOAAQM0efJk3XbbbVqzZo1dYgYAAAAAZ+KIXA8AAAAAUHY2FfjS09P10ksvKT8/37zM09NTHh4e5tc//PCDDh48qLvvvrvcwSUlJWnUqFHq27ev1q1bpwsXLmjOnDlW9c3NzdWTTz6pOnXqaP369QoPD9fo0aNVVFQkSfr444/1448/6quvvtKGDRs0YcIETZ061eLYAAAAAOBaUNm5HgAAAACgfGwq8Lm7u8tkMumPP/7Qe++9p6KiIhkMBrm4XNqMyWTSzJkz5e/vr8GDB5c7uLi4OLVu3VojR46Uv7+/YmJitHr1aqvu4tu0aZPS09MVGxurgIAAjRgxQjk5Odq7d68k6brrrlNsbKxq1aolSWrbtq2ys7NtukMQAAAAAKqDys71AAAAAADl42prB4PBoJSUFC1YsECrV6/WE088YV63aNEi/fjjj/rwww8trvQsq8TERHXr1s382s/PTz4+Pjp8+LBCQkJK7RsSEiIfHx/zsg4dOmj//v0KCwvTHXfcYV6enZ2tt99+W927dzcX/MrCZDIpOzu7zP2rC4PBIC8vL0eHUSFycnJkMpkcHQbsLCcnx+JvoDKVdfxV9rmW81/1VJbx54jf84y/6onfv+VjMplkMBjsus3KzPUAAAAAAOVjc4FPku644w5t3rxZixYtMj/XbvXq1Xrrrbf0+uuvKzg42OptRUdHa+fOnVdcV7duXQUEBBRblpqaWmqBLyMjo8S+f7Vu3TpNmTJFNWvW1Keffmp13FeSn5+vQ4cOlWsb1YGXl5dNY8CZJCcn8yVUNZaSkuLoEHANs3X8Vfa5lvNf9WbL+HPE73nGX/XG79+yc3d3t/s27ZnrAQAAAAAqTpkKfJLk7e2tl19+WceOHdPChQs1c+ZMzZ8/X3feeadN24mJiSnxC5uhQ4cWuzrU09PTqrvkjEajjEZjsb7p6ekWyyIiItS0aVPNmjVLzz77rOLi4myK/6/c3NzUokWLMvevLux9JXFV0rx5c+4gqIZycnKUkpKiZs2aVdu7T1F1lXX8Vfa5lvNf9VSW8eeI3/OMv+qJ37/lc+TIkQrbtr1yPQAAAABAxbGqwLd7927NmjXL/IXOY489Zl538eJFmUwm1atXT0uWLNGSJUskSYWFhcrJydH69euvum1fX9+rrvt7QS4jI8OqK1V9fHx07Ngxi2WZmZnF+rq7uyssLExz585Vt27d9Msvv6hly5albv9KDAaDatSoUaa+cA58+VS9eXl58TMMh6nq44/zX/XG+IMjVfXxV1XZq9hekbkeAAAAAKDiWFXgq127ttq1a6eioiL9+OOP6tixo3ndhQsX9NNPP6l58+YWhbH8/Hz9+eef5QquQ4cOSkhIUGRkpCQpKytLycnJaty4cal9Q0NDtXbtWhUWFprv5Dt48KB69uwpSXr11Vd16623qkePHpL+b3qbyw+RBwAAAIDqzlG5HgAAAACgfKwq8LVp00Zt2rTRiRMn9NFHH+mFF16QdOnB7snJyVqzZo2+//573X333brvvvvsFly/fv00cOBAxcfHq1OnTlqwYIF8fHzUtm1bSVJubq7y8/NVq1atYn3Dw8NVUFCgpUuXavjw4dq2bZsOHDigadOmSZL8/f01adIkeXl56frrr9fs2bPVpk0bBQYG2i1+AAAAAKjKHJXrAQAAAADKx6Zn8P11GpgffvhBkydP1j333KNatWpp7ty5Gj16tP744w89+eSTdgkuODhYUVFRGjJkiLy9vZWdna158+aZ77JbvHixtm7desWpYdzc3DRr1ixFR0dr+fLlOn/+vEaNGmUu4D3++OPKzMzU2LFjlZOTo65du2rRokV2iRsAAAAAnEll53oAAAAAgPKxqcB32e7duzVs2DANGDBAHTp0kHTpjrm33npLTz31lGrVqqVBgwbZJcARI0YoIiJCSUlJCgkJkZ+fn3ldVFSUoqKiSuzbtWtXbdmyRQkJCQoICLCYVsZgMGj06NEaPXq0XeIEAAAAAGdXmbkeAAAAAKDsylTg69ixo5YvX66wsDAdO3bMvLxLly568cUXFRsbq9atW6tdu3Z2CdLf31/+/v5l6uvt7W1+zh4AAAAAoGSVnesBAAAAAMrGxZbGeXl5MplMysvLU1hYmKRLz2YoLCw0txkyZIjat2+vl156Sfn5+faNFgAAAABgd+R6AAAAAOBcbLqDz9/fX9u3b1fNmjXNyy5evKjs7GwVFhbKaDRKkv75z39q9uzZysvLk5ubm30jBgAAAADYlSNyve3bt2vGjBlKTU1Vnz59NGHCBHl4eFjVNyUlRRMmTNCPP/6okJAQvfbaa2rcuHGxdnl5eRowYICGDh2qAQMGlCteAAAqU5M6DZ16+wCAimdTgc/V1dXiGXiS1KZNG/3444/mhE+SOnXqpKVLl1okhwAAAACAqqmyc72kpCSNGjVKI0eOVL9+/TRz5kzNmTNH48aNK7Vvbm6unnzySbVq1Urr16/Xxo0bNXr0aK1Zs0YuLpaT1MyZM0e//PJLuWIFAKAyFV3MUpGpSM/eMrTi92UqkovBpgneAABViF3O4H9N+KRLV3pS3AMAAAAA51ZRuV5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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 进阶分析：因子在不同市场状态下的表现 ---\n",
      "\n",
      "定向冲击因子在高波动市场下的表现:\n",
      "             fwd_return_1d  fwd_return_5d  fwd_return_10d\n",
      "di_quantile                                              \n",
      "0                 0.006045       0.020305       -0.076809\n",
      "1                -0.014321       0.074963        0.062119\n",
      "2                 0.002337      -0.057309        0.007566\n",
      "3                 0.029277       0.064093        0.062086\n",
      "4                -0.009715      -0.030420       -0.030257\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 59
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}