Chapter-13/13.1 Interfacing Between pandas and Model Code（pandas与建模代码间的交互）.ipynb

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    "# CHAPTER 13 Introduction to Modeling Libraries in Python（Python中建模库的介绍）\n",
    "\n",
    "这一章回顾一下之间pandas的一些特性，希望能在我们处理数据的时候有所帮助。然后会简要介绍两个很有用的建模工具：statsmodels和scikit-learn。\n",
    "\n",
    "\n",
    "# 13.1 Interfacing Between pandas and Model Code（pandas与建模代码间的交互）\n",
    "\n",
    "一个通常的工作流程中，在建模之前，会用pandas来加载数据并清理。模型开发过程中，一个很重要的部分就是特征工程（feature engineering），指的是通过数据变换或分析，从原始数据中提取出对建模有用的信息。之前介绍的聚合（aggregation）和GroupBy就经常用于特征工程。\n",
    "\n",
    "至于什么样才是好的特征工程，这就超出了本书的范围。这里会简单介绍如何在数据处理与建模之间切换。\n",
    "\n",
    "连接pandas和其他一些分析库的点，通常是Numpy数组。要想把一个DataFrame变为Numpy数组，使用.values属性："
   ]
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  {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "   x0    x1    y\n",
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   "source": [
    "data = pd.DataFrame({'x0': [1, 2, 3, 4, 5], \n",
    "                     'x1': [0.01, -0.01, 0.25, -4.1, 0.], \n",
    "                     'y': [-1.5, 0., 3.6, 1.3, -2.]})\n",
    "data"
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       "Index(['x0', 'x1', 'y'], dtype='object')"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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    "data.columns"
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       "array([[ 1.  ,  0.01, -1.5 ],\n",
       "       [ 2.  , -0.01,  0.  ],\n",
       "       [ 3.  ,  0.25,  3.6 ],\n",
       "       [ 4.  , -4.1 ,  1.3 ],\n",
       "       [ 5.  ,  0.  , -2.  ]])"
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     "execution_count": 4,
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   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "变回DataFrame的方法是，传入一个二维ndarray，并指定列名："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "   one   two  three\n",
       "0  1.0  0.01   -1.5\n",
       "1  2.0 -0.01    0.0\n",
       "2  3.0  0.25    3.6\n",
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   "source": [
    "df2 = pd.DataFrame(data.values, columns=['one', 'two', 'three'])\n",
    "df2"
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    ".values属性最好用于同质的数据，即数据类型都是数值型。如果有异质的数据，结果会变为python对象："
   ]
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "df3 = data.copy()"
   ]
  },
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   "execution_count": 7,
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       "   x0    x1    y strings\n",
       "0   1  0.01 -1.5       a\n",
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     "execution_count": 7,
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   "source": [
    "df3['strings'] = ['a', 'b', 'c', 'd', 'e']\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0.01, -1.5, 'a'],\n",
       "       [2, -0.01, 0.0, 'b'],\n",
       "       [3, 0.25, 3.6, 'c'],\n",
       "       [4, -4.1, 1.3, 'd'],\n",
       "       [5, 0.0, -2.0, 'e']], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df3.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于一些模型，我们可能希望使用列中的一部分数据。建议使用loc，然后用values进行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_cols = ['x0', 'x1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  0.01],\n",
       "       [ 2.  , -0.01],\n",
       "       [ 3.  ,  0.25],\n",
       "       [ 4.  , -4.1 ],\n",
       "       [ 5.  ,  0.  ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:, model_cols].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一些库对于pandas的支持非常好：能自动把DataFrame转换为numpy，并把模型的参数名字作为输出的列名。对于其他的一些库，就必须要自己手动操作了。\n",
    "\n",
    "在第十二章里，我们学习了pandas的Categorical数据类型和pandas.get_dummies函数。假设我们的数据集中有一个非数值列："
   ]
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   "execution_count": 11,
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      "text/plain": [
       "   x0    x1    y category\n",
       "0   1  0.01 -1.5        a\n",
       "1   2 -0.01  0.0        b\n",
       "2   3  0.25  3.6        a\n",
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   "source": [
    "data['category'] = pd.Categorical(['a', 'b', 'a', 'a', 'b'],\n",
    "                                  categories=['a', 'b'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想要哑变量来代替category这一列，我们可以创建哑变量，去除category列，然后把结果合并起来："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "   category_a  category_b\n",
       "0           1           0\n",
       "1           0           1\n",
       "2           1           0\n",
       "3           1           0\n",
       "4           0           1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dummies = pd.get_dummies(data.category, prefix='category')\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x0    x1    y  category_a  category_b\n",
       "0   1  0.01 -1.5           1           0\n",
       "1   2 -0.01  0.0           0           1\n",
       "2   3  0.25  3.6           1           0\n",
       "3   4 -4.10  1.3           1           0\n",
       "4   5  0.00 -2.0           0           1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies = data.drop('category', axis=1).join(dummies)\n",
    "data_with_dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在不同的统计模型上使用哑变量有一些细微的不同。当我们有更很多非数值型列的时候，使用Patsy的话会更简单易用一些。关于Patsy的内容会在下一节进行介绍。"
   ]
  }
 ],
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