04-time-series-slides.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Programming with Data:<br>Advanced Python and Pandas<br><br>Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.precision', 2)\n",
    "\n",
    "np.random.seed(252)\n",
    "\n",
    "def random_series(dts):\n",
    "    res = pd.Series(np.random.randn(len(dts)), index=dts)\n",
    "    return res\n",
    "\n",
    "def image(name, **kwargs):\n",
    "    from IPython.display import Image\n",
    "    return Image(filename=f'assets/{name}', retina=True, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "No new classes for Series and DataFrame. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "A \"timeseries\" is a `Series` or `DataFrame` with a `DatetimeIndex`\n",
    "or `PeriodIndex`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Create a simple time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* Standard datetime import\n",
    "* Create a scalar time series\n",
    "* Show the new type of index - note no `freq`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "ts = pd.Timestamp('2000-01-01 00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    101.7\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(101.7, index=[ts])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Time Series Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-03   -0.01\n",
       "2000-01-04   -0.09\n",
       "2000-01-05    0.08\n",
       "2000-01-06   -1.65\n",
       "2000-01-07   -0.75\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a year's worth of weekdays\n",
    "dts = pd.date_range('2000-01-01', '2001-12-31', freq='B')\n",
    "ts = pd.Series(np.random.randn(len(dts)), index=dts)\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Selecting from a Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Notice how the slicing selects across non-contiguous dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3091432326146675"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2000-03-20'] # ts.loc['2000-03-20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-24    0.36\n",
       "2000-03-27   -0.82\n",
       "2000-03-28    0.11\n",
       "2000-03-29    1.36\n",
       "2000-03-30   -0.49\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2000-03-24':'2000-03-30']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Select by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-01    1.16\n",
       "2000-02-02    2.18\n",
       "2000-02-03   -0.29\n",
       "2000-02-04    1.28\n",
       "2000-02-07    0.57\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2000-02'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Select by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-03   -0.01\n",
       "2000-01-04   -0.09\n",
       "2000-01-05    0.08\n",
       "2000-01-06   -1.65\n",
       "2000-01-07   -0.75\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2000'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Leading and Lagging\n",
    "\n",
    "* Common time series operation\n",
    "* Useful for calculating things differences, percent change over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Lagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "image/png": {
       "height": 345,
       "width": 577
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image('lag.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Lagging using position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-03     NaN\n",
       "2000-01-04   -0.01\n",
       "2000-01-05   -0.09\n",
       "2000-01-28   -1.72\n",
       "2000-01-31   -0.86\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2k = ts['2000-01'].copy()\n",
    "ts2k.shift(1).iloc[[0, 1, 2, -2, -1]] # show first and last few rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Notice that the `index` is constant. We drop the first value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Leading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "image/png": {
       "height": 348,
       "width": 678
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image('lead.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "TODO: Add example here of lead/lag to calculate a percentage difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Changing Frequencies: Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Resampling is similar to grouping, expect with time and notions of\n",
    "forwards and backwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "dts1 = pd.date_range('2000-01-01', '2000-03-31', freq='D')\n",
    "ts3 = pd.Series(np.random.randn(len(dts1)), index=dts1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "grp = ts3.resample('M')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Resampling is like grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.21\n",
       "2000-02-29   -0.02\n",
       "2000-03-31    0.21\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>monthly_mean</th>\n",
       "      <th>monthly_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            monthly_mean  monthly_std\n",
       "2000-01-31         -0.21         0.89\n",
       "2000-02-29         -0.02         1.05\n",
       "2000-03-31          0.21         0.97"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp.agg(\n",
    "    monthly_mean='mean',\n",
    "    monthly_std='std')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Filling Missing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02     NaN\n",
       "2000-01-03    1.30\n",
       "2000-01-04     NaN\n",
       "2000-01-05   -1.45\n",
       "2000-01-06    1.00\n",
       "2000-01-07     NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_series_4():\n",
    "    dts = pd.date_range('2000-01-02', '2000-01-07', freq='D')\n",
    "    rv = pd.Series(np.random.randn(len(dts)), index=dts)\n",
    "    rv.iloc[[0, 2, 5]] = np.nan\n",
    "    return rv\n",
    "\n",
    "ts4 = make_series_4()\n",
    "ts4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Filling Data Forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02     NaN\n",
       "2000-01-03    1.30\n",
       "2000-01-04    1.30\n",
       "2000-01-05   -1.45\n",
       "2000-01-06    1.00\n",
       "2000-01-07    1.00\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts4.ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Filling Data Backwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    1.30\n",
       "2000-01-03    1.30\n",
       "2000-01-04   -1.45\n",
       "2000-01-05   -1.45\n",
       "2000-01-06    1.00\n",
       "2000-01-07     NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts4.bfill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Filling with limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1.33\n",
       "2000-01-02     NaN\n",
       "2000-01-03     NaN\n",
       "2000-01-04     NaN\n",
       "2000-01-05     NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_series_5():\n",
    "    dts = pd.date_range('2000-01-01', '2000-01-05', freq='D')\n",
    "    rv = pd.Series(np.random.randn(len(dts)), index=dts)\n",
    "    rv[1:] = np.nan\n",
    "    return rv\n",
    "\n",
    "ts5 = make_series_5()\n",
    "ts5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Filling with limits (cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1.33\n",
       "2000-01-02    1.33\n",
       "2000-01-03    1.33\n",
       "2000-01-04     NaN\n",
       "2000-01-05     NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts5.ffill(limit=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Aligning Dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Sample Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Create a weekly and monthly series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "dts_w = pd.bdate_range('2000-01-01', periods=8, freq='W')\n",
    "sp5_weekly = random_series(dts_w)\n",
    "\n",
    "dts_m = pd.bdate_range('2000-01', periods=2, freq='MS')\n",
    "t_bill = pd.Series([0.012, 0.023], index=dts_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### View the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02   -1.35\n",
       "2000-01-09   -0.19\n",
       "2000-01-16   -1.16\n",
       "2000-01-23   -0.61\n",
       "2000-01-30   -1.12\n",
       "2000-02-06   -2.07\n",
       "2000-02-13   -0.50\n",
       "2000-02-20   -0.20\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp5_weekly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.01\n",
       "2000-02-01    0.02\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_bill"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Using `reindex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02   NaN\n",
       "2000-01-09   NaN\n",
       "2000-01-16   NaN\n",
       "2000-01-23   NaN\n",
       "2000-01-30   NaN\n",
       "2000-02-06   NaN\n",
       "2000-02-13   NaN\n",
       "2000-02-20   NaN\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_bill.reindex(sp5_weekly.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Reindex and fill forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    0.01\n",
       "2000-01-09    0.01\n",
       "2000-01-16    0.01\n",
       "2000-01-23    0.01\n",
       "2000-01-30    0.01\n",
       "2000-02-06    0.02\n",
       "2000-02-13    0.02\n",
       "2000-02-20    0.02\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_bill.reindex(sp5_weekly.index, method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Rolling Calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "image/png": {
       "height": 335,
       "width": 531
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image('rolling.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rolling example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "roll = sp5_weekly.rolling(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02     NaN\n",
       "2000-01-09   -0.77\n",
       "2000-01-16   -0.67\n",
       "2000-01-23   -0.88\n",
       "2000-01-30   -0.86\n",
       "2000-02-06   -1.60\n",
       "2000-02-13   -1.28\n",
       "2000-02-20   -0.35\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roll.mean()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "384px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}