1_Python_Data_Tools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python Data Tools\n",
    "===\n",
    "There are a number of excellent libraries available to python for working with data. Almost all of these tools are included in the Anaconda distribution of Python. To use a library you'll need to first `import` it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#necessary in order to view plots in ipython notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where numpy is the name of the library we've just imported. Each time we use a class or function from an imported library, we'll need to specify which library it comes from. For instance, to use the `rand` method from numpy's `random` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.96499052,  0.58450056,  0.79463451,  0.72083158])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.random.rand(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we reference the library name so often, it's common to create an alias for the library name. We can specify the alias as part of the import statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can reference the numpy library using the alias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.68121795,  0.5544354 ,  0.9831872 ,  0.69944449])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy\n",
    "---\n",
    "The numpy library provides a set of tools for creating, manipulating, and operating on multi-dimensional arrays. Many of the operations available from the numpy library could also be performed using python's built in math library and for loops. However, numpy makes these operations easier to perform and executes them *much* more efficiently.\n",
    "\n",
    "**Exercise 1.1:** \n",
    "\n",
    "Below we use numpy to create a sine wave from a 1,000,000 element array. Perform the same operation using a combination of a list, a for-loop, and the math.sin method. A timer is provided so you can record the execution time and compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is a 1000000 element array\n",
      "this operation took 0.061228 seconds\n",
      "this operation took 0.000054 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "x = np.arange(0, 10, 0.00001)\n",
    "print('x is a %d element array' % len(x))\n",
    "\n",
    "t0 = time.time()\n",
    "y_np = np.sin(x)+np.cos(x)\n",
    "print('this operation took %f seconds' % (time.time()-t0))\n",
    "\n",
    "t0 = time.time()\n",
    "# put your code here\n",
    "print('this operation took %f seconds' % (time.time()-t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the exercise above you should have seen that with 1 million element array, numpy is around 10 times faster than a for-loop (try the same exercise with arrays of different sizes to see how your results vary).\n",
    "\n",
    "Numpy is also great for working with multi-dimensional arrays and matrices. Let's look at an example in which we have a 12 element 1-dimensional array that we'd like to convert into a 4x3 2-dimensional array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(1, 13)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = a.reshape(4, 3)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy also provides a way to select elements and slices of an array by their indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0, 1]  # gets the element in the first row and second column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5,  8, 11])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:, 1]  # gets the array of elements in the second column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2, 0:2]  # gets the top left 2x2 square of elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy also allows us to perform boolean operations on an array *and* to use an array of boolean values to select certain elements from an array. Below we use a boolean operation to determine if each element in the array is even (by checking whether it is divisible by 2). Then we use numpy's boolean indexing feature to set all even elements to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True, False],\n",
       "       [ True, False,  True],\n",
       "       [False,  True, False],\n",
       "       [ True, False,  True]], dtype=bool)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a%2 == 0  # modulus 2 is 0 for odd numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  0,  3],\n",
       "       [ 0,  5,  0],\n",
       "       [ 7,  0,  9],\n",
       "       [ 0, 11,  0]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a%2 == 0] = 0  # set all even numbers to 0\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We won't cover all of what numpy can do in this module (including it's very useful matrix operations), but you can get a more thorough introduction here: http://wiki.scipy.org/Tentative_NumPy_Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas\n",
    "---\n",
    "The numpy library treats a 2-dimensional array as an array of arrays. This has the advantage of being efficient but the disadvantage being somewhat cumbersome to work with. In most cases a 2-dimensional array is a collection of data points where each row represents an observation and each column represents a different feature that has been observed.\n",
    "\n",
    "The pandas library stores data in a way that is more congruent with the observation/feature (or row/column) way of thinking about a data set. It provides: \n",
    "1. a *high level interface* to numpy arrays that is much more intuitive and easier to work with and\n",
    "2. a *set of tools* that make every step in the data science pipeline easier to perform\n",
    "\n",
    "Where the base structure of numpy is an array, the base structure of pandas is a dataframe. The two object are very similar, but the pandas dataframe has the advantages of:\n",
    "1. allowing different data types in a single dataframe and\n",
    "2. providing labels for both rows and columns\n",
    "\n",
    "The labeling is particularly useful for columns, as each column typically represents a different feature.  In the example below we take a weeks worth of daily stock data (open and close prices), initially putting it into a numpy array, and then into a pandas dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 563.3,  553.2],\n",
       "       [ 556.6,  549.1],\n",
       "       [ 554.8,  548.9],\n",
       "       [ 556.8,  542.7],\n",
       "       [ 555.3,  544.5],\n",
       "       [ 552.2,  546.4],\n",
       "       [ 558.2,  545.5]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data_np = np.array([\n",
    "    [563.3, 553.2],\n",
    "    [556.6, 549.1],\n",
    "    [554.8, 548.9],\n",
    "    [556.8, 542.7],\n",
    "    [555.3, 544.5],\n",
    "    [552.2, 546.4],\n",
    "    [558.2, 545.5]\n",
    "])\n",
    "columns = [['high', 'low']]\n",
    "dates = np.array(['3/10', '3/11', '3/12', '3/13', '3/16','3/17', '3/18'])\n",
    "data_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>3/10</th>\n",
       "      <td>563.3</td>\n",
       "      <td>553.2</td>\n",
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       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
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       "    <tr>\n",
       "      <th>3/17</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
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       "    <tr>\n",
       "      <th>3/18</th>\n",
       "      <td>558.2</td>\n",
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       "       high    low\n",
       "3/10  563.3  553.2\n",
       "3/11  556.6  549.1\n",
       "3/12  554.8  548.9\n",
       "3/13  556.8  542.7\n",
       "3/16  555.3  544.5\n",
       "3/17  552.2  546.4\n",
       "3/18  558.2  545.5"
      ]
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "data_pd = pd.DataFrame(data_np, columns=columns, index=dates)\n",
    "data_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we need three different variables to fully describe the data with numpy and only one with pandas. Furthermore, pandas comes with a large set of tools for operating on dataframes. Let's say we wanted to sort our data by the low price:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "outputs": [
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       "      <th>3/18</th>\n",
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       "      <th>3/17</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
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       "      <th>3/12</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/11</th>\n",
       "      <td>556.6</td>\n",
       "      <td>549.1</td>\n",
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       "      <th>3/10</th>\n",
       "      <td>563.3</td>\n",
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      "text/plain": [
       "       high    low\n",
       "3/13  556.8  542.7\n",
       "3/16  555.3  544.5\n",
       "3/18  558.2  545.5\n",
       "3/17  552.2  546.4\n",
       "3/12  554.8  548.9\n",
       "3/11  556.6  549.1\n",
       "3/10  563.3  553.2"
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     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "data_pd.sort_values(by=\"low\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or suppose we have another dataset of open and close prices that we'd like to join with our existing data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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       "      <th>3/11</th>\n",
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       "      <th>3/19</th>\n",
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       "      <td>556.4</td>\n",
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       "      <th>3/20</th>\n",
       "      <td>560.1</td>\n",
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      "text/plain": [
       "       open  close\n",
       "3/11  553.6  549.6\n",
       "3/12  551.9  553.9\n",
       "3/13  551.9  545.8\n",
       "3/16  549.4  552.9\n",
       "3/17  550.1  549.3\n",
       "3/18  550.9  557.9\n",
       "3/19  557.8  556.4\n",
       "3/20  560.1  558.8"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2 = pd.DataFrame([\n",
    "        [553.6, 549.6],\n",
    "        [551.9, 553.9 ],\n",
    "        [551.9, 545.8],\n",
    "        [549.4, 552.9],\n",
    "        [550.1, 549.3],\n",
    "        [550.9, 557.9],\n",
    "        [557.8, 556.4],\n",
    "        [560.1, 558.8]\n",
    "    ], \n",
    "    columns=['open', 'close'],\n",
    "    index=['3/11', '3/12', '3/13', '3/16','3/17', '3/18', '3/19', '3/20']\n",
    ")\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the dates for data_pd and df2 don't quite match. This might be a significant annoyance if we had to write a custom function to join these two dataframes, but fortunately we don't because pandas has a tool to do it for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>3/13</th>\n",
       "      <td>556.8</td>\n",
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       "      <td>551.9</td>\n",
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       "      <th>3/16</th>\n",
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       "    <tr>\n",
       "      <th>3/17</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "      <td>550.1</td>\n",
       "      <td>549.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/18</th>\n",
       "      <td>558.2</td>\n",
       "      <td>545.5</td>\n",
       "      <td>550.9</td>\n",
       "      <td>557.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.8</td>\n",
       "      <td>556.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560.1</td>\n",
       "      <td>558.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       high    low   open  close\n",
       "3/10  563.3  553.2    NaN    NaN\n",
       "3/11  556.6  549.1  553.6  549.6\n",
       "3/12  554.8  548.9  551.9  553.9\n",
       "3/13  556.8  542.7  551.9  545.8\n",
       "3/16  555.3  544.5  549.4  552.9\n",
       "3/17  552.2  546.4  550.1  549.3\n",
       "3/18  558.2  545.5  550.9  557.9\n",
       "3/19    NaN    NaN  557.8  556.4\n",
       "3/20    NaN    NaN  560.1  558.8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined = data_pd.join(df2, how='outer')\n",
    "df_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas even has a tool for plotting your data in a jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1108a1590>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rLJ7Odqyd0I26Hpc/pRfsP0Dc8OHG0Yt1a826N0Z1MKgGHl/3OPtT9tO9Xne+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\n+Ey1FJoaO/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108a1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this line is necessary to view plots in jupyter notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "df_joined.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110928a90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rLJ7Odqyd0I26Hpc/pRfsP0Dc8OHG0Yt1a826N0Z1MKgGHl/3OPtT9tO9Xne+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\n+Ey1FJoaO/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9bf050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_joined.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting data\n",
    "--\n",
    "**The .ix operator**\n",
    "\n",
    "You can slice a pandas dataframe in the same way you slice a numpy array using the `.ix` operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3/11</th>\n",
       "      <td>549.1</td>\n",
       "      <td>553.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/12</th>\n",
       "      <td>548.9</td>\n",
       "      <td>551.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/13</th>\n",
       "      <td>542.7</td>\n",
       "      <td>551.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        low   open\n",
       "3/11  549.1  553.6\n",
       "3/12  548.9  551.9\n",
       "3/13  542.7  551.9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.ix[1:4, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.ix` operator works just like selection on numpy arrays. You can select a subset of both rows and columns (as done above), or select a single row or column (shown below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high     556.6\n",
       "low      549.1\n",
       "open     553.6\n",
       "close    549.6\n",
       "Name: 3/11, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not specifying the start or end index, defaults to the first and last index\n",
    "df_joined.ix[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3/11    549.1\n",
       "3/12    548.9\n",
       "Name: low, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.ix[1:3,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you select a single row or column, you get back a pandas Series object, which is a labeled 1-dimensional array similar to a python dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "563.29999999999995"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_joined_one_row is a Series object, all columns, but only the first row\n",
    "df_joined_one_row = df_joined.ix[0,:]\n",
    "\n",
    "# we access a particular value just as if it were a dictionary\n",
    "df_joined_one_row['high']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The .loc operator**\n",
    "\n",
    "The more common way to select data is by label using the `.loc` operator. The `.loc` operator can take \n",
    "- a single label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "553.20000000000005"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.loc['3/10', 'low']  # select the \"low\" value from 3/10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a list or array of labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low      549.1\n",
       "high     556.6\n",
       "close    549.6\n",
       "Name: 3/11, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the \"low\", \"high\", and \"close\" values from 3/11\n",
    "df_joined.loc['3/11', ['low', 'high', 'close']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a label slice object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3/10    553.2\n",
       "3/11    549.1\n",
       "3/12    548.9\n",
       "3/13    542.7\n",
       "Name: low, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.loc['3/10':'3/13', 'low']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a boolean array or boolean series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3/12</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "      <td>551.9</td>\n",
       "      <td>553.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/16</th>\n",
       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
       "      <td>549.4</td>\n",
       "      <td>552.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/17</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "      <td>550.1</td>\n",
       "      <td>549.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       high    low   open  close\n",
       "3/12  554.8  548.9  551.9  553.9\n",
       "3/16  555.3  544.5  549.4  552.9\n",
       "3/17  552.2  546.4  550.1  549.3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns a boolean series which is True where the high value is less than 556 and False otherwise\n",
    "bool_array = df_joined.loc[:, 'high'] < 556\n",
    "\n",
    "# uses the boolean series above to select the \"True\" rows\n",
    "df_joined.loc[bool_array, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- or any combination of the above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3/10</th>\n",
       "      <td>553.2</td>\n",
       "      <td>563.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/11</th>\n",
       "      <td>549.1</td>\n",
       "      <td>556.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/12</th>\n",
       "      <td>548.9</td>\n",
       "      <td>554.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3/13</th>\n",
       "      <td>542.7</td>\n",
       "      <td>556.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        low   high\n",
       "3/10  553.2  563.3\n",
       "3/11  549.1  556.6\n",
       "3/12  548.9  554.8\n",
       "3/13  542.7  556.8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.loc['3/10':'3/13', ['low', 'high']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has a few more selection operators, including a `.query` operator that allows you to [select data with sql-like syntax](http://pandas.pydata.org/pandas-docs/stable/indexing.html#the-query-method-experimental). I won't cover all of those operators in this module, but you can read more [here](http://pandas.pydata.org/pandas-docs/stable/indexing.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a dataframe\n",
    "---\n",
    "We've seen above that we can create a dataframe from a numpy array or from a list of lists. In both cases we have to also specify the column labels and the index labels. Neither labels are *required*, and if we don't specify them pandas defaults to integer index labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>563.3</td>\n",
       "      <td>553.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>556.6</td>\n",
       "      <td>549.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>556.8</td>\n",
       "      <td>542.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>558.2</td>\n",
       "      <td>545.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1\n",
       "0  563.3  553.2\n",
       "1  556.6  549.1\n",
       "2  554.8  548.9\n",
       "3  556.8  542.7\n",
       "4  555.3  544.5\n",
       "5  552.2  546.4\n",
       "6  558.2  545.5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integer index labels are oftentimes fine for rows, but you'll almost always want labels for your columns. You can also create a dataframe from a dictionary where the key is the desired column name and the value is a list of values for that column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>563</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>556</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>556</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>555</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>552</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>558</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high  low\n",
       "0   563  553\n",
       "1   556  549\n",
       "2   554  548\n",
       "3   556  542\n",
       "4   555  544\n",
       "5   552  546\n",
       "6   558  545"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict = {'low': [553, 549, 548, 542, 544, 546, 545],\n",
    "             'high': [563, 556, 554, 556, 555, 552, 558]}\n",
    "pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that if we don't specify the column order with the `columns` param when creating a dataframe from a dictionary, the columns will be ordered alphabetically.\n",
    "\n",
    "Pandas also has tools for dealing with dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-03-10', '2015-03-11', '2015-03-12', '2015-03-13',\n",
       "               '2015-03-14', '2015-03-15', '2015-03-16'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2015-03-10', periods=7)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-10</th>\n",
       "      <td>563.3</td>\n",
       "      <td>553.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-11</th>\n",
       "      <td>556.6</td>\n",
       "      <td>549.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-12</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-13</th>\n",
       "      <td>556.8</td>\n",
       "      <td>542.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-14</th>\n",
       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-15</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-16</th>\n",
       "      <td>558.2</td>\n",
       "      <td>545.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high    low\n",
       "2015-03-10  563.3  553.2\n",
       "2015-03-11  556.6  549.1\n",
       "2015-03-12  554.8  548.9\n",
       "2015-03-13  556.8  542.7\n",
       "2015-03-14  555.3  544.5\n",
       "2015-03-15  552.2  546.4\n",
       "2015-03-16  558.2  545.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dates = pd.DataFrame(data_np, columns=['high', 'low'], index=dates)\n",
    "df_with_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the dates here are of type `datetime`, where before they were of type string. Having your dates as a `datetime` has several advantages. For one, a `datetime` object has *many* useful methods like getting the day of the week or finding the difference between to dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-10</th>\n",
       "      <td>563.3</td>\n",
       "      <td>553.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-11</th>\n",
       "      <td>556.6</td>\n",
       "      <td>549.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-12</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-13</th>\n",
       "      <td>556.8</td>\n",
       "      <td>542.7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-14</th>\n",
       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-15</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-16</th>\n",
       "      <td>558.2</td>\n",
       "      <td>545.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high    low  day_of_week\n",
       "2015-03-10  563.3  553.2            1\n",
       "2015-03-11  556.6  549.1            2\n",
       "2015-03-12  554.8  548.9            3\n",
       "2015-03-13  556.8  542.7            4\n",
       "2015-03-14  555.3  544.5            5\n",
       "2015-03-15  552.2  546.4            6\n",
       "2015-03-16  558.2  545.5            0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dates['day_of_week'] = dates.dayofweek\n",
    "df_with_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>days_since_mar_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-10</th>\n",
       "      <td>563.3</td>\n",
       "      <td>553.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-11</th>\n",
       "      <td>556.6</td>\n",
       "      <td>549.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-12</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "      <td>3</td>\n",
       "      <td>2 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-13</th>\n",
       "      <td>556.8</td>\n",
       "      <td>542.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-14</th>\n",
       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
       "      <td>5</td>\n",
       "      <td>4 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-15</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "      <td>6</td>\n",
       "      <td>5 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-16</th>\n",
       "      <td>558.2</td>\n",
       "      <td>545.5</td>\n",
       "      <td>0</td>\n",
       "      <td>6 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high    low  day_of_week  days_since_mar_10\n",
       "2015-03-10  563.3  553.2            1             0 days\n",
       "2015-03-11  556.6  549.1            2             1 days\n",
       "2015-03-12  554.8  548.9            3             2 days\n",
       "2015-03-13  556.8  542.7            4             3 days\n",
       "2015-03-14  555.3  544.5            5             4 days\n",
       "2015-03-15  552.2  546.4            6             5 days\n",
       "2015-03-16  558.2  545.5            0             6 days"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dates['days_since_mar_10'] = dates - dates[0]\n",
    "df_with_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And when using a dataframes `plot` method, the x-axis labels are always well formatted if the index is a datetime (this is not always the case if the dates are of type string):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110b585d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M4t93XODYol2FCIWIBq2xbBRWWOTix/0n7bbT2xNIzym7/t4qPLA0OMS21N4l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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b16410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we use the .loc operator to select only the columns we want to plot\n",
    "df_with_dates.loc[:,['high', 'low']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** If you have timeseries data, the dates don't have to be the index as we've done here. You could just as easily make them another column. If you chose to go that route, however, you'd need to specify which column to use as the x-axis when using the dataframes `plot` method (using the `x` parameter).\n",
    "\n",
    "We'll see in the next module how to import data from a text file or a sql database directly into a pandas dataframe.\n",
    "\n",
    "Dataframes at a quick glance\n",
    "---\n",
    "The dataframe that we've been working with in this module is relatively small. It is more common whoever to have dataframes with many more rows. In that case, you might still want to print your dataframe to the screen to see what you've got (what are the columns names and types, how sparse is the data, etc.), but you don't need (or want) to see the *all* of the data. In these cases the dataframes `head` and `tail` methods are very useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>days_since_mar_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-10</th>\n",
       "      <td>563.3</td>\n",
       "      <td>553.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-11</th>\n",
       "      <td>556.6</td>\n",
       "      <td>549.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-12</th>\n",
       "      <td>554.8</td>\n",
       "      <td>548.9</td>\n",
       "      <td>3</td>\n",
       "      <td>2 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high    low  day_of_week  days_since_mar_10\n",
       "2015-03-10  563.3  553.2            1             0 days\n",
       "2015-03-11  556.6  549.1            2             1 days\n",
       "2015-03-12  554.8  548.9            3             2 days"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dates.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we've used the `head` method to show only the first 3 rows in our dataframe. Similarly, we could use the `tail` method to see the rows at the end of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>days_since_mar_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-13</th>\n",
       "      <td>556.8</td>\n",
       "      <td>542.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-14</th>\n",
       "      <td>555.3</td>\n",
       "      <td>544.5</td>\n",
       "      <td>5</td>\n",
       "      <td>4 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-15</th>\n",
       "      <td>552.2</td>\n",
       "      <td>546.4</td>\n",
       "      <td>6</td>\n",
       "      <td>5 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-16</th>\n",
       "      <td>558.2</td>\n",
       "      <td>545.5</td>\n",
       "      <td>0</td>\n",
       "      <td>6 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high    low  day_of_week  days_since_mar_10\n",
       "2015-03-13  556.8  542.7            4             3 days\n",
       "2015-03-14  555.3  544.5            5             4 days\n",
       "2015-03-15  552.2  546.4            6             5 days\n",
       "2015-03-16  558.2  545.5            0             6 days"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dates.tail(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframes also come with a `describe` method to get some basic statistics for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>days_since_mar_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>556.742857</td>\n",
       "      <td>547.185714</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.455362</td>\n",
       "      <td>3.501632</td>\n",
       "      <td>2.160247</td>\n",
       "      <td>2 days 03:50:45.332114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>552.200000</td>\n",
       "      <td>542.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>555.050000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1 days 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>556.600000</td>\n",
       "      <td>546.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>557.500000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4 days 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>563.300000</td>\n",
       "      <td>553.200000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high         low  day_of_week       days_since_mar_10\n",
       "count    7.000000    7.000000     7.000000                       7\n",
       "mean   556.742857  547.185714     3.000000         3 days 00:00:00\n",
       "std      3.455362    3.501632     2.160247  2 days 03:50:45.332114\n",
       "min    552.200000  542.700000     0.000000         0 days 00:00:00\n",
       "25%    555.050000  545.000000     1.500000         1 days 12:00:00\n",
       "50%    556.600000  546.400000     3.000000         3 days 00:00:00\n",
       "75%    557.500000  549.000000     4.500000         4 days 12:00:00\n",
       "max    563.300000  553.200000     6.000000         6 days 00:00:00"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dates.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizations are another great way to explore unfamiliar data that we'll cover in a future module."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.2**\n",
    "\n",
    "In the cell below, we've loaded a dataframe of  Facebook likes, Twitter followers, or Wikipedia pageviews data for The Prodigy and Yoko Ono. The \"value\" field is the daily increase or decrease in the metric value (Facebook likes, Twitter followers, or Wikipedia pageviews). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>artist</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>Yoko Ono</td>\n",
       "      <td>Facebook likes</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-02</td>\n",
       "      <td>Yoko Ono</td>\n",
       "      <td>Facebook likes</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-03</td>\n",
       "      <td>Yoko Ono</td>\n",
       "      <td>Facebook likes</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>Yoko Ono</td>\n",
       "      <td>Facebook likes</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-05</td>\n",
       "      <td>Yoko Ono</td>\n",
       "      <td>Facebook likes</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    artist          metric  value\n",
       "0  2014-04-01  Yoko Ono  Facebook likes  177.0\n",
       "1  2014-04-02  Yoko Ono  Facebook likes  146.0\n",
       "2  2014-04-03  Yoko Ono  Facebook likes   81.0\n",
       "3  2014-04-04  Yoko Ono  Facebook likes   69.0\n",
       "4  2014-04-05  Yoko Ono  Facebook likes  103.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading our data frame from a pickle file (we'll cover this in the next module)\n",
    "metrics = pd.read_pickle('artist_metrics_small.pkl')\n",
    "metrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part a:** The elements in the `date` column are of type string. Convert them into datetime objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part b:** Create three new dataframes, one for Facebook data, one for Twitter followers data, and one for Wikipedia pageviews data (hint: use the `.loc` operator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part c:** For each of the new dataframes you created in part b, use the `.pivot` method to create a new dataframe where \"Yoko Ono\" and \"The Prodigy\" are columns and make \"date\" the dataframe's index. Doing this will reduce the length of your dataframes from part b by a factor of 1, and add one extra column to the width. We would say that the dataframe in it's original form was in a \"stacked\" or \"long\" format and after we perform the pivot it's in a \"wide\" format. The long format is often preferable for sparse data (where say one or both artists were missing data for most days), and the wide format is often preferred when plotting or performing analysis. Once you pivot both dataframes to a wide format, plot the results. Be sure to label the axis of your plots and give them titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define the pivotted Facebook dataframe and plot in this cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the pivotted Twitter dataframe and plot in this cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the pivotted Wikipedia dataframe and plot in this cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part d**: Take the pivotted Wikipedia dataframe and add a day_of_week column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part e:** Use the dateframe's `.group_by` method to get the mean value of Wikipedia pageviews for each day of the week. Plot the results for both artists. Give your plot a title, and for extra credit change the index from integers to the days of the week in string format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part f:** Using the results from part d, create a new dataframe with just data for *The Prodigy*. With this new dataframe, create a new \"week_of_year\" column. Now pivot this dataframe, making the day_of_week values the new columns, the values from \"The Prodigy\" column the values, and the \"week_of_year\" the new index. In this new dataframe, the values in the column labeled \"0\" are all the values from Mondays and the values in the column labeled \"6\" are all the values from Sundays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**part g:** Replace the numeric column labels with the list of days as strings defined below. Plot the results as a boxplot (look at the `kind` parameter for the `.plot` method). Add the parameter `sym=''` to the plot method to hide the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "days = ['MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT', 'SUN']\n",
    "# your code here"
   ]
  }
 ],
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