Rsampling

pd_resample.ipynb

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   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>units</th>\n",
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       "    <tr>\n",
       "      <th>2014-05-01 00:00:00</th>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>2014-05-01 00:30:00</th>\n",
       "      <td>11</td>\n",
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       "      <th>2014-05-01 01:00:00</th>\n",
       "      <td>12</td>\n",
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       "      <th>2014-05-01 01:30:00</th>\n",
       "      <td>4</td>\n",
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       "      <th>2014-05-01 02:00:00</th>\n",
       "      <td>10</td>\n",
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      "text/plain": [
       "                     units\n",
       "2014-05-01 00:00:00     19\n",
       "2014-05-01 00:30:00     11\n",
       "2014-05-01 01:00:00     12\n",
       "2014-05-01 01:30:00      4\n",
       "2014-05-01 02:00:00     10"
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     "execution_count": 11,
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   "source": [
    "date_index = pd.date_range('2014-05-01', '2014-05-31', freq='30min')\n",
    "units = np.random.randint(0, 20, len(date_index))\n",
    "\n",
    "df = pd.DataFrame(index=date_index)\n",
    "df['units'] = units\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-05-04</th>\n",
       "      <td>10.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-18</th>\n",
       "      <td>9.563988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-01</th>\n",
       "      <td>9.662045</td>\n",
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      "text/plain": [
       "                units\n",
       "2014-05-04  10.083333\n",
       "2014-05-18   9.563988\n",
       "2014-06-01   9.662045"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.resample('2W').mean() #resampled every two weeks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2014-05-01 00:00:00</th>\n",
       "      <td>19.000000</td>\n",
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       "    <tr>\n",
       "      <th>2014-05-01 00:10:00</th>\n",
       "      <td>14.904594</td>\n",
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       "    <tr>\n",
       "      <th>2014-05-01 00:20:00</th>\n",
       "      <td>12.237927</td>\n",
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       "    <tr>\n",
       "      <th>2014-05-01 00:30:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01 00:40:00</th>\n",
       "      <td>11.190812</td>\n",
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      "text/plain": [
       "                         units\n",
       "2014-05-01 00:00:00  19.000000\n",
       "2014-05-01 00:10:00  14.904594\n",
       "2014-05-01 00:20:00  12.237927\n",
       "2014-05-01 00:30:00  11.000000\n",
       "2014-05-01 00:40:00  11.190812"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.resample('10T').interpolate('quadratic').head()"
   ]
  }
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