Manipulation/pandas 1.ipynb

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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Table of Contents\n",
    "\n",
    "1.[introduction to panda data structure](#intro)\n",
    "\n",
    "   [series](#series)\n",
    "   \n",
    "   [DataFrame & python data structure](#DataFrame)\n",
    "   \n",
    "   [Index Object](#IndexObject)\n",
    "   \n",
    " 2.[Essential Functions](#Functions)\n",
    "   \n",
    "   [reindexing](#reindexing)\n",
    "   \n",
    "   [indexing, selection, filtering](#indexing)\n",
    "   \n",
    "   [ix](#ix)\n",
    "   \n",
    "   [sorting and ranking](#sorting)\n",
    "   \n",
    "   "
   ]
  },
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   "source": [
    "## intro\n",
    "## series\n",
    "A Series is a one-dimensional array-like object containing an array of data (of any NumPy data type) and an associated array of data labels, called its index\n",
    "\n",
    "一个前面有index的数组。"
   ]
  },
  {
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   "execution_count": 2,
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import Series, DataFrame\n",
    "obj = Series([1,2,3,4])\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "692605c1-4151-45b4-94d6-bb3378cde32a",
    "_uuid": "f8bdddba79bf69a28a4bafbd0dba4ced5765f8f5"
   },
   "source": [
    "custom index!!\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 = Series([2,4,6,7],index = ['a','b','c','d'])\n",
    "obj2\n",
    "obj2['a']"
   ]
  },
  {
   "cell_type": "markdown",
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    "_uuid": "b72e5f11056d247218ea869ed4df4af24bdf2841",
    "collapsed": true
   },
   "source": [
    "使用boolean方法筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "_uuid": "9db002bcf883751f6dc4335ad43c927d519258d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    6\n",
       "d    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[obj2 >4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       7.389056\n",
       "b      54.598150\n",
       "c     403.428793\n",
       "d    1096.633158\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.exp(obj2)"
   ]
  },
  {
   "cell_type": "markdown",
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    "_uuid": "127767b794c1b4cc2f771230646659dc9b343dc4",
    "collapsed": true
   },
   "source": [
    "某种程度上series can be considered as a fixed-length, ordered dict。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "_uuid": "02f506e0111857969aa3d86054be9f2b5bba14ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'b' in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "35125ec9-7619-40d0-8c98-a1df7bb09a5c",
    "_uuid": "9cf839bebfb24d58420b6cfed3be1c2b318abc6c"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'r' in obj2"
   ]
  },
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   },
   "source": [
    "所以这样一看，我们可以通过字典来构建series!!\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Texas     71000\n",
       "Oregon    16000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}\n",
    "obj3 = Series(sdata)\n",
    "obj3"
   ]
  },
  {
   "cell_type": "markdown",
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    "_cell_guid": "a492af32-8645-4e5f-8890-82e4b335c126",
    "_uuid": "21dcae4ff3291d3272eae2d57ecf18e2d329e9d1"
   },
   "source": [
    "如下，还可以传入index，但是如果没有对应的value，就会是nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    "_uuid": "44ec82b18df4eeb32692f03f9d9719ca58ecb323"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['California', 'Ohio', 'Oregon', 'Texas']\n",
    "obj4 = Series(sdata, index=states)\n",
    "obj4"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "index可以修改：\n",
    "=="
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob      1\n",
       "Steve    2\n",
       "Jeff     3\n",
       "Ryan     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']\n",
    "obj"
   ]
  },
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   },
   "source": [
    "pd.isnull\n",
    "===\n",
    "去检查是不是有null。"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    "_uuid": "31cb961042afab24e2e7f23b9d13b897d33b0d28"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    False\n",
       "Ohio           True\n",
       "Oregon         True\n",
       "Texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "33b86120-391f-43f5-aca4-da55e6f9e535",
    "_uuid": "a9fa010d7e3f31324f6c42dc37bb410db019a6f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "30418dc1-9431-418a-ae4e-64c24127f595",
    "_uuid": "58ede20fc82b80c0803c9b2c372378437c7ddc15"
   },
   "source": [
    "一个非常重要的特点是，series之间的加法能够加对应的值！\n",
    "===\n",
    "A critical Series feature for many applications is that it automatically aligns differently- indexed data in arithmetic operations:\n",
    "===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "fc1fd779-22ca-4ff6-a26c-efc5874d2b3a",
    "_uuid": "b7e1d91c44833487b9ef45fb6ce645558f70e698"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California         NaN\n",
       "Ohio           70000.0\n",
       "Oregon         32000.0\n",
       "Texas         142000.0\n",
       "Utah               NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 + obj4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2b5bb24c-11ad-422e-88ff-a66755c28cbb",
    "_uuid": "a9eb71057897e78869a5095a57d5d31a7468e471"
   },
   "source": [
    "series的对象和index都有一个name的attribute,就像行的名字，列的名字，表格名字一样的感觉。可以和pandas的一些功能一起使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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    "_uuid": "f642f505dbf35bebf07942815d9647d1fc7e39a9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj4.name = 'population'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "e8ebb074-72c0-46aa-9ff7-ad8eda11523b",
    "_uuid": "e0561106a183e1ae585adbc51372fe01d8573da0",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj4.index.name = 'state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "fcc5c697-9379-49a7-975f-cb22b2a500c0",
    "_uuid": "8f4f80d9e59cd95139850030464f1185684bd004"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
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   },
   "source": [
    "## DataFrame\n",
    "想一个dict of series which use the same index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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   },
   "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 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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002],'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}\n",
    "frame = DataFrame(data)\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "08f732278d7152e771d6a5124948958a5ce49775"
   },
   "source": [
    "## 1 输入dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "d892f716a7407ccb8527299a1c9142cc437ab4cd",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = { 'state': ['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada'],\n",
    "         'year': [2000, 2001, 2002, 2001, 2002],\n",
    "         'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "1461f223e123ec42270cbc5f203b24e2962afff6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pop': [1.5, 1.7, 3.6, 2.4, 2.9],\n",
       " 'state': ['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada'],\n",
       " 'year': [2000, 2001, 2002, 2001, 2002]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "5cc621101463b8b957663855895d7c5968e0618d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state    [Ohino, Ohino, Ohino, Nevada, Nevada]\n",
       "year            [2000, 2001, 2002, 2001, 2002]\n",
       "pop                  [1.5, 1.7, 3.6, 2.4, 2.9]\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dict to sereis\n",
    "#不指定index，则data的key作为index\n",
    "s = pd.Series(data)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "abb6159613ebfdbe944fa5b0305dc95195c68a63"
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohino</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohino</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohino</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0   Ohino  2000  1.5\n",
       "1   Ohino  2001  1.7\n",
       "2   Ohino  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dict to dataframe\n",
    "#不指定column,key作为column\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "13e7a1e8038d575d38a77bcc37c1fe55ed5205fb"
   },
   "source": [
    "## 2 输入list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "00214690d14c463364e1d103172e5838b1a9d52b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2000, 'Ohino', 1.5],\n",
       " [2001, 'Ohino', 1.7],\n",
       " [2002, 'Ohino', 3.6],\n",
       " [2001, 'Nevada', 2.4],\n",
       " [2002, 'Nevada', 2.9]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[2000, 'Ohino', 1.5],\n",
    "        [2001, 'Ohino', 1.7],\n",
    "        [2002, 'Ohino', 3.6],\n",
    "        [2001, 'Nevada', 2.4],\n",
    "        [2002, 'Nevada', 2.9]]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "29978a246bdf15aefdd2b2e1911c7b7e4fb0fc15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     [2000, Ohino, 1.5]\n",
      "1     [2001, Ohino, 1.7]\n",
      "2     [2002, Ohino, 3.6]\n",
      "3    [2001, Nevada, 2.4]\n",
      "4    [2002, Nevada, 2.9]\n",
      "dtype: object\n",
      "one      [2000, Ohino, 1.5]\n",
      "two      [2001, Ohino, 1.7]\n",
      "tree     [2002, Ohino, 3.6]\n",
      "four    [2001, Nevada, 2.4]\n",
      "five    [2002, Nevada, 2.9]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#list to series\n",
    "l = pd.Series(data)\n",
    "print(l)\n",
    "l = pd.Series(data, index = ['one','two','tree','four','five'])\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "b304bd6363bfa1f5547f04e97a2d3ae1b7e879da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0       1    2\n",
      "0  2000   Ohino  1.5\n",
      "1  2001   Ohino  1.7\n",
      "2  2002   Ohino  3.6\n",
      "3  2001  Nevada  2.4\n",
      "4  2002  Nevada  2.9\n",
      "   year   state  population\n",
      "0  2000   Ohino         1.5\n",
      "1  2001   Ohino         1.7\n",
      "2  2002   Ohino         3.6\n",
      "3  2001  Nevada         2.4\n",
      "4  2002  Nevada         2.9\n"
     ]
    }
   ],
   "source": [
    "#list to dataframe\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n",
    "df = pd.DataFrame(data, columns = ['year','state','population'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba0154c80b31bdff1f92e97b08c2a15e6c1508e"
   },
   "source": [
    "## 3 输入array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "f693b16eaa6a2f8d318ae14537aa8f762e4c89e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['2000', 'Ohino', '1.5'],\n",
       "       ['2001', 'Ohino', '1.7'],\n",
       "       ['2002', 'Ohino', '3.6'],\n",
       "       ['2001', 'Nevada', '2.4'],\n",
       "       ['2002', 'Nevada', '2.9']], dtype='<U21')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[2000, 'Ohino', 1.5],\n",
    "        [2001, 'Ohino', 1.7],\n",
    "        [2002, 'Ohino', 3.6],\n",
    "        [2001, 'Nevada', 2.4],\n",
    "        [2002, 'Nevada', 2.9]]\n",
    "data = np.array(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "3f7c8ff06a4c6a86960dd76924ef78080d2f4b91"
   },
   "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 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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state population\n",
       "0  2000   Ohino        1.5\n",
       "1  2001   Ohino        1.7\n",
       "2  2002   Ohino        3.6\n",
       "3  2001  Nevada        2.4\n",
       "4  2002  Nevada        2.9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data,columns = ['year','state','population'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1aa035aede072593ec4108c40d607e29a2f9e5bf"
   },
   "source": [
    "## 4 输入series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "a6b5b829f1db70830394e6d425d0d9dea2b5db55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state    [Ohino, Ohino, Ohino, Nevada, Nevada]\n",
       "year            [2000, 2001, 2002, 2001, 2002]\n",
       "pop                  [1.5, 1.7, 3.6, 2.4, 2.9]\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "86847fd9fb4cc4e61725ab80fedadd513e000b10",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sereis to dict\n",
    "dic = s.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "68babc85555ad9f23b7375ba0765fd275fa0215b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pop': [1.5, 1.7, 3.6, 2.4, 2.9],\n",
       " 'state': ['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada'],\n",
       " 'year': [2000, 2001, 2002, 2001, 2002]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "966f02820c067a79dad8c0fc6851af6c00d47b01"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([list(['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada']),\n",
       "       list([2000, 2001, 2002, 2001, 2002]),\n",
       "       list([1.5, 1.7, 3.6, 2.4, 2.9])], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## sereis to array\n",
    "foo = s.as_matrix()\n",
    "foo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "2b1e76f3743b14f6394bfd8fd577c82ce250ef47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9932ef27d899d02a64644777cb3cb980024ccef6"
   },
   "source": [
    "## 5 dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "639de8040996e73097018a87b854b149b6bf3f4f"
   },
   "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 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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state population\n",
       "0  2000   Ohino        1.5\n",
       "1  2001   Ohino        1.7\n",
       "2  2002   Ohino        3.6\n",
       "3  2001  Nevada        2.4\n",
       "4  2002  Nevada        2.9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "101b08977492263210c05af3843a9f322706b84a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['2000' 'Ohino' '1.5']\n",
      " ['2001' 'Ohino' '1.7']\n",
      " ['2002' 'Ohino' '3.6']\n",
      " ['2001' 'Nevada' '2.4']\n",
      " ['2002' 'Nevada' '2.9']] \n",
      "\n",
      " [['2000' 'Ohino' '1.5']\n",
      " ['2001' 'Ohino' '1.7']\n",
      " ['2002' 'Ohino' '3.6']\n",
      " ['2001' 'Nevada' '2.4']\n",
      " ['2002' 'Nevada' '2.9']] \n",
      "\n",
      " [['2000' 'Ohino' '1.5']\n",
      " ['2001' 'Ohino' '1.7']\n",
      " ['2002' 'Ohino' '3.6']\n",
      " ['2001' 'Nevada' '2.4']\n",
      " ['2002' 'Nevada' '2.9']] \n",
      "\n",
      " [['2000' 'Ohino' '1.5']\n",
      " ['2001' 'Ohino' '1.7']\n",
      " ['2002' 'Ohino' '3.6']\n",
      " ['2001' 'Nevada' '2.4']\n",
      " ['2002' 'Nevada' '2.9']]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n",
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "## df to array\n",
    "foo1 = DataFrame.as_matrix(df)\n",
    "foo2 = df.as_matrix()\n",
    "foo3 = df.values\n",
    "foo4 = np.array(df)\n",
    "print(foo1,'\\n\\n',foo2,'\\n\\n',foo3,'\\n\\n',foo4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "2814124c40aa331f5afd72b8608c329973facdce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'year': {0: '2000', 1: '2001', 2: '2002', 3: '2001', 4: '2002'}, 'state': {0: 'Ohino', 1: 'Ohino', 2: 'Ohino', 3: 'Nevada', 4: 'Nevada'}, 'population': {0: '1.5', 1: '1.7', 2: '3.6', 3: '2.4', 4: '2.9'}} \n",
      "\n",
      "{0: '2000', 1: '2001', 2: '2002', 3: '2001', 4: '2002'}\n"
     ]
    }
   ],
   "source": [
    "## df to dictionary\n",
    "df1 = df.to_dict(orient='dict')\n",
    "print(df1,'\\n')\n",
    "print(df1['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "b20d14543c936ada6bee344204bd7ce6a385e491"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'year': ['2000', '2001', '2002', '2001', '2002'], 'state': ['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada'], 'population': ['1.5', '1.7', '3.6', '2.4', '2.9']} \n",
      "\n",
      "['2000', '2001', '2002', '2001', '2002']\n"
     ]
    }
   ],
   "source": [
    "df2 = df.to_dict(orient = \"list\")\n",
    "print(df2,'\\n')\n",
    "\n",
    "print(df2['year'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "c2766afe954b39afddb9c0cecf91b20a5d237646"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'year': 0    2000\n",
      "1    2001\n",
      "2    2002\n",
      "3    2001\n",
      "4    2002\n",
      "Name: year, dtype: object, 'state': 0     Ohino\n",
      "1     Ohino\n",
      "2     Ohino\n",
      "3    Nevada\n",
      "4    Nevada\n",
      "Name: state, dtype: object, 'population': 0    1.5\n",
      "1    1.7\n",
      "2    3.6\n",
      "3    2.4\n",
      "4    2.9\n",
      "Name: population, dtype: object} \n",
      "\n",
      "0    2000\n",
      "1    2001\n",
      "2    2002\n",
      "3    2001\n",
      "4    2002\n",
      "Name: year, dtype: object\n"
     ]
    }
   ],
   "source": [
    "df3 = df.to_dict(orient = \"series\")\n",
    "print(df3,'\\n')\n",
    "print(df3['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "78ed1d1d47e79b61da90fac97aae4ab9147082ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'year': '2000', 'state': 'Ohino', 'population': '1.5'}, {'year': '2001', 'state': 'Ohino', 'population': '1.7'}, {'year': '2002', 'state': 'Ohino', 'population': '3.6'}, {'year': '2001', 'state': 'Nevada', 'population': '2.4'}, {'year': '2002', 'state': 'Nevada', 'population': '2.9'}] \n",
      "\n",
      "{'year': '2000', 'state': 'Ohino', 'population': '1.5'}\n"
     ]
    }
   ],
   "source": [
    "df4 = df.to_dict(orient='records')\n",
    "print(df4,'\\n')\n",
    "print(df4[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "72b651fa92befa572e6ddd604173d52633afd7b1",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "84be61a8912d75aef7de21a0fc875ec6ebac5037",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "5acf683074f6562ea21d1b20edb26ac93c967cc4",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "ac27868e3ed5e946bd817b702ba34fdcf06fe832",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e0220cf7-333d-42b5-a6d8-f0dcce22ceaf",
    "_uuid": "475a793d2451d0df1212e11a8bb9e09dc78a0e54"
   },
   "source": [
    "Pass column names\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "1b6ae9e9-7682-4286-8e4d-7120b76e7ec9",
    "_uuid": "99f6f78f12f5e5f4224e96b8a1cee487be074e77"
   },
   "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 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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop\n",
       "0  2000   Ohino  1.5\n",
       "1  2001   Ohino  1.7\n",
       "2  2002   Ohino  3.6\n",
       "3  2001  Nevada  2.4\n",
       "4  2002  Nevada  2.9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame1 = DataFrame(data, columns = ['year','state','pop'])\n",
    "frame1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c4c06919-1b72-4556-a06d-a4ccd8230d05",
    "_uuid": "eadcb82701a76c0dc26a9bf17e1fbf7a57904ec9"
   },
   "source": [
    "pass row names -- index\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "a4ec9a87-5de6-4200-b5af-6333d085e5b3",
    "_uuid": "cd016d9e2e392a8d04d2064765aa53ad28540365"
   },
   "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 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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop\n",
       "one    2000   Ohino  1.5\n",
       "two    2001   Ohino  1.7\n",
       "three  2002   Ohino  3.6\n",
       "four   2001  Nevada  2.4\n",
       "five   2002  Nevada  2.9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = DataFrame(data, columns = ['year','state','pop'], index = ['one', 'two', 'three', 'four', 'five'])\n",
    "frame2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "3a6a6728-8078-41da-9fb2-fddc16ea9509",
    "_uuid": "ff3b5e462492a20bdfae46d61192ebb850bd7a5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop'], dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "eb69b7f3-5e9e-4b96-8146-63337032bbe6",
    "_uuid": "c79d77ce4da069d8ae05fb882191663cee184ad1"
   },
   "source": [
    "访问每一列元素，既可以用 “.”，也可以用[]\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "15655d17-9be0-4c14-b8ab-8ffe988050c3",
    "_uuid": "750e6bfbaa0b198a8e6012ce0f71b5811ecfc9f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one       Ohino\n",
       "two       Ohino\n",
       "three     Ohino\n",
       "four     Nevada\n",
       "five     Nevada\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "740db756-070a-4eeb-958e-37a6ebd47c9d",
    "_uuid": "674cacf7e8abd15860b804bbc8d6c2774f1c4023"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      2000\n",
       "two      2001\n",
       "three    2002\n",
       "four     2001\n",
       "five     2002\n",
       "Name: year, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b7750a5b-fa8f-4ef7-ac83-db169612cd47",
    "_uuid": "c72bcfcfa8005c1861e222ccc49f32d9cd1b7d3c"
   },
   "source": [
    "## frame.ix\n",
    "可以用ix去访问特定index的行。\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "0490ee1a-35a2-4cdf-a67a-73df84f39033",
    "_uuid": "01c0ce5116a868e31179df85535181c854be6493"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "year       2001\n",
       "state    Nevada\n",
       "pop         2.4\n",
       "Name: four, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.ix['four']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ba7fbefd-2f3b-439e-b081-050c12390ca6",
    "_uuid": "2dcae36894453c781241c601e27677804193020c"
   },
   "source": [
    "增加列\n",
    "===\n",
    "[]法\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "fc03b53d-1051-4b6d-82ae-edf7124ca1f1",
    "_uuid": "e5596fc4bd49cebdab0fc35368c14796e9a1b086"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
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       "      <td>16.5</td>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000   Ohino  1.5  16.5\n",
       "two    2001   Ohino  1.7  16.5\n",
       "three  2002   Ohino  3.6  16.5\n",
       "four   2001  Nevada  2.4  16.5\n",
       "five   2002  Nevada  2.9  16.5"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['debt'] = 16.5\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "3d353f3a-5b4f-49f0-b06e-8d95b3d1013e",
    "_uuid": "d767d09118271a9f0fbd96584a04487cd28741ac",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 重新赋值\n",
    "frame2.debt = np.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "e5b3b581-1795-454e-9a2f-09e45df65f97",
    "_uuid": "9785a3b1ca30d41ff89f7da0b0a7f1926b9d8d6d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>state</th>\n",
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       "      <th>debt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
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       "      <td>3</td>\n",
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       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000   Ohino  1.5     0\n",
       "two    2001   Ohino  1.7     1\n",
       "three  2002   Ohino  3.6     2\n",
       "four   2001  Nevada  2.4     3\n",
       "five   2002  Nevada  2.9     4"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a9d7577f-e998-4dd5-a8c6-896d953ef8f1",
    "_uuid": "af3428cc085e596e644938e62043a236550e4455"
   },
   "source": [
    "直接赋值series。但series要注意，长度要和dataframe一样，否则会有nan。\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "2b91ae20-fb0f-4758-9e1e-c00fd2e142b8",
    "_uuid": "663961a05b4dec1c259b35f66116179c74f97e9c"
   },
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000   Ohino  1.5   NaN\n",
       "two    2001   Ohino  1.7  -1.2\n",
       "three  2002   Ohino  3.6  -1.5\n",
       "four   2001  Nevada  2.4   NaN\n",
       "five   2002  Nevada  2.9  -1.7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = Series([-1.2, -1.5, -1.7], index = ['two','three','five'])\n",
    "frame2.debt = val\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "159e7848-2afd-4545-bfa7-22946c829010",
    "_uuid": "f91c90ceb7aadf04af5f9062542f91bf2bae0f45"
   },
   "source": [
    "根据已有数据赋值\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "c6d7bda3-942f-41d6-ab03-fffb70e132ac",
    "_uuid": "4000078b547327a2f090a040bce36cb875cd8fd8"
   },
   "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 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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "      <th>eastern</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt  eastern\n",
       "one    2000   Ohino  1.5   NaN    False\n",
       "two    2001   Ohino  1.7  -1.2    False\n",
       "three  2002   Ohino  3.6  -1.5    False\n",
       "four   2001  Nevada  2.4   NaN    False\n",
       "five   2002  Nevada  2.9  -1.7    False"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['eastern'] = frame2.state == 'Ohio'\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "46a39c05-d198-4906-8ecb-01c9bacddece",
    "_uuid": "f9e26701141907eb3413944f421b3cdc49c74a80"
   },
   "source": [
    "删除列\n",
    "===\n",
    "del\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "b5d3fb9b-c0ff-4d58-a1b9-aa3d276c36c5",
    "_uuid": "fe8d3228f13016a48bba33c7d094dc3c25ba0a42",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del frame2['eastern']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "80558de0-8aba-4fbe-8f8a-fc4d415809e1",
    "_uuid": "b75d977378afcdb3515f2fc4c83df1db1aa31f2e"
   },
   "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 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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohino</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000   Ohino  1.5   NaN\n",
       "two    2001   Ohino  1.7  -1.2\n",
       "three  2002   Ohino  3.6  -1.5\n",
       "four   2001  Nevada  2.4   NaN\n",
       "five   2002  Nevada  2.9  -1.7"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "78547828-8a78-4c49-a090-8a058604291a",
    "_uuid": "f647b02cfa507066cef13a9582b78b1924fc4b4e"
   },
   "source": [
    "还可以传入一个nested dict的形式去生成dataframe\n",
    "==\n",
    "外面的key看做是columns, inner key as row indices\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "d4cf2574-2e06-4821-8003-aa97e03c40f6",
    "_uuid": "1466562e2ac9a587a0dbd3966a5dc6e5ef35d787",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop = {'Nevada': {2001: 2.4, 2002: 2.9},'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "61abfbb5-c7b2-4ecc-b215-41f3ebe9b5e6",
    "_uuid": "49311e87f32efe11015d3bd3101c000414bf7f2b"
   },
   "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 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>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2000     NaN   1.5\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3 = DataFrame(pop)\n",
    "frame3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ac9ba08c-b54a-4150-b22a-98115468bbfc",
    "_uuid": "320b8c15ffed6fb422a53ce1be0c97c8a9eefe51"
   },
   "source": [
    ".T随时可以transpose\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "0b1bb4a9-6fde-46fc-8e10-724a0805bef5",
    "_uuid": "5f495877f61a63c2a7aefa8b827218515e3dcbe7"
   },
   "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 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>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2000  2001  2002\n",
       "Nevada   NaN   2.4   2.9\n",
       "Ohio     1.5   1.7   3.6"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fb987cba-8771-4106-8bdc-7bd6dc2b52fb",
    "_uuid": "e023a99887b7ebe5151b976e9a65d93d0aa2d1b8"
   },
   "source": [
    "dataframe的列和行的名字都可以添加及修改！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "7fde0d32-2428-41bb-b8f7-0dc13362d486",
    "_uuid": "a7f7b2be2fec6007090e39e7b9dc3e7108e17191"
   },
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Nevada  Ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.index.name = 'year'; frame3.columns.name = 'state'\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_cell_guid": "f502e6c9-277e-4daf-ba3b-d24319ab8982",
    "_uuid": "263d92ffe8907bcc4e98bf04d6253e9f006ff337"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, 1.5],\n",
       "       [2.4, 1.7],\n",
       "       [2.9, 3.6]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "067598f8-3966-4d51-8fdc-17710eef847a",
    "_uuid": "9d4d0b9c98126d413bf7c3a55ff9448ce4d209a5"
   },
   "source": [
    "## IndexObject\n",
    "index object\n",
    "===\n",
    "虽然我们不需要很了解index作为一个对象，但是作者是想说这个也是panda的一个重要组成部分。他有一个特点是immutable，所以分享给其他数据用作index，会很安全。这个对象既像一个list，又想一个set，因为每一个数是独一无二。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "13282e8d-f3e8-4804-9bd8-d3634823d4dd",
    "_uuid": "09ce3ba8b0cc3073794bbf7f2e4b85bc1535a88b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(np.random.randn(3),index = np.arange(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "4d8d88f1-b829-48a3-83a7-f7eb99048d79",
    "_uuid": "4198140a6b38edc7308964d3edeb1cb952177002"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.925612\n",
       "1   -0.391786\n",
       "2    0.947284\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "4db56b4c-5f5c-4be9-b116-be0da967d151",
    "_uuid": "0b149d6355cc2a6d09128cbacea245c3716bf408",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "6ccfdd48-e370-4812-81e8-b0057ac3e693",
    "_uuid": "181a37dfc611890d6f1b08f8646d07b8c6ef4413"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_cell_guid": "e2f1fea2-ad45-4072-8a42-9690758257a9",
    "_uuid": "7055f77e244789e469bbb40ecf0f834420423bc5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2], dtype='int64')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_cell_guid": "534ebc09-6e60-4128-8684-a7ee3239a18c",
    "_uuid": "bab5d3f7c046aa9ae86db632f434c0a4c91a6967"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 in obj.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0720ecf3-f39e-4fd6-b53b-4ed4ea8f7117",
    "_uuid": "11c9327098a45d6fdff76649fd1268b78da763d6"
   },
   "source": [
    "## Functions\n",
    "## reindexing\n",
    "就是给value重新加不一样的index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_cell_guid": "1a526e43-7774-4683-abb2-fa12f5581b5e",
    "_uuid": "3ef671baf551e9de8c46b19c39efadb6949b6c35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_cell_guid": "89612192-b59d-43c7-b216-9b0e03b28836",
    "_uuid": "6bcf469bb2de33ea8f338ae6b44e8dbd6c72899b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])\n",
    "obj2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cdac3b4c-dc42-43d9-8bd4-6c2880cdf8d5",
    "_uuid": "eb942eac4c09bb6fdd69aef153fa340b16c88d8a"
   },
   "source": [
    "发现有nan，于是在reindex的时候要小心这个！如下操作即可！\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_cell_guid": "04206fc9-8de9-46e6-9110-7510edc62c38",
    "_uuid": "64fe2642b4575bd73a4fd9b77f21b62b9b5c5aff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "db5967d8-7add-42f9-afa9-c234ad1eebae",
    "_uuid": "d520775317193f047385e2f4d9a2fc0b6342a2ce"
   },
   "source": [
    "或者，在时间线分析中，需要interpolation插入，fill of values when reindexing, 于是就有一个method叫ffill(fill values forward)，如下\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_cell_guid": "bb9b71b1-0279-4363-9bf7-cce6d095bbe8",
    "_uuid": "3883dc3eff74fdaf353966a8c856f6a023b496b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 = Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])\n",
    "obj3.reindex(range(6), method = 'ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_cell_guid": "69bcc95e-3e27-4ba6-ada8-00f73ef13b6b",
    "_uuid": "3d3e2161f1cd63d1af6435fa391d5d5cb99c38a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bfill --- fill backward values\n",
    "obj3 = Series(['blue', 'purple', 'yellow'], index=[1, 3, 5])\n",
    "obj3.reindex(range(6), method = 'bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4805eba2-306c-47f3-bd96-0cbd1689a2f6",
    "_uuid": "1418777a32b6be64b13f1e400cd214e47674e33b"
   },
   "source": [
    "默认情况下修改的是行的index，但是列的index也可以改哦！只要传入参数columns即可\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_cell_guid": "98f026e0-07b4-4d26-af77-9108b1aea8d0",
    "_uuid": "6670f66ca9cf05b1dcff7d5a8257f229fd4dbc98"
   },
   "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 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>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'], columns=['Ohio', 'Texas', 'California'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "ad2272bc-3c6d-4956-be76-95c3de6c95c5",
    "_uuid": "871fedb559d64c72633efc5326fa0f8dfe0bf854"
   },
   "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 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>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['Texas', 'Utah', 'California']\n",
    "frame.reindex(columns = states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_cell_guid": "a6a1f28f-4faf-4ffb-8171-4d8c67f845d7",
    "_uuid": "e028657852af522a7773309f265a4f6fb936e3b8"
   },
   "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 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>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a   0.0    1.0         2.0\n",
       "b   NaN    NaN         NaN\n",
       "c   3.0    4.0         5.0\n",
       "d   6.0    7.0         8.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reindex row and columns in one shot\n",
    "frame.reindex(index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "86057ec8-e1ac-48bc-863b-e0245d943f81",
    "_uuid": "75cc5cd709ced72ea9ded8a9b9e0f94ea3447ba8"
   },
   "source": [
    "使用ix可以行和列一起修改了！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_cell_guid": "39a951bd-9756-4f39-9842-a40c492286a7",
    "_uuid": "9c9d28c3bfc3ef37bebd412a72a1fb9d195354d6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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 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>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a    1.0   NaN         2.0\n",
       "b    NaN   NaN         NaN\n",
       "c    4.0   NaN         5.0\n",
       "d    7.0   NaN         8.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.ix[['a', 'b', 'c', 'd'], states]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9e9a0f87-e018-4580-ae95-3b33b238d7a3",
    "_uuid": "0b0f04fa26d16da8f00e8dcd0baac0cdc9879202"
   },
   "source": [
    "## drop\n",
    "entries from an axis\n",
    "删除！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "e7c86164-dce9-4e3b-9dce-50332216c7a2",
    "_uuid": "39aaa0f5390e89f9d81068656da77a42e58dfe3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bc2b8ba3-cec9-479c-8942-f604268b1567",
    "_uuid": "cae9982346b39dcf8926466216f99a9c5831d32a"
   },
   "source": [
    "通过index删除\n",
    "==\n",
    "批量删除，传入list。而且，有了index在手，想删哪里就删哪里！！！！只要说明是那个axis就好！默认不写的话是行index。axis = 1,代表列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_cell_guid": "6c8645b6-b10d-4d77-8e6e-98940de3cb1f",
    "_uuid": "03726a2c46c23c82f476b2762c02c43f9cb02b42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obj = obj.drop('c')\n",
    "new_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_cell_guid": "0362391d-b22f-46d5-8f33-d163eebd2f88",
    "_uuid": "cf04208bd258b6aeee4375dd67731cb0e39fb27d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.drop(['c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_cell_guid": "ba4105b0-fc5b-49a6-88c0-0d660a6980dc",
    "_uuid": "a242e7424fe21832f3d9f5283cd43d19c814bfba"
   },
   "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 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame(np.arange(16).reshape((4, 4)),\n",
    "index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
    "columns=['one', 'two', 'three', 'four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "d883a7c9-0d5c-4b3c-892c-c7f3e73589aa",
    "_uuid": "a62c49457a7b897c7f029e9fc89fee40e9c6307c"
   },
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          two  three\n",
       "Ohio        1      2\n",
       "Colorado    5      6\n",
       "Utah        9     10\n",
       "New York   13     14"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['one','four'],axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5272c933-b399-48fd-a3c7-bddce50a7b42",
    "_uuid": "d310a85f43c7c4049e52aeae1b1f97686866a06a"
   },
   "source": [
    "## indexing\n",
    "selection, filtering\n",
    "==\n",
    "如果用label去切片，那么最后的那个label是包含的，这和正常的slicing不一样！！！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_cell_guid": "41a25c1b-51e8-4ff7-ab61-ad832f02c7c9",
    "_uuid": "8ec9a443c81b7e1a03ebac26d3b173956134e456"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series(range(4), index = ['a','b','c','d'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_cell_guid": "17f672e5-9e5d-48fb-99d9-c11dbcbfc2b8",
    "_uuid": "cf845ca23fdf534e4a1765b85eda5878d0da3745"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b':'d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_cell_guid": "6250ec18-88ac-42a7-8643-146f9f291172",
    "_uuid": "fd6c471731bc2f17245bf814643b8dbeed66a0c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "c    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_cell_guid": "e0cd954f-1344-4a8b-8eba-c3d1cd4d81f7",
    "_uuid": "8a70fe151e791b1bc37bca0175c42b0c1ac1e709"
   },
   "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 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame(np.arange(16).reshape((4, 4)),\n",
    "index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
    "columns=['one', 'two', 'three', 'four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0ac9ae09-8342-4a26-a03f-8a36f67268c1",
    "_uuid": "a2c4a252f0f3d94c43d10f37e233fe6101174689"
   },
   "source": [
    "选取行\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_cell_guid": "0a48be87-bc09-4a35-9068-d741289e4fc2",
    "_uuid": "40d99049b04880eea23ebbfecb0b570324b2c777"
   },
   "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 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6d9eb091-727f-4a4a-ab3d-bb946a059de6",
    "_uuid": "4c7b03524788152bdccb395f9316b041fa5745eb"
   },
   "source": [
    "boolean方法选数据，特定范围的值\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_cell_guid": "6a9dad9a-3798-4188-9fb8-236c805a257b",
    "_uuid": "b3aaf435c495cc37af7fc3e09bcc273620f2ecb2"
   },
   "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 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['three'] > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_cell_guid": "2debf30b-ab20-49bf-9d42-ee4512d82695",
    "_uuid": "b547db8c5eb7fa9cca6c9659eb0b981f37995a0d"
   },
   "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 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one    two  three   four\n",
       "Ohio       True   True   True   True\n",
       "Colorado   True  False  False  False\n",
       "Utah      False  False  False  False\n",
       "New York  False  False  False  False"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_cell_guid": "c52ba45b-88d6-4ea7-a0c0-24260679ed78",
    "_uuid": "98a61e7207c178fdecc9072611b12844446ee88b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[data < 5] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_cell_guid": "ba243a4b-d8ac-4295-b6d1-f33a24719ede",
    "_uuid": "5c7127b0796a48349a047000b4290d593d051a55"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    0      0     0\n",
       "Colorado    0    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "da8d7110-e861-4351-9573-64e1645cefac",
    "_uuid": "d4138e34930bbb7a60d641363d39a611a04f796f"
   },
   "source": [
    "## ix\n",
    "data[列的label]选择列\n",
    "===\n",
    "data.ix[行的label/数字]选择行\n",
    "===\n",
    "data.ix[: , 列的label/数字]切片选行，label选列\n",
    "==\n",
    "data.ix[行的label/数字,列的label/数字]选择行和列\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_cell_guid": "279dd955-5cc4-47c9-a01b-254312543d21",
    "_uuid": "0093ff36aeec2e054f608fedb4a2fb9a24da0f5f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "two      5\n",
       "three    6\n",
       "Name: Colorado, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix['Colorado', ['two', 'three']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_cell_guid": "7f69a7d8-d71f-4d4d-a81e-c7fcb46c277d",
    "_uuid": "96bb091fc1d42788fb026489fa59793032897363"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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 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>four</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          four  one  two\n",
       "Colorado     7    0    5\n",
       "Utah        11    8    9"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[['Colorado', 'Utah'], [3, 0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_cell_guid": "2ea9e185-5ab6-4d0a-b53f-2e06b8653391",
    "_uuid": "82bb4212493118ddcc12b6010eb4efce3775351d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "one       8\n",
       "two       9\n",
       "three    10\n",
       "four     11\n",
       "Name: Utah, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#默认是行的index\n",
    "data.ix[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_cell_guid": "1764b848-53fc-40df-aab9-d35e05df47db",
    "_uuid": "956f39df6c318bce3a79da9abcb595ccbbb9ce55"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ohio        0\n",
       "Colorado    5\n",
       "Utah        9\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[:'Utah', 'two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_cell_guid": "9a05c916-d5a5-4d84-854a-16bbadac5f0f",
    "_uuid": "a8bfd026246f37bf6d5a654cb8e86fee1fe0b1d1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    },
    {
     "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 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "          one  two  three\n",
       "Colorado    0    5      6\n",
       "Utah        8    9     10\n",
       "New York   12   13     14"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#也可以用boolean！！\n",
    "data.ix[data.three > 5, :3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3f286a44-f9b1-4e26-ad87-40e8e23eda34",
    "_uuid": "0588254a9964a6542ad557232f910812fd034960"
   },
   "source": [
    "选择列！！\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_cell_guid": "8addfd2d-9800-4a41-b09e-fc7c65d346f8",
    "_uuid": "72a37a85b2e20cb57a79bee7e5969a20d5168e6f"
   },
   "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 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>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three\n",
       "Ohio        0      0\n",
       "Colorado    0      6\n",
       "Utah        8     10\n",
       "New York   12     14"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['one','three']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1e970e18-1368-4c33-8f55-ec619c5b1f1e",
    "_uuid": "0924bf6dfcf865abd43c367e1952080a3a154ce8"
   },
   "source": [
    "不需要使用如下方法其实！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_cell_guid": "3763dba6-0182-4dd8-b5da-0b4bbd32918c",
    "_uuid": "dfe8a61b27754e47148f89e18c7960d7bffaed78"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
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       "          one  three\n",
       "Ohio        0      0\n",
       "Colorado    0      6\n",
       "Utah        8     10\n",
       "New York   12     14"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.ix[:,['one','three']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_cell_guid": "08684065-4e48-4a93-9243-bcb18d95535e",
    "_uuid": "e304429ae216647a5a614ab65ca41c919bef858f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ohio         0\n",
       "Colorado     5\n",
       "Utah         9\n",
       "New York    13\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_cell_guid": "7f18747d-ab15-4e96-bd11-ac22a969eb15",
    "_uuid": "57ab32386217ba4a47c93cbf644110ee5bcfef81"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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      "text/plain": [
       "          two  three\n",
       "Ohio        0      0\n",
       "Colorado    5      6"
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     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[0:2,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4f97ac94-c0aa-4660-87fa-14b360142616",
    "_uuid": "43f2b5ed89369c01d708941dd510a0f2575d2d69"
   },
   "source": [
    "## Arithmetic \n",
    "and data alignment\n",
    "===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_cell_guid": "cee59c07-4b8f-4e65-9e86-b1f3ef7d2ac7",
    "_uuid": "36240fc3763e6b7346e4dfcbf63c6d893ef24d98",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 =  DataFrame(np.arange(12.).reshape((3, 4)), columns=list('abcd'))\n",
    "df2 =  DataFrame(np.arange(20.).reshape((4, 5)), columns=list('abcde'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_cell_guid": "2c55a22c-a5d0-4b29-aa5b-1ba2aaa63c88",
    "_uuid": "9731988aadea193bcb066042bf82b446e9ed3bc3"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "      a     b     c     d   e\n",
       "0   0.0   2.0   4.0   6.0 NaN\n",
       "1   9.0  11.0  13.0  15.0 NaN\n",
       "2  18.0  20.0  22.0  24.0 NaN\n",
       "3   NaN   NaN   NaN   NaN NaN"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1+df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2bb8fcbb-133b-4c93-8e97-a52854c6ced8",
    "_uuid": "9c0e7992f3d2a4e053c3447b298c19e56b597fb6"
   },
   "source": [
    "如果想避免出现nan,我们需要加上fill_value，让没有这个value的那个变成一个规定的值。\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "_cell_guid": "f6092806-0282-4dd6-81e5-d3b6e69488cf",
    "_uuid": "f78e72c12e85b1836d44e46066fe2e56e3126568"
   },
   "outputs": [
    {
     "data": {
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       "      <td>19.0</td>\n",
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      "text/plain": [
       "      a     b     c     d     e\n",
       "0   0.0   2.0   4.0   6.0   4.0\n",
       "1   9.0  11.0  13.0  15.0   9.0\n",
       "2  18.0  20.0  22.0  24.0  14.0\n",
       "3  15.0  16.0  17.0  18.0  19.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.add(df2, fill_value = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2c101a01-8fcd-40f1-af66-8081182dfdfc",
    "_uuid": "8b58302fa86e8f2157d0c9757c3a952efc362f83"
   },
   "source": [
    "broadcasting\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_cell_guid": "f4bf6e54-df81-4834-b8ff-04fcf3a9e48f",
    "_uuid": "ccf8829dc8f1ed911a474b7cdb510643bfb19ad0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          b     d     e\n",
      "Utah    0.0   1.0   2.0\n",
      "Ohio    3.0   4.0   5.0\n",
      "Texas   6.0   7.0   8.0\n",
      "Oregon  9.0  10.0  11.0\n",
      "b    0.0\n",
      "d    1.0\n",
      "e    2.0\n",
      "Name: Utah, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "frame = DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "print(frame)\n",
    "series = frame.ix[0]\n",
    "print(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "_cell_guid": "36a02c1d-f915-432b-a41e-297afa03d6da",
    "_uuid": "475a9a361ff6beabe190b163677de90d2a3a7448"
   },
   "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 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>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b    d    e\n",
       "Utah    0.0  0.0  0.0\n",
       "Ohio    3.0  3.0  3.0\n",
       "Texas   6.0  6.0  6.0\n",
       "Oregon  9.0  9.0  9.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame - series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_cell_guid": "443281e1-b7af-4b47-bf08-e6d32677fa31",
    "_uuid": "da7797725256fba520b6ed98160ddb956c402d34"
   },
   "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",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b     d     e\n",
       "Utah    0.0   2.0   4.0\n",
       "Ohio    3.0   5.0   7.0\n",
       "Texas   6.0   8.0  10.0\n",
       "Oregon  9.0  11.0  13.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame + series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "_cell_guid": "84d5fefc-f5be-4357-8597-4d24ee63f078",
    "_uuid": "c9bcfca9f2a88b397c75b500cf562f3452867264"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0\n",
       "e    1\n",
       "f    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series2 = Series(range(3), index=['b', 'e', 'f'])\n",
    "series2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "04e9fd8b-134c-4ca4-90da-b5c1ea0ad56e",
    "_uuid": "a7794bf2bd3d0c15270984151abcee722d5b146b"
   },
   "source": [
    "如果对应的加数没有value，那么就会是nan。这个是上面的两个dataframe相加的例子是一样的。\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "_cell_guid": "f5617660-00b8-4373-87c4-decafe055641",
    "_uuid": "890e7d3306f0dfa153be1ff40eefaa93179707d3"
   },
   "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 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>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b   d     e   f\n",
       "Utah    0.0 NaN   3.0 NaN\n",
       "Ohio    3.0 NaN   6.0 NaN\n",
       "Texas   6.0 NaN   9.0 NaN\n",
       "Oregon  9.0 NaN  12.0 NaN"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame + series2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0c535959-1544-47e7-be24-0ffad728dbf9",
    "_uuid": "140ccb50f54674b3168cc3a16878dc8c553b0a94"
   },
   "source": [
    "Function application and mapping\n",
    "==\n",
    "很多numpy的函数elementwise的那种，也可以作用在dataframe上。\n",
    "==\n",
    "dataframe也可以使用.apply函数。\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "_cell_guid": "70418e2f-7379-426e-a6f8-e2743cb12850",
    "_uuid": "7c9ddc4e76fc93417f7b92a82a0a5e826e30606e"
   },
   "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 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>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.656465</td>\n",
       "      <td>0.440515</td>\n",
       "      <td>-1.139722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-0.264573</td>\n",
       "      <td>-1.634157</td>\n",
       "      <td>-0.255566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.668726</td>\n",
       "      <td>0.474205</td>\n",
       "      <td>-0.136463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-1.404178</td>\n",
       "      <td>-0.660561</td>\n",
       "      <td>0.448461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah   -0.656465  0.440515 -1.139722\n",
       "Ohio   -0.264573 -1.634157 -0.255566\n",
       "Texas   0.668726  0.474205 -0.136463\n",
       "Oregon -1.404178 -0.660561  0.448461"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "_cell_guid": "ce2a40f3-bd0c-41c0-bfe1-64de6df560cb",
    "_uuid": "bf6444bb2dc15bf5ea014b407afbcef7f04eeb5f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = lambda x: x.max() - x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "_cell_guid": "39b15c98-0e66-4acc-a950-153a83c571fc",
    "_uuid": "50ebca743c329a9ea076244d101cea8bca72ba60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2.072904\n",
       "d    2.108362\n",
       "e    1.588183\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "_cell_guid": "2defd616-383b-4122-ae49-f1f5197a43ce",
    "_uuid": "3789e23746ec446b21da94324ed26ddc5a54a3e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      1.580237\n",
       "Ohio      1.378591\n",
       "Texas     0.805189\n",
       "Oregon    1.852639\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f,axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f9dc834f-b384-4f1b-83dd-214d151306fd",
    "_uuid": "74e26caa7b99584b06cffdff44bfe830872e3e09"
   },
   "source": [
    "下面的函数，把function的结果作为series传了出来，很厉害！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "_cell_guid": "b8c8197e-1713-4215-b7c8-70a6710c5ddd",
    "_uuid": "307d87f5c797814a4fc5c9c66efc0ff16377db77",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return Series([x.min(), x.max()], index = ['min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "_cell_guid": "4995097d-af5b-4dc4-a5eb-d70bed17ee56",
    "_uuid": "24bd1a0d89308d484ab8c11799fcc97b98df5e36"
   },
   "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 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>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.404178</td>\n",
       "      <td>-1.634157</td>\n",
       "      <td>-1.139722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.668726</td>\n",
       "      <td>0.474205</td>\n",
       "      <td>0.448461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b         d         e\n",
       "min -1.404178 -1.634157 -1.139722\n",
       "max  0.668726  0.474205  0.448461"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2e59261d-191e-4560-8e5d-5e3dedf17d30",
    "_uuid": "65c3682409f81ef0a44e92cfcd8e0fbc84ab113d"
   },
   "source": [
    "下面的例子是展示element-wise python functions作用在df上。这里使用的是.appymap函数。每一个数都保留两位小数。\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "_cell_guid": "321b60a8-794a-46af-9751-25e7ef426de1",
    "_uuid": "2f90b82edae544ee4804279e99ea89d7abb8a7fd"
   },
   "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 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>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.66</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.47</td>\n",
       "      <td>-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-1.40</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b      d      e\n",
       "Utah    -0.66   0.44  -1.14\n",
       "Ohio    -0.26  -1.63  -0.26\n",
       "Texas    0.67   0.47  -0.14\n",
       "Oregon  -1.40  -0.66   0.45"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format = lambda x : '%.2f' % x\n",
    "frame.applymap(format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0ea3f7c5-2b3e-401b-a016-61366a21bf17",
    "_uuid": "6ce764f5f4fea52d86b963059368a3fedfb4fe87"
   },
   "source": [
    "The reason for the name applymap is that Series has a map method for applying an ele- ment-wise function:\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "_cell_guid": "c516cb8e-f804-48fc-8516-0652290c9bef",
    "_uuid": "f3e6f4908a4318efbe2d423d6c2d5c586e15d94a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      -1.14\n",
       "Ohio      -0.26\n",
       "Texas     -0.14\n",
       "Oregon     0.45\n",
       "Name: e, dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['e'].map(format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f89219b4-5651-4fc4-b8bc-7c134c1e9f3b",
    "_uuid": "c5a5150e771c910534e43af077802bf775f727f8"
   },
   "source": [
    "## sorting\n",
    "and ranking\n",
    "\n",
    "Sort index\n",
    "==\n",
    "## sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "_cell_guid": "d1b1ebd2-2098-46cc-9f90-a19a9d05732b",
    "_uuid": "e0c38bc06a42f8de66b25b4d9e978bf4fa67f793"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    0\n",
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series(range(4), index = ['d','a','b','c'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_cell_guid": "8518592f-c88d-4f85-bf66-081a465a8886",
    "_uuid": "9307025c619a7db193a35dc11216bd6863f490b7"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    0\n",
       "dtype: int64"
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     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "obj.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_cell_guid": "9171d2e5-9005-40d4-a84f-b0577e9e31fb",
    "_uuid": "aeaac0f8aff60ad653cca20a680ec602204f8174"
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   "outputs": [
    {
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    "frame = DataFrame(np.arange(8).reshape((2, 4)), index=['three', 'one'], columns=['d', 'a', 'b', 'c'])\n",
    "frame"
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  {
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    "_uuid": "a97aec8020a437b317b869c686a0528b5cc6a8b0"
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   "source": [
    "Sort index可以传入axis参数，这样就知道sort哪个index了！加上ascending参数，可以控制升序/降序\n",
    "=="
   ]
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  {
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    {
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    {
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       "       d  c  b  a\n",
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    "_uuid": "6f85319d2c4b003c6799c39369c2232a71d1548d"
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   "source": [
    "Sort by value\n",
    "==\n",
    "## sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "_cell_guid": "e0877263-7b3d-4145-a27d-771e430081fb",
    "_uuid": "a167d4d2f3207101a4a6d1fb3752c86843154b3b"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -3\n",
       "3    2\n",
       "0    4\n",
       "1    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "obj = Series([4,7,-3,2])\n",
    "obj.sort_values()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4d0dfe21-a4b6-44c4-a986-b7be14048e04",
    "_uuid": "d69ab509c9c6a3fb0a357a82ae312747ac4a5977"
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   "source": [
    "Sort by column\n",
    "==\n",
    "用sort_values()最好！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "_cell_guid": "3cd760d0-3581-490f-ab90-b57c3217b3a5",
    "_uuid": "b7fe1deb70f252678a21653e6b405d11c07c6043"
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   "outputs": [
    {
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    {
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      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: by argument to sort_index is deprecated, please use .sort_values(by=...)\n",
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      "/Users/louis/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: by argument to sort_index is deprecated, please use .sort_values(by=...)\n",
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  {
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    "ranking\n",
    "==\n",
    "如果啥都不改，默认出来的结果，如果一样大，则会出现0.5这样。想要有序，可以加上method = 'first'或者'max'或'min'，效果略有不同。升降序依旧是修改ascending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "_cell_guid": "ae0cf7e0-f3c2-458d-a213-42145c0813a4",
    "_uuid": "a4424ad2d0662deed8ef911c83bbe3e70cae2162"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.5\n",
       "1    1.0\n",
       "2    6.5\n",
       "3    4.5\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    4.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series([7, -5, 7, 4, 2, 0, 4])\n",
    "obj.rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "_cell_guid": "c6e2fd82-d92e-4663-b7a5-b2c6cd27796c",
    "_uuid": "f45903615548c63961bc830b10ef69ef26cf8d87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.0\n",
       "1    1.0\n",
       "2    7.0\n",
       "3    4.0\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank(method='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ec0a60fb-6e39-4d6c-8322-0251b6bbbb7a",
    "_uuid": "54c9254ccb3d184d30dbf4221265d725493fca06"
   },
   "source": [
    "降序！\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "_cell_guid": "338d3588-c3f3-43b9-a776-453e48c0da49",
    "_uuid": "be6268afbc68c1e958f1478dc600b63cba942946"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    7.0\n",
       "2    2.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "5    6.0\n",
       "6    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank(ascending = False, method = 'max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "_cell_guid": "b7747e2f-f323-466a-9716-fd138da451c4",
    "_uuid": "a417f32d1cc9a04cd3d21e224a2a5074407bc1c2"
   },
   "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 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>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b  a    c\n",
       "0  4.3  0 -2.0\n",
       "1  7.0  1  5.0\n",
       "2 -3.0  0  8.0\n",
       "3  2.0  1 -2.5"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame({'b': [4.3, 7, -3, 2], 'a': [0, 1, 0, 1], 'c': [-2, 5, 8, -2.5]})\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b2ad0ab4-728f-4a5f-98d8-6ce235ae1b36",
    "_uuid": "91916b2b959ce0b598e5440f3f2c09f3f666411e"
   },
   "source": [
    "还可以根据axis给rank。\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "_cell_guid": "c940086d-3978-4ba0-be12-c24a057567e9",
    "_uuid": "1e488f8767e8aa34e2836bf9d35aca6486bf20e5"
   },
   "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 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>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b    a    c\n",
       "0  3.0  2.0  1.0\n",
       "1  3.0  1.0  2.0\n",
       "2  1.0  2.0  3.0\n",
       "3  3.0  2.0  1.0"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.rank(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "526f0f66-b8b0-47e3-92f9-d0fe479c977b",
    "_uuid": "fc9a7bbc6fcbf49fd21eb6f664ef822324e4489a"
   },
   "source": [
    "Axis indexes with duplicate values\n",
    "==\n",
    "index.is_unique检测是否index是unique\n",
    "=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "_cell_guid": "51f6cbb9-9d26-47f1-b383-a06bcb0eb5c7",
    "_uuid": "f2a1aa66367c2cd7010c15e8a253b2713c273120"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series(range(5), index=['a', 'a', 'b', 'b', 'c'])\n",
    "obj.index.is_unique"
   ]
  }
 ],
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