{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     3\n",
       "2     5\n",
       "3   NaN\n",
       "4     6\n",
       "5     8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.709714</td>\n",
       "      <td>-0.570070</td>\n",
       "      <td>-0.550845</td>\n",
       "      <td>-1.057212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.628737</td>\n",
       "      <td>0.391646</td>\n",
       "      <td>-1.091479</td>\n",
       "      <td>0.537669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.294052</td>\n",
       "      <td>-1.296279</td>\n",
       "      <td>-0.912759</td>\n",
       "      <td>0.441580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.528582</td>\n",
       "      <td>0.532051</td>\n",
       "      <td>-1.274615</td>\n",
       "      <td>0.146372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.501640</td>\n",
       "      <td>1.164900</td>\n",
       "      <td>2.032659</td>\n",
       "      <td>0.443303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.862346</td>\n",
       "      <td>2.191615</td>\n",
       "      <td>-0.349397</td>\n",
       "      <td>-0.309474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.709714 -0.570070 -0.550845 -1.057212\n",
       "2013-01-02 -0.628737  0.391646 -1.091479  0.537669\n",
       "2013-01-03 -0.294052 -1.296279 -0.912759  0.441580\n",
       "2013-01-04  0.528582  0.532051 -1.274615  0.146372\n",
       "2013-01-05  0.501640  1.164900  2.032659  0.443303\n",
       "2013-01-06  1.862346  2.191615 -0.349397 -0.309474"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cteate a DataFrame\n",
    "dates = pd.date_range('20130101', periods=6)\n",
    "# dates \n",
    "df = pd.DataFrame(np.random.randn(6,4),index = dates,columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    float64\n",
       "B    float64\n",
       "C    float64\n",
       "D    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.709714</td>\n",
       "      <td>-0.570070</td>\n",
       "      <td>-0.550845</td>\n",
       "      <td>-1.057212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.628737</td>\n",
       "      <td>0.391646</td>\n",
       "      <td>-1.091479</td>\n",
       "      <td>0.537669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.709714 -0.570070 -0.550845 -1.057212\n",
       "2013-01-02 -0.628737  0.391646 -1.091479  0.537669"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.501640</td>\n",
       "      <td>1.164900</td>\n",
       "      <td>2.032659</td>\n",
       "      <td>0.443303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.862346</td>\n",
       "      <td>2.191615</td>\n",
       "      <td>-0.349397</td>\n",
       "      <td>-0.309474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-05  0.501640  1.164900  2.032659  0.443303\n",
       "2013-01-06  1.862346  2.191615 -0.349397 -0.309474"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the index, columns, and the underlying numpy data\n",
    "df.index\n",
    "df.columns\n",
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.446582</td>\n",
       "      <td>0.402311</td>\n",
       "      <td>-0.357739</td>\n",
       "      <td>0.033706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.870429</td>\n",
       "      <td>1.235381</td>\n",
       "      <td>1.219708</td>\n",
       "      <td>0.617847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.628737</td>\n",
       "      <td>-1.296279</td>\n",
       "      <td>-1.274615</td>\n",
       "      <td>-1.057212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.095129</td>\n",
       "      <td>-0.329641</td>\n",
       "      <td>-1.046799</td>\n",
       "      <td>-0.195513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.515111</td>\n",
       "      <td>0.461849</td>\n",
       "      <td>-0.731802</td>\n",
       "      <td>0.293976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.664431</td>\n",
       "      <td>1.006688</td>\n",
       "      <td>-0.399759</td>\n",
       "      <td>0.442872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.862346</td>\n",
       "      <td>2.191615</td>\n",
       "      <td>2.032659</td>\n",
       "      <td>0.537669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.446582  0.402311 -0.357739  0.033706\n",
       "std    0.870429  1.235381  1.219708  0.617847\n",
       "min   -0.628737 -1.296279 -1.274615 -1.057212\n",
       "25%   -0.095129 -0.329641 -1.046799 -0.195513\n",
       "50%    0.515111  0.461849 -0.731802  0.293976\n",
       "75%    0.664431  1.006688 -0.399759  0.442872\n",
       "max    1.862346  2.191615  2.032659  0.537669"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看描述性统计\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   NaN\n",
       "2013-01-02   NaN\n",
       "2013-01-03     1\n",
       "2013-01-04     3\n",
       "2013-01-05     5\n",
       "2013-01-06   NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.709714</td>\n",
       "      <td>-0.570070</td>\n",
       "      <td>-0.550845</td>\n",
       "      <td>-1.057212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.080977</td>\n",
       "      <td>-0.178424</td>\n",
       "      <td>-1.642323</td>\n",
       "      <td>-0.519543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.213076</td>\n",
       "      <td>-1.474703</td>\n",
       "      <td>-2.555082</td>\n",
       "      <td>-0.077962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.315507</td>\n",
       "      <td>-0.942652</td>\n",
       "      <td>-3.829697</td>\n",
       "      <td>0.068409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.817147</td>\n",
       "      <td>0.222248</td>\n",
       "      <td>-1.797039</td>\n",
       "      <td>0.511712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>2.679493</td>\n",
       "      <td>2.413864</td>\n",
       "      <td>-2.146435</td>\n",
       "      <td>0.202238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.709714 -0.570070 -0.550845 -1.057212\n",
       "2013-01-02  0.080977 -0.178424 -1.642323 -0.519543\n",
       "2013-01-03 -0.213076 -1.474703 -2.555082 -0.077962\n",
       "2013-01-04  0.315507 -0.942652 -3.829697  0.068409\n",
       "2013-01-05  0.817147  0.222248 -1.797039  0.511712\n",
       "2013-01-06  2.679493  2.413864 -2.146435  0.202238"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return cumulative sum over requested axis.\n",
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.709714</td>\n",
       "      <td>-0.570070</td>\n",
       "      <td>-0.550845</td>\n",
       "      <td>-1.057212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.628737</td>\n",
       "      <td>0.391646</td>\n",
       "      <td>-1.091479</td>\n",
       "      <td>0.537669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.294052</td>\n",
       "      <td>-1.296279</td>\n",
       "      <td>-0.912759</td>\n",
       "      <td>0.441580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.709714 -0.570070 -0.550845 -1.057212\n",
       "2013-01-02 -0.628737  0.391646 -1.091479  0.537669\n",
       "2013-01-03 -0.294052 -1.296279 -0.912759  0.441580"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat function\n",
    "piece = [df[:2],df[2:3],df[3:1]]\n",
    "pd.concat(piece)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval_x  lval_y\n",
       "0  foo       1       4\n",
       "1  foo       1       5\n",
       "2  foo       2       4\n",
       "3  foo       2       5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge function\n",
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n",
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [4, 5]})\n",
    "pd.merge(left,right,on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          A         B         C         D\n",
      "0  0.307377  0.587502 -1.498826 -1.413681\n",
      "1  0.606972 -0.949604  0.330130  0.221957\n",
      "2 -0.224912 -1.682801  0.376430  0.132976\n",
      "3  0.177087  1.054522  1.107316  0.857462\n",
      "4 -1.378083  0.585530  0.877582 -1.344324\n",
      "5 -0.288912  0.378269 -0.178293  1.931936\n",
      "6 -0.196315 -0.300350  2.258136  0.007789\n",
      "7 -0.863666  0.695809 -2.187412 -0.436914\n",
      "A    0.177087\n",
      "B    1.054522\n",
      "C    1.107316\n",
      "D    0.857462\n",
      "Name: 3, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.307377</td>\n",
       "      <td>0.587502</td>\n",
       "      <td>-1.498826</td>\n",
       "      <td>-1.413681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.606972</td>\n",
       "      <td>-0.949604</td>\n",
       "      <td>0.330130</td>\n",
       "      <td>0.221957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.224912</td>\n",
       "      <td>-1.682801</td>\n",
       "      <td>0.376430</td>\n",
       "      <td>0.132976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.177087</td>\n",
       "      <td>1.054522</td>\n",
       "      <td>1.107316</td>\n",
       "      <td>0.857462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.378083</td>\n",
       "      <td>0.585530</td>\n",
       "      <td>0.877582</td>\n",
       "      <td>-1.344324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.288912</td>\n",
       "      <td>0.378269</td>\n",
       "      <td>-0.178293</td>\n",
       "      <td>1.931936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.196315</td>\n",
       "      <td>-0.300350</td>\n",
       "      <td>2.258136</td>\n",
       "      <td>0.007789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.863666</td>\n",
       "      <td>0.695809</td>\n",
       "      <td>-2.187412</td>\n",
       "      <td>-0.436914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.177087</td>\n",
       "      <td>1.054522</td>\n",
       "      <td>1.107316</td>\n",
       "      <td>0.857462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.307377  0.587502 -1.498826 -1.413681\n",
       "1  0.606972 -0.949604  0.330130  0.221957\n",
       "2 -0.224912 -1.682801  0.376430  0.132976\n",
       "3  0.177087  1.054522  1.107316  0.857462\n",
       "4 -1.378083  0.585530  0.877582 -1.344324\n",
       "5 -0.288912  0.378269 -0.178293  1.931936\n",
       "6 -0.196315 -0.300350  2.258136  0.007789\n",
       "7 -0.863666  0.695809 -2.187412 -0.436914\n",
       "8  0.177087  1.054522  1.107316  0.857462"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append function\n",
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])\n",
    "s = df.iloc[3]\n",
    "print(df)\n",
    "print(s)\n",
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A      B         C         D\n",
      "0  foo    one -1.010950 -1.443440\n",
      "1  bar    one -1.023700  0.052766\n",
      "2  foo    two -0.642614 -2.438775\n",
      "3  bar  three -0.442711  0.525121\n",
      "4  foo    two -1.176690 -0.230537\n",
      "5  bar    two -0.771803 -0.347051\n",
      "6  foo    one -1.313567 -1.211388\n",
      "7  foo  three  0.779921 -1.279009\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>-2.238214</td>\n",
       "      <td>0.230836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-3.363901</td>\n",
       "      <td>-6.603149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -2.238214  0.230836\n",
       "foo -3.363901 -6.603149"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "\t\t\t\t\t\t'foo', 'bar', 'foo', 'foo'],\n",
    "\t\t\t\t\t\t'B' : ['one', 'one', 'two', 'three',\n",
    "\t\t\t\t\t\t'two', 'two', 'one', 'three'],\n",
    "\t\t\t\t\t\t'C' : np.random.randn(8),\n",
    "\t\t\t\t\t\t'D' : np.random.randn(8)})\n",
    "# Grouping and then applying a function sum to the resulting groups.\n",
    "\n",
    "print(df)\n",
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263.22"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time Series \n",
    "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n",
    "# print(rng)\n",
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\n",
    "# print(ts)\n",
    "ts.resample('5Min').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ulson_hu/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:830: MatplotlibDeprecationWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  mplDeprecation)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11262d250>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts=pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}