Overview.ipynb

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    "pandas库的优点：\n",
    "* 含有三个好用的数据结构：Series、DataFrame、Panel\n",
    "* 支持普通的坐标index和层次化index\n",
    "* 含有大量方法，方便地处理数据\n",
    "* 含有方便的I/O读写数据方法，支持csv、excel、HDFS等多种常用数据格式\n",
    "* 内存占用少"
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    "  \n",
    "\n"
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    "## 数据结构"
   ]
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    "![](https://c1.staticflickr.com/3/2864/33450400012_13d1327ef2.jpg)"
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   "source": [
    "### 为什么要有多个数据结构？"
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    "pandas内置了三个数据结构，一维的Series，二维的DataFrame，三维的Panel。你可以把DataFrame看做容器，容器元素是Series，同样，你可以把Panel看做容器，容器元素是DataFrame。\n",
    "\n",
    "\n",
    "我们可以对容器进行插入、删除操作，操作方式和python中的字典很相似。"
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    "最常用的数据结构是DataFrame,你完全可以把它看做一个数据库表，DataFrame的index就是每一行的id，column就是数据库表的列名。这样理解的好处是可以避免用用axis=0，axis=1来操作数据，更加直观。\n",
    "\n",
    "    \n",
    "\n"
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    "for col in df.columns:\n",
    "    series = df[col]\n",
    "    # do something with series"
   ]
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   "source": [
    "## 可变性与数据复制"
   ]
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    "Python的数据类型可以分为可变类型和不可变类型。比如，string、tuple就是不可变类型，list、dictionary是可变类型。\n",
    "\n",
    "所有的pandas数据结构都是值可变的，你可以修改他们的值，但不是所有的数据结构都是大小可变的。\n",
    "\n",
    "Series的长度就不可变，一旦创建，长度就不能修改。\n",
    "\n",
    "和Series不同，DataFrame可以增加、删除列。\n",
    "\n",
    "pandas中的大多数方法不会直接在传入的参数上进行操作，而是返回一个新的结果对象。"
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