6.13小节完成，第6章完成。噢耶！

README.rst

@@ -50,12 +50,13 @@
 
 -----------------------------------------------------
 
-++++++++++++++++
++++++++++++++++++++
 How to Contribute
-++++++++++++++++
++++++++++++++++++++
 
 You are welcome to contribute to mango-test as follow
 
+* fork project and commit pull requests
 * add/edit wiki
 * report/fix issue
 * code review
@@ -74,7 +75,7 @@ License
 
 (The Apache License)
 
-Copyright (c) 2013-2014 [WinHong, Inc.](http://www.winhong.com/) and other contributors
+Copyright (c) 2010-2014 [WinHong, Inc.](http://www.winhong.com/) and other contributors
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
 

source/c06/p13_summarizing_and_perform_statistics.rst

@@ -5,14 +5,119 @@
 ----------
 问题
 ----------
-todo...
+你需要处理一个很大的数据集并需要计算数据总和或其他统计量。
+
+|
 
 ----------
 解决方案
 ----------
-todo...
+对于任何涉及到统计、时间序列以及其他相关技术的数据分析问题，都可以考虑使用 `Pandas库 <http://pandas.pydata.org/>`_ 。
+
+为了让你先体验下，下面是一个使用Pandas来分析芝加哥城市的
+`老鼠和啮齿类动物数据库 <https://data.cityofchicago.org/Service-Requests/311-Service-Requests-Rodent-Baiting/97t6-zrhs>`_ 的例子。
+在我写这篇文章的时候，这个数据库是一个拥有大概74,000行数据的CSV文件。
+
+.. code-block:: python
+
+    >>> import pandas
+
+    >>> # Read a CSV file, skipping last line
+    >>> rats = pandas.read_csv('rats.csv', skip_footer=1)
+    >>> rats
+    <class 'pandas.core.frame.DataFrame'>
+    Int64Index: 74055 entries, 0 to 74054
+    Data columns:
+    Creation Date 74055 non-null values
+    Status 74055 non-null values
+    Completion Date 72154 non-null values
+    Service Request Number 74055 non-null values
+    Type of Service Request 74055 non-null values
+    Number of Premises Baited 65804 non-null values
+    Number of Premises with Garbage 65600 non-null values
+    Number of Premises with Rats 65752 non-null values
+    Current Activity 66041 non-null values
+    Most Recent Action 66023 non-null values
+    Street Address 74055 non-null values
+    ZIP Code 73584 non-null values
+    X Coordinate 74043 non-null values
+    Y Coordinate 74043 non-null values
+    Ward 74044 non-null values
+    Police District 74044 non-null values
+    Community Area 74044 non-null values
+    Latitude 74043 non-null values
+    Longitude 74043 non-null values
+    Location 74043 non-null values
+    dtypes: float64(11), object(9)
+
+    >>> # Investigate range of values for a certain field
+    >>> rats['Current Activity'].unique()
+    array([nan, Dispatch Crew, Request Sanitation Inspector], dtype=object)
+    >>> # Filter the data
+    >>> crew_dispatched = rats[rats['Current Activity'] == 'Dispatch Crew']
+    >>> len(crew_dispatched)
+    65676
+    >>>
+
+    >>> # Find 10 most rat-infested ZIP codes in Chicago
+    >>> crew_dispatched['ZIP Code'].value_counts()[:10]
+    60647 3837
+    60618 3530
+    60614 3284
+    60629 3251
+    60636 2801
+    60657 2465
+    60641 2238
+    60609 2206
+    60651 2152
+    60632 2071
+    >>>
+
+    >>> # Group by completion date
+    >>> dates = crew_dispatched.groupby('Completion Date')
+    <pandas.core.groupby.DataFrameGroupBy object at 0x10d0a2a10>
+    >>> len(dates)
+    472
+    >>>
+
+    >>> # Determine counts on each day
+    >>> date_counts = dates.size()
+    >>> date_counts[0:10]
+    Completion Date
+    01/03/2011 4
+    01/03/2012 125
+    01/04/2011 54
+    01/04/2012 38
+    01/05/2011 78
+    01/05/2012 100
+    01/06/2011 100
+    01/06/2012 58
+    01/07/2011 1
+    01/09/2012 12
+    >>>
+
+    >>> # Sort the counts
+    >>> date_counts.sort()
+    >>> date_counts[-10:]
+    Completion Date
+    10/12/2012 313
+    10/21/2011 314
+    09/20/2011 316
+    10/26/2011 319
+    02/22/2011 325
+    10/26/2012 333
+    03/17/2011 336
+    10/13/2011 378
+    10/14/2011 391
+    10/07/2011 457
+    >>>
+嗯，看样子2011年10月7日对老鼠们来说是个很忙碌的日子啊！^_^
+
+|
 
 ----------
 讨论
 ----------
-todo...
+Pandas是一个拥有很多特性的大型函数库，我在这里不可能介绍完。
+但是只要你需要去分析大型数据集合、对数据分组、计算各种统计量或其他类似任务的话，这个函数库真的值得你去看一看。
+