{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ipub": {
     "ignore": true
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Analysing structured data with data frames \n",
    "\n",
    "(c) 2019 [Steve Phelps](mailto:sphelps@sphelps.net)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data frames\n",
    "\n",
    "- The `pandas` module provides a powerful data-structure called a data frame.\n",
    "\n",
    "- It is similar, but not identical to:\n",
    "    - a table in a relational database,\n",
    "    - an Excel spreadsheet,\n",
    "    - a dataframe in R.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Types of data\n",
    "\n",
    "Data frames can be used to represent:\n",
    "\n",
    "- [Panel data](https://en.wikipedia.org/wiki/Panel_data)\n",
    "- [Time series](https://en.wikipedia.org/wiki/Time_series) data\n",
    "- [Relational data](https://en.wikipedia.org/wiki/Relational_model)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Loading data\n",
    "\n",
    "- Data frames can be read and written to/from:\n",
    "    - financial web sites\n",
    "    - database queries\n",
    "    - database tables\n",
    "    - CSV files\n",
    "    - json files\n",
    "    \n",
    "- Beware that data frames are memory resident;\n",
    "    - If you read a large amount of data your PC might crash\n",
    "    - With big data, typically you would read a subset or summary of the data via e.g. a select statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Importing pandas\n",
    "\n",
    "- The pandas module is usually imported with the alias `pd`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Series\n",
    "\n",
    "- A Series contains a one-dimensional array of data, *and* an associated sequence of labels called the *index*.\n",
    "\n",
    "- The index can contain numeric, string, or date/time values.\n",
    "\n",
    "- When the index is a time value, the series is a [time series](https://en.wikipedia.org/wiki/Time_series).\n",
    "\n",
    "- The index must be the same length as the data.\n",
    "\n",
    "- If no index is supplied it is automatically generated as `range(len(data))`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating a series from an array\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03245675,  0.41263151, -0.27993028, -0.95398035, -0.01473876])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.random.randn(5)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.032457\n",
       "b    0.412632\n",
       "c   -0.279930\n",
       "d   -0.953980\n",
       "e   -0.014739\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series = pd.Series(data, index=['a', 'b', 'c', 'd', 'e'])\n",
    "my_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting a series\n",
    "\n",
    "- We can plot a series by invoking the `plot()` method on an instance of a `Series` object.\n",
    "\n",
    "- The x-axis will autimatically be labelled with the series index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "my_series.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating a series with automatic index\n",
    "\n",
    "- In the following example the index is creating automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.032457\n",
       "1    0.412632\n",
       "2   -0.279930\n",
       "3   -0.953980\n",
       "4   -0.014739\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating a Series from a `dict`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
    "my_series = pd.Series(d)\n",
    "my_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Indexing a series with `[]`\n",
    "\n",
    "- Series can be accessed using the same syntax as arrays and dicts.\n",
    "\n",
    "- We use the labels in the index to access each element.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can also use the label like an attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Slicing a series\n",
    "\n",
    "\n",
    "- We can specify a range of labels to obtain a slice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series[['b', 'c']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Arithmetic and vectorised functions\n",
    "\n",
    "- `numpy` vectorization works for series objects too.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
    "squared_values = pd.Series(d) ** 2\n",
    "squared_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3.0\n",
       "b    5.0\n",
       "c    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series({'a' : 0., 'b' : 1., 'c' : 2.})\n",
    "y = pd.Series({'a' : 3., 'b' : 4., 'c' : 5.})\n",
    "x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',\n",
       "               '2000-01-05'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('1/1/2000', periods=5)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.032457\n",
       "2000-01-02    0.412632\n",
       "2000-01-03   -0.279930\n",
       "2000-01-04   -0.953980\n",
       "2000-01-05   -0.014739\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series = pd.Series(data, index=dates)\n",
    "time_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting a time-series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KjAYSgAkiknDKatOAw8aYTsDzwFOOjqu8n4jwr8t706t1JLd9sJ4tB7SSSKmG4IwjgoFAujFmlzGmDHgfSDplnSRgvv3xQuA80UuA1RkICfRn9mQbjUMCuW7+GrKPlVgdklJexxmJoDWw76TnmfZl1a5jjCkHCgCd91FnJLZxVSVRfnEZN7y9lpITWkmklDM5IxFU983+1DKPM1kHEZkuIikikpKTk+OE0JS3SGwdyfNX9OHXvUe4578btZJIqWpUVNbv34UzEkEm0Pak522AAzWtIyIBQCSQf+qGjDGzjTE2Y4wtJkbLRdX/N7pnS/5xQRc+XX+A177daXU4Srmd937ZW6/3OSMRrAE6i0h7EQkCrgIWn7LOYiDZ/vgy4GujX+lUPdx0bieS+rTimeXbWZZ60OpwlHIb2cdKeHrZtnq91+HrCIwx5SJyM7Ac8AfmGWM2i8gMIMUYsxiYC7wtIulUHQlc5ei4yjeJCE9d2ouMvGJu/2ADbZqGktg60uqwlLLcY59vpfRE/e7p4ZTrCIwxS40xXYwxHY0xj9mXPWhPAhhjSowxlxtjOhljBhpjdjljXOWbqiqJ+tM0NJDrF6SQfVQriZRv+z4tl0XrD3Djn+p34aXbtphQ6nSaR4QwJ3kABcdPcL1WEikfVnKiggcWpdKuWSh/1USgfE1Cq8Y8f2UfNuw7wp0LtZJI+aZZ3+1id24RM5MSCQn0r9c2NBEojzayRwvuGtWVzzYc4OWv060ORymX2pNbxKvfpjOmV0uHGnO6bdM5pc7UX4Z3JD2rkOdW7KBjTDgX9dJblyrvZ4zhgUWpBPv78cCYU7v61I0eESiPJyI8cWlP+rdryt8/Ws/GzCNWh6RUg1uy8SCr03L5x8iuxDYOcWhbmgiUVwgO8GfWpP40Cwvm+gUpHCrQSiLlvY6WnGDGki30bB3JNYPbObw9TQTKa0SHBzMn2UZhSTnT307heJlWEinv9Ozy7eQWlvLY+ET8/Rzv36mJQHmV7i0b8+JVfdm0v4B/fLSBynr2XlHKXW3MPMLbP2UweXA7erVp4pRtaiJQXuf8hFjuHd2Nzzcd5MWVaVaHo5TTVFQa7vsklWbhwfx9ZFenbVerhpRXuv6cDqRlFfLiyjQ6Ng9nbO9WVoeklMPe+SmDTfsLeGlCXxqHBDptu3pEoLySiPDo+EQGxkdx50cbWL9PK4mUZ8s+WsK/lm/nnM7RXOzkEmlNBMprBQf48/o1/YiJqKokOlhw3OqQlKq3mZ9vpbSikhlJiTj7Bo+aCJRXaxYezNzkARwvq+C6+SkUl5VbHZJSdbZqRw6fbTjATX/qRPvoMKdvXxOB8npdW0Tw8oS+bD14lDs+0Eoi5VlKTlTw4KJU2keHceOfOjTIGJoIlE84t1tz/nlhd5ZtPsRzK3ZYHY5SZ+z1b3eyJ6+YmUmJBAfUr6lcbbRqSPmMaWe3Jz27kFe+SadzbDhJfVpbHZJSp7Urp5DXv91JUp9WnN05usHG0SMC5TNEhBlJiQxqH8WdCzeybu9hq0NSqka/N5UL9OO+i7o36FiaCJRPCQrw441r+tMyMoTpC9ay/4hWEin3tHjDAX5Iz+OukV1pHuFYU7naaCJQPqdpWBBzk22UnqiqJCoq1Uoi5V4Kjp9g5pKt9G4TycRBjjeVq40mAuWTOjWP4JWr+7H90FFu+2C9VhIpt/Kv5dvJLyrlsfE9ndJUrjYOJQIRiRKRFSKSZv/dtJp1+ojIjyKyWUQ2isiVjoyplLMM7xLDg2MSWLEli2e+3G51OEoBsH7fEd75OYPJZ8WT2DrSJWM6ekRwD7DSGNMZWGl/fqpiYLIxpgcwCnhBRJzTMk8pByUPiWfioDhe/3Yn/12baXU4yseVV1Ry3yebiAkP5u8XdHHZuI4mgiRgvv3xfGDcqSsYY3YYY9Lsjw8A2UD9b66plBOJCI+M7cGQjs249+NNpOzJtzok5cPe/imDzQeO8tDFPYhwYlO52jiaCGKNMQcB7L+bn25lERkIBAE7HRxXKacJ9Pfjtav70apJCDe8vZZ9+cVWh6R80KGCEp79cgfDu8RwYc8WLh271kQgIl+JSGo1P0l1GUhEWgJvA9caYyprWGe6iKSISEpOTk5dNq+UQ5qEBjF3ygDKKiq5fkEKhVpJpFxs5pItnKioZEZSD6c3latNrYnAGHO+MSaxmp9FQJb9A/63D/rs6rYhIo2Bz4H7jTE/nWas2cYYmzHGFhOjs0fKtTrGhPPa1f1Iyy7k1v/8SoVWEikX+XZ7Np9vOsjN53aiXTPnN5WrjaNTQ4uBZPvjZGDRqSuISBDwCbDAGPORg+Mp1aDO6RzDwxcnsHJbNk8v22Z1OMoHVDWV20yHmDCmD2+YpnK1cTQRPAmMEJE0YIT9OSJiE5E59nWuAIYBU0Rkvf2nj4PjKtVgJp0Vz+Sz2jFr1S4+TNlndTjKy736TTp784t5tAGbytXGoaZzxpg84LxqlqcA19kfvwO848g4Srnag2MS2JVTxH2fbCK+WRgD20dZHZLyQunZhbzx3U7G923NkE4N11SuNnplsVLVCPD349WJ/WgbFcoNb6ewN08riZRzGWN44NNUGgX6888LG7apXG00EShVg8jQQOYmD6DSwLT5azhWcsLqkJQX+XT9fn7clcfdo7sRExFsaSyaCJQ6jfbRYbx+dT925xbxN60kUk5SUHyCR5dspU/bJkwYEGd1OJoIlKrNkE7RPJLUg2+35/D40q1Wh6O8wNPLt3G4uIzHxifi54KmcrXRO5QpdQauHtSOtKxC5n6/m87Nw7lqoPXf4pRnWrf3MO/9spepQ9vTo5VrmsrVRo8IlDpD91/UnWFdYrj/01R+3JlndTjKA1U1lUslNiKE20e4rqlcbTQRKHWGAvz9eGViX+Kjw7jxnbXszCm0OiTlYeb/mMHWg0d56OIEwoPdZ0JGE4FSddA4JJB/TxlAgJ8w9a015BeVWR2S8hAHC47z3JfbObdrDKMSXdtUrjaaCJSqo7ZRobyZbONQQQnTF6RQcqLC6pCUB5jx2RbKKw0zkhJd3lSuNpoIlKqHfnFNee6KPqRkHObOhRv1VpfqtL7Zls0XqYe45bzOtI0KtTqcP9BEoFQ9XdSrJXeP6sZnGw7w/Fc7rA5HuanjZRU8uDiVTs3Duf4ca5rK1cZ9zlYo5YFuHN6BjLwiXv46nbioUC63tbU6JOVmXvkmjX35x3l/+mCCAtzzu7cmAqUcICLMHJdI5uHj/POTTbRu2oghHa1rHqbcS1rWMWav2sWl/dowuEMzq8OpkXumJ6U8SKC/H69e3Y/4ZmHc+PZa0rO1rFRVNZW7/9NUQoMC+OeF3awO57Q0ESjlBJGNApk3ZQBBAX5c+9Yv5BWWWh2SstjH6/bz8+587hndjWbh1jaVq40mAqWcpG1UKHOSB5B9tJTrtazUpx0pLuOxpVvpF9eEKz3gvJEmAqWcqE/bJrxwZR/W7T3C3z/aoGWlPuqpZdsoOH6Cx8b3dIumcrXRRKCUk43u2ZJ7R3fj840HeXbFdqvDUS62NiOf//yyj6lD4+nesrHV4ZwRrRpSqgFMH9aBPXnFvPrNTtpFhXHFAPefHlCOO2FvKtcyMoTbznefpnK10USgVAMQEWYk9SDzcPHvZaVDLbwnrXKNt37Yw7ZDx5g1qT9hbtRUrjYOTQ2JSJSIrBCRNPvvpqdZt7GI7BeRVxwZUylP8VtZaceYcG58Zy1pWcesDkk1oANHjvP8Vzs4r1tzLkiItTqcOnH0HME9wEpjTGdgpf15TWYC3zk4nlIepXFIIHOn2AgO8Ofat9aQc0zLSr3VI59tptIYHh7bw+2aytXG0USQBMy3P54PjKtuJRHpD8QCXzo4nlIep03TUOYm28gt1LJSb/XVliyWb87i1vO6uGVTudo4mghijTEHAey/m5+6goj4Ac8Cdzo4llIeq3fbJrxwZV82ZB7hjg/Xa1mpFykuK+ehxZvpEhvOdee0tzqceqk1EYjIVyKSWs1P0hmO8VdgqTFm3xmMNV1EUkQkJScn5ww3r5RnGJXYgvsu7M7STYd4ermWlXqLl79OZ/+R4zw6rieB/p5ZkV/raW1jzPk1vSYiWSLS0hhzUERaAtnVrHYWcI6I/BUIB4JEpNAY84fzCcaY2cBsAJvNpl+ZlNeZdnZ7ducW8cZ3O2nXLJQJA+OsDkk5YEfWMd5ctYvL+7dhYPsoq8OpN0frmxYDycCT9t+LTl3BGHP1b49FZApgqy4JKOULRIRHxvYg8/Bx7v80lTZNG3FO5xirw1L1UFlpuO+TTYSHBHDvhd2tDschjh7HPAmMEJE0YIT9OSJiE5E5jganlDcK8PfjlYl96dw8nL++s44dWlbqkRauy2TNnsPcO7obUWFBVofjEDHGPWdgbDabSUlJsToMpRrMgSPHGffqDwT6+/HJTUNoHhFidUjqDB0uKuPPz35Lx5hwPrzhLLfqJyQia40xtrq8xzPPbCjlBVo1acTc5AHkF5Vx/fwUjpdpWamnePKLbRwrKefR8YlulQTqSxOBUhbq2SaSF6/qw8b9Bdz+gZaVeoKUPfl8kLKPaee0p1sLz2gqVxtNBEpZ7IIeLbj/ogSWbT7EU8u2WR2OOo3fmsq1btKIW8/rbHU4TuM5XZGU8mJTh8aTkVfErFW7iGsWytWD2lkdkqrGvO93sz3rGG9OthEa5D0fn97zlyjlwUSEB8cksC+/mAcXbaZN01CGd9GyUneSebiYF75KY0RCLCM8rKlcbXRqSCk3EeDvx8sT+9ElNoKb3l3H9kNaVupOHl68per32B4WR+J8mgiUciPhwQHMm2IjLNifqW+tIftoidUhKeDLzYf4amsWt53fmdZNGlkdjtNpIlDKzbSMrCorPVxcxnULUiguK7c6JJ9WVFrOw4s30zU2gqlne2ZTudpoIlDKDSW2juTlCX1J3V/Are+vp0LLSi3z0so0DhSU8Nj4RI9tKlcb7/yrlPIC53WP5cExCazYksUTS7daHY5P2nboKHO+382VtrbY4j23qVxttGpIKTc2ZWh79uQVM+f73bSLDmPSYC0rdZXKSsP9n6TSOCSAe0Z3szqcBqWJQCk394C9rPShRVXdSs/t+of7P6kG8NHafaRkHOaZy3rR1MObytVGp4aUcnP+fsJLE/rSvWVjbn53HVsOHLU6JK+XX1TGE19sY2D7KC7r38bqcBqcJgKlPEBYcABzkwcQERLItPlryNKy0gb1xNKtFJaU8+i4RI+7EX19aCJQykO0iAxh7hQbBcdPMG3+Gi0rbSA/78rjo7WZXD+sA11iI6wOxyU0ESjlQXq0iuSViX3ZcuAot/xHy0qdray88vc7x93yZ+9pKlcbTQRKeZg/d4vl4bE9+GprFo99rmWlzjTn+12kZRfyyNgeNArytzocl9GqIaU80OSz4tmTW8y8H3YTHx3K5LPirQ7J4+3LL+allWmM7BHLed29q6lcbTQRKOWh7ruoO3vzi3l48WbaNg3l3G5aVlpfxhgeWrwZPxEeutj7msrVRqeGlPJQ/n7Ci1f1IaFVY25+bx2bDxRYHZLHWr45i6+3ZXPHiC608sKmcrVxKBGISJSIrBCRNPvvpjWsFyciX4rIVhHZIiLxjoyrlKryW1lp40aBTHsrhUMFWlZaV0Wl5Tzy2Wa6tYhgypB4q8OxhKNHBPcAK40xnYGV9ufVWQA8Y4zpDgwEsh0cVyllF9s4hHlTBnCs5ART31pDUamWldbFC1/t4GBBCY+N70mAlzaVq42jf3USMN/+eD4w7tQVRCQBCDDGrAAwxhQaY4odHFcpdZLuLRvz6tX92J51jFv+86uWlZ6hLQeOMu+HPUwYGEf/dtVOaPgERxNBrDHmIID9d3Vnq7oAR0TkYxH5VUSeERHfqctSykX+1LU5D4/twcpt2cxcssXqcNxeZaXh/k830aRRIHeP6mp1OJaqtWpIRL4CWlTz0n11GOMcoC+wF/gAmALMrWas6cB0gLi4uDPcvFLqN5MGtyMjt6iqW2mzUK4d6p03UnGGD1L2sW7vEZ69vDdNQr27qVxtak0Expjza3pNRLJEpKUx5qCItKT6uf9M4FdjzC77ez4FBlNNIp9T/VoAABBASURBVDDGzAZmA9hsNj22Vaoe7r2wqqx05pIttG0ayvledqN1Z8gtLOXJL7YxqH0Ul/RrbXU4lnN0amgxkGx/nAwsqmadNUBTEYmxP/8zoMetSjUQfz/hhav6kNg6klve/5XU/VpWeqrHl26luKycx8b7RlO52jiaCJ4ERohIGjDC/hwRsYnIHABjTAXwD2CliGwCBHjTwXGVUqcRGhTAnMk2mjSq6lZ6sOC41SG5jR935vHxuv1MH9aBTs19o6lcbcQY95yBsdlsJiUlxeowlPJo2w4d5bLXf6RtVCgf3XgW4cG+3UygrLyS0S+uoqyiki9vG+6V/YREZK0xxlaX9/hm0axSPqJbi6qy0h1Zx/jbe+sor6i0OiRLvbl6FztzipgxNtErk0B9aSJQyssN7xLDzKREvtmew4wlW3DXWYCGtjevqqnc6MQW2pfpFL59nKiUj5g4KI6MvCJmrdpFfLMwpp7tW2WlxhgeXJxKgJ/w4MUJVofjdjQRKOUj7h7VjYy8YmZ+voU2TRtxQY/qLg/yTstSD/Ht9hweGJNAy0jfaypXG50aUspH+PkJz1/Zh16tI7n1/fVsyvSNstLC0nIe/mwzCS0bk3xWO6vDcUuaCJTyIY2C/Hkz2UZUWBBT569h/xHvLyt97ssdZB8r5bHxiT7bVK42uleU8jHNI0L497UDKCmrYNpbazhWcsLqkBpM6v4C3vrfbiYOjKNvnO82lauNJgKlfFCX2Aheu6YfadmF3Pzer15ZVlpRabjv01SiwoK4a2Q3q8Nxa5oIlPJR53SO4dFxiXy3I4eHFm/2urLS//yylw37jnD/RQlEhgZaHY5b06ohpXzYhIFxZOQV88Z3O2kfHcZ153SwOiSnyDlWytPLtjGkYzOS+rSyOhy3p4lAKR9318iu7M0v4rGlW2kbFcpILygrfXzpVkpOVDJznDaVOxM6NaSUj/PzE567og+92zTh1vd/ZWPmEatDcsj/0nP55Nf93Di8Ax1jwq0OxyNoIlBKERLoz5xkG9HhwUybn0LmYc+8m2xpeQX3f5pKXFQofz23k9XheAxNBEopAKLDg/n3lAGUnKhg2lspHPXAstLZ3+1iV24RM5J6EBKoTeXOlCYCpdTvOsdG8MY1/dmZU8hN767jhAeVle7JLeLlb9K5qFdL/tRVm8rVhSYCpdT/M7RTNI+P78nqtFyPKSutaiq3mSB/Px4co03l6kqrhpRSf3DFgLbsySvitW93Et8slOnDOlod0mkt3XSIVTtyeOjiBGIbh1gdjsfRRKCUqtY/LuhKRn4xjy/dRtumoYzu2dLqkKp1rOQEj3y2mcTWjZk0WJvK1YdODSmlquXnJzx7eW/6xTXhtg/W8+vew1aHVK1nv9xBTmEpj43rqU3l6smhvSYiUSKyQkTS7L+r7eokIk+LyGYR2SoiL4le4aGURwgJ9OfNyTZiG4dw/YIU9uW7V1lp6v4CFvy4h2sGtaN32yZWh+OxHE2f9wArjTGdgZX25/+PiAwBhgK9gERgADDcwXGVUi7SLDyYeVMGUFZeydS31lBw3D3KSisqDfd9somosGD+MbKr1eF4NEcTQRIw3/54PjCumnUMEAIEAcFAIJDl4LhKKRfq1DycNyb1Z3dukduUlb73cwYbMgt4YEx3IhtpUzlHOJoIYo0xBwHsv/9QvGuM+RH4Bjho/1lujNnq4LhKKRcb0jGaJy7pyffpudz/SaqlZaXZx0p4etl2zu4Uzdje2lTOUbVWDYnIV0B1XajuO5MBRKQT0B1oY1+0QkSGGWNWVbPudGA6QFxc3JlsXinlQpfb2rI3v5iXv04nPjqMv/zJmrLSR5dspbS8khlJPbSpnBPUmgiMMefX9JqIZIlIS2PMQRFpCWRXs9p44CdjTKH9PV8Ag4E/JAJjzGxgNoDNZnP/q1iU8kF3jOhCRl4xTy3bRrtmoVzo4rLS79NyWbzhALee15kO2lTOKRydGloMJNsfJwOLqllnLzBcRAJEJJCqE8U6NaSUhxIRnr6sF7Z2Tbn9g/Wsc2FZacmJCh5YlEp8s1DLjka8kaOJ4ElghIikASPszxERm4jMsa+zENgJbAI2ABuMMZ85OK5SykIhgf7MmtSfFpEhXD/fdWWlb3y3k925Rcwcl6hN5ZxI3LWPiM1mMykpKVaHoZQ6jZ05hVzy2v+IDg/i478ObdDqnd25RYx8YRUje7Tg5Ql9G2wcTycia40xtrq8Ry/DU0rVW8eYcGZN6s/e/GL+8s5aysobpqzUGMODi1IJ9vfjgYu6N8gYvkwTgVLKIYM7NOPJS3rxv5153P/ppgYpK/1s40FWp+Vy56iuNNemck6nTeeUUg67tH8bMvKLeWllGu2ahXGTE+8OdrTkBDOXbKFXm0iuHqRN5RqCJgKllFPcfn5nMvKKeGb5duKiQrnYSRd6Pbt8O3mFpcxLHoC/n14z0BB0akgp5RS/lZUOiG/K3z/awNqMfIe3uTHzCAt+ymDyWfH0bBPphChVdTQRKKWcJjjAn9mTbLRu0ojrF6xlb179y0qrmsqlEhMezB0XdHFilOpUmgiUUk7VNCyIeVMGUGkMU976hYLi+nUrfeenDDbtL+CBMQk0DtGmcg1JE4FSyunaR4cxe5KNzPzj3PBOSp3LSrOOlvDM8u2c0zmaMb3c885o3kQTgVKqQQxsH8XTl/Xip1353Ptx3cpKZy7ZQllFJTOTErWpnAto1ZBSqsGM69uajLxinv9qB+2jQ7n5z51rfc+qHTks2XiQ28/vQnx0mAuiVJoIlFIN6pbzOpGRV8S/vtxB26hQkvq0rnHd35rKdYgO48Y/dXBhlL5NE4FSqkGJCE9c2pPMI8e586ONtGrSiAHxUdWu+9q3O8nIK+bd6wYRHKBN5VxFzxEopRpcVVlpf9o0bcT0BSnsyS36wzq7cgp549udjOvTiqGdoi2I0ndpIlBKuUST0KqyUoCpb63hSHHZ768ZY3hgUSrBgX7cd1GCVSH6LE0ESimXiY8O483JNjIPH2f622spLa8AYPGGA/yQnsddo7oRExFscZS+RxOBUsqlbPFRPHN5L37Znc+9/91EQXFVU7nebZswcaDeq9wKerJYKeVySX1aszevmGdX7GDd3sPkF5Xx1rUDtamcRfSIQClliZv/3IlL+7VhT14xyUPiSWytTeWsokcESilLiAhPXNKT4V1juCAh1upwfJomAqWUZYIC/BjrpPsWqPpzaGpIRC4Xkc0iUikiNd4sWURGich2EUkXkXscGVMppZRzOXqOIBW4BFhV0woi4g+8CowGEoAJIqKFwkop5SYcmhoyxmwFausOOBBIN8bssq/7PpAEbHFkbKWUUs7hiqqh1sC+k55n2pcppZRyA7UeEYjIV0CLal66zxiz6AzGqO5wodrG5CIyHZgOEBenF5YopZQr1JoIjDHnOzhGJtD2pOdtgAM1jDUbmA1gs9nO/C4WSiml6s0VU0NrgM4i0l5EgoCrgMUuGFcppdQZcLR8dLyIZAJnAZ+LyHL78lYishTAGFMO3AwsB7YCHxpjNjsWtlJKKWeRutxH1JVE5Biw3eo4vEgkUGB1EF5E96dz6f50nq7GmIi6vMGdryzeboyp8SI1VTciMtsYM93qOLyF7k/n0v3pPCKSUtf3aNM53/GZ1QF4Gd2fzqX700LuPDWUokcESilVN/X57HTnI4LZVgeglFIeqM6fnW6bCOzXFKh6qK7Jn4i8a1+WKiLzRCTQ6jg9RQ37c66IbBCRjSKyUETCrY7TU5yuCaWIvCwihVbF5g3q89nptolA1c9pmvy9C3QDegKNgOssC9KDnGZ/3m6M6W2M6QXspapEWtXidE0o7R2Mm1gYns9ym0RQw7eum+3PjYhEWx2jh/i9yZ8xpgx4H0gyxiw1dsAvVF3hrWpX0/48CiBVHRcbUUPbFPUH1e5Pe4J4BrjL0ug8UA2fnW+JyG4RWW//6XO6bbhFIjjNt4QfgPOBDAvD8zSnbfJnnxKaBCxzcVyeqsb9KSL/Bg5RdaT1sutD80g17c+bgcXGmIOWROWhamnzf6cxpo/9Z/3ptuMWiYCav3X9aozZY21oHqe2Jn+vAauMMatdFI+nq3F/GmOuBVpRdcX8la4MyoNVtz+DgcvRZFof1X521nUj7pIItFW189TY5E9EHgJigDssiMtTnbZpojGmAvgAuNTFcXmq6vbnHqATkC4ie4BQEUl3fWge6XSfnY/ZixmeF5Hg023EXRLBGbeqVrWqtsmfiFwHjAQmGGMqLY3Qs9S0PzvB7+cILga2WRijJ6luf35qjGlhjIk3xsQDxcaYTpZG6Tlq+uy8l6opywFAFHD36TbiLi0mzrhVtTo9Y0y5iPzW5M8fmGeM2SwiG6g61/Kj/Y5yHxtjZlgYqkeobn9SNRW0WkQaU/UPcQPwF+ui9Bw1/f9pcVierNrPzpPOtZTaz2X943QbcYsri0UkANgBnAfsp+pbw8Tf/gexHy7ajDG5lgWplFJupqbPTiDfGHPQfsT6PFBijLmnpu24xdRQTa2qReQWe5vrNsBGEZljZZxKKeVOTtPm/10R2QRsAqKBR0+3Hbc4IlBKKWUdtzgiUEopZR1NBEop5eMsTwTaYEoppaxleSJQSillLbdIBCISLiIrRWSdiGwSkST78ngR2Soib4rIZhH5UkQaWR2vUkp5E8urhuxTQ02AUGPMUXuX0Z+AzkA7IJ2qawjWi8iHVDWmese6iJVSyru4y5XFAjwuIsOASqp6ZcTaX9t9Uue8tUC868NTSinv5S6J4GqqmqH1N8acsF9JHGJ/rfSk9Sqo6v2ulFLKSdziHAEQCWTbk8C5VE0JKaWUcgFLjwjsfTJKqbqN4mcikgKsRzs5KqWUy1h6slhEegNvGmMGWhaEUkr5OMumhkTkRuA/wP1WxaCUUsoNykeVUkpZy11OFiullLKISxOBiLQVkW/sVwtvFpFb7cujRGSFiKTZfze1LxcReUlE0u333ux30raS7euniUiyK/8OpZTyJi6dGhKRlkBLY8w6EYmg6gKxccAUqu6o86SI3AM0NcbcLSIXAn8DLgQGAS8aYwaJSBSQAtiouj/nWqquQTjssj9GKaW8hEuPCIwxB40x6+yPj1F1R53WQBIw377afKqSA/blC0yVn4Am9mQyElhhjMm3f/ivAEa58E9RSimvYWXVUDzQF/gZiP3tZsv2383tq7UG9p30tkz7spqWK6WUqiNLEoGIhAP/BW4zxhw93arVLDOnWa6UUqqOXJ4IRCSQqiTwrjHmY/viLPuUz2/nEbLtyzOBtie9vQ1w4DTLlVJK1ZGrq4YEmAtsNcY8d9JLi4HfKn+SgUUnLZ9srx4aDBTYp46WAxeISFN7hdEF9mVKKaXqyNVVQ2cDq4FNVLWbBvgnVecJPgTigL3A5caYfHvieIWqE8HFwLXGmBT7tqba3wvwmDHm3y77Q5RSyovolcVKKeXj9MpipZTycZoIlFLKx2kiUEopH6eJQCmlfJwmAqWU8nGaCJRSysdpIlBKKR+niUAppXzc/wH3geesjbQbWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = time_series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Missing values\n",
    "\n",
    "- Pandas uses `nan` to represent missing data.\n",
    "\n",
    "- So `nan` is used to represent missing, invalid or unknown data values.\n",
    "\n",
    "- It is important to note that this only convention only applies within pandas.\n",
    "    - Other frameworks have very different semantics for these values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## DataFrame\n",
    "\n",
    "- A data frame has multiple columns, each of which can hold a *different* type of value.\n",
    "\n",
    "- Like a series, it has an index which provides a label for each and every row. \n",
    "\n",
    "- Data frames can be constructed from:\n",
    "    - dict of arrays,\n",
    "    - dict of lists,\n",
    "    - dict of dict\n",
    "    - dict of Series\n",
    "    - 2-dimensional array\n",
    "    - a single Series\n",
    "    - another DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "## Creating a dict of series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': a    1.0\n",
       " b    2.0\n",
       " c    3.0\n",
       " dtype: float64, 'y': a    4.0\n",
       " b    5.0\n",
       " c    6.0\n",
       " d    7.0\n",
       " dtype: float64, 'z': a    0.1\n",
       " b    0.2\n",
       " c    0.3\n",
       " d    0.4\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_dict = {\n",
    "        'x' : \n",
    "            pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
    "        'y' : \n",
    "            pd.Series([4., 5., 6., 7.], index=['a', 'b', 'c', 'd']),\n",
    "        'z' :\n",
    "            pd.Series([0.1, 0.2, 0.3, 0.4], index=['a', 'b', 'c', 'd'])\n",
    "}\n",
    "\n",
    "series_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Converting the dict to a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x    y    z\n",
       "a  1.0  4.0  0.1\n",
       "b  2.0  5.0  0.2\n",
       "c  3.0  6.0  0.3\n",
       "d  NaN  7.0  0.4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(series_dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotting data frames\n",
    "\n",
    "- When plotting a data frame, each column is plotted as its own series on the same graph.\n",
    "\n",
    "- The column names are used to label each series.\n",
    "\n",
    "- The row names (index) is used to label the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The outer dimension is the column index.\n",
    "\n",
    "- When we retrieve a single column, the result is a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x']['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.x.b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Projections\n",
    "\n",
    "- Data frames can be sliced just like series.\n",
    "- When we slice columns we call this a *projection*, because it is analogous to specifying a subset of attributes in a relational query, e.g. `SELECT x FROM table`.\n",
    "- If we project a single column the result is a series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2.0\n",
       "c    3.0\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice = df['x'][['b', 'c']]\n",
    "slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(slice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Projecting multiple columns\n",
    "\n",
    "- When we include multiple columns in the projection the result is a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x    y\n",
       "a  1.0  4.0\n",
       "b  2.0  5.0\n",
       "c  3.0  6.0\n",
       "d  NaN  7.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice = df[['x', 'y']]\n",
    "slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(slice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vectorization\n",
    "\n",
    "- Vectorized functions and operators work just as with series objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.0\n",
       "b    7.0\n",
       "c    9.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x'] + df['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x     y     z\n",
       "a  1.0  16.0  0.01\n",
       "b  4.0  25.0  0.04\n",
       "c  9.0  36.0  0.09\n",
       "d  NaN  49.0  0.16"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Logical indexing\n",
    "\n",
    "- We can use logical indexing to retrieve a subset of the data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c     True\n",
       "d    False\n",
       "Name: x, dtype: bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x'] >= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x    y    z\n",
       "b  2.0  5.0  0.2\n",
       "c  3.0  6.0  0.3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['x'] >= 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Descriptive statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To quickly obtain descriptive statistics on numerical values use the `describe` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.290994</td>\n",
       "      <td>0.129099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>0.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.5</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x         y         z\n",
       "count  3.0  4.000000  4.000000\n",
       "mean   2.0  5.500000  0.250000\n",
       "std    1.0  1.290994  0.129099\n",
       "min    1.0  4.000000  0.100000\n",
       "25%    1.5  4.750000  0.175000\n",
       "50%    2.0  5.500000  0.250000\n",
       "75%    2.5  6.250000  0.325000\n",
       "max    3.0  7.000000  0.400000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Accessing a single statistic\n",
    "\n",
    "- The result is itself a DataFrame, so we can index a particular statistic like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()['x']['mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Accessing the row and column labels\n",
    "\n",
    "- The row labels (index) and column labels can be accessed:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['x', 'y', 'z'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Head and tail\n",
    "\n",
    "- Data frames have `head()` and `tail()` methods which behave analgously to the Unix commands of the same name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Financial data\n",
    "\n",
    "- Pandas was originally developed to analyse financial data.\n",
    "\n",
    "- We can download tabulated data in a portable format called [Comma Separated Values (CSV)](https://www.loc.gov/preservation/digital/formats/fdd/fdd000323.shtml)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "googl = pd.read_csv('data/GOOGL.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the first few rows\n",
    "\n",
    "- When working with large data sets it is useful to view just the first/last few rows in the dataset.\n",
    "\n",
    "- We can use the `head()` method to retrieve the first rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-13</td>\n",
       "      <td>503.878876</td>\n",
       "      <td>516.941956</td>\n",
       "      <td>503.753754</td>\n",
       "      <td>516.751770</td>\n",
       "      <td>516.751770</td>\n",
       "      <td>3155600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-14</td>\n",
       "      <td>517.477478</td>\n",
       "      <td>520.395386</td>\n",
       "      <td>515.690674</td>\n",
       "      <td>518.133118</td>\n",
       "      <td>518.133118</td>\n",
       "      <td>2331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-15</td>\n",
       "      <td>517.952942</td>\n",
       "      <td>519.519531</td>\n",
       "      <td>515.670654</td>\n",
       "      <td>517.297302</td>\n",
       "      <td>517.297302</td>\n",
       "      <td>2550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-11-18</td>\n",
       "      <td>518.393372</td>\n",
       "      <td>524.894897</td>\n",
       "      <td>515.135132</td>\n",
       "      <td>516.291321</td>\n",
       "      <td>516.291321</td>\n",
       "      <td>3515800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>516.376404</td>\n",
       "      <td>517.892883</td>\n",
       "      <td>512.037048</td>\n",
       "      <td>513.113098</td>\n",
       "      <td>513.113098</td>\n",
       "      <td>2260900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2013-11-13  503.878876  516.941956  503.753754  516.751770  516.751770   \n",
       "1  2013-11-14  517.477478  520.395386  515.690674  518.133118  518.133118   \n",
       "2  2013-11-15  517.952942  519.519531  515.670654  517.297302  517.297302   \n",
       "3  2013-11-18  518.393372  524.894897  515.135132  516.291321  516.291321   \n",
       "4  2013-11-19  516.376404  517.892883  512.037048  513.113098  513.113098   \n",
       "\n",
       "    Volume  \n",
       "0  3155600  \n",
       "1  2331000  \n",
       "2  2550000  \n",
       "3  3515800  \n",
       "4  2260900  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "googl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the last few rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>2019-11-06</td>\n",
       "      <td>1290.089966</td>\n",
       "      <td>1292.989990</td>\n",
       "      <td>1282.270020</td>\n",
       "      <td>1291.010010</td>\n",
       "      <td>1291.010010</td>\n",
       "      <td>1231300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>2019-11-07</td>\n",
       "      <td>1294.280029</td>\n",
       "      <td>1322.650024</td>\n",
       "      <td>1293.750000</td>\n",
       "      <td>1306.939941</td>\n",
       "      <td>1306.939941</td>\n",
       "      <td>2257000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>2019-11-08</td>\n",
       "      <td>1301.520020</td>\n",
       "      <td>1317.109985</td>\n",
       "      <td>1301.520020</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1519600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>1304.000000</td>\n",
       "      <td>1304.900024</td>\n",
       "      <td>1295.869995</td>\n",
       "      <td>1298.280029</td>\n",
       "      <td>1298.280029</td>\n",
       "      <td>861700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>2019-11-12</td>\n",
       "      <td>1298.569946</td>\n",
       "      <td>1309.349976</td>\n",
       "      <td>1294.239990</td>\n",
       "      <td>1297.209961</td>\n",
       "      <td>1297.209961</td>\n",
       "      <td>1442600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date         Open         High          Low        Close  \\\n",
       "1505  2019-11-06  1290.089966  1292.989990  1282.270020  1291.010010   \n",
       "1506  2019-11-07  1294.280029  1322.650024  1293.750000  1306.939941   \n",
       "1507  2019-11-08  1301.520020  1317.109985  1301.520020  1309.000000   \n",
       "1508  2019-11-11  1304.000000  1304.900024  1295.869995  1298.280029   \n",
       "1509  2019-11-12  1298.569946  1309.349976  1294.239990  1297.209961   \n",
       "\n",
       "        Adj Close   Volume  \n",
       "1505  1291.010010  1231300  \n",
       "1506  1306.939941  2257000  \n",
       "1507  1309.000000  1519600  \n",
       "1508  1298.280029   861700  \n",
       "1509  1297.209961  1442600  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "googl.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Converting to datetime values\n",
    "\n",
    "- So far, the `Date` attribute is of type string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2013-11-13'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "googl.Date[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(googl.Date[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In order to work with time-series data, we need to construct an index containing time values.\n",
    "\n",
    "- Time values are of type `datetime` or `Timestamp`.\n",
    "\n",
    "- We can use the function `to_datetime()` to convert strings to time values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2013-11-13\n",
       "1   2013-11-14\n",
       "2   2013-11-15\n",
       "3   2013-11-18\n",
       "4   2013-11-19\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(googl['Date']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Setting the index\n",
    "\n",
    "- Now we need to set the index of the data-frame so that it contains the sequence of dates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2013-11-13 00:00:00')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "googl.set_index(pd.to_datetime(googl['Date']), inplace=True)\n",
    "googl.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(googl.index[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting series\n",
    "\n",
    "- We can plot a series in a dataframe by invoking its `plot()` method.\n",
    "\n",
    "- Here we plot a time-series of the daily traded volume:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = googl['Volume'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Adjusted closing prices as a time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "googl['Adj Close'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Slicing series using date/time stamps\n",
    "\n",
    "- We can slice a time series by specifying a range of dates or times.\n",
    "\n",
    "- Date and time stamps are specified strings representing dates in the required format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "googl['Adj Close']['1-1-2016':'1-1-2017'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Resampling \n",
    "\n",
    "- We can *resample* to obtain e.g. weekly or monthly prices.\n",
    "\n",
    "- In the example below the `'W'` denotes weekly.\n",
    "\n",
    "- See [the documentation](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) for other frequencies.\n",
    "\n",
    "- We group data into weeks, and then take the last value in each week.\n",
    "\n",
    "- For details of other ways to resample the data, see [the documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Resampled time-series plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-11-17    517.297302\n",
       "2013-11-24    516.461487\n",
       "2013-12-01    530.325317\n",
       "2013-12-08    535.470459\n",
       "2013-12-15    530.925903\n",
       "Freq: W-SUN, Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_prices = googl['Adj Close'].resample('W').last()\n",
    "weekly_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_prices.plot()\n",
    "plt.title('Prices for GOOGL sampled at weekly frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Converting prices to log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_rets = np.diff(np.log(weekly_prices))\n",
    "plt.plot(weekly_rets)\n",
    "plt.xlabel('t'); plt.ylabel('$r_t$')\n",
    "plt.title('Weekly log-returns for GOOGL')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Converting the returns to a series\n",
    "\n",
    "- Notice that in the above plot the time axis is missing the dates.\n",
    "\n",
    "- This is because the `np.diff()` function returns an array instead of a data-frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(weekly_rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can convert it to a series thus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-11-24   -0.001617\n",
       "2013-12-01    0.026490\n",
       "2013-12-08    0.009655\n",
       "2013-12-15   -0.008523\n",
       "2013-12-22    0.036860\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_rets_series = pd.Series(weekly_rets, index=weekly_prices.index[1:])\n",
    "weekly_rets_series.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Plotting with the correct time axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Now when we plot the series we will obtain the correct time axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UGs30RQsXTd0QQkXXFtNoph9auGjqhva5aDTTFy1cNHUjqIpc32ZoNJpJoGGECxGdTUQbiWgTEV0W8/7pRPQYETlEdJ7y3kVE9IL/c9HUtVozHoTPRTv0NZrpR0MIFyIyAVwJ4PUAVgF4OxGtUg7bDuC9AH6tfLYTwOcBnARgDYDPE9GMyW6zZvyE+7nUtx0ajWbiaQjhAi4UNjHGtjDGCgB+C+Bc+QDG2DbG2FMAPOWzZwG4nTHWyxjrA3A7gLOnotGa8RH6XLR00WimG40iXBYA2CH93+2/NqGfJaJLiGgdEa3r6ekZU0M1E4cORdZopi+NIlwo5rVq55yqP8sY+zFjbDVjbHVXV1fVjdNMDiKJUisuGk319Azl692EqmgU4dINYJH0/0IAu6bgs5o6oh36Gk1tPN09gDVfvQNbeobr3ZRRaRThshbASiJaSkQJABcCuKnKz94K4HVENMN35L/Of03T4DDt0NdoaqJnOAfGgP0jhXo3ZVQaQrgwxhwAHwEXChsAXM8YW09ElxPROQBARCcSUTeA8wH8iIjW+5/tBfAlcAG1FsDl/muaBkdrLhpNbTguHyuu1/hjxqp3AwSMsZsB3Ky89jnp77XgJq+4z14N4OpJbaBmwtFCRaOpDSFUvANAuDSE5qI5ONF5LhpNbTj+oHEPgEGjhYumbjBtFtNoakJoLgeCWUwLF03d0LXFNJraEJrLgbAg08JFUze0Q1+jqQ0v0Fzq3JAq0MJFUzcCzV7LFo2mKhxtFtNoRkf7XDSa2nD9shYHwpjRwkVTN3RtMY2mNoTm4mjNRaMpjw5F1mhqQ+e5aA5asgUXBac6b6M2i2kOJIbzDh7ZWt/iH9rnojloueiaR/DVmzdUdayuLTZ1PLa9Dxf++MGqBb+mlP97rBtv/8lDyBScurXB1UmUmoOVPYM57B3KVXWspzcLmzIe396Ph7b0Yv/IgVGuvREZybtwPVZXAS1qi2mzmOagw3FZMABGQydRTh15xwUA5IpacxkrIlKrniYpobFozUVz0OF6rGofSqi5TGaLNACQ94VKrujWuSXVsXZbL774p/X1bkYEkbhYz4k9CEWejpoLEV0q/X3YxDZHc6DjeKzqMEkxRrVDf/LJOweWcLnzub342QPb6t2MCOHEXr82HEgO/apL7hNRB4D/AXAYEeUAPAXg/QAunqS2HbTc+MRO7OrP4UOvWl7vptSM63lVd3yd5zJ1HGhmMddjYIz/No24ncynnkaoSOyK/VwOgEEzquZCRAYRfYYx1s8YuxjAFwE8DGAlgP+b7AYejPz5qd343bod9W7GmHA8Vrtw0ZrLpBNoLs6Bobm4QbJg4wjDRsgxcRqgDdUyqubCGPOI6EwAX/X/F1sIPzqZDTuYKbpeMBkcaDhu9WYxnUQ5dQifS/4AMYsFwsVlSDbIloaNUO5+OoYiP05EnyciHQAwBRQcLzBj1JNndg7gh/dsrukzbg2aC9NmsSlDaCzZA0S4CI2l2sjDqaARSq8cSD6XaoXFIgAXAthFRDcS0ZeI6PxJbNdBTcHxGsI2ftOTu/CNvz5X02ecmnwu4nfjD5QDnTBarP79qhoa2ixWx/7qHUBmsaqEC2PsAsbYEQAWg/tcNgFYM5kNO5gpuI2huWQKDjxW/SrJ81htx+tQ5Iq8+bv34cd/r01zLEfo0K9/v6oGobE0UoHGRtAaGiGooFpqsmYyxvIAHvN/NJNEwfFQdFndI2WyBb5qLLoeTMMc9XjR4av2uTTASrCR2bZvBFv3jUzIucJQ5MbRBCrhNoAJSsVThMu+4Txe3J/BCYtnTFkbGiGRs1oaxodCRGcT0UYi2kREl8W8nySi6/z3HyaiJf7rS4goS0RP+D8/nOq2TzQFP1ur3nWgxCq3UOW2d7VG0wQypfHHSV0oTGBgRyPluTDG8OSO/orHBAuVBtpyUd1i+IIfPoi3/eCBKY12bATtqVoaQrgQkQngSgCvB7AKwNuJaJVy2PsB9DHGVoDn23xdem8zY+w4/+eDU9LoSUQIlXqbxkSBvmKVE5xTo51c57lUxvHYxAkXX6g0Qijyrx7ejnOvvB93bdxb9phGcJ6rqFrDFl+rnMrIzukYLTbZrAGwiTG2hTFWAPBbAOcqx5wL4Fr/798DOIOIGiO7SqHgePjlQy+OeXURCpf6rtpEZFGxyoidIMFLO/THjedH3U2U9hr0qQYwi23aOwwA2NJT3uQn+lIjRouJ/mr5JuuBbHHq29BAQrccjSJcFgCQswa7/ddij2GMOQAGAMz031tKRI8T0T1E9IpyFyGiS4hoHRGt6+npmbjWKzy0ZT8++8dn8MQoqn85iu7ETwRDuSLe/7O12Nmfrfoz2WLoc6kGobFUu6qaKIf+rx/ejvde80jFY+58bg8e3rJ/fBeaQorexJpGG8ksZpt8Uq7Ur6rRgj/6m8fxoV9OXbqd6K+i2c1+As5UCpcw12bKLjlmGkW4xGkg6pRT7pjdAA5hjB0P4FIAvyaitriLMMZ+zBhbzRhb3dXVNa4GV0IM4LEO5Mkwi922fg/+9tzemkKLc4Wx+VzcKlebE1Vb7OmdA1i3ra/iMe/72Tr8448fGtd1phKxYp+oPtBI0WK2yacd1dyaKTh4qpsvyNwq8lz+9OQu3PLMSwCA3QNZ7Bue3O0Ewgg23raWOggXVXtqZBpFuHSD59IIFgLYVe4YIrIAtAPoZYzlGWP7AYAx9iiAzQAOnfQWV6A4zolBTOYTaRbrak0CAPYOVj8AM0Xf51K15lJjtNgE+Vwc16t78MNEU5zgoI5GynMJhIvSTz57wzM453v3Y+9grmb/3Ud+/Tgu/9OzZd//9u3PY/2ugTG2mBMGrPD/m5M8gnJwkoXLcN7BJT9fhz2DuZKItUamQQorYC2AlUS0FMBO8ITNdyjH3ATgIgAPAjgPwJ2MMUZEXeBCxiWiZeA1z7ZMXdNLEQNiLGYtz2PjFk5xJCw+oKvdyAuQQpGdKn0uNa6qJqq2WNH1UHA9eB6D0SBFDseL6APVao2j0Ui1xURfVBctL/i+mF0DuUj5l2rYO5RDUyI+XL7geLjiby+AMYYj57ePtdklOSZTZRa76YlduO3ZPehsTtRseq4nDaG5+D6UjwC4FcAGANczxtYT0eVEdI5/2E8BzCSiTeDmLxGufDqAp4joSXBH/wcZY3Xd6Ho8DvmitFKbyFWmGKx7h6rXXGoNRa5Vcwkikcsc7rgeHti0b9TziBXwRE3E46G7L4NfPLht3OcZzwJFxfNY8Gwawyzm+1yU8dHZnAAA7B/O15znMpJ3Y+/toqsfwR8e6wYwfi1QLIaE9jBVZjEhjPOOV3O4fz1pCOECAIyxmxljhzLGljPGvuK/9jnG2E3+3znG2PmMsRWMsTWMsS3+639gjB3JGDuWMfYyxtif6nkfwPjMYvIAmEjNRQzSoVz5/b//tmEPTv6vvyFXdMEYk6LFqvW5+KuqKlabjLFAqJQ7+nt3bcI7rnoYD26u7Ih3JsGMOFbeedXD+H83rkffSGFc5xHaohAKrsdw6tfuxB8f31nzuWShKxYs2/dncMPj3eNq41gRfVHtVzOFcBkplBUumYKDgWyxZHIdyTsli7Gi6+Ge53vw6f97GsD4+4ejREMKTWkgW0R3X2bMATyjIYRLwfF0nsvBznjs5RHhMqGay+jnemHvMHYP5DCYK6LghqskuU07+7N4dtdg7OdrKU0hH1LOjPbi/kxwzUoEJqQqnvdk5w6JNvdlxilclGixXNHFzv5sEMZbC3I/Eqv7367djkuvfzJ2BfzSQG7CKgPEEQpOhlzRxV99p3youRRCLVgRQK/+77tx7BdvQ0bSUhw/2VT9blUBP17NVs0xIT/GaDDr4Ht3bsJHfj05hUuSMZqLNotNc4bzDl7zrbvx2PZopFJxHCtpeQBM5Epctl1nC/ETbGAGczzkCuG15RXmqV+7E2+44t6K16jGlCEEClEoaLbvz+Doz98aTGwpWwyqygIhfN6jC45KmttYcFwvVtiOV7iE0WJRITOcr7398nMR58sUXDAWXyX5S395Fh/9zeRVeApD7V189o/P4IO/fBTP7BxA0g79guU0lz1+QMqw9D2O5OM3Qts3HP0OxrtYc5QthkXbBrJFDOedMX031SDyaQquF/QLbRab5qzfOYAtPSP46l82RF4vKhNDLcjO8/Gssu/euDfS2WU1etdAvCYgJ03Kk071ZrHqVXZxiBg4jDH8/tEdGMo7geknaXGzgzwpPLBpH8698v5I+RCnBs1lPMJl72AOb/ruvdgtPb/v3rkJb7jiXqzfNRAR2r0j47PDq9qvWHSIqgm1ICZd06BgASHamolZaPQM5fHSQPWBH7Ui7m2k4OD5PUMAxCZz/P29Q3lJc4nvS8P58PkO+89E9bnsH4n6F8etufhNCU1T/HwD2SLyjld20QYAT3cPYHNPbVrn3sEcllz2F9y/iZuF80VX0lxqbf3Uo4XLOEjZfPJTB6i8MquVglu6ygR4ouCNT1Rnb+8bKeC916zFTU+E0dzyCnBPmYkjyGtxvIhwKcT05DjnqWwPHi0CTGguBgnhAoz41xchnrI5QHD7hj14ckc/3nXVw8gUHIzknZo0xaHc2Cf9jXuG8MzOQWx8aSh4bcNurrXs6M1EJo/ekfHlXJQIF/+3WKXXgliktKWsULgUhXApFVaD2SL6MqV+jYkiFJQuRvwFUMo2gsm6ZzAf5rlEAlzCex+KaC7xwqVXNYuN0yQq2hQkU/qPZ9AXLnnHK9vvP/WHp/BfN2+Ifa8c9/nBLD9/cBsA/txcpjWXgwLR8VXTwngczPkyPpdr7t+KXz+8HQCwuWe4YkjxiD9hyBOH7NcoF44qVrgFx4t8tuh4+OPjO/GZG54OXtsf47CWNZbR+j5TNReEQropwaNwQuEStldEuw3lHbzxivtw5OdvDaPFyjxvecAPZseuuYj2yd+RWGBki26wCgcmQnMJHfqeVGNsZAyai/hsR1Mi+I4zFTSXwWwRrseq0vL+43dP4pand9fUHtnEJ67veeHiZO9QLlZz2SX53mQhK4SLOt5KzGIT7NAXwmYwVwwWkuWuMZAtoqeGSE3xGSAMHCjIPhdpgHX3ZfBft2xoOCe/Fi7jQOSBqKu/wjjMYuWixfoyhcCO/8+/eBTfvu35Uc8hmwHkQVpuEs5Kocc5xSz29xd6IpNI73CpcJFXmaN19EBz8YWLx1jwHMVgSvoTt/wcd/VnMa89BQCBb0aEtJZ73rLWNh7NJa7yQloIl4KHJ3f0oylhwjZpAnwu4b0UpCTRkXH4XNrSNnIOjwTMFSsIF1+oqGYlFc9j+N2j3fjQr2rzzwitLJN3g+s7nhesxvcO5SW/hvzdhwsq2SwmBI2qOexXMvYrmU0ZY7hr496KGoE6sYsxJcxiQHl/5kjBCRZkecetqtqzWAiJfBoeLSY0urCdl/3hafzoni2TFq02VrRwGQflBmgtDmYVuUik6LCMMfRnisFquF8SNHGoTmAgOtmXm4Rlh35WcehnC27EYbovZuKRr1GtcDENySzmTxIig1uUJZUn8139WZy8fGYQnim/X+55yz6jwXEIlzjNJZ0INZd1L/bhuEUd6GpJlphkakXOXi/4SaLAGM1i/vfWnrbBGD+faha774V9uOreLXBcL/DVjSYg4zSbq+/biu6+TMXPiT4+UnCCydjxWDBhZgouhvPh64Kd/eF55WvLgkb+bvbXoLk81T2Ai69Zi4e2lg97D0xSyu+hnFM2SfXGJ3biz0/tQibvBn3isM/+Fe/+Ka+F95Yr78f/PRYfEi40F9HH8o4XhPnLlggxTnqG8hHtWeX6dTtqNs2NBy1caoQxhu/c8Ty27RsJOpJq6w3MYmOITpEFgjjvUN6B4zH0ZQo8/0SZ6GXu3rg3MB3Jk6o8SMtVOZY1F9Xnkim4yDtukAAXr7kw6e/K96469D3GkPXLzQQ2bUkDzBVdrN3Wi71DeRzS2YRD57QE5xKmonIrU/l+qzH1OK6HS69/Ai8oAzVbwSy2fziPDbsHsXrxDMxoTkxAnkvUPBpoLuMxi6VtAPw+VLPYu376ML78lw2RIJDRTHv92eg99o4UcPmfnw0mznLIPtdj2wcAACAASURBVBc5j0dekIi+L2vcO/tCs5jcTjmJUR5zqum2kuYiBOlwhf6hFo0U/T1XdAN/jjour75vK35y71YUXA+ZQpjo+eCW/RjJO3hiRz8uvf7J2OuJhZC4bt5xY/Nc2lL8e/3gLx/F6/7n72Wj1m5bvwd/elKtqjV5aOFSI/tHCvjOHS/gpid3BR1JnazHZRaLcej3j4SdbDDrIFt0Y0NIswUX7/vZWvziwRf5uSKai2RmGUVzKao+F1/YeCyM4IpbmcvJk6Ol1TDFoQ+Eq3LHZegdKQSr93zRww/v2Yzzf/ggGAPmd6Rx2Jy2ks+Ve94RzaWKbOrnXhrC/z22E/923RMA+I6QclKpHKjhK1m4b9M+eAw4YUknOpsTsT6pONRJVSAL56hZbOwO/dl+fbmBbLEkakwg+6RGC0roz4TPciBTDEx2o+XIFGNMfEUprwoIFzrygmWXFIgiLxLkdsiaQ0m0WIXxKIRspe0lAp+L4lR3PBZ8L+rzHMo7eEmKLpT7hcjfmtWSiL2eEJoZKdAmrsRSaypaxStTRrj0ZwqR/KDJRguXGhGdet9wPqKxMMbw7K5BMMbGZRYryKHIvvCSzRN7hnLwWHy01oi/5704Xh4oUc2lnHAJfTXy+QtSmKUQCnGT53g0F8ZCE83jO/pwwpdvD6Kv5IgwAFjQkcY5x82P3LdoZxzy6newCs1FrKYt08D6XQN41X/fjZ/etzVWcxHP+KluXhTxmAXtmNGUqNrn8m/XPYFLr3+i5PWi4iMTi46xhCKL9s71fVV9mWJwL6omJGsBcZrLoy/2Yov/vfRLx77YO1JWqxrIFPHOqx4KfAJxhVlVIRv6NcJj5HuX2ym342cPbMN9L/Aoq1KzWPnxKASdvLhTCat+l/o9hBanmsVG8k7EkS9rtMJ8OLs1FXu9AV9oylGccZqL8MkE15QE3N0b9wamsL5MIdbHNllo4VIjQm3uGYoKl4e39uINV9yLK+/aFAyI3pECrr5va01hg9EkSn7+3kzpaifOcSjaI1bn6uANrjGKQ1/4WARFN9RkRMftHcnjwc37cbe0m+BYfC5Rhz4/967+HBgDdvv3OlJwIgNofkcarzy0C9887xgAYeRZLT6X29a/FAkplhHCKGFSkO9x36Z9sT4X+Vk2J0x0NNnobE6U9bns6M1EqvM+t3swdqVfVPqBuE6m4NYchir6xZw2IVwKwfdZorlIPqk4Afm2HzyI13zrHgB8JSzYtj9T1qS0ed8w7t+0H2+58n4MZIuxixvZ56K+LpBNXnLbZM3lB3dvxs8e2FrSPqA6zaXSMUJjEXI/asbzfS7Kom8450QiJ+VtAYSZTwh9FXGPmWLY7+KqIqtjTdYI33vNWvzo71swkCmiP1OMaD+TjRYuNSKijVTN5cX9fIL4wd2bg1XnY9v7cfmfn8Wzu+PLpchc+8A2XHzNI0HnTtlGaBaTBokIx4wLJxbtEas6eRBHhEtZzSXsxFlpIBfc0gSx3pECvn/3Jnz79jBqLRItVmWeixkTiqwGSozkneC5vPqwLiyckQaAiFMfqORzkVa/vvnik394Cj+6ZzMA4Lt/eyGosnDP8z1BlJFlGIEzNVNwY81isqCZ35EGEaGzOYGhnBM7ib7iG3fhjVfcF/y/f6QQ6wdSo/vk6/RmCjjne/fh0Rerq88qzn9IZxMAviJWQ5IFstlQXfmrORyy9rClZzhi688VuRD8/t2bIiv3tVt7Y6tsuy6L7TNOTIALoJjkFN/Pbn9BoJpJKyVRDgeay+jRYpXK3ssC0PNYRIsAEElO3bqPay4zmuLNYsI6IFcrKCq5NkDpfcomc1Hl4vEdfYGGNxbtdyxo4VIjgxHNJfxSd/phkiMFtyQHpZpNjJ7xN7wSE2Rryg46TZ9kntgVaC6lA0W0R6w+5cnWqUJziUSL+X83J0wUHVbi49k3XECu6EZWSfJgi8usZozh27dtxKa9Q4G2YUqai5qvINozkneRdzzYJuGai9cE0WRJRbiU97lEzXW5oov+TBE9w3lkCy6+dfvzeOv3H8Bgroj3XvMIfuXnE1kmBT6hXNFFtlCaTyELkPkdXOjN8GtkjWYac/0gjTjhIk+EBSe6X826bb14qnsAl//pWdzy9O6KmeEAFwIGAQt8obxvOB9GnykTjeg7nc2lpr0hxZYvzDbLupqxdltvRLh092WwqWcY3/jrxkgI+0C2GDvJO54XW/BUXrDImml/Gc0F4MKFMVbSHyoF2GRGMa0CoYku0GA8Fph1BfI4ifNvyH6jZ3ZyDTYu8dJxvdj+I8Z4NPqz1BQnWOD3yfte2Bd8ZqpMY1q41IjQXFSzmBx++dj2aLx5Nfb3vONhpOAEUSetKSvoNPJA2u0LsTifi+jYcjKkQHQsg8qv4LKBU9ND0eWTedI2fbNYqfmEJ1uGr8sCJW5V158p4oo7N+EvT70Uai5Shr4qVMS5h33NJWFGu6uquaiTyUC2iIFMsSRqTqyke4byke9tzwA3x700yJ9xwjSCe8pIEVbyYC5ENBdu3uj0V6KqaUwNg+bRf9FQ2qCdSo05+TsTK9qXBnP40K8ewyW/WFfyefU5tKXtIFpstzTBqYKpz5+oF89sKmn/PiUJsD9bRHPCxKsPm4212/oi7+/ozQZakOzn6s/yftPutyW432rMYtKzlgWZKlx6RwrB+7KzO++Gi68ll/0Ff34qjJwSDvmKZjE1z8VjaFGc6aLvMsZic6pk5/6T/q6bcffd6/eNsm2R3lP7vTwmxXt3SuZrLVwaFLHKHCm4EV9ItxQmqU6s1WRrFxwPHgvP35qyJYd+ER1NNhKmEfpclGCC/7zhadyzsSdyzrhQ5JRtxg4gxhhyUn6M43qwTQO2yetRlZgYfFONvEqSB4lY3d3+7J7AZCiE7FCuGEzaltiVMKZgp+x0LjheiTBJmNHNodQ2HvvF23Dm/9wTbZfH0DMcCpftvaFwuWMDH4BC07RMChy82YhZrIzm0i40Fz5xqpOz7ONhjAVmp1zRKzGhqZqm/J3tG+KfE4fc+8K+wJb+l6d2I1d0sas/iw9cuxZDuSIGskW0p21YpoG2lBWpjaZONL0jBRgEzGpJlixg1Iz3/kwRHU0JnLZyFgqOh7uk/rejLxP0ZdnUJnwuJyyeETmX67HY6tgRISs9d7ndA9lioAELxPcqwnQB/hwZY4Gv44q/vSCdb/RdV9UoMddjaE5EhctXb34OH/7VY/jMDU/jPTEh2bJgF/01biFWKSRaboO4Lxn52Yg+uKVnRHp/asxijbIT5QGDbMLo7ssiYRoouB72DpYvx1JNnSmxMhUCqy1lBR2jL1PAjKYEkpYTCBfXY752YWD9rsHAlBN3Tn68B8sgJCwjdgAV3TBah2suXOW3TSM28bDgeCiYHkYKPOP7urU7IrXPRH2xj/7mMVx44iH4wjlHBqviwVwxeI5t/spPnoACzSUwi5URLiWaSziohMmhZygfFbIuC7Z67s0UIs70m33zjVgJW6YRRO9li25stFhUc+HCZWYzD/ntUxYVz0nCJVf0IhnkI3kHHZLtXa2OLV9HaFbydHrvCz1I2SY+/OvHcPSCdhw+txV3bNiLPz+1OxAuADfZyZnu6kSzf7iAtrSNpGWUTFqqeXcgW0B72sZJSzthEPDA5nBjt96RQnBNuf8M+sJFDZ8tutVoLpLJSZpA+/3xIbdvm+/PaE/bkS0bCq4XCCL5HCPVOPSDopGhcFE1sH3DeTyxox/zO1LBzpqCjiY7dpvxuPE4WhpDpaRoOVgjU3AxuzUZ2SRwNDPqRKE1lxqRTRg7ejPB3vQvVRQu1WkuQDixCacwIISLjRlNiYhDUKykb3kmvraT6nMxDOLCMKbj5hRTT9Hlk3nCNErqcSVMIyjU5/p1r372wDY8vDV0MDsuw7C/gZO4D2HeG8w6wYTT0cQHZyQRTlnRFV2G4YJTIkxEQmfc/Ypifws60sHgNQ2C43no8X1ijAFPdg8gbZuY3ZrEcy9FAy8SZiiIMwUn1FzkZxXrc/E1l4yquYTnHyk42CdpNqrfJerIdiP3titmf5stPSPBXjJP7xzAJimMWxYuHWm7RHORbf69I3m0pWwkLTOmVpdiFvM16qaEhc7mJIouQ9o20Zay0CcFKoj+kzANX3NhsE0DK2eHibCu58XuOaQ69IWfbSTvBEJipOAGG40JtvnasirEvnbLc0GxUXmSzQQO/Qo+F8WhH2cWA7hmHudH62pJliSetqas+MCAUYRLxKFfdNGStHDmEbMBhCY+kedz8vKZkc9qs1iDInealwZzaEvbvumofGfYsHsQ3/jrc7H1hL5/9ya8/Kt/k4RLAbZJmNGUCCbcvpEiZjQl0NFkR1ZyuQIvwX3L0y/FXjeiuUiaSKxwke20vs/FMgzY/oQgI/xB8t4g6mDwJLOPEMhxmouY9OTIpLhq0n0jhVF9Lt19WTy/Zwg7+7O4wS/bn7KNwKGfsgw4Hous4h57sQ+LOtOY15EuSaCzDJI26/JG1VwWdfrCxddA1Cz9PdKqNZN3I5pLqXBRHPpuZeGyvTcT8R+JuSdTcDGQ4T4XgBevjLRD+e6G8w7StomkbZQ4imWfCmMM/dlisDgQCZotKYuHYmfC71j0n67WZODQt00DX/mHowMB4fh5LiK6KXgOHsMP7t6MN333XuQdL9haOO94aE6EZtH2pqgGsd0XtG2KZnHN/duCauG1aC6eF+6cGggZxkpyTMS51O+TiGuNqn9oWVdLrMY2WkV1VXM5/pAO/OQ9q0GEIPBEjKmTl2nhckAwlHOC1RBjQNo20OrbdVtjOhoAPLGjH9+/ezOee2kId23cizf8773Bivgbf92IlwZzgbOxL1NEwjTQlrYxmOPVafszBXQ0JdCRjq7OskUXP71vC7aUyYiOhCIzBtMgbu6IEXKR/VscxleXFsG2qIxwCXf+G8k7Jaq247Fg5TSSd/H49j5s91eTg7liYAYTwmXdi+GGa3Grtt6RQlAdQKBGi93+7B687n/+jp/ey/Mc1izp5I5i/37TCZP7XKRJcmd/FotmNGF+mVwD+RmGARNRzWXNkk785p9ejoUzeKivbRpolcyaggFpYhkpOBGBqpbskENi5Qx9QI4Y5O2wDPKFSyh0rGBVr2gu0iTcnDCRKTiRySZbdGFbvJ+o0VU9UntzRQ/9mfC8QoNvSVr+JFoIHNriuc1u84WL4yFhEtYs7cQjnzkDQJhEKQqBChzXw9f/+hye2TmIfNGNTOZNkr+jQxIiBoWai+xzEQjfXySyq1BZc5HDpMXfjuvFjnnXYyVaXnPCQlMiqg2etmIWLIPGpLnI7Sk4HpKWCSJCk21KuWj8Pg+d2xr5rCizNNlo4VIjg7kilsxshvAfpmwzEDZtabvEsSizuWcY63cO4NndgyXVbYXm0J8pIGEZ6PCLDA7l+N4aM5rsyMQA8AH+o3u24JWHduHEJVEHKVAaLSZ8Lrmih9uf3QPGWJAFn4vktbjcn+NrLmrJlDa/bXJ9KjVU2fW8YPIczBXxD99/AFfcuQkAF9DCLCYmp/s37cPSWc2R/B6ZgWxxVIe+YNv+ERw6pxWHzGyC44YVE1K2yX0uQ/nA1wMAi2c2xyayOR6LTDZxpd2LroeWlFViepgZk0jZny0Eq+1MwYmUJxHanesxDOaKJY7svOMFBQrF5CFCg1fOacWOPq65iK2CxdSjmsXknIrOlkTJd5cpuLBNg5vFlIlWnjAHc0X0juTR1cKFymxZuDTxe1dX77N9zUX4CgGeS8SfI/e5pBThoq7QI8IlGR4rj43Zrakgok71iQCl0WXA6NFike0kJId+S5kFpdqHm5NmRHD+/H1rcM3FJwam2nKfF/OJat5TQ5HFLp5NSSsYl+L7muX7AAVac2lQhnJOkIUNRIVLS9IqWXnJbN47HCmxIiMm296RAmzTCAbFnsE8skUXM5oTJQMlW3QxmCti1fy2WPVcLf9i+sLi7y/04J9+vg4f/c3jOONb9+Dp7oEYzUVEixkl+Q1iNSg6+IjkjxC4Xhgyu1vZnGwwWwzs8MJs8fTOAZyyfGYw2aj0Z2KEixV/bK7oImXz0vcFPzgB4N+V6zHsHcrhiHlhbbJ/OH5BUMZfpqhoDCKkVl7Rx4VIA9wEIlbIm3uGcceze9CXKQZ+mZG8i/3DheAexET8nqsfxjFfuA1X3bc1OK+oitzZlIiYggQrZ7eguzeL7b0ZLO9qBhBGG+0b4vvRd8RoLjObkzwKTtZcfOGS8B36sj8m6jAfgceAOf5zE5pLc9LEjCZu/pEd+abBk0v7/dBwcd+W7zdz/ZL76viRq0M7HkOLJFDkSC1ZQ+losgNtW52UgfjUALFwUJ3rz700iHf/9OHIvTyzcxDfueN5uGXMYiqWQWhOWkFCrmijbRoVNBf+nQjhNaslKiBUoZv0+0qTr40CoeYysyWBf3nVchw5n/f5g86hT0RnE9FGItpERJfFvJ8kouv89x8moiXSe5/2X99IRGdNZjuH89wsJr7slG0EHaApaZasvGQ27xsJKw8rKxs5JyBhGcEkINT7GU2JErtyJu+g6DKkLLMkJFK9hvC5JKQooD8/xQMBdg9kS0w9jstgmRQ7caoDdjjnlNyP44XRUOomSYO+5tKcMIMVLACcuKSzrOaXLbolZrDKwsWAZRhw3DDMN22bKHoe+kaKQXIZABy9sB1z29Ml5+FaT/mBDyA2ig3guS5icP/g7s34198+joGIcHGwvTcTOLWFWezp7rA0jNiRU4QiJy0DyyUnuGDl7BYUXA/7hgtY3hU9n9jSWixMDpsTmkhmNidKTJrZoouEacTuArqjNxuYgUTAgBDKoeZiY0aTXaK5JC1u6h3IFgKHPoCIzyVOc1H9lFGzWHisPHEnbTMYT6rPBYgGj6iJhWo/vn/Tftz7wj48tzuM9Htwy358544XkHc8v5+Vt1bYJheqzYnowlOYeE2D4vuYv4ARc4va9z1VuAjNJWFJDv0CkpaBpoSJT559OG788KmRe51sGkK4EJEJ4EoArwewCsDbiWiVctj7AfQxxlYA+B8AX/c/uwrAhQCOBHA2gO/755sUhnJFtCbtYKWWsszA59KcsJBOlH+kXHOJ78RymYiWpBVMBtv2CeFil/hcRDmHpG0EE5FMZLMwj/tc4oRFyjajJfZ9B7LIc1FRhUtshWSPleRFyO+JYAj57O1pu2SgygNSbXtc2wCuFaQsE5ZJcFwWRBzxrXRZsHK+7pKX44HLXgMAsZqL43mxZhK1cKVdRnMRz2V7bwYjfnl5kSXfny1i095hnLikE4DYE8SNJBymbBMGhdFiCcvAiq4Y4SJtP7DM11zESltEkIn+dOrKWcGxs1qSfnJoeM1MwYVlUolw6RnKY99wHi/z81M27+X9cm4bv58uv/hiS9LEjOYEskU3oukkLa6Ni4k00Fz879v1Q+HTimamOrtl4SL/LQultGRaFeZPWeOTTzmYLYIxVrb4qUgxeLG3dI8axnj7Ky0om5MW2tI2mpNmRBgmpfsXAu6p7v4S06sQLpZJgVnUNCjic8kX3UBYNSXMwKeydzCH2W1JkP9By+TRnweVcAGwBsAmxtgWxlgBwG8BnKsccy6Aa/2/fw/gDOJP7VwAv2WM5RljWwFs8s83KQiHfiBcEqFZrClhlqj1wlF82opZ2LpvpKrS3rPbUpLmwjt1hx8tJiNsx0nLiFXP1ZL7pkGwY1bZHmOBz0dk8DsuQ8I3j6i0Kk7SuPI2rscqlp3v7suiLWVDqrgP2zSCQpbhtcL7StrVaS4D2SJSthnkIBUUn4vjca3spGUzA01iblucWYyVmEn4ZB/NQYnVXPyy+4wxdEsTk9CYnt45AMdjOG5RBwzi2p8qjC2TkLZN7B3MBwIxTnNZs3QmTlg8A8cu6sBpK7oAhJrLgBI4IZuPZrcl4XgsUlW44PBFhdgFVPQhEb4rfHubVc2lLYwWE34dIdgAvlKXF0diYSA0l2IFh75MSyJec4kKFymKTNx3jAYj7mP5Z24OIsFUc7WILBTBKCrGaMIlYeG0FbOwZunM6EIpMAvyCMZswcXbfvAAfvMIz1cLzGJ+/7eM0EJimwS5mQU3DNFuSpiB5rJnMI85SsXltGQ2m2waRbgsALBD+r/bfy32GMaYA2AAwMwqPwsAIKJLiGgdEa3r6emJO6Qijl8GpTUV1VzEgG2O8blcfOpSPHDZa3D2UXORdzzs8CeaStVJu1qSaPcHYqC5NNuRiBggLPOdss1Yx2LRDW3mrr/KitNcckUvyHNpS9tBnotlUuyqXI3AKae5qNvMyuzsy6A1ZQWrKoAPGlVzkYVLSShyTNsA/lyStsE1FylaTPhcRJi1zBxfoC+b1Ry85nilmfPtabtkC+hEjAa1aEYaBcfDjt4sdks5UEK4POZHxx0xrw0tSQvDUml2cc+2aeCNx8zDjU/swpaeES5cupoj10mYBjqbE/jDh07BjR8+NdBc1GR3eXL9j7MOQ9IyAtPuXsVsmTCNwIYvJjkhXE5YzDWtLfuGI+Zb4dhvTlrobC7NXUraRsRnGO42Sv7q3fNDkUs1F9lUGtFcErLmYkh/h+doixGqMmu39UU0mRLNxc+J2rY/fndNy6CK1orWlIUvnHMkLn3toUiV1Vw87BvOo+iGkYzCN9ssCRRhkkyYRpDnImqoifM1J6xAeOwdygVCX8B9MgeX5hJn31Bn3nLHVPNZ/iJjP2aMrWaMre7q6qqxiXwle9aRc3DY3JZgMKVsI6K5qINjdlsS8zvSwTFiNV9wvbL7uc9uS4ZmMcnnIgaKEGADo2gumYKLpZ++GTc83h1oLqrtFuA+CrGtbFvK5hn6Hjf3yBO4MC20paPXUqvnAsD6XYN4YHP5LWP3+Zng8pdnW0aJz0WeFFUNgai8sEzZJiyDm8EKks+FCxtWYlJLWAb+/slX4/2vWBq8VnT5wJU11daUjUzBxZV3bQoc/nGay2FzufP0ro17IxP9nLYUiICNe4Zgm4RlXc1oTdkYyjlBHonwm9iGgY+/9lB4jGHjniEkTCMIdxaU+KHKCFx5kvnwq1dg45dfH3yPqk+M15SLmsU27B7E3LZUUJF6R28W89pTweJgdlsSlkGY2ZyIrfKbMKPCRX5mPGJK+FyUPBeXRe5RdujL0WIpy8SZR8zGJacviyzwxLiLc+wDpdUzSjQXPydoR4xZjLfdKBvEM0MK/AGAphjNRdy7mBeCoJHAoc8/YxlhykPCMiMJxoyF52tKmHh+zzA+cO1a7BnMl+wVk06YB51DvxvAIun/hQDU/TiDY4jIAtAOoLfKz04I6YSJH717Nc4+al6ouUjRYs1JK1DVP/jK5fjaW4/Gm4/hm1qJVZYwIRUcryR/RDC7NYmE74gTkVYdUiiy6LDCLJayzYpRKzc+sQuOy1eAcX6KXNHFtn0jSNkG5nekuObiVyGW97MXE71qFlN3/AMQlOKXB3XKNvCulx8S/N+WsiI7USbMUueofK24SbycaSxlmcF7ooI0Fy4eHM8LaprJtKXsyApXBAMkTAP/duZKAOGz/+atG3HLMy9FwmplhOP8jg17Iq8L5y5jXIjYpuFrLsWg5pkQLqZBmNeeDvZhSVgGjpjXhveduhTHLeoAgMB8JTBivuNV89piN6RqSfJ7LRUukkPfX0Fv3DOMw+a2RkxRsimxKWHhun8+GW9fc0hQFVom6fct+RoCy+C+MddjSFhGxFTqelHhUk5zSSdMXHXRifjMG46IPBPxfZYzi72klGNRNVWh1b1YRnMxDZQVLle8/Xh8/W3HRNooED4S4XMRWr5YcIoq4MFxJgUmsqRlwPMYPI8FQijwufjC6I4NezGcd8poLgeXWWwtgJVEtJSIEuAO+puUY24CcJH/93kA7mTc5nMTgAv9aLKlAFYCqLyJ9wTQFYkW4x23KWEGHaglaeLCNYcEPgTxpYsImqLrxcbbA2FYp1jpNSdMbrP2V4SivIgIqUxaRmyIqiBp8RW8ZVLsZJwrunh+zxBWzG7hhS1dPgHbpgHb8ttvh1qZugos57hf0JHGJ88+HACfGJ/70uvxvlNDzaAtHfW5WCaVaC5yklpcXktZ4SJF8WR8B2c6YQYRYHaZCB85MEL4XBKWgXeetBh//uhpeP1Rc6X28FVnXBvam2zMbUvhXn9XRHFfvFwKv8bhfnJbc9KMmMWEaUtMdKI/2CbX7D735lVBWKm60uftir52hl8WREWYUnuULSJsywgmq7zDy8Ps6M1gycymyOSu5gadsHgGWlN2bM5Q0jIjWpfcRss0giRKkVslKHpRzbBcnov8HOTJPp0wsWZJZ0mhTIEoSXTsog6sWdIZMYvlii4GskWe9V4mY940jGBcfOYNh+P8ExYGwv2wua1Y1BnesziOSPY58crbJZpLkSdGiuNs04iYyIbyDpZ95mbcup4vXgJNU0l8VX0uTbZ1cJnFfB/KRwDcCmADgOsZY+uJ6HIiOsc/7KcAZhLRJgCXArjM/+x6ANcDeBbAXwF8mDE26U8vVnNJWEEHUlezaqhwZc2Fd4gwqzrhn8OEZRBakzxZUzhiR9NcEpYZ5LnEChfHwwt7hnHo7Nag9ljRZdzZ6AcenLJiVrCCrCZaDADe+rIFgY9A3ItsGolz6Ku+kKQVmuZUhz7/DD/Bl95yFN56fOhqS9tmoJ3kCi6IEAnDjtNcgOj3JMxe4rs8akF7xOwpTEblBNxS339jmxSEHLen7eC7OtzPtUknTOSKHnqG8uhosoM91cUkIMJ85RW8mPzjnMli5d6etvGW4+bjktOXxbZPfI+xPhf/WgWHL4KG8w4WdTZF2nC8rz2ptKVsfOTVK6JtsqJ9r0Rz8bxI/TuB40aFt+xblM1MKSte0CQsA9d/8GS87WULI+0R/VD03W+dfyzmtKcigTZC2MdF6MltFwvKD5y2YnZsfwAAIABJREFUDN88/9igja3JqLYkKgokTCOM4PLvXbQj1Fx46H2YbBr6XOQ2fuJ3TwII+8ZOpTSQqrmkE2ZZQTnRNIRwAQDG2M2MsUMZY8sZY1/xX/scY+wm/+8cY+x8xtgKxtgaxtgW6bNf8T93GGPslqlo7yEzm3DWkXOwZmlnxCyWLidclFDhglteuAjBJcxg4jcRoT1tB1Fpss9FdOi4PJFAczHiHfR7B/N4aTCHlXNaYfvlYcR+Lu96+WJ86uzDceU7XhZMWqpztJzjvilhBe0Sg7mzOYHjD+GTksdYiVlMbb9lUjBZxPkTxMRz/KIO/OOJoXWUR4uF1W/Fql/Y1K0Y8yAQXRk7nq/lSMfKk6v4/sr5OdYs5c7vf3/dYZjfkUba1/6E5nKYr7mkbW4H3zfMM97F85XLpsj3yu/PKGlP8Ez89iyd1YzvXHh8iRlTIPptnM9FXCvveNjh1yxb1NkUCcB4o2/yjePjrz0U377gWJxz7PxI22f4fVl+pqZvGvKY6KMU3IfjsajfL6K5SCZXOeclJnxdfU5iTIWmZaOkqKtw5ssJtyqGwSP6ElKkY0vKgmlQiVYp2iW3xTJVs1gYiiwLF2E+BeJL5ovFxhfPORL/esbKoM7dHCUKciod+rrk/hhJWtz/AoSlI+QSD6rdu0nRXIquF1s5FQhXqu89ZSlStonXHB6aNRbOSGN2WxIp2wjMYilpld6etks0iaRlwPE8mBRvFhPl6Q+d04IX9gwF0WK2YeCoBe04akE7v47vcFe1JHUr1/CeQ41KCBciwhUXHo93XvUwTlsxK/AzAL7mojw30zDQlLCC5FIVMXkkLAOOF129imeSKbqwjWgkml2mEkBEuLieH2osJ+iFnxMTUznN5UOvWo63rzkEc9u5H0sk9gnt6Ii5wrRlIld00TOUR1drMvAPiElEaLIEWchV0lzCZ1IJMVmpmotlymYxL9gbZZESTCAWQXGYBuGtL1uI9bsG/fbytsxrT/P6eYoWU3R5VJ8pLYCSFk+ClWvKNZfJ0I9qLqVRWWoIfto2kbTCMZS2uY9ODjMXBT6Xj6K5pPxzCVqSNlqSTkQQAwiiyuT+ZCkOfdnnkrRDs5jsc4kTDuJ5rpzTio+/thXP7h7Ejt5siVnsc29ehTJBqhOOFi4TwPKuZhw2pxWr5rVhk7+Hw2hmsXwFs5iY4M4+ai7Olmz8APDT956IhGXg/k37g/L7SctAyu/HHX52tGwCsk0DnscHfDJmlf2sH2a6vKsl2O9FFK6USforNHUFXa4idFPCLNFcAL4C/vsnXw0A+NOTYeyFHeNzsc3Q7BC7Spcco/JKMWmbwVScLbiwFK2ovOYS43ORjpU1LSFc4rRBgE9yc9v5+T52xkp87AweFNCUNNGetjHH10jSvnDpzRRwxNy2QHMRz1UsNuQSJEKAVPK5xD0vGTFZqSHxPM+Ff/a/b92IjXt4drpYDf/xw6cGprvRCDUs/hzmd6Tx7O7ByAQpNBfXY1Hh4ie9phPhM5fNYinbgEE8KTISiuz3F9n81Jwwce5x87F/uID7Nu1Dyub+UTkoJmESii7fBvumJ3dhR28GBgFHLyyvuYiyNnL1jNakFZsakLZLs+1Nw4DrsmBBKMoi5YuqWczA2162ED+9byvOPGIO/iJtHa2eEwBOWT4Tz+4aLInsnBdTiWKyaBiz2IFMR1MCt378dKyc01rW56JmHhddrySE95iF7Th1RbQAosos32zSkrQCE0/SCjUEkQsjO8Jdj/kRUvFmMZFw19mSCEryx+WCpCw+6cgdOS75UBBnFlOJ+Fys0mgxS3KYVooWs02jZPUq3ssUnKCOU3DeMgJhVnMSHzhtKU5cMiPIc5GfmWxCEnlG5cxi5TjnWO4HEROfqJCQLbhoTpolE4Iwi8kBICn/3uR7Foj7Hq1dSSmiTtZCElKGvhAsQBi5d9yijpKQ6ErX4L8N/7PtJceI1bvLwhJF4rNFL5oPJmuWXNPlx6ZjzGJyfyEi/O+Fx+OVh/IUhJQdDSEW/aXgeLh+3Q588vdP4bt3bsLyrpbYSDuBSYSPvmYFfv6+MG+7LW3HRqfFLZJELpaYC7JFXjRWmMWswCxGWDW/Ddu+9kYc6kciNsVEnwnee8oS/P2Try7RnqYSrblMMKLDqitjsfGWEAgFx8PugSwWz2wKwhz/9YyVOOOIOVVdp0UJ8RX+EOH8b0lZgaqdd9xgVSjHw8urRyKe/cw1Fy6M1MlcZL3Lg2NRZ1PZBLMmv+QFUXnhUo3PJV3RvxBG08gWopQV7uPCizFSRKCUixYzDMJn37QK/3nD09jSM1KSx7JCypAfGMUsVo63Ks5l4WR1PYamhFXi0xKTm7zRlPi+44IcxHOqpl2tSQv7nQIWzUgHgtM24wM/xoLalg+9agUOmdmMs48MNXJepseTNBf+3SRtI9izSCBrBAnLgG0QCoh36Mfdg9BqUnbUhC3GhrxTJQAcvaC9bAKmaDuvnhFqcp8469DAVC4TJ/SE1iabsof9UkBJK/QbWhEflbjPcAyr/YCIUEY5nzK05jLBpCWVXEUOnSw4Hnb1Z3FIZ1NQvr+WAS1rJkmLm59mtyYDJ7Fshss7PBJHXhWevrILp62YFZTvaElaPFLHX705fqFLmWMWtuOExTMiq6RKK9gmm+8x8aFXLscbjp4Xe0wkiTImWswyKIyyqaC5yI5/QPih+NmzRbdqzUVuS9Et1VxeddhsPPTpMzCjyQ6i9cqZxaqF+1x49YcmqZyQQJjFZM1FTNoVNZcq+pPQBA6RQmZFyX2Zb0j5GrUgtE65UOM5x86PlPkxDSNIopTNYinLRNGLbn8saygJywh8KXHlX+IWI+K9lBWmDYhnaPsh0XKS4VEL2mN3mwzbXjqDHz63LTb0OdBcpLZaBqHoedg/kg920xzMFYNilJZkFhOIZ5eScoJGM4HWg8Zr0QFOuWgxoDTMdWd/Dgs60qHZp4ZJSjYPJG2+4n/gstfgXS9fDCCq2eSLXok9+9A5LfjlB07CbN+sJVZntq9dOV5pQcYPvGIZfvCuE2BLRfRExnalNn7y7MODyCkVWWs3jVKfi2VKZrG4PBcpmialmDnEClgUY5TPXa7oZXBd31RTcEvzWOa2p9CUsIIcifEObNFnHI+hKWGWCL6Zfk7VmZJWG0zacQ59q/r+JHJp5HwMnucSfvbS1x6KC05cVPLZahDnidOwBJZBfqkixPpcZM0lJeV+JKTFiNzeSmbUpkBzCc1igY/GP16uiXfMwvaIsFetTGYNZqdA6EXCsA0wxv1roqjpUM6R8lxCs1j4mdCcOrO5NJKwUdBmsQkmVcYsBkSdxUN5B/uG85jXng7U21o6iCw8gjpFphHY4mXNJu+4YVVkYRbz3xfHCxuxPEjLtUeUXck7XmRSUmlKlE58ceeSUbUlW8ojqOxzIWWCMZB3+P/ZAt9jPKK5lIkWC973c3wKjhuvhSbMINJovJpLWpp4hZa2bFZzoO2ZBuGR/zwjUvgx0FziHPo1aC5i3xf5e0woz1ItmFoLyQomTYEpbSct179L+sElQriI5EPLMFB0+XgR37usCVVarAUCxZY0FyXUff9wHpZB+NYFx+KExTMifVT0e7nt1SLMb7KglecJkZjNNRe+AVhoFpM0FxJmQxOzLIPXJXOmKASsBhpP3B3gNFUyi0mai6hVNL8jVZONXCCER9IyIp1/RlMC/7h6EV4jZWXnHaG5hCvSZsn2DISlySPOxgoDRxy3qILmooZfx6FeQR2spuRziRcuYe0lkkKtRW0xgJvFuOYSP6jjsE1urlDzXARNUqTReFeNssYl+s+dn3gVPnHWYcHrs1tTkeuEjvLxmcXERnCyWcwyjMhkVs5fVg3C5FSpLbZJQTFQw6AgSlFUsRbCRfR1S9ZczNKqxBV9LpJwSUkmMvn4/cMFzGxJ4NzjFpQsflQhOVo/khHbEMtzg9zfRSHRwaxT6tCPmBHF8zFw+blHYems5qCqQyOhhcsEc/SCdrxu1Rysml8avihrLlv3ceEim8VqMa+U20TIMAhfP+8YHLswzJ4WwkVOohQTvxiIIhJIFgiVVuTCHCNvuqXSFLPHjIqhai6mqrkYQZsqJQwKASBHUclmMdXnMrpZTJgr4jXKpoQV+AJGO9doyH6EppgQ1jhSFUKRA1NUjOAph2oWkxmPcEnao7fFNCjQBuQ+mrLCWnDyOeTcJtssLRxZ0eciRWzJWox83v0jhbJO/IRyH2r/HY1UwoxqLlKfFBF7Q8LnIpnFrBiBlLZNrFnaibs+8aqqdsScarRwmWBmNCfw4/esjkSPCOSJ+0W/2vH8jnSouZTZEz6OoIhdmaJ58oQYiRbzO6kQdGKAifBXWQBWmjRFOY+ZLeUT6ZrKtE2mxIatOvSllWm85sKTI+WwXv47WqPKNqIJmqObxaKCSaVJcSyPh4jmUsUzA0ZJoqxBcxHHzFFCkWXi+nK1qKHIcVhGmJNlSrXFUjaP+JM1FyD8bmzTgG0YJQK2Un8JBI8ULSY+bwfCJV+2irL6bEbrRyoLOtKRrHlZcxF5T5v2Dkt5LqXRYobS1xuVxhN30xi5uKQIIZzbngoERE0+l2RU81BxpPpD+aJfCTgSiiw+Hy3pUrXm4jt9K9mcR4vIAkqFS2meC4UhnHHlX5SsftnRHREmikO/GrMYEObIqMTlVYwV+fPVaHuAlEQZ02eqmdAFf/noaVi/axCWGSYkqver7iNUC5XK1AgsM9RcTAq/Z/GM80UPq+a14b2nLOHHG6Hw5Ds0xmsuqpYhv5eyDcnnUmoWWzYrPitfHaO1+FwA4BfvX1NSV00wqyWJNx87H1ffvxVFlyEpLZDkihKyWayR0cJlClFNHmL/l5Q1+gBUCc1i8ZORvPIKfS6EQ+e04qgFbUHIsupzkTWXSsIhaZmx7W1NWWXL2sSh2rTjosVE2Yy4661e0hmpyiybi+RBnJZ8MMDoTnhxrMfirytH/s2vYBqshqjPpUqzmBUKUZVqkygBXi5kpZ+UZ/kJtCXCZTwO/QrtFFgGBaXjTTNcrYvP5hwXJy3rDCLWwve5T0I9dSWHvhDeTRGHflS48A0B478H1QxWq3BR67zJmnrSNvCFN6/CX5/h2fdRs5isufDfcWHojYQWLlOIWhZfaAtj0lxSlTWXRZ1NuOVfX4Gr7t2Ke1/oCfJc5ran8OePviI4LhAuQXl/WXMpP3BEUigA3PyxV8AyCbNakrBMwjFfuK3q+1CvEKu5VMhzefOx8/HmY8MCiimbJ20mzKhwaUpaUc1llElBvvdKmsusluS4zROy5lJp6wSZhTPS+MirV+CMw0vL6cs+iVoQCYnq/ZYrfFkNS2c145WHduG4hfEVlIEKPhf/uRSVnKswa91AW8oq+S4rhT/Paknim+cdg9ccPhu/enh75DqyMCoVAv5e9yVm3PH522ShkTBNzGxJYtW8NjzZPeALz9JosYJvlUhV2VfqhRYuU4hYlYptbcXqKFWDjVwwmuYCiC10TeQdD55fzlxFCKdwq2bZ5zKKWcwflHHBC9Uy2krQMgmrF8zAK1bOKqnwGkfKMpGyzGD7XIHYrkAwquZiVtZyhM+lUp5Ptcjb5Fbr0DcMikSTyYwl+hAIn33CGt/qXKY5aeFaqTRKHJZhBPuQmERSYmTYfkNZGIicqK+97ZiSBYrh15mLq6MHAOev5hpQmFBZqhm3KZpL2jaDMknRto9TuEifF8LwuEUdeLJ7INioDohGi+X9yLpG11wa22g3zRATt+i4QlsQq+1aOmrrKJqLIGmbQZ5L3PlDzSXcNkAwWrRYrTW14hjd58J3X/zF+0+qSkNI2kaJgxbggt1UfDCVkNsRN0mL5zSnrXxAQ7WMxaFfiVrMYjJykcSphPtcfLOYFHQia3TqwkAcs6AjHWuWFFWOK5FSzGJyxJxqFhOaqqcU+Ryv5iJ/Xgi3l/nZ/dv2jcRGi4kKAtrnogk4ZfksvOmYedjZn8WugVzQgcWGWLUUmRO7X44Wbpq0eNKXbRglkVhAOIDjQpErTcCnLJ9Zsg/IWCCoq+RoG2sN801aYf6C/NnmpBlxio42gUYizWLaICaCzubxC5exOPQrMVbNRY7CmkpMg4Iy8Ly4amk0lJqjNFq/+NTZhwd+xXKI556O0UJVs9jSmc3oGcpHNuoSbR8Pcj8UY/nso+biH1cvwiWvXBZUTpfvN+cI4dLYmosWLlPIUQva8b13vAzv+MlDAGRTlFVVNrvMaD4XQco2wRjfnCxec1HMYnKIbYVJ5oOvXF5Te8shmiTaFrefSy3MakkE+9zLq71mxecyap6LWVlzEdvRzozZL75WxCQhr9rHQ6KKxMU4wigsfu9/+NDJcON3U5hQ5H4ZqS1WJh/ENozYSDCZC9ccMup1g1Iw/nMiIizoSGNnf7ZEc/nBu16Gvz23F799ZHtkt8fxmsXiNJekZeLr5/FabmKvJVkIia0YxhulONlo4VIHxKAXHfj9py0tu895OYT5pBrNRRC3ylo8sxkp28D8Du7PkCfk8Q6cqhDCJdhTXNj9eeRSLRnQAF+xih0cI5pLQg1Nri5aDIgXsn1+/anOCRQuvIL0+J95LRn6MraiuZywOL4e3EQTSRAkis3hUcPIJ6JQo5zzIjikswk7+7MlE/fMliQuWL0oCAIQxPkxayHO5yKzZFYzXrFyFo5ZGG5VIHKCtFlMU4IYvMLnsqizqWKNrjgMg9CStEYdZKOVc3n5spl4+gtnxZpC1Eztavn8m1cF+8ePhnDoi8lctDFtmzwstkbNZUZzAqIerSwgmhKK5jLOaLGLT12CR7b24pzjym/1Wy2i5lut2ms5ghVwjVqQeD5TbRZTNZfzVi/EITPTkYVTVLgYNZtL45BLwQiOP6QDD27ZX3a3RteLqnLjduhHttAu/f5bkhZ+8f6TIq/965krMVJw8LYTFpYc30ho4VIHhFOwXCx9tVx0yuLY0t4ykQFaZkCWm0zGaqK5+NSlVR8rWiQmD7m0xUC2WLPmIqP6XGopuS+/3xmz6+Kyrhbc+vHTx9w2lbRtluxWOlbGrrmUBkJMBapWsqAjjX84fiHufG5P+LqyMJiIKsBq4UoA+LczD8WCGemSHWAFqplwYn0u1d3TrJYkvn3BceO67lSghUsdEA65SpsQVcN/nHX4qMfIqnYt5cGB2oryjRUjWC0LDcYXLv7AH8/KUDYxqZrLqBn60rGV6qdNFGkpqW+8HLWgHWuWdFbc+z0OuSDkVCJ/x3JouhWTlQ4AZx01d0KCSeS9XQQJy8A7T1pc9jOq5jIZ0WLThboLFyLqBHAdgCUAtgG4gDHWF3PcRQA+6//7ZcbYtf7rdwOYB0B42V7HGNs7ua0eH8IhN17NpRpG87lUYipWsKWaSzQMtZoSMtXQnLRKao1VQr6u2FBtMknZxoRpLgs60rj+gyfX/DkxmdvW5C8qItdV9jcJX49Per1g9dj2llFZ1tWMd7988f9v7/5jJSvvOo6/PzNz78L+aPf3srJsYWFLQUJhe0EQ3NLuAgUawKqxFXFN2ZCqibS1ERoSq5ImYI2aGGOyXdpQtWilKITEtrBWU60lbGu3gtQuFISFLUuBBUTDj+XrH+ecu7OzM/fO7Dxz5szczyuZ3JkzZ84+3z137vc8z3l+cP765V1/ppisdMFknVdeO5B0EGWq3/WqqEI0NwA7ImI9sCN/fYg8AX0K+CngbOBTkprbg66KiDPyR6UTCzA9vXi7dbZTa24W67UW0Ov9jiNRXKi23nNZljdFpUrAC+bVe6q5NL/fzwj1bs2fbBwygHUYpnvslT3Opc108kDL1PTpyzRRr3HTlad1NTi3UDRpF+Oc+v2/6jc5VdnQay7AFcAF+fPbgH8Crm/Z52Lg3oh4HkDSvcD7gNvLKWJa08ml7JpLr4PqSriCLZquJlp6i7377Sv4rYtO7rlpp5MFkw3ejIN3aWed/qXkP7A3XnbK0KdNPzjOpdw/ePUOyeXQaeZLLVJHB/LfoYXzGux7+dWem5pbldIjc0iqkFxWRcRegIjYK6ldn9xjgSebXu/JtxU+L+kA8GWyJrO2fT0kXQtcC7B27ez94AelaBbr955LNzqtHdGNMq5gW5vFmgfynXFc5/moejV/sj7dRbl5iv5Oym4aOu+k7ptmBmUi74WVojt0r/9uodNYpEHUXI5EcculuBDo1EmmW2XXEstUSnKRdB/QrvvFjd0eos22IoFcFRFPSVpEllyuBr7Q7iARsQ3YBjA1NTW0dUGLqS7KaG7p1J2zG2Xc2K3pYDKBw5NMKgvmNaZHV3dz7OJLv7CCizANSvOkkWXqNKFop6nph6lYuKxowkzZFXnclPLNiYjNnd6T9Iyk1XmtZTXQ7p7JHg42nQGsIWs+IyKeyn++LOmLZPdk2iaXqlizZD4//p/XSmljP6rD/EwzKdb1KOMXv7hInk4qLT9TaV57ppsmr6Lyu2JR/9O7jIpGy0zSpf27HZrFOtVohqnoilxcdPS6EmWrqsQ1CFWok90NbMmfbwHuarPPV4GLJC3Jb+RfBHxVUkPScgBJE8D7gQdLKHNftm+Z4rO/MlVKzaV5MGO3v8ibTlkFlNNbrOh9U7SvF1/W1M0g0sH5qLpJmkvykfcfPu/4pOWosmHVXDoll07bh6noilwsA5FyVuRxU4U6/83AlyRdAzwB/AKApCngIxGxNSKel3QT8ED+md/Pty0gSzITQB24D/hs+SH0ZvnCeVx46qpS/q16TbxzzVvZtefFrj/zpx86k6f3/1+SgWqzKZawnTjsnkv6L93BJrfZ41q+cB67P33JUP7YDkujXjtsGd8y1Ds0f1Wx5lJcDB09UUPqf/qXqsQ1CENPLhHxHLCpzfadwNam158DPteyzyvAuwZdxlF38WnHsGvPi10PPDtqos66RL20ZvP6gWLt9ENrLIP40k2P4+jy2HMpsQCctGLh9Cy8ZWqepqZ2SHJpX4sZpm1XT3HrvzzGuhULk0xa6hv6NtK2nr8OIX7xrDSDz1I68OahN9kHOdail5rLXHTd5vVD+XdPWnXwQqbTFD1VucI/98RlnHviMl594wAf2HDs7B+YRVXiGgQnlzlgslHj1y5IM0V+akUzQ2uNJdWV6mSjNj3wrdO0/jZcp64+uJJpp0GUVbvCn9eos3JR/x1yyh5TVCYnFxuqNw603HNJnAB2/c5F089rNSGVP0DSZjbT1PqFcT1lrrmYDUjRpbNYcqCeuFmsdTLIRk2uuVRYXe3vs1St5pLKuMYF1eiKbHPYOeuW8me/tIFPXprN8Hyk08V3q1EbzlgOm9lvbsru9zRfDDR3Hx/XK/xxjQtcc7Ehk8Rlp6+efn3W8Uu56crT2LA23dQvzbKxHOP7hR5VH9u8no+8ex3zW2aGbtRqvH7gQGV6i6U2rnGBay5WMRP1Glef87aB9eiq1zXWTRGjStJhiQUOn9B03BRdr49bOvg1g8rmmovNKb7nMlpa55wbR1/48Nm845hFwy5Gck4uNqfUhzTFiR2Z4lyNc/PRxrevGHYRBsLfMptTGrXaWP+hGjeNMW8WG2dOLjanNOquuYySgzUXn7NR42Yxm1Pec/JKTlxZzrxp1r/ihr5zy+hxcrE55Xcv/8lhF8F6UNRYXHMZPT5jZlZZE43x7y02rpxczKyyWuecs9Hh5GJmlTU9zsVjk0aOk4uZVdZ0V+Q+16q38jm5mFllzYUR+uPKycXMKmui7nsuo8rJxcwqq+Gay8hycjGzypqs16gpmzXZRsvQk4ukpZLulbQ7/7mkw35fkbRf0j0t20+QdH/++b+RNFlOyc1s0Bo1L5Ewqqpw1m4AdkTEemBH/rqdzwBXt9l+C/DH+edfAK4ZSCnNrHSNes1NYiOqCsnlCuC2/PltwJXtdoqIHcDLzduU1ZXfC9wx2+fNbPRM1uWb+SOqCnOLrYqIvQARsVfSyh4+uwzYHxFv5K/3AMd22lnStcC1AGvXrj3C4ppZWX7uXWs4yRONjqRSkouk+4Bj2rx1Y7+HbrMtOu0cEduAbQBTU1Md9zOzajh9zWJOX7N42MWwI1BKcomIzZ3ek/SMpNV5rWU1sK+HQ/8YWCypkdde1gBP91lcMzPrUxXuudwNbMmfbwHu6vaDERHA14GfP5LPm5nZYFQhudwMXChpN3Bh/hpJU5K2FztJ+gbwt8AmSXskXZy/dT3wcUmPkN2DubXU0puZ2WGGfkM/Ip4DNrXZvhPY2vT6Zzp8/ofA2QMroJmZ9awKNRczMxszTi5mZpack4uZmSXn5GJmZskp680790h6FvjvhIdcTjbuZhw4lmpyLNUzLnFAd7G8LSJWdHOwOZtcUpO0MyKmhl2OFBxLNTmW6hmXOCB9LG4WMzOz5JxczMwsOSeXdLYNuwAJOZZqcizVMy5xQOJYfM/FzMySc83FzMySc3IxM7PknFw6kHScpK9LeljSQ5Kuy7cvlXSvpN35zyX59ndI+jdJr0r6RJvj1SX9u6R7RjkWSY9L+g9J35W0c8RjWSzpDknfz4937ijGIunk/HwUj5ckfXQUY8nf+1h+jAcl3S7pqBGO5bo8jodG4JxcJel7+eObkt7ZdKz3SfovSY9IuqGrAkSEH20ewGpgQ/58EfAD4FTgD4Ab8u03ALfkz1cCZwGfBj7R5ngfB74I3DPKsQCPA8vH4bwAtwFb8+eTwOJRjaXpmHXgR2SD3UYuFrJlyh8Djs5ffwn41RGN5TTgQWA+2Qz09wHrKxzHTwNL8ueXAPc3/U49CqzLvye7gFNn+/ddc+kgIvZGxHfy5y8DD5P94l9B9keJ/OeV+T77IuIB4PXWY0laA1wGbG99rwwpYxm2VLFIeguwkXz9n4h4LSL2lxJEbkDnZRPwaESknH1iVoljaQBHS2qQ/WEudXXZhLGcAnwrIv43spVy/xncJZyFAAADe0lEQVT42RJCIC9Xr3F8MyJeyLd/i2xlX8iWNHkkIn4YEa8Bf50fY0ZOLl2QdDxwJnA/sCoi9kJ28siuWmbzJ8BvA28OqIhdSxBLAF+T9G1J1w6qnN3oM5Z1wLPA5/Pmyu2SFgywuDNKcF4KHwRuT12+XvQTS0Q8Bfwh8ASwF3gxIr42yPLOpM/z8iCwUdIySfOBS4HjBlfazo4gjmuAf8ifHws82fTennzbjJxcZiFpIfBl4KMR8dIRfP79wL6I+HbywvVelr5iyZ0XERvIqs2/IWljsgL2IEEsDWAD8OcRcSbwClkTQekSnRckTQKXk63YOhQJvi9LyK6KTwB+Algg6ZfTlrLrsvQVS0Q8DNwC3At8haw56Y2khexCr3FIeg9Zcrm+2NRmt1nHsDi5zEDSBNlJ+auIuDPf/Iyk1fn7q4F9sxzmPOBySY+TVSffK+kvB1TkjhLFQkQ8nf/cB/wdQ1gFNFEse4A9EXF//voOsmRTqlTnJXcJ8J2IeCZ9SWeXKJbNwGMR8WxEvA7cSXYvoFQJvy+3RsSGiNgIPA/sHlSZ2+k1DkmnkzXfXxHZKsGQfVeaa1xr6KKp0smlA0kia49/OCL+qOmtu4Et+fMtwF0zHSciPhkRayLieLImi3+MiFKvxFLFImmBpEXFc+Aisqp/aRKelx8BT0o6Od+0CfjPxMWdUapYmnyIITWJJYzlCeAcSfPzY24iu1dQmpTnRdLK/Oda4AOUeH56jSMv453A1RHxg6b9HwDWSzohrx1/MD/GzPrpjTDOD+B8sqrf94Dv5o9LgWXADrIrkB3A0nz/Y8gy/EvA/vz5W1qOeQHD6S2WJBay+xS78sdDwI2jGkv+3hnAzvxYf0/eU2ZEY5kPPAe8ddS/L8DvAd8nu3D5C2DeCMfyDbKLll3AporHsR14oWnfnU3HupSst9mj3X7vPf2LmZkl52YxMzNLzsnFzMySc3IxM7PknFzMzCw5JxczM0vOycWsIpTN0vzrwy6HWQpOLmbVsRhwcrGx4ORiVh03AycqW5PlM8MujFk/PIjSrCLymWvviYjThlwUs7655mJmZsk5uZiZWXJOLmbV8TLZcrRmI8/JxawiIls/418lPegb+jbqfEPfzMySc83FzMySc3IxM7PknFzMzCw5JxczM0vOycXMzJJzcjEzs+ScXMzMLLn/B0MiDPGOGNhaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(weekly_rets_series)\n",
    "plt.title('GOOGL weekly log-returns'); plt.xlabel('t'); plt.ylabel('$r_t$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting a return histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_rets_series.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    313.000000\n",
       "mean       0.002937\n",
       "std        0.032039\n",
       "min       -0.099918\n",
       "25%       -0.013341\n",
       "50%        0.004653\n",
       "75%        0.021327\n",
       "max        0.229571\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_rets_series.describe()"
   ]
  }
 ],
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