code/08.07-analyzing_titanic_dataset.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "***\n",
    "***\n",
    "# Introduction to the Basics of Statistics\n",
    "***\n",
    "***\n",
    "\n",
    "王成军 \n",
    "\n",
    "wangchengjun@nju.edu.cn\n",
    "\n",
    "计算传播网 http://computational-communication.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 一、使用Pandas清洗泰坦尼克数据\n",
    "- 练习使用Pandas\n",
    "\n",
    "# 二、分析天涯回帖数据\n",
    "- 学习使用Statsmodels\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-09T02:37:59.029680Z",
     "start_time": "2019-06-09T02:37:58.730142Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/matplotlib.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-09T02:38:03.430005Z",
     "start_time": "2019-06-09T02:38:03.032389Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/pandas_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Statsmodels\n",
    "\n",
    "http://statsmodels.sourceforge.net/\n",
    "\n",
    "![](./img/statsmodels_hybi_banner.png)\n",
    "\n",
    "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. \n",
    "\n",
    "An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. \n",
    "\n",
    "Researchers across fields may find that statsmodels fully meets their needs for statistical computing and data analysis in Python. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 使用pandas清洗泰坦尼克数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 从本机读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-09T03:18:33.241860Z",
     "start_time": "2019-06-09T03:18:32.903737Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('../data/tatanic_train.csv',\\\n",
    "                    sep = \",\", header=0)\n",
    "test = pd.read_csv('../data/tatanic_test.csv',\\\n",
    "                    sep = \",\", header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "You can easily explore a DataFrame  \n",
    " - .describe() summarizes the columns/features of the DataFrame, including the count of observations, mean, max and so on. \n",
    " - Another useful trick is to look at the dimensions of the DataFrame. This is done by requesting the .shape attribute of your DataFrame object. (ex. your_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-09T02:44:09.665713Z",
     "start_time": "2019-06-09T02:44:09.642932Z"
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
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       "   Unnamed: 0  PassengerId  Survived  Pclass  \\\n",
       "0           0            1         0       3   \n",
       "1           1            2         1       1   \n",
       "2           2            3         1       3   \n",
       "3           3            4         1       1   \n",
       "4           4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     "metadata": {},
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   "source": [
    "train.head()"
   ]
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   "execution_count": 6,
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     "end_time": "2019-06-09T02:44:31.363652Z",
     "start_time": "2019-06-09T02:44:31.318446Z"
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    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>222.500000</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>667.500000</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  PassengerId    Survived      Pclass         Age  \\\n",
       "count  891.000000   891.000000  891.000000  891.000000  714.000000   \n",
       "mean   445.000000   446.000000    0.383838    2.308642   29.699118   \n",
       "std    257.353842   257.353842    0.486592    0.836071   14.526497   \n",
       "min      0.000000     1.000000    0.000000    1.000000    0.420000   \n",
       "25%    222.500000   223.500000    0.000000    2.000000   20.125000   \n",
       "50%    445.000000   446.000000    0.000000    3.000000   28.000000   \n",
       "75%    667.500000   668.500000    1.000000    3.000000   38.000000   \n",
       "max    890.000000   891.000000    1.000000    3.000000   80.000000   \n",
       "\n",
       "            SibSp       Parch        Fare  \n",
       "count  891.000000  891.000000  891.000000  \n",
       "mean     0.523008    0.381594   32.204208  \n",
       "std      1.102743    0.806057   49.693429  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    7.910400  \n",
       "50%      0.000000    0.000000   14.454200  \n",
       "75%      1.000000    0.000000   31.000000  \n",
       "max      8.000000    6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T02:35:07.376970Z",
     "start_time": "2018-05-22T02:35:07.372484Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape#, len(train)\n",
    "#train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T02:35:22.116104Z",
     "start_time": "2018-05-22T02:35:22.110688Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passengers that survived vs passengers that passed away\n",
    "train[\"Survived\"][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T02:35:09.971726Z",
     "start_time": "2018-05-22T02:35:09.964471Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passengers that survived vs passengers that passed away\n",
    "train[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-24T07:48:28.030128Z",
     "start_time": "2018-04-24T07:48:28.022796Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.616162\n",
       "1    0.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As proportions\n",
    "train[\"Survived\"].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-24T07:49:09.779501Z",
     "start_time": "2018-04-24T07:49:09.773728Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T02:35:29.080928Z",
     "start_time": "2018-05-22T02:35:29.063171Z"
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   "outputs": [
    {
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PassengerId  Survived  Pclass  \\\n",
       "1           1            2         1       1   \n",
       "2           2            3         1       3   \n",
       "3           3            4         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Sex']=='female'][:3]#[train['Pclass'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T02:35:37.271424Z",
     "start_time": "2018-05-22T02:35:37.261587Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived    Fare\n",
       "0         0  7.2500\n",
       "4         0  8.0500\n",
       "5         0  8.4583"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Males that survived vs males that passed away\n",
    "train[[\"Survived\", 'Fare']][train[\"Sex\"] == 'male'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-24T07:52:03.568885Z",
     "start_time": "2018-04-24T07:52:03.558210Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    468\n",
       "1    109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Males that survived vs males that passed away\n",
    "train[\"Survived\"][train[\"Sex\"] == 'male'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    233\n",
       "0     81\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Females that survived vs Females that passed away\n",
    "train[\"Survived\"][train[\"Sex\"] == 'female'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.811092\n",
       "1    0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized male survival\n",
    "train[\"Survived\"][train[\"Sex\"] == 'male'].value_counts(normalize = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.742038\n",
       "0    0.257962\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized female survival\n",
    "train[\"Survived\"][train[\"Sex\"] == 'female'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:21:39.818993Z",
     "start_time": "2018-05-08T06:21:39.481247Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.0\n",
      "1    0.0\n",
      "2    0.0\n",
      "Name: Child, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Create the column Child, and indicate whether child or not a child. Print the new column.\n",
    "train[\"Child\"] = float('NaN')\n",
    "train.Child[train.Age < 5] = 1\n",
    "train.Child[train.Age >= 5] = 0\n",
    "print(train.Child[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T09:44:23.078810",
     "start_time": "2017-05-14T09:44:23.070244"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.675\n",
       "0    0.325\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized Survival Rates for under 18\n",
    "train.Survived[train.Child == 1].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.618968\n",
       "1    0.381032\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized Survival Rates for over 18\n",
    "train.Survived[train.Child == 0].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-09T03:19:20.183965Z",
     "start_time": "2019-06-09T03:19:20.128088Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass                  1         2         3\n",
       "Sex    Age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 80]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 80]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(train['Age'], [0, 18, 80])\n",
    "train.pivot_table('Survived', ['Sex', age], 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-09T03:20:49.924017Z",
     "start_time": "2019-06-09T03:20:49.838487Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(-0.001, 14.454]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318182</td>\n",
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       "      <th>(18, 80]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098039</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
       "Pclass                         1         2         3                 1   \n",
       "Sex    Age                                                               \n",
       "female (0, 18]               NaN  1.000000  0.714286          0.909091   \n",
       "       (18, 80]              NaN  0.880000  0.444444          0.972973   \n",
       "male   (0, 18]               NaN  0.000000  0.260870          0.800000   \n",
       "       (18, 80]              0.0  0.098039  0.125000          0.391304   \n",
       "\n",
       "Fare                                 \n",
       "Pclass                  2         3  \n",
       "Sex    Age                           \n",
       "female (0, 18]   1.000000  0.318182  \n",
       "       (18, 80]  0.914286  0.391304  \n",
       "male   (0, 18]   0.818182  0.178571  \n",
       "       (18, 80]  0.030303  0.192308  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.qcut(train['Fare'], 2)\n",
    "train.pivot_table('Survived', ['Sex', age], [fare, 'Pclass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T02:57:55.545955Z",
     "start_time": "2018-04-29T02:57:55.507921Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    1\n",
      "2    0\n",
      "Name: Survived, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Create a copy of test: test_one\n",
    "test_one = test\n",
    "# Initialize a Survived column to 0\n",
    "test_one['Survived'] = 0\n",
    "# Set Survived to 1 if Sex equals \"female\" and print the `Survived` column from `test_one`\n",
    "test_one.Survived[test_one.Sex =='female'] = 1\n",
    "\n",
    "print(test_one.Survived[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#Convert the male and female groups to integer form\n",
    "train[\"Sex\"][train[\"Sex\"] == \"male\"] = 0\n",
    "train[\"Sex\"][train[\"Sex\"] == \"female\"] = 1\n",
    "\n",
    "#Impute the Embarked variable\n",
    "train[\"Embarked\"] = train[\"Embarked\"].fillna('S')\n",
    "\n",
    "#Convert the Embarked classes to integer form\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"S\"] = 0\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"C\"] = 1\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"Q\"] = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 分析天涯回帖数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:30:51.091468Z",
     "start_time": "2018-05-08T06:30:51.074911Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>author</th>\n",
       "      <th>author_page</th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>贾也</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>194675</td>\n",
       "      <td>2703</td>\n",
       "      <td>2012-10-29 07:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁波镇海PX项目引发群体上访 当地政府发布说明(转载)</td>\n",
       "      <td>/post-free-2839539-1.shtml</td>\n",
       "      <td>无上卫士ABC</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>88244</td>\n",
       "      <td>1041</td>\n",
       "      <td>2012-10-24 12:41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title                        link   author  \\\n",
       "0   【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml       贾也   \n",
       "1  宁波镇海PX项目引发群体上访 当地政府发布说明(转载)  /post-free-2839539-1.shtml  无上卫士ABC   \n",
       "\n",
       "                     author_page   click  reply              time  \n",
       "0  http://www.tianya.cn/50499450  194675   2703  2012-10-29 07:59  \n",
       "1  http://www.tianya.cn/74341835   88244   1041  2012-10-24 12:41  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/tianya_bbs_threads_list.txt',\\\n",
    "                 sep = \"\\t\", names = ['title','link', \\\n",
    "                        'author','author_page',\\\n",
    "                        'click','reply','time'])\n",
    "df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T10:09:12.096905",
     "start_time": "2017-05-14T10:09:12.094341"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# df=df.rename(columns = {0:'title', 1:'link', \\\n",
    "#                         2:'author',3:'author_page',\\\n",
    "#                         4:'click', 5:'reply', 6:'time'})\n",
    "# df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:31:25.520254Z",
     "start_time": "2018-05-08T06:31:25.506960Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author_page</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>152</td>\n",
       "      <td>27452</td>\n",
       "      <td>1020</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     author_page followed_num fans_num post_num comment_num\n",
       "0  http://www.tianya.cn/50499450          152    27452     1020        1341\n",
       "1  http://www.tianya.cn/74341835            0        1        2           5"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = pd.read_csv('../data/tianya_bbs_threads_author_info.txt', \n",
    "                 sep = \"\\t\", names = ['author_page','followed_num',\\\n",
    "                        'fans_num','post_num', \\\n",
    "                        'comment_num'])\n",
    "da[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T10:10:30.950757",
     "start_time": "2017-05-14T10:10:30.948044"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# da=da.rename(columns = {0:'author_page', 1:'followed_num',\\\n",
    "#                         2:'fans_num',3:'post_num', \\\n",
    "#                         4:'comment_num'})\n",
    "# # da[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:43:50.967553Z",
     "start_time": "2018-05-08T06:43:50.961418Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "467"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([df,da], axis=1)\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:43:51.842126Z",
     "start_time": "2018-05-08T06:43:51.826047Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>author</th>\n",
       "      <th>author_page</th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>time</th>\n",
       "      <th>author_page</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>贾也</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>194675</td>\n",
       "      <td>2703</td>\n",
       "      <td>2012-10-29 07:59</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>152</td>\n",
       "      <td>27452</td>\n",
       "      <td>1020</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁波镇海PX项目引发群体上访 当地政府发布说明(转载)</td>\n",
       "      <td>/post-free-2839539-1.shtml</td>\n",
       "      <td>无上卫士ABC</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>88244</td>\n",
       "      <td>1041</td>\n",
       "      <td>2012-10-24 12:41</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宁波准备停止PX项目了,元芳,你怎么看?</td>\n",
       "      <td>/post-free-2848797-1.shtml</td>\n",
       "      <td>牧阳光</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>82779</td>\n",
       "      <td>625</td>\n",
       "      <td>2012-10-28 19:11</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>816</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title                        link   author  \\\n",
       "0   【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml       贾也   \n",
       "1  宁波镇海PX项目引发群体上访 当地政府发布说明(转载)  /post-free-2839539-1.shtml  无上卫士ABC   \n",
       "2         宁波准备停止PX项目了,元芳,你怎么看?  /post-free-2848797-1.shtml      牧阳光   \n",
       "\n",
       "                     author_page   click  reply              time  \\\n",
       "0  http://www.tianya.cn/50499450  194675   2703  2012-10-29 07:59   \n",
       "1  http://www.tianya.cn/74341835   88244   1041  2012-10-24 12:41   \n",
       "2  http://www.tianya.cn/36535656   82779    625  2012-10-28 19:11   \n",
       "\n",
       "                     author_page followed_num fans_num post_num comment_num  \n",
       "0  http://www.tianya.cn/50499450          152    27452     1020        1341  \n",
       "1  http://www.tianya.cn/74341835            0        1        2           5  \n",
       "2  http://www.tianya.cn/36535656           19       28      816        1268  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:43:53.710650Z",
     "start_time": "2018-05-08T06:43:53.705251Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:43:54.670830Z",
     "start_time": "2018-05-08T06:43:54.664458Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# extract date from datetime\n",
    "# date = map(lambda x: x[:10], data.time)\n",
    "date = [i[:10] for i in data.time]\n",
    "#date = [i[:10] for i in data.time]\n",
    "data['date'] = pd.to_datetime(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:44:35.339668Z",
     "start_time": "2018-05-08T06:44:35.333573Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2012-10-29\n",
       "1   2012-10-24\n",
       "2   2012-10-28\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:45:38.304152Z",
     "start_time": "2018-05-08T06:45:38.287852Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tslib.Timestamp"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert str to datetime format\n",
    "data.time = pd.to_datetime(data.time)\n",
    "data['month'] = data.time.dt.month\n",
    "data['year'] = data.time.dt.year\n",
    "data['day'] = data.time.dt.day\n",
    "type(data.time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T06:45:43.179084Z",
     "start_time": "2018-05-08T06:45:43.160844Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1534.957173</td>\n",
       "      <td>18.907923</td>\n",
       "      <td>7.432548</td>\n",
       "      <td>2012.620985</td>\n",
       "      <td>17.961456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11099.249834</td>\n",
       "      <td>144.869921</td>\n",
       "      <td>3.084860</td>\n",
       "      <td>1.795269</td>\n",
       "      <td>9.491730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>194675.000000</td>\n",
       "      <td>2703.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               click        reply       month         year         day\n",
       "count     467.000000   467.000000  467.000000   467.000000  467.000000\n",
       "mean     1534.957173    18.907923    7.432548  2012.620985   17.961456\n",
       "std     11099.249834   144.869921    3.084860     1.795269    9.491730\n",
       "min        11.000000     0.000000    1.000000  2006.000000    1.000000\n",
       "25%        42.500000     0.000000    5.000000  2013.000000    8.000000\n",
       "50%        84.000000     0.000000    6.000000  2013.000000   23.000000\n",
       "75%       322.000000     4.000000   11.000000  2013.000000   25.000000\n",
       "max    194675.000000  2703.000000   12.000000  2015.000000   31.000000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n",
    "#data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Statsmodels\n",
    "\n",
    "http://statsmodels.sourceforge.net/\n",
    "\n",
    "![](./img/statsmodels_hybi_banner.png)\n",
    "\n",
    "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. \n",
    "\n",
    "An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. \n",
    "\n",
    "Researchers across fields may find that statsmodels fully meets their needs for statistical computing and data analysis in Python. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Features include:\n",
    "\n",
    "- Linear regression models\n",
    "- Generalized linear models\n",
    "- Discrete choice models\n",
    "- Robust linear models\n",
    "- Many models and functions for time series analysis\n",
    "- Nonparametric estimators\n",
    "- A collection of datasets for examples\n",
    "- A wide range of statistical tests\n",
    "- Input-output tools for producing tables in a number of formats and for reading Stata files into NumPy and Pandas.\n",
    "- Plotting functions\n",
    "- Extensive unit tests to ensure correctness of results\n",
    "- Many more models and extensions in development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:24:03.144707Z",
     "start_time": "2018-04-29T03:24:02.273484Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:25:29.039227Z",
     "start_time": "2018-04-29T03:25:29.020762Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1534.957173</td>\n",
       "      <td>18.907923</td>\n",
       "      <td>7.432548</td>\n",
       "      <td>2012.620985</td>\n",
       "      <td>17.961456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11099.249834</td>\n",
       "      <td>144.869921</td>\n",
       "      <td>3.084860</td>\n",
       "      <td>1.795269</td>\n",
       "      <td>9.491730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>194675.000000</td>\n",
       "      <td>2703.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               click        reply       month         year         day\n",
       "count     467.000000   467.000000  467.000000   467.000000  467.000000\n",
       "mean     1534.957173    18.907923    7.432548  2012.620985   17.961456\n",
       "std     11099.249834   144.869921    3.084860     1.795269    9.491730\n",
       "min        11.000000     0.000000    1.000000  2006.000000    1.000000\n",
       "25%        42.500000     0.000000    5.000000  2013.000000    8.000000\n",
       "50%        84.000000     0.000000    6.000000  2013.000000   23.000000\n",
       "75%       322.000000     4.000000   11.000000  2013.000000   25.000000\n",
       "max    194675.000000  2703.000000   12.000000  2015.000000   31.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:25:54.054309Z",
     "start_time": "2018-04-29T03:25:54.047326Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1534.9571734475376, 11087.35990002894, 716825)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.mean(data.click), np.std(data.click), np.sum(data.click)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:26:34.293470Z",
     "start_time": "2018-04-29T03:26:34.246370Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1534.9571734475376, 122929549.55276974, 11087.35990002894, 716825.0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不加权的变量描述\n",
    "d1 = sm.stats.DescrStatsW(data.click, \\\n",
    "                          weights=[1 for i in data.click])\n",
    "d1.mean, d1.var, d1.std, d1.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:27:35.602911Z",
     "start_time": "2018-04-29T03:27:35.595833Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83335.963986409959, 6297145701.6868114, 79354.556905617035, 735856562.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加权的变量描述\n",
    "d1 = sm.stats.DescrStatsW(data.click, weights=data.reply)\n",
    "d1.mean, d1.var, d1.std, d1.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.0"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(data.click) # np.percentile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:29:02.851951Z",
     "start_time": "2018-04-29T03:29:02.682499Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.click)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:29:14.087518Z",
     "start_time": "2018-04-29T03:29:13.987957Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.reply, color = 'green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:15:46.168302Z",
     "start_time": "2018-05-08T07:15:46.037390Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(data.click+1), color='green')\n",
    "plt.hist(np.log(data.reply+1), color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:17:54.632395Z",
     "start_time": "2018-05-08T07:17:54.547770Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the height and weight to see\n",
    "plt.boxplot([np.log(data.click+1)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:32:31.557601Z",
     "start_time": "2018-04-29T03:32:31.439426Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the height and weight to see\n",
    "plt.boxplot([data.click, data.reply])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:19:24.158128Z",
     "start_time": "2018-05-08T07:19:24.151960Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def transformData(dat):\n",
    "    results = []\n",
    "    for i in dat:\n",
    "        if i != 'na':\n",
    "            results.append( int(i))\n",
    "        else:\n",
    "            results.append(0)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:19:33.400955Z",
     "start_time": "2018-05-08T07:19:33.366011Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1534.957173</td>\n",
       "      <td>18.907923</td>\n",
       "      <td>15.713062</td>\n",
       "      <td>839.421842</td>\n",
       "      <td>146.336188</td>\n",
       "      <td>434.556745</td>\n",
       "      <td>7.432548</td>\n",
       "      <td>2012.620985</td>\n",
       "      <td>17.961456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11099.249834</td>\n",
       "      <td>144.869921</td>\n",
       "      <td>120.221465</td>\n",
       "      <td>7589.853870</td>\n",
       "      <td>577.441999</td>\n",
       "      <td>1989.458332</td>\n",
       "      <td>3.084860</td>\n",
       "      <td>1.795269</td>\n",
       "      <td>9.491730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>194675.000000</td>\n",
       "      <td>2703.000000</td>\n",
       "      <td>1817.000000</td>\n",
       "      <td>108449.000000</td>\n",
       "      <td>10684.000000</td>\n",
       "      <td>24848.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               click        reply  followed_num       fans_num      post_num  \\\n",
       "count     467.000000   467.000000    467.000000     467.000000    467.000000   \n",
       "mean     1534.957173    18.907923     15.713062     839.421842    146.336188   \n",
       "std     11099.249834   144.869921    120.221465    7589.853870    577.441999   \n",
       "min        11.000000     0.000000      0.000000       0.000000      0.000000   \n",
       "25%        42.500000     0.000000      0.000000       0.000000      4.000000   \n",
       "50%        84.000000     0.000000      0.000000       1.000000     16.000000   \n",
       "75%       322.000000     4.000000      1.000000       4.000000     84.000000   \n",
       "max    194675.000000  2703.000000   1817.000000  108449.000000  10684.000000   \n",
       "\n",
       "        comment_num       month         year         day  \n",
       "count    467.000000  467.000000   467.000000  467.000000  \n",
       "mean     434.556745    7.432548  2012.620985   17.961456  \n",
       "std     1989.458332    3.084860     1.795269    9.491730  \n",
       "min        0.000000    1.000000  2006.000000    1.000000  \n",
       "25%        0.000000    5.000000  2013.000000    8.000000  \n",
       "50%        9.000000    6.000000  2013.000000   23.000000  \n",
       "75%       88.000000   11.000000  2013.000000   25.000000  \n",
       "max    24848.000000   12.000000  2015.000000   31.000000  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fans_num = transformData(data.fans_num)\n",
    "data.followed_num = transformData(data.followed_num )\n",
    "data.post_num = transformData(data.post_num )\n",
    "data.comment_num = transformData(data.comment_num )\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:34:16.472261Z",
     "start_time": "2018-04-29T03:34:16.000422Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Plot the height and weight to see\n",
    "plt.boxplot([np.log(data.click+1), np.log(data.reply+1), \n",
    "             np.log(data.fans_num+1),\\\n",
    "             np.log(data.followed_num + 1)], \n",
    "            labels = ['$Click$', '$Reply$', '$Fans$',\\\n",
    "                      '$Followed$'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas自身已经包含了boxplot的功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:20:48.196074Z",
     "start_time": "2018-05-08T07:20:47.654656Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dne13aCIyQgy0DfKgxVsNcns7yPaOL6mpqVx44YVqBykyxNavX8+XvvSlTuvGjRunMWtlwIqLi7nzzjtJS0vr+NHV0NDAxo0b/Q5NREYI36pH460NMvymHeS+ffvUDlJkmBQUFLBx40bS0tIwM9LS0ti4caPa1kpMmJnfIYjICKT2AxGefPJJQqEQLS0tagcpMozU8UxiKRgMsnPnzk4dP3fu3KkZB0Ukampi4cnMzOTkyZPk5+fT3NxMcnIySUlJagcpIjLCaMZBERksNbHw3HbbbTQ1NZGRkQFARkYGTU1N3HbbbT5HJiIi/ZGdnU1lZWWndZWVleqkJyJRUxMLTygU4tZbb+XXv/41AL/+9a+59dZbCYVCPkcmIiL9UVxcTGFhYacmc4WFhepXIiJRUxMLT3V1NSdOnOCyyy7j8OHDXHbZZXz/+9/nV7/6ld+hiYhIP2jGQREZLDWx8CQmJnLmzJlO686cOUNiYqJPEYmMHfn5+SQkJJCXl0dCQgL5+fl+hyQj3I4dO6iurqatrY3q6mp27Njhd0giMoKoiYWnpaWFxsZGAoEAu3fvJhAI0NjYSEtLi9+hiYxq+fn57N27l6KiInbt2kVRURF79+5VkiwDpmNKRAZLTSwifOITn+DLX/4yzjnMjKysLH7xi1/4HZbIqPbGG2+wfPlyNm3aREVFBZs2bQJgy5YtPkcmI5WOKREZLDWxiPCLX/wC5xwAzjklxyLDwDnH2rVrO61bu3Ztx2dRpL+cc1x33XXk5OSwYMECcnJyuO6663RMiUjUfKtBjlcTJkygvr6+415EhpaZ8Sd/8iccO3aso0PV1KlTNQOaDEpRURFtbW0AHDhwgKKiIp8jEpGRRG2QI6SkpHDJJZdgZlxyySWkpKT4HZLIqJeTk0N5eTmHDh2ira2NQ4cOUV5eTk5Ojt+hxaVAIEBqaip5eXmkpqYSCAT8DinuJCQk0NbWxpw5cygrK2POnDm0tbWRkKCvPBGJjs4WEa699lqOHj2Kc46jR49y7bXX+h1SXCorK+t06bKsrMzvkGQEO336NOPGjeu4YlNfX8+4ceM4ffq0z5HFn0AgwJYtW3jsscfYs2cPjz32GFu2bFGS3EVbWxvTp0+nurqagoICqqurmT59ekeNsohIX3xLkM1skZltraur8yuE87z11ls0NzcD0NzczFtvveVzRPGnrKyM4uJiSkpKeP311ykpKaG4uFhJsgxYbW0tr776Ks45QqEQzjleffVVamtr/Q4t7mzbto1169axcuVKUlNTWblyJevWrWPbtm1+hxZ3rrnmGsaNGwfAuHHjuOaaa3yOSERGEnXS8yQlhZtjt9cwtN+3r5ewYDBIaWkpeXl5JCUlkZeXR2lpKcFg0O/QREa9pqam89rSFhUV0dTU5FNE8eu1115jyZIl7Nq1iyVLlvDaa6/5HZKIjCBqYuHpabxjjYPcWU1NDbm5uZ3W5ebmUlNT41NEMtJlZmZyzz33dJoW+J577iEzM9Pv0OJOSkrKeUOVbdmyRf0lumjv4Pn000+zaNEinn766U7rRUT6ourRLto7d7TfS2fZ2dlUVlaSl5fXsa6yspLs7Gwfo5KRbP369XzpS19iyZIlHDp0iJkzZ9La2sqGDRv8Di3uLF26lFWrVgEwe/ZsNmzYwKpVqzRCQxfOOVJSUjpq1tva2jotiwxEdz+wNHTg6KUEuYv09HTq6upIT09XJ6FuFBcXU1hYSGlpKa2trYRCIQoLC9XEQgasoKCAHTt28MYbb+Cc49ChQyxcuJCCggK/Q4s7JSUlAKxZs4ampiZSUlIoKirqWC9hZsall17KBx980DHxU/uyyED0dPXBzJQkj1JqYtHF6dOnaWtrU3Lcg4KCAoLBIIFAgPz8fAKBAMFgUMlMDzTiR98CgQD79u3jiSeeYM+ePTzxxBPs27dPIzP0YN68eWRlZZGQkEBWVhbz5s3zO6S445yjtra208RPkcsiA7Vv3z7eeOMN9u3b53coMsRUgyz9VlBQQEFBARUVFcyfP9/vcOJW+4gf7bXtiYmJFBYWAugHRYTIkRkqKipYuXIlEK4lVc1oZzqm+kdN5qTd1c9ePajn5+wIj8v+wOEHzls3mH3/9As/HVRcMoScc77cgEXA1qysLBcPAAc4M+t0Hy4i6U4oFPI7hLg2Z84ct2/fPufcb8pq3759bs6cOT5GFX8A19DQ4Jz7TTk1NDTos9cNHVPRAVxiYqJLTk52gEtOTnaJiYk6pnqh83nvIvOB9rJSjtC7eDymgLddlHmqhnnrwkVckhMZjJqaGmprazs1saitrdWIH11oZIbo1dTU8OKLL3aaSe/FF1/UMdWN1tZWMjIyAMjIyKC1tdXniGQ0MDPy8vI0IsoYoCYWIkNk2rRpPPzwwzz//PMdl8Pvuusupk2b5ndocUUjM0TvoosuYuvWraxfv57Zs2dTXV3Nww8/zEUXXeR3aHHp3LlzJCQkcO7cOb9DkRHu+eef56677up2vYxOSpBFhlDXWgbVOpxPIzNE76OPPiI9PZ3rrruO1tZWrrvuOtLT0/noo4/8Di0utXe2VqdrGaz2Nv7BYJCamhqys7MpLi5W2/9RTAmyyBA5cuQIO3bsIBAIdJxQ161bx7333ut3aHGnpKSEkpISdfzsQ0tLC1OnTuWGG27oWDd79mxOnTrlY1QiY4M6qI8tGuZNZIhkZ2eTmZlJVVUV5eXlVFVVkZmZqUlVZMDMjOrqahISwqfuhIQEqqurdWWiB3PmzKGsrIw5c+b4HYqIjDBKkLto/6LRF44MVvukKpFTKBcWFlJcXOx3aDJCtXcevuCCCzAzLrjggk7r5TcmTpxIdXU1BQUFVFdXM3HiRL9DEpERJOomFmY2HrjcOffzXrb5FPCXwCeBlc656sGHOLw0ikXfysrK1A4rCu1lEtnEQpOqSCzU19d3upfzffzxxx2PnXOdlkVE+tJngmxmFwL/CNwAvAD8hbf+DmAd0Ao85pzb7pz7GRAwsz8DrgNGXIIsvdNEBf2jNmsyFNqnt9U0tyIiQyOaJhZtQAmwsn2FmU0EngRyvdtjZnap97cngfuB8phHK74LBoOUlpaSl5dHUlISeXl5lJaWEgwG/Q5NZMxoHyNaY0WLiAyNPhNk51y9c64caIlYnQ98zzn3gXPuGLAPWOBt/xCwFFg1BPGKz2pqasjNze20Ljc3VxMViAyjxsbGTvciIhJbAx3m7XLgUMRyLXCZmX0OuAW4hHANc7fMbBmwDGDKlClUVFQMMIzhEe/xDacZM2awZMkSKisrOXz4MDNmzCA3N5cZM2aonHpRX1+v8omCymngVG7nu/jiizl9+nTHPaiceqLPXvRUVtEZ6eU00AR5HOGmF+3agFbn3KvAq3092Tm3FdgKMHfuXBdPbTO7a9sXT/H57XOf+xxbtmxh3bp1HTN5tc96pnLqmdogR0flNHAqt/N1N1GIyql7+uxFT2UVnZFeTgNNkI8C8yOWM4G3+rMDM1sELMrKyhpgCENDo1j0LhQKsWrVKrZv394xMsOqVat46aWX/A5NREREJCYGOg7y60C+mU02s6nAPGBv7MKSeFVTU8MjjzzSafKLRx55RG2QZVDKysrIyclhwYIF5OTkUFZW5ndIIiIyhkUzzNtE4B1gIpBqZvMJd8IrBn7gbfaQc66hPy/snNsF7Jo7d+7SfkU8xBITEzuGL2ttbfU7nLiTnZ3No48+yksvvdRRg3zbbbdpdrgeaMzovmnoQBkqOp+LyED1mSA75z4GemoHsSOm0QyDvmbIaz+JRp5Me3rOWGyGkZeXx7p167ptgyydlZWVUVRUxNmzZ2lra+PnP/95Rzkp8fuNyKED29uslZaWEggEVE49GD9+PI2NjaSmpnL27Fm/wxERGXV8m2razBaZ2da6urphfV3nXLe3zMxMLrjgApKTkwFITk7mggsuIDMzs8fnjEWRbZBvueUWtm/fzqpVqwiFQn6HFndWrFhBfX09jz/+OHv27OHxxx+nvr6eFStW+B1aXNHQgf139uxZnHNKjvtw4YUXYmZceOGFfociIiOMbwmyc26Xc25Zenq6XyF0sn79etLS0pg+fTpgTJ8+nbS0NNavX+93aHFFbZCjd+rUKR5//HFWrlxJamoqK1eu5PHHH+fUqVN+hxZXsrOzqays7LSusrJSzXZ6YGZMnTqVhIQEpk6d2udVsbHs9OnTOOc6jWIhIhKNMVeD3JOCggI2btxIWloamJGWlsbGjRt1ibcLJTP9k5OT0+uyQHFxMYWFhYRCIVpaWgiFQhQWFlJcXOx3aHHJOcexY8doa2vj2LFjY/ZqVjQmTJjQ6V5EJFoDHeZt0OKxk15BQQEFBQXMWr2bqsdv8TucuNSezLR3qGpPZjTV9PmSkpK4++67+da3vtVRVnfffTdJSb597OJS+4/QQCDQ0ZkxGAzqx2k3kpKSaGlpITU1taMNcmNjo46pbpgZ9fX1QHjCgsix7UVE+qKzqvSLkpnoFRUVsWnTJu666y5OnDjB5MmT+fWvf83999/vd2hxp/3H6UgfWH6otbS0AOdPNd2+Xn6jazKs5FhE+kNNLKTfCgoKOrVBVnLcvZKSEu6//35Onz5NW1sbp0+f5v7776ekpMTv0GSES0hI6HQv3ZsyZUqnexGRaKmTnsgQKikpobGxkVAoRGNjo5JjGbTk5GTefPNN3njjDd58882OkXfkfMePH+90LyISLTWxkH4LBAJs27aNpqYmUlJSWLp0qRI/kWHS3NzMZz/7Wdra2khISKCtrc3vkERERh0lyNIvgUCALVu2nDdRCKAkWWSYtCfFSo5FRIaG2iBLv2zbto1169Z1Gtt33bp1bNu2ze/QRERERGJCbZClX5qams6bVrqoqIimpiafIpLRoKysjJycHBYsWEBOTg5lZWV+hySjQOTMqCIi/aEmFtIvKSkpbNmyhZUrV3as27JlCykpKT5GJSNZWVkZxcXFHWNrJyYmUlhYCKARUmRQmpubO92LiERr1CTI1zy6l7qzsTsJzlq9Oyb7SR+fzLuP3BiTfcWDpUuXdrQ5nj17Nhs2bGDVqlXn1SqLRCsYDFJaWkpeXl7HOMilpaUEAgElyCIi4gvfEmQzWwQsysrKisn+6s42836MZr+L5WQFsUq040V7R7w1a9Z0jGJRVFSkDnoyYDU1NeTm5nZal5ubS01NjU8RyWjRPnueZtETkf5SG2TpN43tK7GUnZ1NZWVlp3WVlZVkZ2f7FJGMFu1JsZJjEekvTcMk/aYOVRJLxcXFFBYWEgqFaGlpIRQKUVhYSHFxsd+hiYjIGDVq2iDL8FCHKom19uMmEAhQU1NDdnY2wWBQx5MM2rx583jwwQd56qmn2L9/v9/hiMgIohpk6ZfIDlVJSUnk5eVRWlpKMBj0OzQZwQoKCqiqqqK8vJyqqiolxxIT+/fv5/bbb1dyLCL9pgRZ+kUdqkRERGS0U4Is/aIOVSISL8ys21usnyMiY4+mmpZ+UYcqkaHVUwKnxO98zrlubytWrOh2+xUrVvT4HBGRSBrmTfqloKCAYDBIIBAgPz+fQCCgDlW9yM/PJyEhgby8PBISEsjPz/c7pLikkVF+o6cEzjnH888/3+1znn/+eSV+EUpKSlixYkXHDJ8pKSmsWLFCQ1KKSNQ0ioXIEMnPz2fv3r0sX76cm2++mddee43NmzeTn5/P66+/7nd4cUMjo0SvvTyCwSAHqmuYMzub4uJilVM3SkpKKCkpYdbq3TGbREpExg4lyNIvSmai98Ybb7B8+XI2bdpERUUFmzZtAmDLli0+RxZfNNV0/xQUFFBQUMCs1bupUuInIjIk1ElP+kXDvEXPOcfatWs7rVu7du2YvezdE42MIiIi8UYJsvRLTU0NtbW1ndqL1tbWKpnphpmRm5tLamoqeXl5pKamkpubO+o7TvWXRkYREZF4oyYW0i/Tpk0jEAhw8cUXA9DQ0EAgEGDatGk+RxZ/MjMzqaqqYvz48ZgZCQkJVFVVcfnll/sdWlwpLi7mzjvvJC0tjUOHDjFz5kwaGhrYuHGj36GJiMgYFXUNspmNN7Or+thmrpmVmNlLZvangw9P4s2ZM2eor68nEAglIXQ0AAAeKElEQVSwe/duAoEA9fX1nDlzxu/Q4s7Ro0dJTk7m7NmzOOc4e/YsycnJHD161O/Q4pZq10VEJB70mSCb2YVm9hJwHHg4Yv0dZnbQzH5pZku81e845wLAEmDRkEQsvjp16hS33HILa9as4aabbmLNmjXccsstnDp1yu/Q4k5LSwsvv/wyzjlCoRDOOV5++WVaWlr8Di2uBINBdu7cycGDBykvL+fgwYPs3LlT7dpFRMQ30TSxaANKgFeB3wUws4nAk95yK/ATM9vlnPvQe85fAbo+Okr96Ec/Ys+ePR2jWNx1111+hxS3qqqquOmmmzotS2fqpCciIvGmzwTZOVcPlJvZvRGr84HvOec+ADCzfcACM/sWsBb4jnPuP4YgXvFZUlISTU1NndY1NTWRlKTm7F1lZGSwevVqEhMTmT17Nhs2bGD16tVkZGT4HVpcyc7OZt68efz4xz/GOYeZcf3116uTnoiI+GagWc3lwKGI5VrgMuAx4PeAS80sxzm3tbsnm9kyYBnAlClTqKioGGAYncVqP/X19THbF8QurnjQ2tpKfX09N9xwQ8e65ORkWltbR9X7jIX777+fDRs28PDDD3fUtqempnL//ferrCKcOXOGAwcOkJqaSmNjIykpKbz99ttcccUVKqc+qHyio3LqW6y/90YzlVV0Rno5DTRBHke46UW7NqDVOfdwD9t34iXOWwHmzp3r5s+fP8AwInx3NzHZD3RMVhATMYwrHlx88cWcPn2axMTEjqSvpaWFiy++eFS9z1iYP38+s2fPJhgMUlNTw6c+9SnNetaNgwcPkpqaytSpUzl8+DBTp07l2LFjHDx4UMdUb0bZuWXIqJyiEtPvvVFOZRWdkV5OA02QjwLzI5Yzgbf6swMzWwQsysrKGmAI4oe6ujoALrnkEk6cONFx375eZCBeeOEFFi1a1HFC3bVrF7feeqvfYYlPrnl0L3Vnm2O2v1mrd8dkP+njk3n3kRtjsi8RiW8DTZBfB9aa2WTCI2HMA+7rzw6cc7uAXXPnzl06wBg6mZi9mqufXR2LXYU9G5vdTMwGGD3Twba2tpKcnMzx48cBOH78OMnJyTQ3x+7LbLTQtNzRW716NbfffjtNTU2kpKRw5ZVX+h3SkIjHxC8ek766s828H6NptGNZixWrRFtE4l+fCbI3YsU7wEQg1czmA0uBYuAH3mYPOeca+vPCsa5B/rjmcZ1Qh0lzczNPPvkks2fPprq6moceesjvkOJSMBjkrrvuIhAIUFNTQ3Z2NnfddRfBYFAJcoSkpCSqq6uZN28eDz74IE899RT79+8flR0/4zHxG43nKBGRwYpmFIuPgZ6y2B0DfeFY1yDL8IrseCbdq66u5syZM+fVIL///vt+hxZ3EhIS2L9/P/v37+9YFhER8Ytv30JmtsjMtqrt6sjU2tra6V7ON27cOFasWEFeXh5JSUnk5eWxYsUKxo0b53docaWlpYWMjAxmzZqFmTFr1iwyMjI0oYqIiPjGtwTZObfLObcsPT3drxBkEKZOnUpCQgJTp071O5S4de7cOdauXcsVV1zBggULuOKKK1i7di3nzp3zO7S4YmbcfvvtHDx4kH379nHw4EFuv/12TTstIiK+8a2Rn0axGNk+/PBD2tra+PDDD/veeIyaPn06H3/8MQDOOSDcfnv69Ol+hhV3nHM8/fTT/Mu//AsnTpxg8uTJfPjhhx1lJiIiMtx8S5DVBnnkGjduHM45WltbSUhIIDExUbWiPbjgggvYvn17RxvkxYsX+x1S3MnMzOTkyZOcOnUK5xynTp0iJSWFSZMm+R2aiIiMUeoJI/2SkJBAc3MzGRkZmBkZGRk0NzerU1U3jhw5wm233cZNN93EwoULuemmm7jttts4cuSI36HFna7NKdS8QkRE/KQmFtIvbW3hCRQjx0EGdDm8G9OmTeM73/kOe/bs6ahBvuuuu5g2bZrfocWV2tpaEhISmDx5MidOnGDSpEmcOHGC2tpav0MTEZExSp30pF+SkpLOG9otMTFxVI5ZGwsfffQR+fn5LFy4kPz8fD766CO/Q4o7ZsZ9993H0aNHKS8v5+jRo9x3332qRRYREd8oq5F+aR9669Zbb+WLX/wizzzzDK+88orPUcWnrjWgzc3NNDc3c+bMGZ8iik/OOV588UX27NnDoUOHmDlzJvX19boqISIivhlVCXJMZ4T6bmz2lT4+OSb7iScJCQm88sorHYlxQkJCR9MLOd/y5cu5+eabee2119i8ebPf4cSdpKQkPvroI+rq6nDO8cEHH2BmuiohIiK+GTVtkGM1fSuEE+1Y7m+0aWtrY9asWXz1q1/lK1/5imaG64WZsXnz5o7E2MxUM9pFSkoKDQ2/mam+ubkZgLS0NL9CEhGRMU5tkGXA7rnnHr9DiHtdk2Elx+eLTI6jWS8iIjLUNDaXDMjhw4dxznH48GG/Q4l78+bN48UXX2TevHl+hxK3EhISSE4ON0dKTk7WsIEiIuIrNfKTAWlvc6y2x3374Q9/yO23366krxeRx1Fra6uOKxER8ZW+sWVApkyZwjPPPMOUKVP8DiWuTZo0qaNZhXNOs8P1Qj+6RERGBjPr85aXlxfVdvE6pKdvCbKZLTKzrXV1dX6FIINw/PhxvvjFL3ZMFCLdO3nyZEdns7S0NE6ePOlzRCIiIoPjnOvzNnPVq1FtF699c9RJTwZkwoQJne7lfFdffTUA9fX1ne7b14uIiEh8UhMLGZBHH32UPXv28Oijj/odStxqa2vjiiuu6LTuiiuuUBOCHrTP0Nh1pkYREZHhpk56MiCrVq2ipaVFkzn0orq6mpSUlE7rjh49SlNTk08RxbfW1tZO9yIiMvyueXQvdWebY7KvWE3glj4+mXcfuTEm+4qWshvpt4SEhI4pp1taWjSTXi8aGxvPW47XDgkiIiJtsx5iot9BdBHOMH46rK+pBFn6rWsyrOS4ez11PIjXDgkiIiIf1zwek9mEKyoqmD9//uADInY10f2hUSxERERERCJoFAsRiQvLly9n165dLF++3O9QRERkjFMTCxGJC5s3b2bz5s1+hyEiIqJh3kREREREIqkGWURkmEzMXs3Vz66O3Q6fHfwuJmYDDL5DjojIaKIEWURkmMSqdzjEroe4H73DRUTinZpYiIjvUlJSOiZViXwsIiLih5jWIJtZMvAZIMk593os9y0io1fk7IKaaVBERPwWdQ2ymY03s6v62Oxq4HrgpkFFJXGvfTY4zQonIiIio02fCbKZXWhmLwHHgYcj1t9hZgfN7JdmtgTAOfcfwAtDFq3EhaSkJJKSks57LN1LTk7udC/dS0hI6HQvIrFhZn3e8vLyotpOlSIyVkST2bQBJcCrwO8CmNlE4ElvuRX4iZntcs59OFSBxkq0H25b1/c2Y3XK4JkzZ5KamkpNTQ1XXXUVjY2NvPfee36HFZe6JnsJCQmamrsbV111FT//+c+B8NTlkcsi0rNrHt1L3dnmXreZuerVmL5mXx0708cn8+4jN8b0NUWGW58JsnOuHig3s3sjVucD33POfQBgZvuABcA/D0WQsRRNUhvL+cNHo/fee4958+axc+dOnnrqKQ4cOOB3SHGrra2Nz33uc3zxi1/kmWee4ZVXXvE7pLh07tw5nHMdn70rrrjC75BERoS6s80xGRkllt97GhlFRoOBXhu/HDgUsVwLXGZm1xBuhnG1ma10zm3o7slmtgxYBjBlyhQqKioGGMbQqK+vj7uYhlteXl6vf9+/fz/79+/vtK632vlQKBSTuEaiV1555bzEeCweX70dU++//363x89oPKZi9b+P5XkqHo/HeCwnGL1lNRbKKVbGQo6gY2rgCfI4wk0v2rUBrc65d4HFfT3ZObfVzI4CiyZOnHh9vNXWqga595r2QCDAtm3baGpqIiUlhaVLl1JSUjKM0Y18Y/H46u2YmjBhAg0NDR3LaWlp1NfXD0dYw+u7u2P2v4/ZeSqGMcXKxENXEzjU93ZROxmb3UzMhvnzfxqbncVKjP5/Mf3ei8NjKpZGfY6gYwoYeIJ8FJgfsZwJvNWfHTjndgG75s6du3SAMYhPSkpKKCkpYdbq3TGb9GC0Gj9+PLt376a1tZXExERuueUWzp4963dYcac9GdYxJRCfE6qAmg6IjCUDTZBfB9aa2WTCI2HMA+7rzw7MbBGwKCsra4AhiMS/tLQ0lixZwqFDh5g5cyZpaWlKkEVEJK7F7Mfgd2Ozn/Txwz8KVJ8JsjdixTvARCDVzOYDS4Fi4AfeZg855xq630P3VIMso52Zce2113L06FHMjLS0NLKysigvL/c7NBERkW7F6urNSL8iGM0oFh8DPVXz7hjoC6sGWUa7hQsXsnfv3o7lAwcOcODAAW68UcMfiYiIxDPfRuR3zu1yzi1LT0/3KwSRIfX222/3a72IiIjEB01ZJTJETp06xZw5c3DOEQqFcM4xZ84cTp065XdoIiIi0gvf5ghWEwsZC06dOtVpLN/LLrvMx2hEZLSZmL2aq59dHZudPRub3UzMBhi5bU9FwMcEWZ30ZCw4evQo8+bN48EHH+Spp546b3IVEZHBiNWQeBoOT6QzNbEQGWJ1dXW0tLRQV1fndygiIiISBTWxEBliBw4coKCgwO8wRGSU0pi1IrGnJhYiQ2zKlCk8/vjjrF69muPHj/sdjoiMIhqzVmRoqImFyBC78sormTBhAldeeaXfoYiIiEgU1MRiDLrm0b3UnW2Oyb5idWkvfXwy7z4y+ibQmDx5Mvv37+/onDd58mROnDjhc1Sxp2MqejHtwBSDS+K6HC4icj41sRiD6s42q9fzMDEznHMdZTV16lS/QxoSOqaiE8tL2LokLiIydHxLkEVGu5SUFI4fP95pHOT29SIiIhK/1AZZZIikpaX1a72IiIjEByXIIkNEU02LiIiMTL4lyGa2yMy2avIEGc1ee+21XpdFREQk/viWIDvndjnnlqWnp/sVgsiQu/nmm3tdFhERkfijJhYiQyQjI4MDBw6Qk5PDsWPHyMnJ4cCBA2RkZPgdmoiIiPRCo1iIDJGTJ08yadKkTlNNZ2RkcPLkSZ8jExERkd6oBllkCJ08ebJTJz0lxyIiIvFPCbKIiIiISAQlyCIiIiIiEXxrg2xmi4BFWVlZfoUgEhNdZ8obDOdczPYlIiIiA6Nh3kQGyTnX523mqlej2k5ERET8pyYWIiIiIiIRlCCLiIiIiERQgiwiIiIiEkEJsoiIiIhIBCXIIiIiIiIRok6QzWy8mV3VxzafMrMyM/u2mV0/+PBERERERIZXnwmymV1oZi8Bx4GHI9bfYWYHzeyXZrbEW/0g8ACwDAgMRcAiIiIiIkMpmolC2oAS4FXgdwHMbCLwpLfcCvzEzHYBlzrnPvS2uXBIIhYRERERGUJ91iA75+qdc+VAS8TqfOB7zrkPnHPHgH3AAuBjM8sws4uBD4ckYhERERGRITTQqaYvBw5FLNcClwEbga1AI/DVnp5sZssIN8NgypQpVFRUDDCMoVFfXx93McVaLN5frMsp3sr8L8sbaGiO3f5mrd496H2kJcM/LEiLQTSxNTF7NVc/uzo2O3s2NruZmA0VFfFXVrEUb5+ZWIrVexvt56lo5OXlRbWdrYtuf6FQaBDRjHxjIUfoy5g4pqKZ/tabAvde4Bve41XAVyP+9jjwQLT7irxdf/31Lt6EQiG/QxhSM1e9GpP9xLKcYhVTLMUypliVVTyWk3M6pvwwmt9fPH72nBvdZT7av/diSWUVnXgsJ+BtF2V+OtBh3o4C0yOWM4H/6c8OzGyRmW2tq6sbYAgiIiIiIrE30AT5dSDfzCab2VRgHrC3Pztwzu1yzi1LT08fYAgiIiIiIrHXZxtkb8SKd4CJQKqZzQeWAsXAD7zNHnLONfTnhc1sEbAoKyurXwGLiIiIiAylPhNk59zHQE9Z7I6BvrBzbhewa+7cuUsHug8RERERkVjzbapptUEWERERkXjkW4KsNsgiIiIiEo9UgywiIiIiEkE1yCIiIiIiESw8brKPAZh9SOdZ+eLBJcCv/A5iBFA5RU9lFR2VU/RUVtFROUVH5RQ9lVV04rGcZjrnLo1mQ98T5HhkZm875+b6HUe8UzlFT2UVHZVT9FRW0VE5RUflFD2VVXRGejn51sRCRERERCQeKUEWEREREYmgBLl7W/0OYIRQOUVPZRUdlVP0VFbRUTlFR+UUPZVVdEZ0OakNsoiIiIhIBNUgi4iIiIhEGNMJspnNMrNfeo9vMbMHe9l2h5ndPXzRjTxmNt/M3vQ7DhERGRpmdq+ZfcPvOCT+jLZjY0wnyJGcc7udc0/5HYfEnpldZ2Y1Zra9h793JPZm9rdm9jfDEFPHj7ORqK8yldgxsz8ys//jdxyjkZkVm9klfscR71ROMhYpQZYemZn5HUOMrAb+zjm3xO9ARhGV6fD5PDDT7yBGqaVAqt9BjAAqJxlzxlyCbGafM7P/NLODwN9FrO+4NGBmF5rZs2b2SzN7z8ymRGw33szeGa3NLdprNs3sOeBNM/srM6v2agtv87bZYWZrzexHZvY/ZvZQl31kmVlte4JtZgvN7BUf3k67ycTfbI0jnco0Ct7n6WdmtsHM/tvM9pvZ5WY208y+633WfmJmv+9t/2kz+6mZvW9m/9fMVgN/DDxhZpt7eI0dZvaEmb1lZsfM7HZvfaerId4+M72Y3jOzzWZ21My2mNkXzOy/vM/6lTF67x3nWjP7OzO73cwOeOXwz2Z2obddhZmt9/5WbWa5ZvZv3nsp8ra518xe8srsQ2/5783ssLcu2dtuoVeevzCzr0Y891tm9qr3fp/w1n8HmA78m5nd28N7eN/M1njnv/8ys+yImHO9x5FN9aKKc6hE87/t5djrTzldFLHdxqF8T8PJzCrN7EbvsXn//0+b2Q+9Y+obZpZgZmlmtscr6yozu9p7zg6v7H9pZrf4+26Gh5ktaT+WgD/w1t0QcR7b6ZXZN81sWcTzvm9mv+Vb4NFwzo2ZGzAL+B/gSm/5t4Ffeo/vBb7hPX4aeAIwYALhX847gLuBfyJcc+b7+xnCMmoBPgMsAJ4HEoFLgcOEf1TtACq8cpkMHAE+AcwH3vT28+/AXO/xJqDAp/dTApwFaoE1wE7gF0AN8Hlvm8i4/xb4G+/xTOC7wC+BnwC/D6QAJ4Fx3jb/CfyJ93gt8BfAeOBZ4GfAD4FPRRxvPwGqgY3tx14Pcb/vxVsD/BeQ7a2vAHIj/leRx+9LXrwfest/7/3PvgskD1GZ3g/81It3J5DgbdMCPAy8B7wNTPHW/w3hxPoQ8L972H8F8H+Bd734/4+3fgdwd8R2LRH/v38DXvDe+xrgr4H/9so/w+fPUytwi7f8iPc+vgf8ubdurlceKcDLwJ956zO6e9/dvMYO4DUgCfgs8POux3LEMZUZEdN1hM9vx4CnvW02AOti9L4jz7Wf9v4f07zlp4AnIv7fT3mP/8E7ri4B/jdwPOL4PgRcSPhzdA64h/C56d+Bm4AM4C3gIq8sKoHf8p77AeFz2CTg18D0yDLp63PoPf47YGsUn8Ne4xym463H/20vx15U5eRtd4TwuT8DOA1c7tdnLMbl95fA173HvwOUe8fU5d66MuCPvLL9LW/dCmBHxGfxu4S/J83v9zMM5fW/gIPesTDOe+/fAK71PgMJXvnNB24BXvWeNxWo8Tv+vm5jrQb5D4FvO+fe85Y/7GG7zwNBF1bvnGv01hcBFwBfGeI4/fYr59y/Ev7SmQ8cIJyApADttenPO+canXMnCJ9Euv4SLANuNTMDFgK+1CA75wKEP6B/RvjDXOWc+yTh9/YPFnF1oBv/CDznnMsinPg+763/d+B3zOxSwifKPG/97wNvEE7Ofuic+xThJGWdmSUB/wz8pXNuNnAqyvizgReBHjuQRrgOuAO4mfD4k28DVxD+svtsNK8XZUyRZbqf8Pv+BOEv5894myUCtc65Kwn/UCg0s4uBLxP+P3yScMLUkynOuWsIJ8rRfN6uJVxG1xJOkJOcc58g/EV+Z3/e3xA47pzb7T3+JuFaljnOuX8CcM69TTjO/0U4qXvYzP7QORfVMeL5tnOuhXDyE01zjA+cc+845+oJ/8D5F2/9jwgn0YPV9Vw7F3jJOXfEW94C3BCx/S7vfj/wb865Xznn/hNIN7Nx3t++55z7yDn374QrL3Y651qBH3sxzyNchj8Eqggfj5/wnrvPOfehc+4k4fPZjH68l53tr090ZdtXnEOtt//tVfR87EH05bTPOXfCO0arGZ73NRxeJPzdAOFE+DtADvCGmf2M8DE2yyvbmWb2deDPCdewt3vJOdfmvExwlPss8B3vWDgHfMtbf4hws5wdhI+h6cBe4Fozu4BwjrXz/N3Fl7GWIKcSrtmKZrvmbtZ/SDjhuCCWQcWheu8+CVjrnPuUd5vinDvq/S2yfMYDZ7rsYyfhX4y/C7ztnGsY0oijcxPhmlucc+8TTvo/3d2GZpZGz18kbxBOBG8Avg78tpmlAOnOuUOEE9SHvBPq3wOXec+rc85933uJ56KIdyR8MXd3IgRwhL9s4Dfxf+Rt//+Ay7wvmZ680OW5fXnHOfeBc+4D4GjE82OV8A1G18/Kr7vZxoBW59zXgAeANWZW0o/XaAJwzjUT/nEC4XNdYsQ2kZf3z0U8bm1/fjfPGaiu59okoC1i2bzX7RpPZCzt8SR02QbCZdU15iSgPOJ8lemca//CjtxnZBlFo/25PZVt12YTfcU51Hr733Yn8n8RbTk1RrndiOJV+PzSzK4l/H3xU+BnEcfUTOfcU17Tn/uAbUCQcBm26+28Ntp0zZVSvPs9hI+prxC+2mLeuWk3cCPhHx//PHxhDsxYS5D3AX9qZtO85Wk9bFcO/H8AZnaRmY331n8beB14zszGQtlVAveY2QSvPdZnIv72R2aWaGZXEP5V/VbkE71E5SMgQLg2OR709SXddduu2rdvT5AXEP7AnwI+Rzjhbn/uH3kn1Kucc79DzyeS3oyEL+bzToTe+jbvhAhe/F6y/juEm6V8v73tYw96fe/dtOUc6oRvMDLN7He9x/cRLrP/NrPFAF47vIuBn5vZVc65SsLnn/aRK84Cl3hXY/rjfcI16nhtZ6cO6l30T9dz7TvAn5hZewxLCV+OjaUfAX9gZlkQHp0miucMumzpXBMe75ro4djr43kDLaeRqIxw87D/JnxMTTez34OO0XvSCdcqf8859y6Q71uk/vsh8MdenpRGuBkqhMtnJ+EfC5Hn+TJgMXCRc+5nwxrpAIyFJK+Dd8nuq8C/ep0qevqCXgH8npm9D4ToXGNcTDjZWTeEocaLbxNOkn9G+AQaWdt6jPAl/L3ACu+SXFc7CZ88Yv1FOFBvAl8CMLMZhN/PD7rb0DlXRw9fJN5xdAUw0zn3X4QTw78inDhDuMyWe8+bYGbXEy7DT5jZNd429w3wPbxPfH0x93QiPI93Ap3knNtE+LLvb/fztd4nvt57tA4D95nZe4R/lD9O+EtimXce+gfC7Y6bgQfN7BDhNuxrvOfvJHze+X/9fN1vAxeYWTnhJkJH+9g+Zro5134aeNRb/gWQTrjNfixf8wjhZjZveGW9IoqnPUP4HNbfTtdPAF80s5cJnwtGkp6Ovd4MtJxGou8QruEsc86dJdyGfId3TK0nXMnyDOEy/Bmda93HFO+K6AuEm+N8j99UEq0jXBHyAuG+JO3+Ffg9wuem+DcUDZt1G903+ug0FLHdHXgdH32OtwLIJdx++hXCHcfeAT7r/X0+3XfS+yThD/0vCSfS10Xs85+Abd7j3yZc03mht3wJ8CrhxOgAsNBb/0eEk7wawjUUfXXSy+wmvk8R7uj3MuFE67xOpt5yY8TjrwNFQ1SmXyHc9KiccDvSu72/t0Rsey/hjhuXEu5w+AvCV2Im9bZv7/GsiPc4xfs/vEa4s19kJ703I57/M8LtBCHc5vlxH4+9jvh10023+L8Rvrp3GBjvdyyj8Ua4f8AMv+OI5mZewCJRM7MdhBOSb/ayTRLh5PIh59wPhys2kXhiZrMIf1ayYrS/rpPLbHDhGnkZBDP7NOf3C7jTOfdjP+IR/5jZ/YRH2CnyO5bRxsxuBh5wzv2h37FEo7t2liKDYmaXEb68sk3Jce/G8hezmU0n/CMq0kPOuZf9iGckiFWiLZ05594CVLZjnJm9SPiqz019bCr9ZGZPEu64v8jvWKKlGmQRERERkQhjqpOeiIiIiEhflCCLiIiIiERQgiwiIiIiEkEJsoiIiIhIBCXIIiIiIiIRlCCLiIiIiET4/wFemigMDHwjwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "data.boxplot(return_type='dict')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:35:58.885726Z",
     "start_time": "2018-04-29T03:35:58.199058Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools import plotting\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 10))\n",
    "plotting.scatter_matrix(data[['click', 'reply',\\\n",
    "                              'post_num','comment_num']]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 更多使用pandas.plotting绘图的操作见：\n",
    "http://pandas.pydata.org/pandas-docs/version/0.15.0/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:38:47.178409Z",
     "start_time": "2018-04-29T03:38:44.249998Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x119406898>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn # conda install seaborn\n",
    "seaborn.pairplot(data, vars=['click', 'reply', \\\n",
    "                             'post_num', 'comment_num'],\n",
    "                  kind='reg')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:40:54.155408Z",
     "start_time": "2018-04-29T03:40:48.299423Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x119640f60>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 599.5x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.pairplot(data, vars=['click', 'reply', 'post_num'],\n",
    "                 kind='reg', hue='year')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:29:23.365118Z",
     "start_time": "2018-05-08T07:29:23.014080Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lmplot(y='reply', x='click', data=data, #logx = True,\n",
    "               size = 5) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# values_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-29T03:42:41.001263Z",
     "start_time": "2018-04-29T03:42:40.993785Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    304\n",
       "2014     63\n",
       "2007     34\n",
       "2012     33\n",
       "2015     20\n",
       "2011      6\n",
       "2009      6\n",
       "2006      1\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:30:39.916714Z",
     "start_time": "2018-05-08T07:30:39.905537Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year\n",
       "2006     1\n",
       "2007    34\n",
       "2009     6\n",
       "2011     6\n",
       "2012    33\n",
       "2013   304\n",
       "2014    63\n",
       "2015    20"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = data.year.value_counts()\n",
    "dd = pd.DataFrame(d)\n",
    "dd = dd.sort_index(axis=0, ascending=True)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:30:43.933076Z",
     "start_time": "2018-05-08T07:30:43.928522Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2006, 2007, 2009, 2011, 2012, 2013, 2014, 2015], dtype='int64')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:30:51.676959Z",
     "start_time": "2018-05-08T07:30:51.671688Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2006-01-01',\n",
       " '2007-01-01',\n",
       " '2009-01-01',\n",
       " '2011-01-01',\n",
       " '2012-01-01',\n",
       " '2013-01-01',\n",
       " '2014-01-01',\n",
       " '2015-01-01']"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_date_str = list(map(lambda x: str(x) +'-01-01', dd.index))\n",
    "dd_date_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:31:03.022566Z",
     "start_time": "2018-05-08T07:31:03.015431Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2006-01-01', '2007-01-01', '2009-01-01', '2011-01-01',\n",
       "               '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_date = pd.to_datetime(list(dd_date_str))\n",
    "dd_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:31:19.755981Z",
     "start_time": "2018-05-08T07:31:19.599508Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dd_date, dd.year, 'r-o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:32:09.223546Z",
     "start_time": "2018-05-08T07:32:09.215923Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year\n",
       "2006     1\n",
       "2007    35\n",
       "2009    41\n",
       "2011    47\n",
       "2012    80\n",
       "2013   384\n",
       "2014   447\n",
       "2015   467"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = dd.cumsum()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:32:43.849303Z",
     "start_time": "2018-05-08T07:32:43.606515Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = data.year.value_counts()\n",
    "dd = pd.DataFrame(d)\n",
    "dd = dd.sort_index(axis=0, ascending=True)\n",
    "ds = dd.cumsum()\n",
    "\n",
    "def getDate(dat):\n",
    "    dat_date_str = list(map(lambda x: str(x) +'-01-01', dat.index))\n",
    "    dat_date = pd.to_datetime(dat_date_str)\n",
    "    return dat_date\n",
    "\n",
    "ds.date = getDate(ds)\n",
    "dd.date = getDate(dd)\n",
    "\n",
    "plt.plot(ds.date, ds.year, 'g-s', label = '$Cumulative\\: Number\\:of\\: Threads$')\n",
    "plt.plot(dd.date, dd.year, 'r-o', label = '$Yearly\\:Number\\:of\\:Threads$')\n",
    "plt.legend(loc=2,numpoints=1,fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:34:49.196769Z",
     "start_time": "2018-05-08T07:34:49.181342Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>2006</th>\n",
       "      <td>1214</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278</td>\n",
       "      <td>291</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>28290</td>\n",
       "      <td>514</td>\n",
       "      <td>22</td>\n",
       "      <td>137</td>\n",
       "      <td>8041</td>\n",
       "      <td>10344</td>\n",
       "      <td>281</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>18644</td>\n",
       "      <td>186</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>531</td>\n",
       "      <td>571</td>\n",
       "      <td>39</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2889</td>\n",
       "      <td>28</td>\n",
       "      <td>84</td>\n",
       "      <td>28</td>\n",
       "      <td>332</td>\n",
       "      <td>661</td>\n",
       "      <td>50</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>463720</td>\n",
       "      <td>5933</td>\n",
       "      <td>2779</td>\n",
       "      <td>59511</td>\n",
       "      <td>12315</td>\n",
       "      <td>32498</td>\n",
       "      <td>322</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>63140</td>\n",
       "      <td>937</td>\n",
       "      <td>571</td>\n",
       "      <td>43265</td>\n",
       "      <td>24359</td>\n",
       "      <td>40362</td>\n",
       "      <td>2458</td>\n",
       "      <td>6111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>57764</td>\n",
       "      <td>772</td>\n",
       "      <td>2216</td>\n",
       "      <td>16664</td>\n",
       "      <td>11266</td>\n",
       "      <td>98025</td>\n",
       "      <td>233</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>81164</td>\n",
       "      <td>436</td>\n",
       "      <td>1649</td>\n",
       "      <td>272391</td>\n",
       "      <td>11217</td>\n",
       "      <td>20186</td>\n",
       "      <td>80</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       click  reply  followed_num  fans_num  post_num  comment_num  month  \\\n",
       "year                                                                        \n",
       "2006    1214     24             0         2       278          291      8   \n",
       "2007   28290    514            22       137      8041        10344    281   \n",
       "2009   18644    186            17        12       531          571     39   \n",
       "2011    2889     28            84        28       332          661     50   \n",
       "2012  463720   5933          2779     59511     12315        32498    322   \n",
       "2013   63140    937           571     43265     24359        40362   2458   \n",
       "2014   57764    772          2216     16664     11266        98025    233   \n",
       "2015   81164    436          1649    272391     11217        20186     80   \n",
       "\n",
       "       day  \n",
       "year        \n",
       "2006    24  \n",
       "2007   512  \n",
       "2009    78  \n",
       "2011    72  \n",
       "2012   819  \n",
       "2013  6111  \n",
       "2014   579  \n",
       "2015   193  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg = data.groupby('year').sum()\n",
    "dg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:35:10.114270Z",
     "start_time": "2018-05-08T07:35:10.102158Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>2006</th>\n",
       "      <td>1214</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278</td>\n",
       "      <td>291</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>29504</td>\n",
       "      <td>538</td>\n",
       "      <td>22</td>\n",
       "      <td>139</td>\n",
       "      <td>8319</td>\n",
       "      <td>10635</td>\n",
       "      <td>289</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>48148</td>\n",
       "      <td>724</td>\n",
       "      <td>39</td>\n",
       "      <td>151</td>\n",
       "      <td>8850</td>\n",
       "      <td>11206</td>\n",
       "      <td>328</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>51037</td>\n",
       "      <td>752</td>\n",
       "      <td>123</td>\n",
       "      <td>179</td>\n",
       "      <td>9182</td>\n",
       "      <td>11867</td>\n",
       "      <td>378</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>514757</td>\n",
       "      <td>6685</td>\n",
       "      <td>2902</td>\n",
       "      <td>59690</td>\n",
       "      <td>21497</td>\n",
       "      <td>44365</td>\n",
       "      <td>700</td>\n",
       "      <td>1505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>577897</td>\n",
       "      <td>7622</td>\n",
       "      <td>3473</td>\n",
       "      <td>102955</td>\n",
       "      <td>45856</td>\n",
       "      <td>84727</td>\n",
       "      <td>3158</td>\n",
       "      <td>7616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>635661</td>\n",
       "      <td>8394</td>\n",
       "      <td>5689</td>\n",
       "      <td>119619</td>\n",
       "      <td>57122</td>\n",
       "      <td>182752</td>\n",
       "      <td>3391</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>716825</td>\n",
       "      <td>8830</td>\n",
       "      <td>7338</td>\n",
       "      <td>392010</td>\n",
       "      <td>68339</td>\n",
       "      <td>202938</td>\n",
       "      <td>3471</td>\n",
       "      <td>8388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       click  reply  followed_num  fans_num  post_num  comment_num  month  \\\n",
       "year                                                                        \n",
       "2006    1214     24             0         2       278          291      8   \n",
       "2007   29504    538            22       139      8319        10635    289   \n",
       "2009   48148    724            39       151      8850        11206    328   \n",
       "2011   51037    752           123       179      9182        11867    378   \n",
       "2012  514757   6685          2902     59690     21497        44365    700   \n",
       "2013  577897   7622          3473    102955     45856        84727   3158   \n",
       "2014  635661   8394          5689    119619     57122       182752   3391   \n",
       "2015  716825   8830          7338    392010     68339       202938   3471   \n",
       "\n",
       "       day  \n",
       "year        \n",
       "2006    24  \n",
       "2007   536  \n",
       "2009   614  \n",
       "2011   686  \n",
       "2012  1505  \n",
       "2013  7616  \n",
       "2014  8195  \n",
       "2015  8388  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgs = dg.cumsum()\n",
    "dgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:35:15.469250Z",
     "start_time": "2018-05-08T07:35:15.463398Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def getDate(dat):\n",
    "    dat_date_str = list(map(lambda x: str(x) +'-01-01', dat.index))\n",
    "    dat_date = pd.to_datetime(dat_date_str)\n",
    "    return dat_date\n",
    "\n",
    "dg.date = getDate(dg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:35:18.713928Z",
     "start_time": "2018-05-08T07:35:18.090102Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "plt.plot(dg.date, dg.click, 'r-o', label = '$Yearly\\:Number\\:of\\:Clicks$')\n",
    "plt.plot(dg.date, dg.reply, 'g-s', label = '$Yearly\\:Number\\:of\\:Replies$')\n",
    "plt.plot(dg.date, dg.fans_num, 'b->', label = '$Yearly\\:Number\\:of\\:Fans$')\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend(loc=4,numpoints=1,fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:36:01.827873Z",
     "start_time": "2018-05-08T07:36:01.820166Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2006      1214\n",
       "2007     28290\n",
       "2009     18644\n",
       "2011      2889\n",
       "2012    463720\n",
       "2013     63140\n",
       "2014     57764\n",
       "2015     81164\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('year')['click'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:36:23.553834Z",
     "start_time": "2018-05-08T07:36:23.546230Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2006     1214.000000\n",
       "2007      832.058824\n",
       "2009     3107.333333\n",
       "2011      481.500000\n",
       "2012    14052.121212\n",
       "2013      207.697368\n",
       "2014      916.888889\n",
       "2015     4058.200000\n",
       "Name: click, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('year')['click'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 常用的统计分析方法\n",
    "- t检验\n",
    "- 卡方检验\n",
    "- 相关\n",
    "- 回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# T-test\n",
    "\n",
    "http://statsmodels.sourceforge.net/devel/stats.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# RQ: 转载的文章的点击量是否显著地小于原创的文章？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:39:46.552904Z",
     "start_time": "2018-05-08T07:39:46.548117Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "repost = []\n",
    "for i in df.title:\n",
    "    if u'转载' in i:\n",
    "        repost.append(1)\n",
    "    else:\n",
    "        repost.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:40:13.662551Z",
     "start_time": "2018-05-08T07:40:13.659170Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df['repost'] = repost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:40:43.514847Z",
     "start_time": "2018-05-08T07:40:43.504854Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repost</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        click  reply\n",
       "repost              \n",
       "0       263.0    1.5\n",
       "1        56.0    0.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('repost').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:41:55.262842Z",
     "start_time": "2018-05-08T07:41:55.255286Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    194675\n",
       "2     82779\n",
       "3     45304\n",
       "5     27026\n",
       "6     24026\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['click'][df['repost']==0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:41:59.999623Z",
     "start_time": "2018-05-08T07:41:59.991674Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     88244\n",
       "4     38132\n",
       "13     4990\n",
       "16     3720\n",
       "18     3421\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['click'][df['repost']==1][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:44:55.052738Z",
     "start_time": "2018-05-08T07:44:55.042984Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=56.577005918931135, pvalue=1.1032740874872203e-303)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.ttest_ind(np.log(df.click+1), df.repost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:45:46.929842Z",
     "start_time": "2018-05-08T07:45:46.924394Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56.577005918931143, 1.1032740874869695e-303, 932.0)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.ttest_ind(np.log(df.click+1), df.repost)\n",
    "# test statistic, pvalue and degrees of freedom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A chi-squared test\n",
    "\n",
    "https://en.wikipedia.org/wiki/Chi-squared_test\n",
    "\n",
    "- also referred to as χ² test (or chi-square test), is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true. \n",
    "- A chi-squared test can then be used to `reject the null hypothesis that the data are independent`.\n",
    "- Test statistics that follow a chi-squared distribution arise from an `assumption of independent normally distributed data`, which is valid in many cases due to `the central limit theorem`. \n",
    "- Chi-squared tests are often constructed from a `sum of squared errors`, or through the `sample variance`.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Suppose there is a city of 1 million residents with `four neighborhoods`: A, B, C, and D. \n",
    "\n",
    "- A random sample of 650 residents of the city is taken and their occupation is recorded as \"blue collar\", \"white collar\", or \"no collar\". \n",
    "\n",
    "- `The null hypothesis` is that each person's neighborhood of residence is independent of the person's occupational classification. The data are tabulated as:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "|               |  A           |    B          |C              | D  | Total|\n",
    "| -------------|:-------------:|:-------------:|:-------------:|-----:|-----:|\n",
    "| White collar| 90 | 60 | 104 |95 | 349|\n",
    "| Blue collar| 30 | 50 | 51 | 20| 151|\n",
    "| No coloar| 30 | 40 | 45 | 35|150|\n",
    "| Total | 150 | 150| 200| 150| 650|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let us take the sample living in neighborhood A, 150/650, to estimate what proportion of the whole 1 million people live in neighborhood A. \n",
    "- Similarly we take 349/650 to estimate what proportion of the 1 million people are white-collar workers. \n",
    "- By the assumption of independence under the hypothesis we should \"expect\" the number of `white-collar workers in neighborhood A` to be\n",
    "\n",
    "$\n",
    "\\frac{150}{650} \\frac{349}{650} 650 = 80.54\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Then in that \"cell\" of the table, we have\n",
    "\n",
    "$\\frac{(\\text{observed}-\\text{expected})^2}{\\text{expected}} = \\frac{(90-80.54)^2}{80.54}$.\n",
    "\n",
    "The sum of these quantities over all of the cells is the `test statistic`. \n",
    "\n",
    "Under the null hypothesis, it has approximately a chi-square distribution whose number of `degrees of freedom` are\n",
    "\n",
    "$ (\\text{number of rows}-1)(\\text{number of columns}-1) = (3-1)(4-1) = 6. $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If the test statistic is improbably large according to that chi-square distribution, then one rejects the null hypothesis of independence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./img/600px-Chi-square_distributionCDF-English.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# scipy.stats.chisquare(f_obs, f_exp=None, ddof=0, axis=0)[source]\n",
    "- Calculates a one-way chi square test.\n",
    "- The chi square test tests the null hypothesis that the categorical data has the given frequencies.\n",
    "\n",
    "Parameters:\t\n",
    "- f_obs : array_like Observed frequencies in each category.\n",
    "- f_exp : array_like, optional Expected frequencies in each category. By default the categories are assumed to be equally likely.\n",
    "- ddof : int, optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=3.5, pvalue=0.62338762774958223)"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chisquare\n",
    "chisquare([16, 18, 16, 14, 12, 12], \\\n",
    "          f_exp=[16, 16, 16, 16, 16, 8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/scipy_org_logo.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:55:25.036879Z",
     "start_time": "2018-05-08T07:55:25.030415Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.62338762775\n",
      "0.62338762775\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "# p_value = chi2.sf(chi_statistic, df)\n",
    "print(chi2.sf(3.5, 5))\n",
    "print(1 - chi2.cdf(3.5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T07:56:48.468473Z",
     "start_time": "2018-05-08T07:56:48.460657Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.77721397],\n",
       "       [ 0.77721397,  1.        ]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.corrcoef(data.click, data.reply)\n",
    "\n",
    "np.corrcoef(np.log(data.click+1), \\\n",
    "                  np.log(data.reply+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>click</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963966</td>\n",
       "      <td>0.143595</td>\n",
       "      <td>0.158116</td>\n",
       "      <td>0.097502</td>\n",
       "      <td>0.085615</td>\n",
       "      <td>0.038788</td>\n",
       "      <td>-0.024827</td>\n",
       "      <td>0.048361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reply</th>\n",
       "      <td>0.963966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199270</td>\n",
       "      <td>0.159387</td>\n",
       "      <td>0.090342</td>\n",
       "      <td>0.123341</td>\n",
       "      <td>0.040165</td>\n",
       "      <td>-0.041208</td>\n",
       "      <td>0.058738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>followed_num</th>\n",
       "      <td>0.143595</td>\n",
       "      <td>0.199270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407656</td>\n",
       "      <td>0.211677</td>\n",
       "      <td>0.499612</td>\n",
       "      <td>-0.036037</td>\n",
       "      <td>0.051187</td>\n",
       "      <td>-0.020604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fans_num</th>\n",
       "      <td>0.158116</td>\n",
       "      <td>0.159387</td>\n",
       "      <td>0.407656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341724</td>\n",
       "      <td>0.145387</td>\n",
       "      <td>-0.084243</td>\n",
       "      <td>0.102301</td>\n",
       "      <td>-0.045883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>post_num</th>\n",
       "      <td>0.097502</td>\n",
       "      <td>0.090342</td>\n",
       "      <td>0.211677</td>\n",
       "      <td>0.341724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.514695</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>-0.011786</td>\n",
       "      <td>-0.033254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comment_num</th>\n",
       "      <td>0.085615</td>\n",
       "      <td>0.123341</td>\n",
       "      <td>0.499612</td>\n",
       "      <td>0.145387</td>\n",
       "      <td>0.514695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.118703</td>\n",
       "      <td>0.069160</td>\n",
       "      <td>-0.119840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.038788</td>\n",
       "      <td>0.040165</td>\n",
       "      <td>-0.036037</td>\n",
       "      <td>-0.084243</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>-0.118703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.236920</td>\n",
       "      <td>0.535354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>-0.024827</td>\n",
       "      <td>-0.041208</td>\n",
       "      <td>0.051187</td>\n",
       "      <td>0.102301</td>\n",
       "      <td>-0.011786</td>\n",
       "      <td>0.069160</td>\n",
       "      <td>-0.236920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0.048361</td>\n",
       "      <td>0.058738</td>\n",
       "      <td>-0.020604</td>\n",
       "      <td>-0.045883</td>\n",
       "      <td>-0.033254</td>\n",
       "      <td>-0.119840</td>\n",
       "      <td>0.535354</td>\n",
       "      <td>-0.046699</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 click     reply  followed_num  fans_num  post_num  \\\n",
       "click         1.000000  0.963966      0.143595  0.158116  0.097502   \n",
       "reply         0.963966  1.000000      0.199270  0.159387  0.090342   \n",
       "followed_num  0.143595  0.199270      1.000000  0.407656  0.211677   \n",
       "fans_num      0.158116  0.159387      0.407656  1.000000  0.341724   \n",
       "post_num      0.097502  0.090342      0.211677  0.341724  1.000000   \n",
       "comment_num   0.085615  0.123341      0.499612  0.145387  0.514695   \n",
       "month         0.038788  0.040165     -0.036037 -0.084243 -0.070024   \n",
       "year         -0.024827 -0.041208      0.051187  0.102301 -0.011786   \n",
       "day           0.048361  0.058738     -0.020604 -0.045883 -0.033254   \n",
       "\n",
       "              comment_num     month      year       day  \n",
       "click            0.085615  0.038788 -0.024827  0.048361  \n",
       "reply            0.123341  0.040165 -0.041208  0.058738  \n",
       "followed_num     0.499612 -0.036037  0.051187 -0.020604  \n",
       "fans_num         0.145387 -0.084243  0.102301 -0.045883  \n",
       "post_num         0.514695 -0.070024 -0.011786 -0.033254  \n",
       "comment_num      1.000000 -0.118703  0.069160 -0.119840  \n",
       "month           -0.118703  1.000000 -0.236920  0.535354  \n",
       "year             0.069160 -0.236920  1.000000 -0.046699  \n",
       "day             -0.119840  0.535354 -0.046699  1.000000  "
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P4e6jRrn/4Aepe72pU93POSfEe8cd7l98kbrXFpGMiX53pvX3c16NaeBOxZFH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159a7350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(df.click, df.reply, 'r-o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l8cz/2P6Uvcx/Wu6VSLNLXZBx9zvN7JCc5GOAB939EQAzux44DRg0s/2AzwBL\nzWxVO9dwKpo1X6HkN/84aMDUTbuuuviqsmoCBZvY8i0HkxwAUMJyMSJSfakLMgW8BHg08foxQuDB\n3Z8Dzp7pBMn9ZFp187JiZs3XStY3/0SgG+kbCRt5QW1rAfEggePICizxCswx9b2IFFbNzcpizRJk\nKqZNy1pcPEggR3IFZhGZXu4X8HbatOxx4ODE6wOjtKJp++XCzWl3/ewuVq5aWdcO7dyO9biJLdm8\nBupoF2mEdth+2cjuyL8XODzqq3kSeDdwZiknbIeazIzrf+0kbx/F2O6xvCOpamlKx3oU/LKa1yB7\nTbJN2f0927ZvY3z3+MQy/7FkE1m5zWOa0S/trKW3Xzaz64AMsMDMfgOsdve1ZnYu8D0mhzA/0MBi\nplLvEb3ZD+g4fVPvtMeboXM8X22m0GCAajSRqfYkUh2pCzLu/p4C6euoYFpeOzSXVfLte2BwoKIm\ns+GHh+ke7Gb76HbGxsfAowPbYNZzs+jY2cHQPkMzXyOxARnHTS6g+ezIs7AD5u07D4Dto9vZvW03\ns++ePZEGsGtkF0Pzh6YsJ5PWZjfNx5E0aofmsqprh+aySh5KI3NHKmoy6zm0J/8iln0wvnyc8b5x\nhpcPs2HTDNeINyDrmyxXcgHNiZ+j12OMZaV1r+tm+JRhhsmpsaV0fovm40gaVbO5rKMK5REREcmr\nrWoyrd5cNpPFCxcz9IMhnt7yNOMd45MHdgG/V/+ysClnsUyY/BeZbDYTkbpSc1kZ2qG5rJBku3/v\nYb2MDo6GB3vO0vgzNdFM138wo2gdsaFdU/tLgKlliZvNakD9ICLTa+nRZVJ9hYYLlzqqrKL+g32A\n3WH1gWqUpRLqBxGpHwWZNlbNJVeS81iymuN2MdHs1b2tO8zlybMzZbIsQxuHYOfk0OuhjUOTo8vW\nTY4uYwcs2GfBRD6A4bnDTbWUv+bjSKtrmyCjPpmpqrnkSjH7xcQP/ykjv6pclmai5jlJo2r2yZi7\nz5yryZmZt+rnTPYv3DNwD7v27AKgY2fHxPyRXb/dxeh7pi6S2b2umzOWnTHxoFu5aiW33H1L1oKa\n8XyUzs5OxjrHJtdh2AHMAQ6AntEeTs2cOlGOKTtfRn0ts2+azR7fE2o5u8J74zzLDlw2JcjUqu+k\nUBBctmkZixcuVn+NSMTMcPeKtlFpm5pMqyq0+vF43/jE/JFZN87K+97cuTEbNm9g+MT8KwKMMZbd\nER/3oSyZ+vZSAAANoUlEQVSH0XWjBcuR7GsZ2zdxjj6yf57psyXVsO9E/TUi1aUg0wY6rIO91u01\nddOvKv32u+Z0FTw267lZjPeNT3u97h3dde2DmK4fpN5ruIm0urYJMu3cJzOvax5Llyyt2Tf03sN6\nCx7ba95e2bP081i6ZGldm6Kmu1be4dUibUbzZMrQzvNkRERKoWVlRESkKbRNTaYZxaOr4nkie3wP\nMDlyrMu76O7qZhnLgDyjy6I5JT1ze4qajzH88DCz/2v2xHUAxveMw/Mwe+5suIkwOmzPOGwPI086\nbuzgrp13sXv2buwOo7OzM2ul5G1bt02OKtszDg62IwxW6bwu5B3yyVUAkqO4qj2HZKbRaitXrWRo\n4xDdg91Zx7u8i8WZ0q6pVQVEAgWZFJsY6ZSzm2U8cmykb4TeQ3urNr+k59AehpZPXUl52abs4cXx\nEGDHGY/+A/KuihyvwpzkfQ7LYawv5B1hZHLuTCKoVPthPNPIsUKj65ZuKr3PSKPURAI1l4mISM20\nTZBZs2ZN1UZLiIi0sv7+/qoNlmqb5jKNLhMRKY5Gl4mISFNom5pMM0qubDxldNm6MLqsmjPlix3N\nFW9+lrvGWe6qyPFqyvyArLxd3gU/IGul5ULXqqaZPl81R7NpdWWRQAtkNlgpQ13zLWC5ddtWfLuH\n4cQvDBXTeNixdRre5RB/9NEw7Lizs5MF+yzg1MypeUdNJcuUDHDJRTe7vGvi/YU+w/DDw/Qc2lPU\nZytXtYYKa8ixyFRaILMFlDLUdboFLB3PHir8TfB9fMqilo4zxhjDDBdcp6uYRTdH+iYX1yz0GboH\nuxlaNHVIdDWH8VZrqLCGHIvUhvpkRESkZtomyGgIs4hIcTSEuQwawiwiUhwNYRYRkabQNjWZtCpl\nqGu+ocNZo8tuzBld9rzhNxQeXVZoOG2yTFNGl62bHF0209Df4bnDLN20tKjPVq5qDRXWkGOR2tAQ\nZhERyasaQ5jVXCYiIjWjICMiIjWjPpk6WZJZwvCOPBMpt8LSY/L3WyRnmsez/ZPLy0BYYiaevQ/k\nnam/53d7JlYDiPP3HtE70d+QOykzXg6m94j8S74k8w9tDH1EXd6VlV8z5UtfRUCrDkgrUpCpk+Ed\nw4ycMjIlfdaNs4qaaV5otv9433jW7P18M/WTG4fF+YcXDU9cY8r1F4X3DC/KuV6+/NF1RvpGsvNr\npnzJqwho1QFpRWouExGRmmmbIKMZ/yIixdGM/zJoxr+ISHE0419ERJpC29RkGq1nbg+sy3NgN0XN\nio9n+xcaXTaRP89M/T2/2zOxGsDE6LJNvVPeE5sYXVZoQ7FN2XknRpdt6p2at42VuoqAVh2QVqQZ\n/yIikpdm/IuISKopyIiISM0oyIiISM0oyIiISM0oyIiISM0oyIiISM0oyIiISM00fZAxs3lm9hUz\nu9LM3tPo8qSd1m+bpHsxSfdiku5FdbXCjP+3Aze4+7fN7HrgulpfsJJ9QoY2DrH5uc24O4wCcwEj\n/Lybyd/IWEi3DsPn+WQegK7o761AN7AdbI/R2d3J2O6xcCyZdzswDuwNPAGzDpoFwF6792LL/VtY\nuWol1956LaO7RyE5ZzVZpi6w3xkLFy6cdi+a+D7Ex+IVAQC2j25n7PmxcK4dhnd5uN7oZPn3+J6s\nPW+m29um0j1W+vv7yWQyJb+vFfd9KfdetCLdi+pKXU3GzK42s81m9ouc9JPNbNDMNpjZqsShA4FH\no5/H61HGDZs3sJ71rF+U/Sffgwfgnp/fM5Fn+MRh/HSHM4CDgXcRfp4P7BelHUwIHvuBd/pknvjY\nGcDRUZ7TQ5p3OmNvHwvH4vPFeaNzxT+Pv2Oc8XeMs61z28TnGX3baDjXGYk/ByfKdAb4Ps5w1/DE\nZ433P8m9F8ljwyeGfXRGThkJ5Zsfnesgn7zeweHcY28fY/wd44x1jU1cZ/jE4Ymfk9dJ3uvcb57T\nva7Gt9SJz53n91/K+YvJWyhPsem1vhfTaeS9KCatXvei1HO32r1IXZAB1gJvSiaYWQdweZT+cuBM\nM1sSHX6UEGggfN+vj4eLz7pleEsRmQqkbyvy+rn5thT4uRYeLiFvMWUplKfAdfRgLZzervei0Q/W\nmcpSaf5muhepXLvMzA4BbnH3I6PXrwNWu/sp0eu/AdzdLzazeYQANArc6e5fy3O+9H1IEZEmUOna\nZc3SJ/MSJpvEAB4DjgFw9+3An0335kpvkoiIlCeNzWUiItIimiXIPE7ofo4dGKWJiEiKpTXIGNmd\n+PcCh5vZIWY2B3g38K2GlExERIqWuiBjZtcBdwGLzew3ZvYBdx8HzgW+B/wKuN7dH2hkOUVEZGap\nHF0mIiKtIXU1mXoxs0Vm9mUz+0ajy9JoZnaamV1lZl8zs5MaXZ5GMrMlZnaFmX3dzM5qdHkaLVq2\n6V4ze3Ojy9JIZrbMzG6P/m0c3+jyNJIFf2dmXzSzP5kpf9sGGXff5O4fbHQ50sDdb3b3lcDZhDn4\nbcvdB939bEK/3xsbXZ4UWAV8vdGFSAEHngdeQJhC0c5OIwy+2kUR96JlgkwZy9G0rAruxQXAl+pT\nyvoo516Y2anAt4Hr61nWWiv1XpjZG4D7gaep52oadVDqvXD32939LcDfAH9b7/LWUhn/j/QCP3L3\njwEfnvEC7t4Sf4BjgaXALxJpHcBDwCHAbGAAWJLzvhsaXfY03AvgIuCERpc9Dfcike/mRpe/kfcC\n+Dvgc8CtwDcbXf40/LsA5gDfaHT5G/zv4r3AO6Ofr5/p/M0y439G7n5ntBxN0jHAg+7+CEC0SvNp\nwKCZ7Qd8BlhqZqvc/eL6lrh2yrgX5wInAvPN7HB3b86lhPMo414sI6zsPRfoq2tha6zUe+HuF0Rp\n7weeqWtha6yMfxdvI6yd2E1YxqpllHovgJuAy8zsOGD9TOdvmSBTwHTL0TxH6INoF9Pdi8uAyxpR\nqAaZ7l6sp4j/cVpIwXsRc/dr6lqixpnu38U3gW82olANMt29GAWK7s9umT4ZERFJn1YPMlqOZpLu\nxSTdi0m6F5N0LyZV7V60WpDRcjSTdC8m6V5M0r2YpHsxqWb3omWCjJajmaR7MUn3YpLuxSTdi0m1\nvhdaVkZERGqmZWoyIiKSPgoyIiJSMwoyIiJSMwoyIiJSMwoyIiJSMwoyIiJSMwoyIiJSMwoyIiJS\nMwoyIiUws5eZ2cXRVrzfi7ZpvtLMDo2Of8XMDjCza6ONoPbkLqNuZvuZ2YNm9tESr32cmf0oOueN\n1ftUIrXT6kv9i1SFme1N2MDrJOCTwAXuPhYd2x+40sxGgGPc/SngfWb2D8Dp8Z4cCXOBrcDPSymD\nu98R7XfzPHBHRR9IpE4UZERmYGaLCNsxPwcsdfctyePu/oyZfRJ4gOx9eY4nz9407v4E8Joyi3MM\nYXfGO8t8v0hdqblMZBpmti/wXcL/K2/ODTAxdx8ChoAfRu/bGziK6m+AlgF+B/ysyucVqQnVZESm\n90/A4cCJ7r51hrxPMbll8/GEwFTtILMM+LG776nyeUVqQkFGpAAzOwZ4F7De3fuLeMvliUCUAZ50\n94cS5zsCOAc4BLjW3b+e55qvBD4KjAA7gJ3AZ9x9l5l1Aq8HPhvlnQOsBmYB+7n7ypxzHQR8mlDz\n2RH9+Wy0fa5IXai5TKSwswAHri4ms7vfkHiZIVGLiQLEXxL26Pg+8Inc95vZGYSmuX90978i9AOd\nD7wpynIM8ELgdjObDfwf4PPAs8CKqGkvPtfc6DqXuPv5wL8AHyEMXBCpGwUZkcKWR3//sJQ3mdl8\nYCnZTWVvA27ysIHTW4H7c95zFHAN8HF3/2WUvA+hn+en0esMsAsYAD5FCCDPAPOBr7r7bxOnPAl4\nEfBo9HoLISDdVspnEamUNi0TKcDMtgO73X1+EXkPj5vGzOytwM3Ay6IBAXHT1WPAIuBB4E3u/v3E\n+28DXgos8gL/U5rZrVGe/yA0ez0zTXmOIwS5jcB3gG+4u0akSd2pJiNS2AhhPsu0zMyA8xJJGWBz\nHGAA3P3RKHh8EHgkJ8DsD5wAfGuaABP3x3wNuA/4rJm9tlCZ3P0OYBVhuPM5hCa2s2f6LCLVpiAj\nUtiPgReZWdcM+c4mPPxjGeB2ADNbEiea2SxgBfDl6PWi6NBhgDHZLJbPMcA8Qo3kG4S+m+9Hnf+Y\nWXfuG9z9Enc/GHgF8BNCsBGpKwUZkcL+kfD/yFmFMpjZG4D57n5X9PqFhP6Y/ihLsoZzMqGfZG3U\ncf/hKP2p6O/n85z/CDM7ljB0eau7x6sEjBGCzt7R688l3nOTmQ3Er939AeBaQtOZSF0pyIgUEPVh\nfAz4ezN7b9QsBkysP3YB8Cp3vyjxtg7CiLRfR0Og704cWwz8wt2fJNQq/i26zsPA9whzayaY2UlR\nvrsJtaNkn0rcyb/VzF5P9iCD1xBGlsXnOQB4D/C3pXx+kWpQx7/IDMzsdYShxIcBzxBqHI8BV7n7\nYJ78HyX0sTwYDR+O018MXEeYrT/g7l9NHJsPfJEweuy3wAsIky6vj47fDXwh8dqAK6N8v3H3TyfO\ndRyh5tNFGHk2D7jC3e+ryg0RKYGCjIiI1Iyay0REpGYUZEREpGYUZEREpGYUZEREpGYUZEREpGYU\nZEREpGYUZEREpGYUZEREpGYUZEREpGb+B6hyub1EfQuwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a58d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.click, df.reply, 'gs')\n",
    "plt.xlabel('$Clicks$', fontsize = 20)\n",
    "plt.ylabel('$Replies$', fontsize = 20)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.title('$Allowmetric\\,Law$', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T11:39:13.077083",
     "start_time": "2017-05-14T11:39:13.074074"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T11:39:55.606454",
     "start_time": "2017-05-14T11:39:47.158571"
    },
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "dat = sm.datasets.get_rdataset(\"Guerry\", \"HistData\").data\n",
    "# Fit regression model (using the natural log of one of the regressors)\n",
    "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', \\\n",
    "                  data=dat).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有些使用windows的同学无法运行上述代码：\n",
    "\n",
    "## 在spyder中打开terminal\n",
    "# 输入: pip install -U patsy\n",
    "\n",
    "https://groups.google.com/forum/#!topic/pystatsmodels/KcSzNqDxv-Q\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Lottery   R-squared:                       0.348\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "Method:                 Least Squares   F-statistic:                     22.20\n",
      "Date:                Sat, 16 Apr 2016   Prob (F-statistic):           1.90e-08\n",
      "Time:                        23:39:42   Log-Likelihood:                -379.82\n",
      "No. Observations:                  86   AIC:                             765.6\n",
      "Df Residuals:                      83   BIC:                             773.0\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         246.4341     35.233      6.995      0.000       176.358   316.510\n",
      "Literacy           -0.4889      0.128     -3.832      0.000        -0.743    -0.235\n",
      "np.log(Pop1831)   -31.3114      5.977     -5.239      0.000       -43.199   -19.424\n",
      "==============================================================================\n",
      "Omnibus:                        3.713   Durbin-Watson:                   2.019\n",
      "Prob(Omnibus):                  0.156   Jarque-Bera (JB):                3.394\n",
      "Skew:                          -0.487   Prob(JB):                        0.183\n",
      "Kurtosis:                       3.003   Cond. No.                         702.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Inspect the results\n",
    "print results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T11:44:02.130944",
     "start_time": "2017-05-14T11:44:02.119637"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "reg = smf.ols('reply ~ click + followed_num', \\\n",
    "              data=data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  reply   R-squared:                       0.933\n",
      "Model:                            OLS   Adj. R-squared:                  0.933\n",
      "Method:                 Least Squares   F-statistic:                     3231.\n",
      "Date:                Sun, 17 Apr 2016   Prob (F-statistic):          4.30e-273\n",
      "Time:                        02:04:27   Log-Likelihood:                -2354.7\n",
      "No. Observations:                 467   AIC:                             4715.\n",
      "Df Residuals:                     464   BIC:                             4728.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept       -1.4024      1.766     -0.794      0.428        -4.873     2.068\n",
      "click            0.0125      0.000     78.660      0.000         0.012     0.013\n",
      "followed_num     0.0749      0.015      5.117      0.000         0.046     0.104\n",
      "==============================================================================\n",
      "Omnibus:                      374.515   Durbin-Watson:                   1.938\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            97373.297\n",
      "Skew:                          -2.416   Prob(JB):                         0.00\n",
      "Kurtosis:                      73.575   Cond. No.                     1.14e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.14e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-14T11:06:31.541560",
     "start_time": "2017-05-14T11:06:31.508827"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      np.log(reply + 1)   R-squared:                       0.606\n",
      "Model:                            OLS   Adj. R-squared:                  0.603\n",
      "Method:                 Least Squares   F-statistic:                     236.9\n",
      "Date:                Sun, 14 May 2017   Prob (F-statistic):           4.03e-93\n",
      "Time:                        11:06:31   Log-Likelihood:                -596.73\n",
      "No. Observations:                 467   AIC:                             1201.\n",
      "Df Residuals:                     463   BIC:                             1218.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   -2.6009      0.189    -13.778      0.000        -2.972    -2.230\n",
      "np.log(click + 1)            0.6872      0.029     24.083      0.000         0.631     0.743\n",
      "np.log(followed_num + 1)     0.0118      0.034      0.347      0.729        -0.055     0.079\n",
      "month                        0.0172      0.013      1.275      0.203        -0.009     0.044\n",
      "==============================================================================\n",
      "Omnibus:                       26.408   Durbin-Watson:                   1.904\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               44.572\n",
      "Skew:                          -0.389   Prob(JB):                     2.10e-10\n",
      "Kurtosis:                       4.299   Cond. No.                         44.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg1 = smf.ols('np.log(reply+1) ~ np.log(click+1) \\\n",
    "+np.log(followed_num+1)+month', data=data).fit()\n",
    "print reg1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RfAMs2CHkgd5fn8IbTg9f99cGmNGguPjhhx/KJZdcIsccc4w8/PDDsn///ga3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2AAAg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\nAAqbO4Qq90AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c52ae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "fig = sm.graphics.plot_partregress_grid(reg1, fig = fig)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "moore = sm.datasets.get_rdataset(\"Moore\", \"car\",\n",
    "                                 cache=True) # load data\n",
    "data = moore.data\n",
    "data = data.rename(columns={\"partner.status\" :\n",
    "                             \"partner_status\"}) # make name pythonic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>partner_status</th>\n",
       "      <th>conformity</th>\n",
       "      <th>fcategory</th>\n",
       "      <th>fscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>low</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>high</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>low</td>\n",
       "      <td>7</td>\n",
       "      <td>low</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low</td>\n",
       "      <td>10</td>\n",
       "      <td>low</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  partner_status  conformity fcategory  fscore\n",
       "0            low           8       low      37\n",
       "1            low           4      high      57\n",
       "2            low           8      high      65\n",
       "3            low           7       low      20\n",
       "4            low          10       low      36"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# 参考文献\n",
    "- scipy-lectures \n",
    "    - http://www.scipy-lectures.org/index.html\n",
    "    - http://www.scipy-lectures.org/packages/statistics/index.html"
   ]
  }
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