10-minutes-to-pandas.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "http://pandas.pydata.org/pandas-docs/stable/10min.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 Minutes to pandas\n",
    "\n",
    "This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the [Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customarily, we import as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Creation\n",
    "\n",
    "See the [Data Structure Intro section](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a Series by passing a list of values, letting pandas create a default integer index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('20130101', periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>2013-01-01</th>\n",
       "      <td>-0.103909</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-0.193043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.175704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>0.286561</td>\n",
       "      <td>-0.963335</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.103909 -0.172618 -1.144955 -0.298930\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951\n",
       "2013-01-04 -0.741502  1.032604 -0.354010 -0.193043\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  0.175704\n",
       "2013-01-06 -0.690284 -0.143495  0.286561 -0.963335"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DataFrame by passing a dict of objects that can be converted to series-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'A':1.,\n",
    "                   'B':pd.Timestamp('20130102'),\n",
    "                   'C':pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                   'D':np.array([3]*4,dtype='int32'),\n",
    "                   'E':pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                   'F':'foo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>foo</td>\n",
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having specific [dtypes](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be completed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2.<TAB>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing Data\n",
    "\n",
    "See the [Basics section](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the top & bottom rows of the frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2013-01-01</th>\n",
       "      <td>-0.103909</td>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-0.298930</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-0.193043</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
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       "                   A         B         C         D\n",
       "2013-01-01 -0.103909 -0.172618 -1.144955 -0.298930\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951\n",
       "2013-01-04 -0.741502  1.032604 -0.354010 -0.193043\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  0.175704"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-0.193043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.175704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>0.286561</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04 -0.741502  1.032604 -0.354010 -0.193043\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  0.175704\n",
       "2013-01-06 -0.690284 -0.143495  0.286561 -0.963335"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the index, columns, and the underlying numpy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.10390871, -0.17261779, -1.1449554 , -0.2989298 ],\n",
       "       [ 0.04139422, -1.46548199, -2.19714313,  1.28603241],\n",
       "       [-0.07985712, -0.16312819,  0.57951835, -0.55695091],\n",
       "       [-0.74150238,  1.03260405, -0.35400969, -0.19304348],\n",
       "       [-1.54756006, -0.18038578,  0.46350889,  0.17570413],\n",
       "       [-0.69028367, -0.14349467,  0.28656095, -0.96333513]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe shows a quick statistic summary of your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
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       "      <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>-0.520286</td>\n",
       "      <td>-0.182084</td>\n",
       "      <td>-0.394420</td>\n",
       "      <td>-0.091754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.602654</td>\n",
       "      <td>0.790508</td>\n",
       "      <td>1.092070</td>\n",
       "      <td>0.774477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>-0.963335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.728698</td>\n",
       "      <td>-0.178444</td>\n",
       "      <td>-0.947219</td>\n",
       "      <td>-0.492446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.397096</td>\n",
       "      <td>-0.167873</td>\n",
       "      <td>-0.033724</td>\n",
       "      <td>-0.245987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.085870</td>\n",
       "      <td>-0.148403</td>\n",
       "      <td>0.419272</td>\n",
       "      <td>0.083517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>1.286032</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  -0.520286 -0.182084 -0.394420 -0.091754\n",
       "std    0.602654  0.790508  1.092070  0.774477\n",
       "min   -1.547560 -1.465482 -2.197143 -0.963335\n",
       "25%   -0.728698 -0.178444 -0.947219 -0.492446\n",
       "50%   -0.397096 -0.167873 -0.033724 -0.245987\n",
       "75%   -0.085870 -0.148403  0.419272  0.083517\n",
       "max    0.041394  1.032604  0.579518  1.286032"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.103909</td>\n",
       "      <td>0.041394</td>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.741502</td>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.690284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>-0.143495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.286561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.298930</td>\n",
       "      <td>1.286032</td>\n",
       "      <td>-0.556951</td>\n",
       "      <td>-0.193043</td>\n",
       "      <td>0.175704</td>\n",
       "      <td>-0.963335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A   -0.103909    0.041394   -0.079857   -0.741502   -1.547560   -0.690284\n",
       "B   -0.172618   -1.465482   -0.163128    1.032604   -0.180386   -0.143495\n",
       "C   -1.144955   -2.197143    0.579518   -0.354010    0.463509    0.286561\n",
       "D   -0.298930    1.286032   -0.556951   -0.193043    0.175704   -0.963335"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.298930</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-0.103909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.286032</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>0.041394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.556951</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>-0.079857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.193043</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.741502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.175704</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>-1.547560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.963335</td>\n",
       "      <td>0.286561</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>-0.690284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01 -0.298930 -1.144955 -0.172618 -0.103909\n",
       "2013-01-02  1.286032 -2.197143 -1.465482  0.041394\n",
       "2013-01-03 -0.556951  0.579518 -0.163128 -0.079857\n",
       "2013-01-04 -0.193043 -0.354010  1.032604 -0.741502\n",
       "2013-01-05  0.175704  0.463509 -0.180386 -1.547560\n",
       "2013-01-06 -0.963335  0.286561 -0.143495 -0.690284"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.175704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.103909</td>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-0.298930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>0.286561</td>\n",
       "      <td>-0.963335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-0.193043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  0.175704\n",
       "2013-01-01 -0.103909 -0.172618 -1.144955 -0.298930\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951\n",
       "2013-01-06 -0.690284 -0.143495  0.286561 -0.963335\n",
       "2013-01-04 -0.741502  1.032604 -0.354010 -0.193043"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc, .iloc and .ix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the indexing documentation [Indexing and Selecting Data](http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing) and [MultiIndex / Advanced Indexing](http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single column, which yields a Series, equivalent to df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.103909\n",
       "2013-01-02    0.041394\n",
       "2013-01-03   -0.079857\n",
       "2013-01-04   -0.741502\n",
       "2013-01-05   -1.547560\n",
       "2013-01-06   -0.690284\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting via [], which slices the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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>-0.103909</td>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-0.298930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.103909 -0.172618 -1.144955 -0.298930\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-0.193043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951\n",
       "2013-01-04 -0.741502  1.032604 -0.354010 -0.193043"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20130102':'20130104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by Label\n",
    "\n",
    "See more in [Selection by Label](Selection by Label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a cross section using a label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.103909\n",
       "B   -0.172618\n",
       "C   -1.144955\n",
       "D   -0.298930\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selection by Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.103909</td>\n",
       "      <td>-0.172618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01 -0.103909 -0.172618\n",
       "2013-01-02  0.041394 -1.465482\n",
       "2013-01-03 -0.079857 -0.163128\n",
       "2013-01-04 -0.741502  1.032604\n",
       "2013-01-05 -1.547560 -0.180386\n",
       "2013-01-06 -0.690284 -0.143495"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing label slicing, both endpoints are included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-02  0.041394 -1.465482\n",
       "2013-01-03 -0.079857 -0.163128\n",
       "2013-01-04 -0.741502  1.032604"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102':'20130104',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduction in the dimensions of the returned object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.041394\n",
       "B   -1.465482\n",
       "Name: 2013-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a scalar value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.10390870843510555"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by Position\n",
    "\n",
    "See more in [Selection by Position](http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select via the position of the passed integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.741502\n",
       "B    1.032604\n",
       "C   -0.354010\n",
       "D   -0.193043\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By integer slices, acting similar to numpy/python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-04 -0.741502  1.032604\n",
       "2013-01-05 -1.547560 -0.180386"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By lists of integer position locations, similar to the numpy/python style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-2.197143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>0.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>0.463509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2013-01-02  0.041394 -2.197143\n",
       "2013-01-03 -0.079857  0.579518\n",
       "2013-01-05 -1.547560  0.463509"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing rows explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <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>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing columns explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-1.144955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.143495</td>\n",
       "      <td>0.286561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2013-01-01 -0.172618 -1.144955\n",
       "2013-01-02 -1.465482 -2.197143\n",
       "2013-01-03 -0.163128  0.579518\n",
       "2013-01-04  1.032604 -0.354010\n",
       "2013-01-05 -0.180386  0.463509\n",
       "2013-01-06 -0.143495  0.286561"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a value explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4654819927914335"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting fast access to a scalar (equiv to the prior method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4654819927914335"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a single column’s values to select data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A where operation for getting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.286032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.175704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286561</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01       NaN       NaN       NaN       NaN\n",
       "2013-01-02  0.041394       NaN       NaN  1.286032\n",
       "2013-01-03       NaN       NaN  0.579518       NaN\n",
       "2013-01-04       NaN  1.032604       NaN       NaN\n",
       "2013-01-05       NaN       NaN  0.463509  0.175704\n",
       "2013-01-06       NaN       NaN  0.286561       NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the isin() method for filtering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2['E'] = ['one','one', 'two','three','four','three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.103909</td>\n",
       "      <td>-0.172618</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-0.298930</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>1.286032</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-0.193043</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.175704</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>0.286561</td>\n",
       "      <td>-0.963335</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01 -0.103909 -0.172618 -1.144955 -0.298930    one\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  1.286032    one\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951    two\n",
       "2013-01-04 -0.741502  1.032604 -0.354010 -0.193043  three\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  0.175704   four\n",
       "2013-01-06 -0.690284 -0.143495  0.286561 -0.963335  three"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>-0.556951</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>0.175704</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03 -0.079857 -0.163128  0.579518 -0.556951   two\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  0.175704  four"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two','four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting a new column automatically aligns the data by the indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102',periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-02    1\n",
       "2013-01-03    2\n",
       "2013-01-04    3\n",
       "2013-01-05    4\n",
       "2013-01-06    5\n",
       "2013-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['F'] = s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting values by label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.at[dates[0],'A'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Settomg values by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.iat[0,1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting by assigning with a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[:,'D'] = np.array([5] * len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of the prior setting operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>0.463509</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>0.286561</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -1.144955  5  NaN\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  5  1.0\n",
       "2013-01-03 -0.079857 -0.163128  0.579518  5  2.0\n",
       "2013-01-04 -0.741502  1.032604 -0.354010  5  3.0\n",
       "2013-01-05 -1.547560 -0.180386  0.463509  5  4.0\n",
       "2013-01-06 -0.690284 -0.143495  0.286561  5  5.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A where operation with setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2[df2 > 0] = -df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
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       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>-5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>-0.579518</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>-1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.547560</td>\n",
       "      <td>-0.180386</td>\n",
       "      <td>-0.463509</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.690284</td>\n",
       "      <td>-0.143495</td>\n",
       "      <td>-0.286561</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -1.144955 -5  NaN\n",
       "2013-01-02 -0.041394 -1.465482 -2.197143 -5 -1.0\n",
       "2013-01-03 -0.079857 -0.163128 -0.579518 -5 -2.0\n",
       "2013-01-04 -0.741502 -1.032604 -0.354010 -5 -3.0\n",
       "2013-01-05 -1.547560 -0.180386 -0.463509 -5 -4.0\n",
       "2013-01-06 -0.690284 -0.143495 -0.286561 -5 -5.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data\n",
    "\n",
    "pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the Missing Data section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1.loc[dates[0]:dates[1],'E'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-01  0.000000  0.000000 -1.144955  5  NaN  1.0\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  5  1.0  1.0\n",
       "2013-01-03 -0.079857 -0.163128  0.579518  5  2.0  NaN\n",
       "2013-01-04 -0.741502  1.032604 -0.354010  5  3.0  NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To drop any rows that have missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  5  1.0  1.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-2.197143</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.079857</td>\n",
       "      <td>-0.163128</td>\n",
       "      <td>0.579518</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.741502</td>\n",
       "      <td>1.032604</td>\n",
       "      <td>-0.354010</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-01  0.000000  0.000000 -1.144955  5  5.0  1.0\n",
       "2013-01-02  0.041394 -1.465482 -2.197143  5  1.0  1.0\n",
       "2013-01-03 -0.079857 -0.163128  0.579518  5  2.0  5.0\n",
       "2013-01-04 -0.741502  1.032604 -0.354010  5  3.0  5.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the boolean mask where values are nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2013-01-01  False  False  False  False   True  False\n",
       "2013-01-02  False  False  False  False  False  False\n",
       "2013-01-03  False  False  False  False  False   True\n",
       "2013-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations\n",
    "\n",
    "See the [Basic section on Binary Ops](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-binop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stats\n",
    "\n",
    "Operations in general exclude missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing a descriptive statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.502968\n",
       "B   -0.153314\n",
       "C   -0.394420\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same operation on the other axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.963761\n",
       "2013-01-02    0.475754\n",
       "2013-01-03    1.467307\n",
       "2013-01-04    1.587418\n",
       "2013-01-05    1.547113\n",
       "2013-01-06    1.890557\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    NaN\n",
       "2013-01-02    NaN\n",
       "2013-01-03    1.0\n",
       "2013-01-04    3.0\n",
       "2013-01-05    5.0\n",
       "2013-01-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.079857</td>\n",
       "      <td>-1.163128</td>\n",
       "      <td>-0.420482</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-3.741502</td>\n",
       "      <td>-1.967396</td>\n",
       "      <td>-3.354010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-6.547560</td>\n",
       "      <td>-5.180386</td>\n",
       "      <td>-4.536491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D    F\n",
       "2013-01-01       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-02       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-03 -1.079857 -1.163128 -0.420482  4.0  1.0\n",
       "2013-01-04 -3.741502 -1.967396 -3.354010  2.0  0.0\n",
       "2013-01-05 -6.547560 -5.180386 -4.536491  0.0 -1.0\n",
       "2013-01-06       NaN       NaN       NaN  NaN  NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying functions to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.144955</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.041394</td>\n",
       "      <td>-1.465482</td>\n",
       "      <td>-3.342099</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.038463</td>\n",
       "      <td>-1.628610</td>\n",
       "      <td>-2.762580</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.779965</td>\n",
       "      <td>-0.596006</td>\n",
       "      <td>-3.116590</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-2.327525</td>\n",
       "      <td>-0.776392</td>\n",
       "      <td>-2.653081</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-3.017809</td>\n",
       "      <td>-0.919887</td>\n",
       "      <td>-2.366520</td>\n",
       "      <td>30</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D     F\n",
       "2013-01-01  0.000000  0.000000 -1.144955   5   NaN\n",
       "2013-01-02  0.041394 -1.465482 -3.342099  10   1.0\n",
       "2013-01-03 -0.038463 -1.628610 -2.762580  15   3.0\n",
       "2013-01-04 -0.779965 -0.596006 -3.116590  20   6.0\n",
       "2013-01-05 -2.327525 -0.776392 -2.653081  25  10.0\n",
       "2013-01-06 -3.017809 -0.919887 -2.366520  30  15.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.588954\n",
       "B    2.498086\n",
       "C    2.776661\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramming\n",
    "\n",
    "See more at [Histogramming and Discretization](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-discretization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    4\n",
       "2    6\n",
       "3    0\n",
       "4    2\n",
       "5    6\n",
       "6    0\n",
       "7    3\n",
       "8    3\n",
       "9    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "6    2\n",
       "4    2\n",
       "3    2\n",
       "2    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String Methods\n",
    "\n",
    "Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses [regular expressions](https://docs.python.org/2/library/re.html) by default (and in some cases always uses them). See more at [Vectorized String Methods](http://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat\n",
    "\n",
    "pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.\n",
    "\n",
    "See the [Merging section](http://pandas.pydata.org/pandas-docs/stable/merging.html#merging)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenating pandas objects together with concat():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.289722</td>\n",
       "      <td>0.379969</td>\n",
       "      <td>-1.439630</td>\n",
       "      <td>1.240801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.036719</td>\n",
       "      <td>0.173213</td>\n",
       "      <td>1.949933</td>\n",
       "      <td>0.279324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.109630</td>\n",
       "      <td>0.160297</td>\n",
       "      <td>0.829894</td>\n",
       "      <td>0.964573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729877</td>\n",
       "      <td>-1.055404</td>\n",
       "      <td>1.383973</td>\n",
       "      <td>-2.291546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.216032</td>\n",
       "      <td>0.573527</td>\n",
       "      <td>0.418476</td>\n",
       "      <td>0.925592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.540735</td>\n",
       "      <td>-0.976064</td>\n",
       "      <td>-1.829465</td>\n",
       "      <td>0.188040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.081610</td>\n",
       "      <td>0.283918</td>\n",
       "      <td>-1.407060</td>\n",
       "      <td>-1.561537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.401048</td>\n",
       "      <td>-0.228690</td>\n",
       "      <td>0.843180</td>\n",
       "      <td>-0.408505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.910873</td>\n",
       "      <td>0.783724</td>\n",
       "      <td>0.305810</td>\n",
       "      <td>0.036870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.576333</td>\n",
       "      <td>-1.507909</td>\n",
       "      <td>0.208141</td>\n",
       "      <td>-2.234579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.289722  0.379969 -1.439630  1.240801\n",
       "1 -0.036719  0.173213  1.949933  0.279324\n",
       "2  1.109630  0.160297  0.829894  0.964573\n",
       "3  0.729877 -1.055404  1.383973 -2.291546\n",
       "4 -0.216032  0.573527  0.418476  0.925592\n",
       "5  0.540735 -0.976064 -1.829465  0.188040\n",
       "6 -0.081610  0.283918 -1.407060 -1.561537\n",
       "7  0.401048 -0.228690  0.843180 -0.408505\n",
       "8  1.910873  0.783724  0.305810  0.036870\n",
       "9  1.576333 -1.507909  0.208141 -2.234579"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# break it into pieces\n",
    "pieces = [df[:3], df[3:7], df[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.289722</td>\n",
       "      <td>0.379969</td>\n",
       "      <td>-1.439630</td>\n",
       "      <td>1.240801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.036719</td>\n",
       "      <td>0.173213</td>\n",
       "      <td>1.949933</td>\n",
       "      <td>0.279324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.109630</td>\n",
       "      <td>0.160297</td>\n",
       "      <td>0.829894</td>\n",
       "      <td>0.964573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729877</td>\n",
       "      <td>-1.055404</td>\n",
       "      <td>1.383973</td>\n",
       "      <td>-2.291546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.216032</td>\n",
       "      <td>0.573527</td>\n",
       "      <td>0.418476</td>\n",
       "      <td>0.925592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.540735</td>\n",
       "      <td>-0.976064</td>\n",
       "      <td>-1.829465</td>\n",
       "      <td>0.188040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.081610</td>\n",
       "      <td>0.283918</td>\n",
       "      <td>-1.407060</td>\n",
       "      <td>-1.561537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.401048</td>\n",
       "      <td>-0.228690</td>\n",
       "      <td>0.843180</td>\n",
       "      <td>-0.408505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.910873</td>\n",
       "      <td>0.783724</td>\n",
       "      <td>0.305810</td>\n",
       "      <td>0.036870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.576333</td>\n",
       "      <td>-1.507909</td>\n",
       "      <td>0.208141</td>\n",
       "      <td>-2.234579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.289722  0.379969 -1.439630  1.240801\n",
       "1 -0.036719  0.173213  1.949933  0.279324\n",
       "2  1.109630  0.160297  0.829894  0.964573\n",
       "3  0.729877 -1.055404  1.383973 -2.291546\n",
       "4 -0.216032  0.573527  0.418476  0.925592\n",
       "5  0.540735 -0.976064 -1.829465  0.188040\n",
       "6 -0.081610  0.283918 -1.407060 -1.561537\n",
       "7  0.401048 -0.228690  0.843180 -0.408505\n",
       "8  1.910873  0.783724  0.305810  0.036870\n",
       "9  1.576333 -1.507909  0.208141 -2.234579"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join\n",
    "\n",
    "SQL style merges. See the [Database style joining](http://pandas.pydata.org/pandas-docs/stable/merging.html#merging-join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "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>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</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  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append\n",
    "\n",
    "Append rows to a dataframe. See the [Appending](http://pandas.pydata.org/pandas-docs/stable/merging.html#merging-concatenation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "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",
       "        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>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>-1.139126</td>\n",
       "      <td>-2.117558</td>\n",
       "      <td>0.997819</td>\n",
       "      <td>1.077369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.288744</td>\n",
       "      <td>-0.289108</td>\n",
       "      <td>0.850340</td>\n",
       "      <td>-0.866612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.227410</td>\n",
       "      <td>-0.683216</td>\n",
       "      <td>-0.398086</td>\n",
       "      <td>-1.304895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.039209</td>\n",
       "      <td>0.440401</td>\n",
       "      <td>-0.321543</td>\n",
       "      <td>0.473590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.323316</td>\n",
       "      <td>0.279076</td>\n",
       "      <td>0.359758</td>\n",
       "      <td>-0.434016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.238919</td>\n",
       "      <td>0.442138</td>\n",
       "      <td>-1.086708</td>\n",
       "      <td>-0.670710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.135859</td>\n",
       "      <td>-0.682790</td>\n",
       "      <td>0.058572</td>\n",
       "      <td>0.790719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.654896</td>\n",
       "      <td>0.135274</td>\n",
       "      <td>0.177353</td>\n",
       "      <td>1.749737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -1.139126 -2.117558  0.997819  1.077369\n",
       "1 -0.288744 -0.289108  0.850340 -0.866612\n",
       "2 -0.227410 -0.683216 -0.398086 -1.304895\n",
       "3 -0.039209  0.440401 -0.321543  0.473590\n",
       "4  1.323316  0.279076  0.359758 -0.434016\n",
       "5 -1.238919  0.442138 -1.086708 -0.670710\n",
       "6 -1.135859 -0.682790  0.058572  0.790719\n",
       "7 -0.654896  0.135274  0.177353  1.749737"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.139126</td>\n",
       "      <td>-2.117558</td>\n",
       "      <td>0.997819</td>\n",
       "      <td>1.077369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.288744</td>\n",
       "      <td>-0.289108</td>\n",
       "      <td>0.850340</td>\n",
       "      <td>-0.866612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.227410</td>\n",
       "      <td>-0.683216</td>\n",
       "      <td>-0.398086</td>\n",
       "      <td>-1.304895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.039209</td>\n",
       "      <td>0.440401</td>\n",
       "      <td>-0.321543</td>\n",
       "      <td>0.473590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.323316</td>\n",
       "      <td>0.279076</td>\n",
       "      <td>0.359758</td>\n",
       "      <td>-0.434016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.238919</td>\n",
       "      <td>0.442138</td>\n",
       "      <td>-1.086708</td>\n",
       "      <td>-0.670710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.135859</td>\n",
       "      <td>-0.682790</td>\n",
       "      <td>0.058572</td>\n",
       "      <td>0.790719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.654896</td>\n",
       "      <td>0.135274</td>\n",
       "      <td>0.177353</td>\n",
       "      <td>1.749737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.039209</td>\n",
       "      <td>0.440401</td>\n",
       "      <td>-0.321543</td>\n",
       "      <td>0.473590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -1.139126 -2.117558  0.997819  1.077369\n",
       "1 -0.288744 -0.289108  0.850340 -0.866612\n",
       "2 -0.227410 -0.683216 -0.398086 -1.304895\n",
       "3 -0.039209  0.440401 -0.321543  0.473590\n",
       "4  1.323316  0.279076  0.359758 -0.434016\n",
       "5 -1.238919  0.442138 -1.086708 -0.670710\n",
       "6 -1.135859 -0.682790  0.058572  0.790719\n",
       "7 -0.654896  0.135274  0.177353  1.749737\n",
       "8 -0.039209  0.440401 -0.321543  0.473590"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping\n",
    "\n",
    "By “group by” we are referring to a process involving one or more of the following steps\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "See the [Grouping section](http://pandas.pydata.org/pandas-docs/stable/groupby.html#groupby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n",
    "                                    'B' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],\n",
    "                                    'C' : np.random.randn(8),\n",
    "                                     'D' : np.random.randn(8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.722163</td>\n",
       "      <td>0.701216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.175957</td>\n",
       "      <td>-0.703938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.491254</td>\n",
       "      <td>0.286199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.434780</td>\n",
       "      <td>-0.930009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.639582</td>\n",
       "      <td>0.341224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.415795</td>\n",
       "      <td>1.285277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.870471</td>\n",
       "      <td>0.343075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.239411</td>\n",
       "      <td>0.206354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -1.722163  0.701216\n",
       "1  bar    one -1.175957 -0.703938\n",
       "2  foo    two  0.491254  0.286199\n",
       "3  bar  three -0.434780 -0.930009\n",
       "4  foo    two -0.639582  0.341224\n",
       "5  bar    two -0.415795  1.285277\n",
       "6  foo    one -0.870471  0.343075\n",
       "7  foo  three -0.239411  0.206354"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping and then applying a function sum to the resulting groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>-2.026532</td>\n",
       "      <td>-0.348671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-2.980373</td>\n",
       "      <td>1.878068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -2.026532 -0.348671\n",
       "foo -2.980373  1.878068"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.175957</td>\n",
       "      <td>-0.703938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.434780</td>\n",
       "      <td>-0.930009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.415795</td>\n",
       "      <td>1.285277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-2.592634</td>\n",
       "      <td>1.044291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.239411</td>\n",
       "      <td>0.206354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.148327</td>\n",
       "      <td>0.627423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one   -1.175957 -0.703938\n",
       "    three -0.434780 -0.930009\n",
       "    two   -0.415795  1.285277\n",
       "foo one   -2.592634  1.044291\n",
       "    three -0.239411  0.206354\n",
       "    two   -0.148327  0.627423"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping\n",
    "\n",
    "See the sections on [Hierarchical Indexing](http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-hierarchical) and [Reshaping](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-stacking)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],\n",
    "                                ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.622577</td>\n",
       "      <td>0.013970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.650400</td>\n",
       "      <td>-0.897182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.584350</td>\n",
       "      <td>0.899746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.497796</td>\n",
       "      <td>1.466334</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.622577  0.013970\n",
       "      two    -1.650400 -0.897182\n",
       "baz   one    -0.584350  0.899746\n",
       "      two     0.497796  1.466334"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stack() method “compresses” a level in the DataFrame’s columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stacked = df2.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.622577\n",
       "               B    0.013970\n",
       "       two     A   -1.650400\n",
       "               B   -0.897182\n",
       "baz    one     A   -0.584350\n",
       "               B    0.899746\n",
       "       two     A    0.497796\n",
       "               B    1.466334\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack() is unstack(), which by default unstacks the **last level**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.497796</td>\n",
       "      <td>1.466334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.622577  0.013970\n",
       "      two    -1.650400 -0.897182\n",
       "baz   one    -0.584350  0.899746\n",
       "      two     0.497796  1.466334"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "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",
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       "\n",
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       "        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>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.622577</td>\n",
       "      <td>-1.650400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.013970</td>\n",
       "      <td>-0.897182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.584350</td>\n",
       "      <td>0.497796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.899746</td>\n",
       "      <td>1.466334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.622577 -1.650400\n",
       "      B  0.013970 -0.897182\n",
       "baz   A -0.584350  0.497796\n",
       "      B  0.899746  1.466334"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "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",
       "\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>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.622577</td>\n",
       "      <td>-0.584350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.013970</td>\n",
       "      <td>0.899746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>-1.650400</td>\n",
       "      <td>0.497796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.897182</td>\n",
       "      <td>1.466334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.622577 -0.584350\n",
       "       B  0.013970  0.899746\n",
       "two    A -1.650400  0.497796\n",
       "       B -0.897182  1.466334"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables\n",
    "\n",
    "See the section on [Pivot Tables](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-pivot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n",
    "                                    'B' : ['A', 'B', 'C'] * 4,\n",
    "                                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "                                    'D' : np.random.randn(12),\n",
    "                                    'E' : np.random.randn(12)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "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",
       "        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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.099809</td>\n",
       "      <td>0.363045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.811048</td>\n",
       "      <td>-1.293170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.657838</td>\n",
       "      <td>0.834876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.302633</td>\n",
       "      <td>1.352126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.107606</td>\n",
       "      <td>-1.160453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>-1.085314</td>\n",
       "      <td>1.069624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.605681</td>\n",
       "      <td>-0.128090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.465062</td>\n",
       "      <td>2.210912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.028709</td>\n",
       "      <td>-1.564908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.583481</td>\n",
       "      <td>-0.301294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.445948</td>\n",
       "      <td>-1.073863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.288841</td>\n",
       "      <td>-0.732611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo  1.099809  0.363045\n",
       "1     one  B  foo  0.811048 -1.293170\n",
       "2     two  C  foo  1.657838  0.834876\n",
       "3   three  A  bar  1.302633  1.352126\n",
       "4     one  B  bar  1.107606 -1.160453\n",
       "5     one  C  bar -1.085314  1.069624\n",
       "6     two  A  foo -0.605681 -0.128090\n",
       "7   three  B  foo  0.465062  2.210912\n",
       "8     one  C  foo  1.028709 -1.564908\n",
       "9     one  A  bar -0.583481 -0.301294\n",
       "10    two  B  bar  1.445948 -1.073863\n",
       "11  three  C  bar -0.288841 -0.732611"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can produce pivot tables from this data very easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "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>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.583481</td>\n",
       "      <td>1.099809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.107606</td>\n",
       "      <td>0.811048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-1.085314</td>\n",
       "      <td>1.028709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>1.302633</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.465062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.288841</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.605681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.445948</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.657838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A -0.583481  1.099809\n",
       "      B  1.107606  0.811048\n",
       "      C -1.085314  1.028709\n",
       "three A  1.302633       NaN\n",
       "      B       NaN  0.465062\n",
       "      C -0.288841       NaN\n",
       "two   A       NaN -0.605681\n",
       "      B  1.445948       NaN\n",
       "      C       NaN  1.657838"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series\n",
    "\n",
    "pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the [Time Series section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#timeseries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=100, freq='S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    24164\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5Min').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time zone representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06   -0.019355\n",
       "2012-03-07   -1.760411\n",
       "2012-03-08    0.638083\n",
       "2012-03-09   -0.044210\n",
       "2012-03-10    0.128250\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts_utc = ts.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00   -0.019355\n",
       "2012-03-07 00:00:00+00:00   -1.760411\n",
       "2012-03-08 00:00:00+00:00    0.638083\n",
       "2012-03-09 00:00:00+00:00   -0.044210\n",
       "2012-03-10 00:00:00+00:00    0.128250\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Convert to another time zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00   -0.019355\n",
       "2012-03-06 19:00:00-05:00   -1.760411\n",
       "2012-03-07 19:00:00-05:00    0.638083\n",
       "2012-03-08 19:00:00-05:00   -0.044210\n",
       "2012-03-09 19:00:00-05:00    0.128250\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Converting between time span representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=5, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31   -0.423920\n",
       "2012-02-29    1.683558\n",
       "2012-03-31   -0.828436\n",
       "2012-04-30    1.318218\n",
       "2012-05-31    2.687456\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ps = ts.to_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01   -0.423920\n",
       "2012-02    1.683558\n",
       "2012-03   -0.828436\n",
       "2012-04    1.318218\n",
       "2012-05    2.687456\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01   -0.423920\n",
       "2012-02-01    1.683558\n",
       "2012-03-01   -0.828436\n",
       "2012-04-01    1.318218\n",
       "2012-05-01    2.687456\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(prng)), prng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-03-01 09:00   -0.760827\n",
       "1990-06-01 09:00    1.218552\n",
       "1990-09-01 09:00    0.247632\n",
       "1990-12-01 09:00    1.421341\n",
       "1991-03-01 09:00   -0.022992\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categoricals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see the [categorical introduction](http://pandas.pydata.org/pandas-docs/stable/categorical.html#categorical) and the [API documentation](http://pandas.pydata.org/pandas-docs/stable/api.html#api-categorical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'a', 'e']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the raw grades to a categorical data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reorder the categories and simultaneously add the missing categories (methods under Series .cat return a new Series per default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad, bad, medium, good, very good]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting is per order in the categories, not lexical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "5   6         e   very bad\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "0   1         a  very good\n",
       "3   4         a  very good\n",
       "4   5         a  very good"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"grade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by a categorical column shows also empty categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "[Plotting](http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization) docs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68e3c16208>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UmjxjDJNrCjGm0p5Wyf9I4Efw59UH0OHy46TDh5BotqkuEjT43767D8fawjUf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\nIIFPEASRI/x/j/ZNbG8EksIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68e3c3a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On DataFrame, plot() is a convenience to plot all of the columns with labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "                  columns=['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f68e54b3828>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68e3c67080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TzbuR8OIB4Qr56PKC40M2iHqxIDJWnloBG16DsMMwqykcXwI39MVIok6Avavw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\nRCaerhzn60EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68e3b2e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(); df.plot(); plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Data In/Out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV\n",
    "[Writing to a csv file](http://pandas.pydata.org/pandas-docs/stable/io.html#io-store-in-csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Reading from a csv file](http://pandas.pydata.org/pandas-docs/stable/io.html#io-read-csv-table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "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>Unnamed: 0</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>2000-01-01</td>\n",
       "      <td>0.927267</td>\n",
       "      <td>-0.655360</td>\n",
       "      <td>0.094857</td>\n",
       "      <td>-1.871880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-0.373781</td>\n",
       "      <td>-2.045079</td>\n",
       "      <td>1.418237</td>\n",
       "      <td>-2.196747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-1.249909</td>\n",
       "      <td>-1.246632</td>\n",
       "      <td>1.661808</td>\n",
       "      <td>-3.458926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>-0.656336</td>\n",
       "      <td>-0.469489</td>\n",
       "      <td>0.569028</td>\n",
       "      <td>-2.996160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-1.995435</td>\n",
       "      <td>-1.011206</td>\n",
       "      <td>0.107986</td>\n",
       "      <td>-2.140169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>-2.172933</td>\n",
       "      <td>-1.052446</td>\n",
       "      <td>1.323676</td>\n",
       "      <td>-1.778703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>-2.287587</td>\n",
       "      <td>1.695631</td>\n",
       "      <td>0.473357</td>\n",
       "      <td>-0.841066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000-01-08</td>\n",
       "      <td>-2.905971</td>\n",
       "      <td>0.800349</td>\n",
       "      <td>2.352456</td>\n",
       "      <td>-0.685280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-09</td>\n",
       "      <td>-2.606441</td>\n",
       "      <td>-0.588098</td>\n",
       "      <td>2.356357</td>\n",
       "      <td>0.741564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>-2.310799</td>\n",
       "      <td>-1.041783</td>\n",
       "      <td>3.231915</td>\n",
       "      <td>0.675458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>-2.688030</td>\n",
       "      <td>-0.277686</td>\n",
       "      <td>3.231317</td>\n",
       "      <td>-0.255091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000-01-12</td>\n",
       "      <td>-3.181948</td>\n",
       "      <td>-0.684816</td>\n",
       "      <td>1.425616</td>\n",
       "      <td>-0.552122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000-01-13</td>\n",
       "      <td>-3.317855</td>\n",
       "      <td>-0.553129</td>\n",
       "      <td>1.496993</td>\n",
       "      <td>0.256157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000-01-14</td>\n",
       "      <td>-1.828533</td>\n",
       "      <td>0.260922</td>\n",
       "      <td>1.164751</td>\n",
       "      <td>2.498890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-15</td>\n",
       "      <td>-1.347107</td>\n",
       "      <td>-0.430444</td>\n",
       "      <td>2.293750</td>\n",
       "      <td>3.847516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000-01-16</td>\n",
       "      <td>-1.217012</td>\n",
       "      <td>-1.019374</td>\n",
       "      <td>1.128881</td>\n",
       "      <td>4.884444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2000-01-17</td>\n",
       "      <td>-1.782090</td>\n",
       "      <td>-0.107942</td>\n",
       "      <td>0.257155</td>\n",
       "      <td>5.731322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>-0.095268</td>\n",
       "      <td>-0.476927</td>\n",
       "      <td>0.458392</td>\n",
       "      <td>7.072993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-19</td>\n",
       "      <td>0.428479</td>\n",
       "      <td>0.210762</td>\n",
       "      <td>-1.008959</td>\n",
       "      <td>5.021705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-01-20</td>\n",
       "      <td>1.620658</td>\n",
       "      <td>0.300587</td>\n",
       "      <td>-2.291179</td>\n",
       "      <td>4.852714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000-01-21</td>\n",
       "      <td>2.087325</td>\n",
       "      <td>0.485488</td>\n",
       "      <td>-5.027487</td>\n",
       "      <td>5.937913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000-01-22</td>\n",
       "      <td>1.538669</td>\n",
       "      <td>0.680880</td>\n",
       "      <td>-2.044547</td>\n",
       "      <td>6.073013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000-01-23</td>\n",
       "      <td>0.119228</td>\n",
       "      <td>-0.042240</td>\n",
       "      <td>-3.205062</td>\n",
       "      <td>5.324347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2000-01-24</td>\n",
       "      <td>1.864645</td>\n",
       "      <td>0.232322</td>\n",
       "      <td>-3.375407</td>\n",
       "      <td>4.011785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-01-25</td>\n",
       "      <td>3.145529</td>\n",
       "      <td>1.382313</td>\n",
       "      <td>-3.297949</td>\n",
       "      <td>3.495598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>2.280387</td>\n",
       "      <td>3.042588</td>\n",
       "      <td>-2.351957</td>\n",
       "      <td>4.294514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2000-01-27</td>\n",
       "      <td>1.356020</td>\n",
       "      <td>2.943523</td>\n",
       "      <td>-2.413912</td>\n",
       "      <td>4.959743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>1.500158</td>\n",
       "      <td>3.164491</td>\n",
       "      <td>-2.669364</td>\n",
       "      <td>3.671910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>-0.364471</td>\n",
       "      <td>4.339372</td>\n",
       "      <td>-2.809318</td>\n",
       "      <td>2.779277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2000-01-30</td>\n",
       "      <td>-0.921427</td>\n",
       "      <td>4.827069</td>\n",
       "      <td>-2.181659</td>\n",
       "      <td>5.141560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>2002-08-28</td>\n",
       "      <td>5.827510</td>\n",
       "      <td>104.673001</td>\n",
       "      <td>7.124674</td>\n",
       "      <td>7.145980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2002-08-29</td>\n",
       "      <td>5.613269</td>\n",
       "      <td>106.552193</td>\n",
       "      <td>6.700777</td>\n",
       "      <td>7.117724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2002-08-30</td>\n",
       "      <td>5.349486</td>\n",
       "      <td>105.710806</td>\n",
       "      <td>8.359958</td>\n",
       "      <td>7.488860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2002-08-31</td>\n",
       "      <td>4.750149</td>\n",
       "      <td>105.237533</td>\n",
       "      <td>9.496718</td>\n",
       "      <td>7.976448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>3.360691</td>\n",
       "      <td>106.316214</td>\n",
       "      <td>8.608041</td>\n",
       "      <td>9.425424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2002-09-02</td>\n",
       "      <td>4.312636</td>\n",
       "      <td>106.411475</td>\n",
       "      <td>7.938806</td>\n",
       "      <td>9.332906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2002-09-03</td>\n",
       "      <td>4.753717</td>\n",
       "      <td>107.787946</td>\n",
       "      <td>7.748690</td>\n",
       "      <td>9.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2002-09-04</td>\n",
       "      <td>4.000538</td>\n",
       "      <td>106.873161</td>\n",
       "      <td>7.172724</td>\n",
       "      <td>9.213243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2002-09-05</td>\n",
       "      <td>4.957935</td>\n",
       "      <td>105.941817</td>\n",
       "      <td>6.075070</td>\n",
       "      <td>9.598816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>2002-09-06</td>\n",
       "      <td>3.789246</td>\n",
       "      <td>107.103667</td>\n",
       "      <td>4.496978</td>\n",
       "      <td>10.522629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2002-09-07</td>\n",
       "      <td>4.770705</td>\n",
       "      <td>108.505168</td>\n",
       "      <td>5.988134</td>\n",
       "      <td>11.189726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>2002-09-08</td>\n",
       "      <td>5.003487</td>\n",
       "      <td>106.186994</td>\n",
       "      <td>6.553292</td>\n",
       "      <td>10.350248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>2.873467</td>\n",
       "      <td>107.056675</td>\n",
       "      <td>6.537440</td>\n",
       "      <td>9.794494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2002-09-10</td>\n",
       "      <td>2.598204</td>\n",
       "      <td>106.226322</td>\n",
       "      <td>5.485649</td>\n",
       "      <td>9.514056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2002-09-11</td>\n",
       "      <td>3.885142</td>\n",
       "      <td>105.969850</td>\n",
       "      <td>5.903802</td>\n",
       "      <td>8.661462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2002-09-12</td>\n",
       "      <td>4.053432</td>\n",
       "      <td>105.372240</td>\n",
       "      <td>6.245537</td>\n",
       "      <td>8.576424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2002-09-13</td>\n",
       "      <td>5.374972</td>\n",
       "      <td>104.445601</td>\n",
       "      <td>4.256982</td>\n",
       "      <td>9.097150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>2002-09-14</td>\n",
       "      <td>4.338918</td>\n",
       "      <td>104.591901</td>\n",
       "      <td>4.707085</td>\n",
       "      <td>9.563868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>2002-09-15</td>\n",
       "      <td>3.796332</td>\n",
       "      <td>103.432584</td>\n",
       "      <td>4.238648</td>\n",
       "      <td>8.989455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>2002-09-16</td>\n",
       "      <td>3.753540</td>\n",
       "      <td>104.520819</td>\n",
       "      <td>3.860953</td>\n",
       "      <td>8.292399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2002-09-17</td>\n",
       "      <td>2.475195</td>\n",
       "      <td>104.426465</td>\n",
       "      <td>3.875685</td>\n",
       "      <td>7.961432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>2002-09-18</td>\n",
       "      <td>1.657759</td>\n",
       "      <td>103.392401</td>\n",
       "      <td>4.266075</td>\n",
       "      <td>6.111522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2002-09-19</td>\n",
       "      <td>2.066962</td>\n",
       "      <td>103.567310</td>\n",
       "      <td>5.715965</td>\n",
       "      <td>7.473733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2002-09-20</td>\n",
       "      <td>-0.155168</td>\n",
       "      <td>104.395953</td>\n",
       "      <td>6.255315</td>\n",
       "      <td>8.504980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>1.183379</td>\n",
       "      <td>102.505430</td>\n",
       "      <td>7.145172</td>\n",
       "      <td>9.602046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>1.484499</td>\n",
       "      <td>101.769360</td>\n",
       "      <td>7.834935</td>\n",
       "      <td>8.422189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>-0.050587</td>\n",
       "      <td>100.566900</td>\n",
       "      <td>7.622388</td>\n",
       "      <td>8.259038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>0.886284</td>\n",
       "      <td>101.412981</td>\n",
       "      <td>8.293838</td>\n",
       "      <td>9.151834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>0.816529</td>\n",
       "      <td>100.559120</td>\n",
       "      <td>8.293766</td>\n",
       "      <td>8.807655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>0.893607</td>\n",
       "      <td>99.115923</td>\n",
       "      <td>8.167909</td>\n",
       "      <td>9.218166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0         A           B         C          D\n",
       "0    2000-01-01  0.927267   -0.655360  0.094857  -1.871880\n",
       "1    2000-01-02 -0.373781   -2.045079  1.418237  -2.196747\n",
       "2    2000-01-03 -1.249909   -1.246632  1.661808  -3.458926\n",
       "3    2000-01-04 -0.656336   -0.469489  0.569028  -2.996160\n",
       "4    2000-01-05 -1.995435   -1.011206  0.107986  -2.140169\n",
       "5    2000-01-06 -2.172933   -1.052446  1.323676  -1.778703\n",
       "6    2000-01-07 -2.287587    1.695631  0.473357  -0.841066\n",
       "7    2000-01-08 -2.905971    0.800349  2.352456  -0.685280\n",
       "8    2000-01-09 -2.606441   -0.588098  2.356357   0.741564\n",
       "9    2000-01-10 -2.310799   -1.041783  3.231915   0.675458\n",
       "10   2000-01-11 -2.688030   -0.277686  3.231317  -0.255091\n",
       "11   2000-01-12 -3.181948   -0.684816  1.425616  -0.552122\n",
       "12   2000-01-13 -3.317855   -0.553129  1.496993   0.256157\n",
       "13   2000-01-14 -1.828533    0.260922  1.164751   2.498890\n",
       "14   2000-01-15 -1.347107   -0.430444  2.293750   3.847516\n",
       "15   2000-01-16 -1.217012   -1.019374  1.128881   4.884444\n",
       "16   2000-01-17 -1.782090   -0.107942  0.257155   5.731322\n",
       "17   2000-01-18 -0.095268   -0.476927  0.458392   7.072993\n",
       "18   2000-01-19  0.428479    0.210762 -1.008959   5.021705\n",
       "19   2000-01-20  1.620658    0.300587 -2.291179   4.852714\n",
       "20   2000-01-21  2.087325    0.485488 -5.027487   5.937913\n",
       "21   2000-01-22  1.538669    0.680880 -2.044547   6.073013\n",
       "22   2000-01-23  0.119228   -0.042240 -3.205062   5.324347\n",
       "23   2000-01-24  1.864645    0.232322 -3.375407   4.011785\n",
       "24   2000-01-25  3.145529    1.382313 -3.297949   3.495598\n",
       "25   2000-01-26  2.280387    3.042588 -2.351957   4.294514\n",
       "26   2000-01-27  1.356020    2.943523 -2.413912   4.959743\n",
       "27   2000-01-28  1.500158    3.164491 -2.669364   3.671910\n",
       "28   2000-01-29 -0.364471    4.339372 -2.809318   2.779277\n",
       "29   2000-01-30 -0.921427    4.827069 -2.181659   5.141560\n",
       "..          ...       ...         ...       ...        ...\n",
       "970  2002-08-28  5.827510  104.673001  7.124674   7.145980\n",
       "971  2002-08-29  5.613269  106.552193  6.700777   7.117724\n",
       "972  2002-08-30  5.349486  105.710806  8.359958   7.488860\n",
       "973  2002-08-31  4.750149  105.237533  9.496718   7.976448\n",
       "974  2002-09-01  3.360691  106.316214  8.608041   9.425424\n",
       "975  2002-09-02  4.312636  106.411475  7.938806   9.332906\n",
       "976  2002-09-03  4.753717  107.787946  7.748690   9.509000\n",
       "977  2002-09-04  4.000538  106.873161  7.172724   9.213243\n",
       "978  2002-09-05  4.957935  105.941817  6.075070   9.598816\n",
       "979  2002-09-06  3.789246  107.103667  4.496978  10.522629\n",
       "980  2002-09-07  4.770705  108.505168  5.988134  11.189726\n",
       "981  2002-09-08  5.003487  106.186994  6.553292  10.350248\n",
       "982  2002-09-09  2.873467  107.056675  6.537440   9.794494\n",
       "983  2002-09-10  2.598204  106.226322  5.485649   9.514056\n",
       "984  2002-09-11  3.885142  105.969850  5.903802   8.661462\n",
       "985  2002-09-12  4.053432  105.372240  6.245537   8.576424\n",
       "986  2002-09-13  5.374972  104.445601  4.256982   9.097150\n",
       "987  2002-09-14  4.338918  104.591901  4.707085   9.563868\n",
       "988  2002-09-15  3.796332  103.432584  4.238648   8.989455\n",
       "989  2002-09-16  3.753540  104.520819  3.860953   8.292399\n",
       "990  2002-09-17  2.475195  104.426465  3.875685   7.961432\n",
       "991  2002-09-18  1.657759  103.392401  4.266075   6.111522\n",
       "992  2002-09-19  2.066962  103.567310  5.715965   7.473733\n",
       "993  2002-09-20 -0.155168  104.395953  6.255315   8.504980\n",
       "994  2002-09-21  1.183379  102.505430  7.145172   9.602046\n",
       "995  2002-09-22  1.484499  101.769360  7.834935   8.422189\n",
       "996  2002-09-23 -0.050587  100.566900  7.622388   8.259038\n",
       "997  2002-09-24  0.886284  101.412981  8.293838   9.151834\n",
       "998  2002-09-25  0.816529  100.559120  8.293766   8.807655\n",
       "999  2002-09-26  0.893607   99.115923  8.167909   9.218166\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HDF5\n",
    "Reading and writing to [HDFStores](http://pandas.pydata.org/pandas-docs/stable/io.html#io-hdf5)\n",
    "\n",
    "Writing to a HDF5 Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading from a HDF5 Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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>2000-01-01</th>\n",
       "      <td>0.927267</td>\n",
       "      <td>-0.655360</td>\n",
       "      <td>0.094857</td>\n",
       "      <td>-1.871880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.373781</td>\n",
       "      <td>-2.045079</td>\n",
       "      <td>1.418237</td>\n",
       "      <td>-2.196747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-1.249909</td>\n",
       "      <td>-1.246632</td>\n",
       "      <td>1.661808</td>\n",
       "      <td>-3.458926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.656336</td>\n",
       "      <td>-0.469489</td>\n",
       "      <td>0.569028</td>\n",
       "      <td>-2.996160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.995435</td>\n",
       "      <td>-1.011206</td>\n",
       "      <td>0.107986</td>\n",
       "      <td>-2.140169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-2.172933</td>\n",
       "      <td>-1.052446</td>\n",
       "      <td>1.323676</td>\n",
       "      <td>-1.778703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-2.287587</td>\n",
       "      <td>1.695631</td>\n",
       "      <td>0.473357</td>\n",
       "      <td>-0.841066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-2.905971</td>\n",
       "      <td>0.800349</td>\n",
       "      <td>2.352456</td>\n",
       "      <td>-0.685280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>-2.606441</td>\n",
       "      <td>-0.588098</td>\n",
       "      <td>2.356357</td>\n",
       "      <td>0.741564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>-2.310799</td>\n",
       "      <td>-1.041783</td>\n",
       "      <td>3.231915</td>\n",
       "      <td>0.675458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>-2.688030</td>\n",
       "      <td>-0.277686</td>\n",
       "      <td>3.231317</td>\n",
       "      <td>-0.255091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-3.181948</td>\n",
       "      <td>-0.684816</td>\n",
       "      <td>1.425616</td>\n",
       "      <td>-0.552122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-3.317855</td>\n",
       "      <td>-0.553129</td>\n",
       "      <td>1.496993</td>\n",
       "      <td>0.256157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>-1.828533</td>\n",
       "      <td>0.260922</td>\n",
       "      <td>1.164751</td>\n",
       "      <td>2.498890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>-1.347107</td>\n",
       "      <td>-0.430444</td>\n",
       "      <td>2.293750</td>\n",
       "      <td>3.847516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>-1.217012</td>\n",
       "      <td>-1.019374</td>\n",
       "      <td>1.128881</td>\n",
       "      <td>4.884444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>-1.782090</td>\n",
       "      <td>-0.107942</td>\n",
       "      <td>0.257155</td>\n",
       "      <td>5.731322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>-0.095268</td>\n",
       "      <td>-0.476927</td>\n",
       "      <td>0.458392</td>\n",
       "      <td>7.072993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>0.428479</td>\n",
       "      <td>0.210762</td>\n",
       "      <td>-1.008959</td>\n",
       "      <td>5.021705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>1.620658</td>\n",
       "      <td>0.300587</td>\n",
       "      <td>-2.291179</td>\n",
       "      <td>4.852714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>2.087325</td>\n",
       "      <td>0.485488</td>\n",
       "      <td>-5.027487</td>\n",
       "      <td>5.937913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>1.538669</td>\n",
       "      <td>0.680880</td>\n",
       "      <td>-2.044547</td>\n",
       "      <td>6.073013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>0.119228</td>\n",
       "      <td>-0.042240</td>\n",
       "      <td>-3.205062</td>\n",
       "      <td>5.324347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>1.864645</td>\n",
       "      <td>0.232322</td>\n",
       "      <td>-3.375407</td>\n",
       "      <td>4.011785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>3.145529</td>\n",
       "      <td>1.382313</td>\n",
       "      <td>-3.297949</td>\n",
       "      <td>3.495598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>2.280387</td>\n",
       "      <td>3.042588</td>\n",
       "      <td>-2.351957</td>\n",
       "      <td>4.294514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>1.356020</td>\n",
       "      <td>2.943523</td>\n",
       "      <td>-2.413912</td>\n",
       "      <td>4.959743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>1.500158</td>\n",
       "      <td>3.164491</td>\n",
       "      <td>-2.669364</td>\n",
       "      <td>3.671910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>-0.364471</td>\n",
       "      <td>4.339372</td>\n",
       "      <td>-2.809318</td>\n",
       "      <td>2.779277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>-0.921427</td>\n",
       "      <td>4.827069</td>\n",
       "      <td>-2.181659</td>\n",
       "      <td>5.141560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td>5.827510</td>\n",
       "      <td>104.673001</td>\n",
       "      <td>7.124674</td>\n",
       "      <td>7.145980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>5.613269</td>\n",
       "      <td>106.552193</td>\n",
       "      <td>6.700777</td>\n",
       "      <td>7.117724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>5.349486</td>\n",
       "      <td>105.710806</td>\n",
       "      <td>8.359958</td>\n",
       "      <td>7.488860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>4.750149</td>\n",
       "      <td>105.237533</td>\n",
       "      <td>9.496718</td>\n",
       "      <td>7.976448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>3.360691</td>\n",
       "      <td>106.316214</td>\n",
       "      <td>8.608041</td>\n",
       "      <td>9.425424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>4.312636</td>\n",
       "      <td>106.411475</td>\n",
       "      <td>7.938806</td>\n",
       "      <td>9.332906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>4.753717</td>\n",
       "      <td>107.787946</td>\n",
       "      <td>7.748690</td>\n",
       "      <td>9.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>4.000538</td>\n",
       "      <td>106.873161</td>\n",
       "      <td>7.172724</td>\n",
       "      <td>9.213243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>4.957935</td>\n",
       "      <td>105.941817</td>\n",
       "      <td>6.075070</td>\n",
       "      <td>9.598816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>3.789246</td>\n",
       "      <td>107.103667</td>\n",
       "      <td>4.496978</td>\n",
       "      <td>10.522629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>4.770705</td>\n",
       "      <td>108.505168</td>\n",
       "      <td>5.988134</td>\n",
       "      <td>11.189726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>5.003487</td>\n",
       "      <td>106.186994</td>\n",
       "      <td>6.553292</td>\n",
       "      <td>10.350248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>2.873467</td>\n",
       "      <td>107.056675</td>\n",
       "      <td>6.537440</td>\n",
       "      <td>9.794494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>2.598204</td>\n",
       "      <td>106.226322</td>\n",
       "      <td>5.485649</td>\n",
       "      <td>9.514056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>3.885142</td>\n",
       "      <td>105.969850</td>\n",
       "      <td>5.903802</td>\n",
       "      <td>8.661462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>4.053432</td>\n",
       "      <td>105.372240</td>\n",
       "      <td>6.245537</td>\n",
       "      <td>8.576424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>5.374972</td>\n",
       "      <td>104.445601</td>\n",
       "      <td>4.256982</td>\n",
       "      <td>9.097150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>4.338918</td>\n",
       "      <td>104.591901</td>\n",
       "      <td>4.707085</td>\n",
       "      <td>9.563868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>3.796332</td>\n",
       "      <td>103.432584</td>\n",
       "      <td>4.238648</td>\n",
       "      <td>8.989455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>3.753540</td>\n",
       "      <td>104.520819</td>\n",
       "      <td>3.860953</td>\n",
       "      <td>8.292399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>2.475195</td>\n",
       "      <td>104.426465</td>\n",
       "      <td>3.875685</td>\n",
       "      <td>7.961432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>1.657759</td>\n",
       "      <td>103.392401</td>\n",
       "      <td>4.266075</td>\n",
       "      <td>6.111522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>2.066962</td>\n",
       "      <td>103.567310</td>\n",
       "      <td>5.715965</td>\n",
       "      <td>7.473733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>-0.155168</td>\n",
       "      <td>104.395953</td>\n",
       "      <td>6.255315</td>\n",
       "      <td>8.504980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>1.183379</td>\n",
       "      <td>102.505430</td>\n",
       "      <td>7.145172</td>\n",
       "      <td>9.602046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>1.484499</td>\n",
       "      <td>101.769360</td>\n",
       "      <td>7.834935</td>\n",
       "      <td>8.422189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>-0.050587</td>\n",
       "      <td>100.566900</td>\n",
       "      <td>7.622388</td>\n",
       "      <td>8.259038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>0.886284</td>\n",
       "      <td>101.412981</td>\n",
       "      <td>8.293838</td>\n",
       "      <td>9.151834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>0.816529</td>\n",
       "      <td>100.559120</td>\n",
       "      <td>8.293766</td>\n",
       "      <td>8.807655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>0.893607</td>\n",
       "      <td>99.115923</td>\n",
       "      <td>8.167909</td>\n",
       "      <td>9.218166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A           B         C          D\n",
       "2000-01-01  0.927267   -0.655360  0.094857  -1.871880\n",
       "2000-01-02 -0.373781   -2.045079  1.418237  -2.196747\n",
       "2000-01-03 -1.249909   -1.246632  1.661808  -3.458926\n",
       "2000-01-04 -0.656336   -0.469489  0.569028  -2.996160\n",
       "2000-01-05 -1.995435   -1.011206  0.107986  -2.140169\n",
       "2000-01-06 -2.172933   -1.052446  1.323676  -1.778703\n",
       "2000-01-07 -2.287587    1.695631  0.473357  -0.841066\n",
       "2000-01-08 -2.905971    0.800349  2.352456  -0.685280\n",
       "2000-01-09 -2.606441   -0.588098  2.356357   0.741564\n",
       "2000-01-10 -2.310799   -1.041783  3.231915   0.675458\n",
       "2000-01-11 -2.688030   -0.277686  3.231317  -0.255091\n",
       "2000-01-12 -3.181948   -0.684816  1.425616  -0.552122\n",
       "2000-01-13 -3.317855   -0.553129  1.496993   0.256157\n",
       "2000-01-14 -1.828533    0.260922  1.164751   2.498890\n",
       "2000-01-15 -1.347107   -0.430444  2.293750   3.847516\n",
       "2000-01-16 -1.217012   -1.019374  1.128881   4.884444\n",
       "2000-01-17 -1.782090   -0.107942  0.257155   5.731322\n",
       "2000-01-18 -0.095268   -0.476927  0.458392   7.072993\n",
       "2000-01-19  0.428479    0.210762 -1.008959   5.021705\n",
       "2000-01-20  1.620658    0.300587 -2.291179   4.852714\n",
       "2000-01-21  2.087325    0.485488 -5.027487   5.937913\n",
       "2000-01-22  1.538669    0.680880 -2.044547   6.073013\n",
       "2000-01-23  0.119228   -0.042240 -3.205062   5.324347\n",
       "2000-01-24  1.864645    0.232322 -3.375407   4.011785\n",
       "2000-01-25  3.145529    1.382313 -3.297949   3.495598\n",
       "2000-01-26  2.280387    3.042588 -2.351957   4.294514\n",
       "2000-01-27  1.356020    2.943523 -2.413912   4.959743\n",
       "2000-01-28  1.500158    3.164491 -2.669364   3.671910\n",
       "2000-01-29 -0.364471    4.339372 -2.809318   2.779277\n",
       "2000-01-30 -0.921427    4.827069 -2.181659   5.141560\n",
       "...              ...         ...       ...        ...\n",
       "2002-08-28  5.827510  104.673001  7.124674   7.145980\n",
       "2002-08-29  5.613269  106.552193  6.700777   7.117724\n",
       "2002-08-30  5.349486  105.710806  8.359958   7.488860\n",
       "2002-08-31  4.750149  105.237533  9.496718   7.976448\n",
       "2002-09-01  3.360691  106.316214  8.608041   9.425424\n",
       "2002-09-02  4.312636  106.411475  7.938806   9.332906\n",
       "2002-09-03  4.753717  107.787946  7.748690   9.509000\n",
       "2002-09-04  4.000538  106.873161  7.172724   9.213243\n",
       "2002-09-05  4.957935  105.941817  6.075070   9.598816\n",
       "2002-09-06  3.789246  107.103667  4.496978  10.522629\n",
       "2002-09-07  4.770705  108.505168  5.988134  11.189726\n",
       "2002-09-08  5.003487  106.186994  6.553292  10.350248\n",
       "2002-09-09  2.873467  107.056675  6.537440   9.794494\n",
       "2002-09-10  2.598204  106.226322  5.485649   9.514056\n",
       "2002-09-11  3.885142  105.969850  5.903802   8.661462\n",
       "2002-09-12  4.053432  105.372240  6.245537   8.576424\n",
       "2002-09-13  5.374972  104.445601  4.256982   9.097150\n",
       "2002-09-14  4.338918  104.591901  4.707085   9.563868\n",
       "2002-09-15  3.796332  103.432584  4.238648   8.989455\n",
       "2002-09-16  3.753540  104.520819  3.860953   8.292399\n",
       "2002-09-17  2.475195  104.426465  3.875685   7.961432\n",
       "2002-09-18  1.657759  103.392401  4.266075   6.111522\n",
       "2002-09-19  2.066962  103.567310  5.715965   7.473733\n",
       "2002-09-20 -0.155168  104.395953  6.255315   8.504980\n",
       "2002-09-21  1.183379  102.505430  7.145172   9.602046\n",
       "2002-09-22  1.484499  101.769360  7.834935   8.422189\n",
       "2002-09-23 -0.050587  100.566900  7.622388   8.259038\n",
       "2002-09-24  0.886284  101.412981  8.293838   9.151834\n",
       "2002-09-25  0.816529  100.559120  8.293766   8.807655\n",
       "2002-09-26  0.893607   99.115923  8.167909   9.218166\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excel\n",
    "\n",
    "Reading and writing to [MS Excel](http://pandas.pydata.org/pandas-docs/stable/io.html#io-excel)\n",
    "\n",
    "Writing to an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading from an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "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",
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       "    .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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.927267</td>\n",
       "      <td>-0.655360</td>\n",
       "      <td>0.094857</td>\n",
       "      <td>-1.871880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.373781</td>\n",
       "      <td>-2.045079</td>\n",
       "      <td>1.418237</td>\n",
       "      <td>-2.196747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-1.249909</td>\n",
       "      <td>-1.246632</td>\n",
       "      <td>1.661808</td>\n",
       "      <td>-3.458926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.656336</td>\n",
       "      <td>-0.469489</td>\n",
       "      <td>0.569028</td>\n",
       "      <td>-2.996160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.995435</td>\n",
       "      <td>-1.011206</td>\n",
       "      <td>0.107986</td>\n",
       "      <td>-2.140169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-2.172933</td>\n",
       "      <td>-1.052446</td>\n",
       "      <td>1.323676</td>\n",
       "      <td>-1.778703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-2.287587</td>\n",
       "      <td>1.695631</td>\n",
       "      <td>0.473357</td>\n",
       "      <td>-0.841066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-2.905971</td>\n",
       "      <td>0.800349</td>\n",
       "      <td>2.352456</td>\n",
       "      <td>-0.685280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>-2.606441</td>\n",
       "      <td>-0.588098</td>\n",
       "      <td>2.356357</td>\n",
       "      <td>0.741564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>-2.310799</td>\n",
       "      <td>-1.041783</td>\n",
       "      <td>3.231915</td>\n",
       "      <td>0.675458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>-2.688030</td>\n",
       "      <td>-0.277686</td>\n",
       "      <td>3.231317</td>\n",
       "      <td>-0.255091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-3.181948</td>\n",
       "      <td>-0.684816</td>\n",
       "      <td>1.425616</td>\n",
       "      <td>-0.552122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-3.317855</td>\n",
       "      <td>-0.553129</td>\n",
       "      <td>1.496993</td>\n",
       "      <td>0.256157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>-1.828533</td>\n",
       "      <td>0.260922</td>\n",
       "      <td>1.164751</td>\n",
       "      <td>2.498890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>-1.347107</td>\n",
       "      <td>-0.430444</td>\n",
       "      <td>2.293750</td>\n",
       "      <td>3.847516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>-1.217012</td>\n",
       "      <td>-1.019374</td>\n",
       "      <td>1.128881</td>\n",
       "      <td>4.884444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>-1.782090</td>\n",
       "      <td>-0.107942</td>\n",
       "      <td>0.257155</td>\n",
       "      <td>5.731322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>-0.095268</td>\n",
       "      <td>-0.476927</td>\n",
       "      <td>0.458392</td>\n",
       "      <td>7.072993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>0.428479</td>\n",
       "      <td>0.210762</td>\n",
       "      <td>-1.008959</td>\n",
       "      <td>5.021705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>1.620658</td>\n",
       "      <td>0.300587</td>\n",
       "      <td>-2.291179</td>\n",
       "      <td>4.852714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>2.087325</td>\n",
       "      <td>0.485488</td>\n",
       "      <td>-5.027487</td>\n",
       "      <td>5.937913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>1.538669</td>\n",
       "      <td>0.680880</td>\n",
       "      <td>-2.044547</td>\n",
       "      <td>6.073013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>0.119228</td>\n",
       "      <td>-0.042240</td>\n",
       "      <td>-3.205062</td>\n",
       "      <td>5.324347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>1.864645</td>\n",
       "      <td>0.232322</td>\n",
       "      <td>-3.375407</td>\n",
       "      <td>4.011785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>3.145529</td>\n",
       "      <td>1.382313</td>\n",
       "      <td>-3.297949</td>\n",
       "      <td>3.495598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>2.280387</td>\n",
       "      <td>3.042588</td>\n",
       "      <td>-2.351957</td>\n",
       "      <td>4.294514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>1.356020</td>\n",
       "      <td>2.943523</td>\n",
       "      <td>-2.413912</td>\n",
       "      <td>4.959743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>1.500158</td>\n",
       "      <td>3.164491</td>\n",
       "      <td>-2.669364</td>\n",
       "      <td>3.671910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>-0.364471</td>\n",
       "      <td>4.339372</td>\n",
       "      <td>-2.809318</td>\n",
       "      <td>2.779277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>-0.921427</td>\n",
       "      <td>4.827069</td>\n",
       "      <td>-2.181659</td>\n",
       "      <td>5.141560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td>5.827510</td>\n",
       "      <td>104.673001</td>\n",
       "      <td>7.124674</td>\n",
       "      <td>7.145980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>5.613269</td>\n",
       "      <td>106.552193</td>\n",
       "      <td>6.700777</td>\n",
       "      <td>7.117724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>5.349486</td>\n",
       "      <td>105.710806</td>\n",
       "      <td>8.359958</td>\n",
       "      <td>7.488860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>4.750149</td>\n",
       "      <td>105.237533</td>\n",
       "      <td>9.496718</td>\n",
       "      <td>7.976448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>3.360691</td>\n",
       "      <td>106.316214</td>\n",
       "      <td>8.608041</td>\n",
       "      <td>9.425424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>4.312636</td>\n",
       "      <td>106.411475</td>\n",
       "      <td>7.938806</td>\n",
       "      <td>9.332906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>4.753717</td>\n",
       "      <td>107.787946</td>\n",
       "      <td>7.748690</td>\n",
       "      <td>9.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>4.000538</td>\n",
       "      <td>106.873161</td>\n",
       "      <td>7.172724</td>\n",
       "      <td>9.213243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>4.957935</td>\n",
       "      <td>105.941817</td>\n",
       "      <td>6.075070</td>\n",
       "      <td>9.598816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>3.789246</td>\n",
       "      <td>107.103667</td>\n",
       "      <td>4.496978</td>\n",
       "      <td>10.522629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>4.770705</td>\n",
       "      <td>108.505168</td>\n",
       "      <td>5.988134</td>\n",
       "      <td>11.189726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>5.003487</td>\n",
       "      <td>106.186994</td>\n",
       "      <td>6.553292</td>\n",
       "      <td>10.350248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>2.873467</td>\n",
       "      <td>107.056675</td>\n",
       "      <td>6.537440</td>\n",
       "      <td>9.794494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>2.598204</td>\n",
       "      <td>106.226322</td>\n",
       "      <td>5.485649</td>\n",
       "      <td>9.514056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>3.885142</td>\n",
       "      <td>105.969850</td>\n",
       "      <td>5.903802</td>\n",
       "      <td>8.661462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>4.053432</td>\n",
       "      <td>105.372240</td>\n",
       "      <td>6.245537</td>\n",
       "      <td>8.576424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>5.374972</td>\n",
       "      <td>104.445601</td>\n",
       "      <td>4.256982</td>\n",
       "      <td>9.097150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>4.338918</td>\n",
       "      <td>104.591901</td>\n",
       "      <td>4.707085</td>\n",
       "      <td>9.563868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>3.796332</td>\n",
       "      <td>103.432584</td>\n",
       "      <td>4.238648</td>\n",
       "      <td>8.989455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>3.753540</td>\n",
       "      <td>104.520819</td>\n",
       "      <td>3.860953</td>\n",
       "      <td>8.292399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>2.475195</td>\n",
       "      <td>104.426465</td>\n",
       "      <td>3.875685</td>\n",
       "      <td>7.961432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>1.657759</td>\n",
       "      <td>103.392401</td>\n",
       "      <td>4.266075</td>\n",
       "      <td>6.111522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>2.066962</td>\n",
       "      <td>103.567310</td>\n",
       "      <td>5.715965</td>\n",
       "      <td>7.473733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>-0.155168</td>\n",
       "      <td>104.395953</td>\n",
       "      <td>6.255315</td>\n",
       "      <td>8.504980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>1.183379</td>\n",
       "      <td>102.505430</td>\n",
       "      <td>7.145172</td>\n",
       "      <td>9.602046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>1.484499</td>\n",
       "      <td>101.769360</td>\n",
       "      <td>7.834935</td>\n",
       "      <td>8.422189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>-0.050587</td>\n",
       "      <td>100.566900</td>\n",
       "      <td>7.622388</td>\n",
       "      <td>8.259038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>0.886284</td>\n",
       "      <td>101.412981</td>\n",
       "      <td>8.293838</td>\n",
       "      <td>9.151834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>0.816529</td>\n",
       "      <td>100.559120</td>\n",
       "      <td>8.293766</td>\n",
       "      <td>8.807655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>0.893607</td>\n",
       "      <td>99.115923</td>\n",
       "      <td>8.167909</td>\n",
       "      <td>9.218166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A           B         C          D\n",
       "2000-01-01  0.927267   -0.655360  0.094857  -1.871880\n",
       "2000-01-02 -0.373781   -2.045079  1.418237  -2.196747\n",
       "2000-01-03 -1.249909   -1.246632  1.661808  -3.458926\n",
       "2000-01-04 -0.656336   -0.469489  0.569028  -2.996160\n",
       "2000-01-05 -1.995435   -1.011206  0.107986  -2.140169\n",
       "2000-01-06 -2.172933   -1.052446  1.323676  -1.778703\n",
       "2000-01-07 -2.287587    1.695631  0.473357  -0.841066\n",
       "2000-01-08 -2.905971    0.800349  2.352456  -0.685280\n",
       "2000-01-09 -2.606441   -0.588098  2.356357   0.741564\n",
       "2000-01-10 -2.310799   -1.041783  3.231915   0.675458\n",
       "2000-01-11 -2.688030   -0.277686  3.231317  -0.255091\n",
       "2000-01-12 -3.181948   -0.684816  1.425616  -0.552122\n",
       "2000-01-13 -3.317855   -0.553129  1.496993   0.256157\n",
       "2000-01-14 -1.828533    0.260922  1.164751   2.498890\n",
       "2000-01-15 -1.347107   -0.430444  2.293750   3.847516\n",
       "2000-01-16 -1.217012   -1.019374  1.128881   4.884444\n",
       "2000-01-17 -1.782090   -0.107942  0.257155   5.731322\n",
       "2000-01-18 -0.095268   -0.476927  0.458392   7.072993\n",
       "2000-01-19  0.428479    0.210762 -1.008959   5.021705\n",
       "2000-01-20  1.620658    0.300587 -2.291179   4.852714\n",
       "2000-01-21  2.087325    0.485488 -5.027487   5.937913\n",
       "2000-01-22  1.538669    0.680880 -2.044547   6.073013\n",
       "2000-01-23  0.119228   -0.042240 -3.205062   5.324347\n",
       "2000-01-24  1.864645    0.232322 -3.375407   4.011785\n",
       "2000-01-25  3.145529    1.382313 -3.297949   3.495598\n",
       "2000-01-26  2.280387    3.042588 -2.351957   4.294514\n",
       "2000-01-27  1.356020    2.943523 -2.413912   4.959743\n",
       "2000-01-28  1.500158    3.164491 -2.669364   3.671910\n",
       "2000-01-29 -0.364471    4.339372 -2.809318   2.779277\n",
       "2000-01-30 -0.921427    4.827069 -2.181659   5.141560\n",
       "...              ...         ...       ...        ...\n",
       "2002-08-28  5.827510  104.673001  7.124674   7.145980\n",
       "2002-08-29  5.613269  106.552193  6.700777   7.117724\n",
       "2002-08-30  5.349486  105.710806  8.359958   7.488860\n",
       "2002-08-31  4.750149  105.237533  9.496718   7.976448\n",
       "2002-09-01  3.360691  106.316214  8.608041   9.425424\n",
       "2002-09-02  4.312636  106.411475  7.938806   9.332906\n",
       "2002-09-03  4.753717  107.787946  7.748690   9.509000\n",
       "2002-09-04  4.000538  106.873161  7.172724   9.213243\n",
       "2002-09-05  4.957935  105.941817  6.075070   9.598816\n",
       "2002-09-06  3.789246  107.103667  4.496978  10.522629\n",
       "2002-09-07  4.770705  108.505168  5.988134  11.189726\n",
       "2002-09-08  5.003487  106.186994  6.553292  10.350248\n",
       "2002-09-09  2.873467  107.056675  6.537440   9.794494\n",
       "2002-09-10  2.598204  106.226322  5.485649   9.514056\n",
       "2002-09-11  3.885142  105.969850  5.903802   8.661462\n",
       "2002-09-12  4.053432  105.372240  6.245537   8.576424\n",
       "2002-09-13  5.374972  104.445601  4.256982   9.097150\n",
       "2002-09-14  4.338918  104.591901  4.707085   9.563868\n",
       "2002-09-15  3.796332  103.432584  4.238648   8.989455\n",
       "2002-09-16  3.753540  104.520819  3.860953   8.292399\n",
       "2002-09-17  2.475195  104.426465  3.875685   7.961432\n",
       "2002-09-18  1.657759  103.392401  4.266075   6.111522\n",
       "2002-09-19  2.066962  103.567310  5.715965   7.473733\n",
       "2002-09-20 -0.155168  104.395953  6.255315   8.504980\n",
       "2002-09-21  1.183379  102.505430  7.145172   9.602046\n",
       "2002-09-22  1.484499  101.769360  7.834935   8.422189\n",
       "2002-09-23 -0.050587  100.566900  7.622388   8.259038\n",
       "2002-09-24  0.886284  101.412981  8.293838   9.151834\n",
       "2002-09-25  0.816529  100.559120  8.293766   8.807655\n",
       "2002-09-26  0.893607   99.115923  8.167909   9.218166\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gotchas\n",
    "If you are trying an operation and you see an exception like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-142-5c782b38cd2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I was true\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m   1120\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "if pd.Series([False, True, False]):\n",
    "    print(\"I was true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [Comparisons](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-compare) for an explanation and what to do.\n",
    "\n",
    "See [Gotchas](http://pandas.pydata.org/pandas-docs/stable/gotchas.html#gotchas) as well."
   ]
  }
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