{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " price\n", "fecha_hora \n", "01/08/2016 00:00 55.4450\n", "01/08/2016 01:00 40.1169\n", "01/08/2016 02:00 34.0645\n", "01/08/2016 03:00 31.1261\n", "01/08/2016 04:00 29.7240" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices = pd.read_csv(\"../data/lbrprices.csv\", index_col='fecha_hora')\n", "prices.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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25 rows × 75 columns

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" ], "text/plain": [ " price Ph-24 Ph-25 Ph-26 Ph-27 Ph-28 Ph-29 Ph-30 \\\n", "fecha_hora \n", "01/08/2016 00:00 55.4450 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 01:00 40.1169 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 02:00 34.0645 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 03:00 31.1261 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 04:00 29.7240 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 05:00 32.3707 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 06:00 35.1250 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 07:00 38.6416 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 08:00 48.4632 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 09:00 49.4265 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 10:00 45.0187 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 11:00 45.5868 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 12:00 47.5454 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 13:00 47.6542 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 14:00 46.3099 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 15:00 48.1719 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 16:00 47.4186 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 17:00 45.7651 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 18:00 48.9220 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 19:00 50.3570 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 20:00 45.0160 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 21:00 50.7729 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 22:00 48.3555 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 23:00 49.8514 NaN NaN NaN NaN NaN NaN NaN \n", "02/08/2016 00:00 42.2087 55.445 NaN NaN NaN NaN NaN NaN \n", "\n", " Ph-31 Ph-32 ... Ph-183 Ph-184 Ph-185 Ph-186 \\\n", "fecha_hora ... \n", "01/08/2016 00:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 01:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 02:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 03:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 04:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 05:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 06:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 07:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 08:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 09:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 10:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 11:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 12:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 13:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 14:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 15:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 16:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 17:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 18:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 19:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 20:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 21:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 22:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 23:00 NaN NaN ... NaN NaN NaN NaN \n", "02/08/2016 00:00 NaN NaN ... NaN NaN NaN NaN \n", "\n", " Ph-187 Ph-188 Ph-189 Ph-190 Ph-191 Ph-192 \n", "fecha_hora \n", "01/08/2016 00:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 01:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 02:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 03:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 04:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 05:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 06:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 07:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 08:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 09:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 10:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 11:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 12:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 13:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 14:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 15:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 16:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 17:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 18:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 19:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 20:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 21:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 22:00 NaN NaN NaN NaN NaN NaN \n", "01/08/2016 23:00 NaN NaN NaN NaN NaN NaN \n", "02/08/2016 00:00 NaN NaN NaN NaN NaN NaN \n", "\n", "[25 rows x 75 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices_shifted = pd.concat([prices]+[prices['price'].shift(i) for i in range(24,73)]+[prices['price'].shift(i) for i in range(168,193)], axis=1)\n", "prices_shifted.columns = ['price']+['Ph-{}'.format(i) for i in range(24,73)] + ['Ph-{}'.format(i) for i in range(168,193)]\n", "prices_shifted.head(25)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prices_shifted['day_of_week'] = pd.to_datetime(prices_shifted.index)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "prices_shifted['day_of_week'] = prices_shifted.day_of_week.dt.day_name()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['price', 'Ph-24', 'Ph-25', 'Ph-26', 'Ph-27', 'Ph-28', 'Ph-29', 'Ph-30',\n", " 'Ph-31', 'Ph-32', 'Ph-33', 'Ph-34', 'Ph-35', 'Ph-36', 'Ph-37', 'Ph-38',\n", " 'Ph-39', 'Ph-40', 'Ph-41', 'Ph-42', 'Ph-43', 'Ph-44', 'Ph-45', 'Ph-46',\n", " 'Ph-47', 'Ph-48', 'Ph-49', 'Ph-50', 'Ph-51', 'Ph-52', 'Ph-53', 'Ph-54',\n", " 'Ph-55', 'Ph-56', 'Ph-57', 'Ph-58', 'Ph-59', 'Ph-60', 'Ph-61', 'Ph-62',\n", " 'Ph-63', 'Ph-64', 'Ph-65', 'Ph-66', 'Ph-67', 'Ph-68', 'Ph-69', 'Ph-70',\n", " 'Ph-71', 'Ph-72', 'Ph-168', 'Ph-169', 'Ph-170', 'Ph-171', 'Ph-172',\n", " 'Ph-173', 'Ph-174', 'Ph-175', 'Ph-176', 'Ph-177', 'Ph-178', 'Ph-179',\n", " 'Ph-180', 'Ph-181', 'Ph-182', 'Ph-183', 'Ph-184', 'Ph-185', 'Ph-186',\n", " 'Ph-187', 'Ph-188', 'Ph-189', 'Ph-190', 'Ph-191', 'Ph-192',\n", " 'day_of_week'],\n", " dtype='object')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices_shifted.columns" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fecha_hora\n", "01/08/2016 00:00 Friday\n", "01/08/2016 01:00 Friday\n", "01/08/2016 02:00 Friday\n", "01/08/2016 03:00 Friday\n", "01/08/2016 04:00 Friday\n", "Name: day_of_week, dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices_shifted.day_of_week.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "prices_shifted['month'] = pd.to_datetime(prices_shifted.index).month_name()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fecha_hora
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10 rows × 77 columns

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" ], "text/plain": [ " price Ph-24 Ph-25 Ph-26 Ph-27 Ph-28 Ph-29 Ph-30 \\\n", "fecha_hora \n", "01/08/2016 00:00 55.4450 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 01:00 40.1169 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 02:00 34.0645 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 03:00 31.1261 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 04:00 29.7240 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 05:00 32.3707 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 06:00 35.1250 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 07:00 38.6416 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 08:00 48.4632 NaN NaN NaN NaN NaN NaN NaN \n", "01/08/2016 09:00 49.4265 NaN NaN NaN NaN NaN NaN NaN \n", "\n", " Ph-31 Ph-32 ... Ph-185 Ph-186 Ph-187 Ph-188 \\\n", "fecha_hora ... \n", "01/08/2016 00:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 01:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 02:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 03:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 04:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 05:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 06:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 07:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 08:00 NaN NaN ... NaN NaN NaN NaN \n", "01/08/2016 09:00 NaN NaN ... NaN NaN NaN NaN \n", "\n", " Ph-189 Ph-190 Ph-191 Ph-192 day_of_week month \n", "fecha_hora \n", "01/08/2016 00:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 01:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 02:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 03:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 04:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 05:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 06:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 07:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 08:00 NaN NaN NaN NaN Friday January \n", "01/08/2016 09:00 NaN NaN NaN NaN Friday January \n", "\n", "[10 rows x 77 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices_shifted.head(10)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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20520 rows × 94 columns

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" ], "text/plain": [ " price Ph-24 Ph-25 Ph-26 Ph-27 Ph-28 \\\n", "fecha_hora \n", "09/08/2016 00:00 95.3264 54.8461 45.1982 44.7123 46.2995 42.9422 \n", "09/08/2016 01:00 82.8532 52.2179 54.8461 45.1982 44.7123 46.2995 \n", "09/08/2016 02:00 57.9764 61.9198 52.2179 54.8461 45.1982 44.7123 \n", "09/08/2016 03:00 67.6407 36.8105 61.9198 52.2179 54.8461 45.1982 \n", "09/08/2016 04:00 66.4892 34.8274 36.8105 61.9198 52.2179 54.8461 \n", "09/08/2016 05:00 68.6807 34.5824 34.8274 36.8105 61.9198 52.2179 \n", "09/08/2016 06:00 66.9259 49.4481 34.5824 34.8274 36.8105 61.9198 \n", "09/08/2016 07:00 66.8057 55.6721 49.4481 34.5824 34.8274 36.8105 \n", "09/08/2016 08:00 85.2455 57.0974 55.6721 49.4481 34.5824 34.8274 \n", "09/08/2016 09:00 79.1283 67.0368 57.0974 55.6721 49.4481 34.5824 \n", "09/08/2016 10:00 79.4960 54.4681 67.0368 57.0974 55.6721 49.4481 \n", "09/08/2016 11:00 95.8666 54.4450 54.4681 67.0368 57.0974 55.6721 \n", "09/08/2016 12:00 80.8384 54.0250 54.4450 54.4681 67.0368 57.0974 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"10/08/2016 03:00 38.4742 67.6407 57.9764 82.8532 95.3264 68.2423 \n", "10/08/2016 04:00 39.7849 66.4892 67.6407 57.9764 82.8532 95.3264 \n", "10/08/2016 05:00 39.2187 68.6807 66.4892 67.6407 57.9764 82.8532 \n", "... ... ... ... ... ... ... \n", "10/12/2018 18:00 106.4128 69.6707 67.0344 58.5975 58.9433 60.2477 \n", "10/12/2018 19:00 129.2578 73.3712 69.6707 67.0344 58.5975 58.9433 \n", "10/12/2018 20:00 117.1150 69.1453 73.3712 69.6707 67.0344 58.5975 \n", "10/12/2018 21:00 144.8820 70.0111 69.1453 73.3712 69.6707 67.0344 \n", "10/12/2018 22:00 103.7000 66.9332 70.0111 69.1453 73.3712 69.6707 \n", "10/12/2018 23:00 88.4655 59.1993 66.9332 70.0111 69.1453 73.3712 \n", "11/12/2018 00:00 70.4762 51.8596 59.1993 66.9332 70.0111 69.1453 \n", "11/12/2018 01:00 63.9543 47.1863 51.8596 59.1993 66.9332 70.0111 \n", "11/12/2018 02:00 58.5600 46.0043 47.1863 51.8596 59.1993 66.9332 \n", "11/12/2018 03:00 52.1070 45.0487 46.0043 47.1863 51.8596 59.1993 \n", "11/12/2018 04:00 53.9686 45.3595 45.0487 46.0043 47.1863 51.8596 \n", "11/12/2018 05:00 61.5639 48.9978 45.3595 45.0487 46.0043 47.1863 \n", "11/12/2018 06:00 74.7410 67.7778 48.9978 45.3595 45.0487 46.0043 \n", "11/12/2018 07:00 85.1159 80.6690 67.7778 48.9978 45.3595 45.0487 \n", "11/12/2018 08:00 94.7120 105.9934 80.6690 67.7778 48.9978 45.3595 \n", "11/12/2018 09:00 100.5628 110.8088 105.9934 80.6690 67.7778 48.9978 \n", "11/12/2018 10:00 96.5661 116.1816 110.8088 105.9934 80.6690 67.7778 \n", "11/12/2018 11:00 106.0722 122.7567 116.1816 110.8088 105.9934 80.6690 \n", "11/12/2018 12:00 106.3128 125.5741 122.7567 116.1816 110.8088 105.9934 \n", "11/12/2018 13:00 105.6875 112.8655 125.5741 122.7567 116.1816 110.8088 \n", "11/12/2018 14:00 106.1539 130.4650 112.8655 125.5741 122.7567 116.1816 \n", "11/12/2018 15:00 102.3658 136.3701 130.4650 112.8655 125.5741 122.7567 \n", "11/12/2018 16:00 103.0934 130.1201 136.3701 130.4650 112.8655 125.5741 \n", "11/12/2018 17:00 101.9319 110.6386 130.1201 136.3701 130.4650 112.8655 \n", "11/12/2018 18:00 125.8868 106.4128 110.6386 130.1201 136.3701 130.4650 \n", "11/12/2018 19:00 101.8847 129.2578 106.4128 110.6386 130.1201 136.3701 \n", "11/12/2018 20:00 109.3477 117.1150 129.2578 106.4128 110.6386 130.1201 \n", "11/12/2018 21:00 109.5164 144.8820 117.1150 129.2578 106.4128 110.6386 \n", "11/12/2018 22:00 101.5718 103.7000 144.8820 117.1150 129.2578 106.4128 \n", "11/12/2018 23:00 75.2138 88.4655 103.7000 144.8820 117.1150 129.2578 \n", "\n", " Ph-29 Ph-30 Ph-31 Ph-32 ... \\\n", "fecha_hora ... \n", "09/08/2016 00:00 44.3551 46.3440 45.6826 42.6676 ... \n", "09/08/2016 01:00 42.9422 44.3551 46.3440 45.6826 ... \n", "09/08/2016 02:00 46.2995 42.9422 44.3551 46.3440 ... \n", "09/08/2016 03:00 44.7123 46.2995 42.9422 44.3551 ... \n", "09/08/2016 04:00 45.1982 44.7123 46.2995 42.9422 ... \n", "09/08/2016 05:00 54.8461 45.1982 44.7123 46.2995 ... \n", "09/08/2016 06:00 52.2179 54.8461 45.1982 44.7123 ... \n", "09/08/2016 07:00 61.9198 52.2179 54.8461 45.1982 ... \n", "09/08/2016 08:00 36.8105 61.9198 52.2179 54.8461 ... \n", "09/08/2016 09:00 34.8274 36.8105 61.9198 52.2179 ... \n", "09/08/2016 10:00 34.5824 34.8274 36.8105 61.9198 ... \n", "09/08/2016 11:00 49.4481 34.5824 34.8274 36.8105 ... \n", "09/08/2016 12:00 55.6721 49.4481 34.5824 34.8274 ... \n", "09/08/2016 13:00 57.0974 55.6721 49.4481 34.5824 ... \n", "09/08/2016 14:00 67.0368 57.0974 55.6721 49.4481 ... \n", "09/08/2016 15:00 54.4681 67.0368 57.0974 55.6721 ... \n", "09/08/2016 16:00 54.4450 54.4681 67.0368 57.0974 ... \n", "09/08/2016 17:00 54.0250 54.4450 54.4681 67.0368 ... \n", "09/08/2016 18:00 56.3392 54.0250 54.4450 54.4681 ... \n", "09/08/2016 19:00 66.5473 56.3392 54.0250 54.4450 ... \n", "09/08/2016 20:00 58.8738 66.5473 56.3392 54.0250 ... \n", "09/08/2016 21:00 60.1973 58.8738 66.5473 56.3392 ... \n", "09/08/2016 22:00 59.7477 60.1973 58.8738 66.5473 ... \n", "09/08/2016 23:00 59.9345 59.7477 60.1973 58.8738 ... \n", "10/08/2016 00:00 56.9154 59.9345 59.7477 60.1973 ... \n", "10/08/2016 01:00 64.5378 56.9154 59.9345 59.7477 ... \n", "10/08/2016 02:00 58.5528 64.5378 56.9154 59.9345 ... \n", "10/08/2016 03:00 58.9723 58.5528 64.5378 56.9154 ... \n", "10/08/2016 04:00 68.2423 58.9723 58.5528 64.5378 ... \n", "10/08/2016 05:00 95.3264 68.2423 58.9723 58.5528 ... \n", "... ... ... ... ... ... \n", "10/12/2018 18:00 61.7710 63.1583 59.9739 57.9043 ... \n", "10/12/2018 19:00 60.2477 61.7710 63.1583 59.9739 ... \n", "10/12/2018 20:00 58.9433 60.2477 61.7710 63.1583 ... \n", "10/12/2018 21:00 58.5975 58.9433 60.2477 61.7710 ... \n", "10/12/2018 22:00 67.0344 58.5975 58.9433 60.2477 ... \n", "10/12/2018 23:00 69.6707 67.0344 58.5975 58.9433 ... \n", "11/12/2018 00:00 73.3712 69.6707 67.0344 58.5975 ... \n", "11/12/2018 01:00 69.1453 73.3712 69.6707 67.0344 ... \n", "11/12/2018 02:00 70.0111 69.1453 73.3712 69.6707 ... \n", "11/12/2018 03:00 66.9332 70.0111 69.1453 73.3712 ... \n", "11/12/2018 04:00 59.1993 66.9332 70.0111 69.1453 ... \n", "11/12/2018 05:00 51.8596 59.1993 66.9332 70.0111 ... \n", "11/12/2018 06:00 47.1863 51.8596 59.1993 66.9332 ... \n", "11/12/2018 07:00 46.0043 47.1863 51.8596 59.1993 ... \n", "11/12/2018 08:00 45.0487 46.0043 47.1863 51.8596 ... \n", "11/12/2018 09:00 45.3595 45.0487 46.0043 47.1863 ... \n", "11/12/2018 10:00 48.9978 45.3595 45.0487 46.0043 ... \n", "11/12/2018 11:00 67.7778 48.9978 45.3595 45.0487 ... \n", "11/12/2018 12:00 80.6690 67.7778 48.9978 45.3595 ... \n", "11/12/2018 13:00 105.9934 80.6690 67.7778 48.9978 ... \n", "11/12/2018 14:00 110.8088 105.9934 80.6690 67.7778 ... \n", "11/12/2018 15:00 116.1816 110.8088 105.9934 80.6690 ... \n", "11/12/2018 16:00 122.7567 116.1816 110.8088 105.9934 ... \n", "11/12/2018 17:00 125.5741 122.7567 116.1816 110.8088 ... \n", "11/12/2018 18:00 112.8655 125.5741 122.7567 116.1816 ... \n", "11/12/2018 19:00 130.4650 112.8655 125.5741 122.7567 ... \n", "11/12/2018 20:00 136.3701 130.4650 112.8655 125.5741 ... \n", "11/12/2018 21:00 130.1201 136.3701 130.4650 112.8655 ... \n", "11/12/2018 22:00 110.6386 130.1201 136.3701 130.4650 ... \n", "11/12/2018 23:00 106.4128 110.6386 130.1201 136.3701 ... \n", "\n", " month_December month_February month_January month_July \\\n", "fecha_hora \n", "09/08/2016 00:00 0 0 0 0 \n", "09/08/2016 01:00 0 0 0 0 \n", "09/08/2016 02:00 0 0 0 0 \n", "09/08/2016 03:00 0 0 0 0 \n", "09/08/2016 04:00 0 0 0 0 \n", "09/08/2016 05:00 0 0 0 0 \n", "09/08/2016 06:00 0 0 0 0 \n", "09/08/2016 07:00 0 0 0 0 \n", "09/08/2016 08:00 0 0 0 0 \n", "09/08/2016 09:00 0 0 0 0 \n", "09/08/2016 10:00 0 0 0 0 \n", "09/08/2016 11:00 0 0 0 0 \n", "09/08/2016 12:00 0 0 0 0 \n", "09/08/2016 13:00 0 0 0 0 \n", "09/08/2016 14:00 0 0 0 0 \n", "09/08/2016 15:00 0 0 0 0 \n", "09/08/2016 16:00 0 0 0 0 \n", "09/08/2016 17:00 0 0 0 0 \n", "09/08/2016 18:00 0 0 0 0 \n", "09/08/2016 19:00 0 0 0 0 \n", "09/08/2016 20:00 0 0 0 0 \n", "09/08/2016 21:00 0 0 0 0 \n", "09/08/2016 22:00 0 0 0 0 \n", "09/08/2016 23:00 0 0 0 0 \n", "10/08/2016 00:00 0 0 0 0 \n", "10/08/2016 01:00 0 0 0 0 \n", "10/08/2016 02:00 0 0 0 0 \n", "10/08/2016 03:00 0 0 0 0 \n", "10/08/2016 04:00 0 0 0 0 \n", "10/08/2016 05:00 0 0 0 0 \n", "... ... ... ... ... \n", "10/12/2018 18:00 0 0 0 0 \n", "10/12/2018 19:00 0 0 0 0 \n", "10/12/2018 20:00 0 0 0 0 \n", "10/12/2018 21:00 0 0 0 0 \n", "10/12/2018 22:00 0 0 0 0 \n", "10/12/2018 23:00 0 0 0 0 \n", "11/12/2018 00:00 0 0 0 0 \n", "11/12/2018 01:00 0 0 0 0 \n", "11/12/2018 02:00 0 0 0 0 \n", "11/12/2018 03:00 0 0 0 0 \n", "11/12/2018 04:00 0 0 0 0 \n", "11/12/2018 05:00 0 0 0 0 \n", "11/12/2018 06:00 0 0 0 0 \n", "11/12/2018 07:00 0 0 0 0 \n", "11/12/2018 08:00 0 0 0 0 \n", "11/12/2018 09:00 0 0 0 0 \n", "11/12/2018 10:00 0 0 0 0 \n", "11/12/2018 11:00 0 0 0 0 \n", "11/12/2018 12:00 0 0 0 0 \n", "11/12/2018 13:00 0 0 0 0 \n", "11/12/2018 14:00 0 0 0 0 \n", "11/12/2018 15:00 0 0 0 0 \n", "11/12/2018 16:00 0 0 0 0 \n", "11/12/2018 17:00 0 0 0 0 \n", "11/12/2018 18:00 0 0 0 0 \n", "11/12/2018 19:00 0 0 0 0 \n", "11/12/2018 20:00 0 0 0 0 \n", "11/12/2018 21:00 0 0 0 0 \n", "11/12/2018 22:00 0 0 0 0 \n", "11/12/2018 23:00 0 0 0 0 \n", "\n", " month_June month_March month_May month_November \\\n", "fecha_hora \n", "09/08/2016 00:00 0 0 0 0 \n", "09/08/2016 01:00 0 0 0 0 \n", "09/08/2016 02:00 0 0 0 0 \n", "09/08/2016 03:00 0 0 0 0 \n", "09/08/2016 04:00 0 0 0 0 \n", "09/08/2016 05:00 0 0 0 0 \n", "09/08/2016 06:00 0 0 0 0 \n", "09/08/2016 07:00 0 0 0 0 \n", "09/08/2016 08:00 0 0 0 0 \n", "09/08/2016 09:00 0 0 0 0 \n", "09/08/2016 10:00 0 0 0 0 \n", "09/08/2016 11:00 0 0 0 0 \n", "09/08/2016 12:00 0 0 0 0 \n", "09/08/2016 13:00 0 0 0 0 \n", "09/08/2016 14:00 0 0 0 0 \n", "09/08/2016 15:00 0 0 0 0 \n", "09/08/2016 16:00 0 0 0 0 \n", "09/08/2016 17:00 0 0 0 0 \n", "09/08/2016 18:00 0 0 0 0 \n", "09/08/2016 19:00 0 0 0 0 \n", "09/08/2016 20:00 0 0 0 0 \n", "09/08/2016 21:00 0 0 0 0 \n", "09/08/2016 22:00 0 0 0 0 \n", "09/08/2016 23:00 0 0 0 0 \n", "10/08/2016 00:00 0 0 0 0 \n", "10/08/2016 01:00 0 0 0 0 \n", "10/08/2016 02:00 0 0 0 0 \n", "10/08/2016 03:00 0 0 0 0 \n", "10/08/2016 04:00 0 0 0 0 \n", "10/08/2016 05:00 0 0 0 0 \n", "... ... ... ... ... \n", "10/12/2018 18:00 0 0 0 0 \n", "10/12/2018 19:00 0 0 0 0 \n", "10/12/2018 20:00 0 0 0 0 \n", "10/12/2018 21:00 0 0 0 0 \n", "10/12/2018 22:00 0 0 0 0 \n", "10/12/2018 23:00 0 0 0 0 \n", "11/12/2018 00:00 0 0 0 1 \n", "11/12/2018 01:00 0 0 0 1 \n", "11/12/2018 02:00 0 0 0 1 \n", "11/12/2018 03:00 0 0 0 1 \n", "11/12/2018 04:00 0 0 0 1 \n", "11/12/2018 05:00 0 0 0 1 \n", "11/12/2018 06:00 0 0 0 1 \n", "11/12/2018 07:00 0 0 0 1 \n", "11/12/2018 08:00 0 0 0 1 \n", "11/12/2018 09:00 0 0 0 1 \n", "11/12/2018 10:00 0 0 0 1 \n", "11/12/2018 11:00 0 0 0 1 \n", "11/12/2018 12:00 0 0 0 1 \n", "11/12/2018 13:00 0 0 0 1 \n", "11/12/2018 14:00 0 0 0 1 \n", "11/12/2018 15:00 0 0 0 1 \n", "11/12/2018 16:00 0 0 0 1 \n", "11/12/2018 17:00 0 0 0 1 \n", "11/12/2018 18:00 0 0 0 1 \n", "11/12/2018 19:00 0 0 0 1 \n", "11/12/2018 20:00 0 0 0 1 \n", "11/12/2018 21:00 0 0 0 1 \n", "11/12/2018 22:00 0 0 0 1 \n", "11/12/2018 23:00 0 0 0 1 \n", "\n", " month_October month_September \n", "fecha_hora \n", "09/08/2016 00:00 0 1 \n", "09/08/2016 01:00 0 1 \n", "09/08/2016 02:00 0 1 \n", "09/08/2016 03:00 0 1 \n", "09/08/2016 04:00 0 1 \n", "09/08/2016 05:00 0 1 \n", "09/08/2016 06:00 0 1 \n", "09/08/2016 07:00 0 1 \n", "09/08/2016 08:00 0 1 \n", "09/08/2016 09:00 0 1 \n", "09/08/2016 10:00 0 1 \n", "09/08/2016 11:00 0 1 \n", "09/08/2016 12:00 0 1 \n", "09/08/2016 13:00 0 1 \n", "09/08/2016 14:00 0 1 \n", "09/08/2016 15:00 0 1 \n", "09/08/2016 16:00 0 1 \n", "09/08/2016 17:00 0 1 \n", "09/08/2016 18:00 0 1 \n", "09/08/2016 19:00 0 1 \n", "09/08/2016 20:00 0 1 \n", "09/08/2016 21:00 0 1 \n", "09/08/2016 22:00 0 1 \n", "09/08/2016 23:00 0 1 \n", "10/08/2016 00:00 1 0 \n", "10/08/2016 01:00 1 0 \n", "10/08/2016 02:00 1 0 \n", "10/08/2016 03:00 1 0 \n", "10/08/2016 04:00 1 0 \n", "10/08/2016 05:00 1 0 \n", "... ... ... \n", "10/12/2018 18:00 1 0 \n", "10/12/2018 19:00 1 0 \n", "10/12/2018 20:00 1 0 \n", "10/12/2018 21:00 1 0 \n", "10/12/2018 22:00 1 0 \n", "10/12/2018 23:00 1 0 \n", "11/12/2018 00:00 0 0 \n", "11/12/2018 01:00 0 0 \n", "11/12/2018 02:00 0 0 \n", "11/12/2018 03:00 0 0 \n", "11/12/2018 04:00 0 0 \n", "11/12/2018 05:00 0 0 \n", "11/12/2018 06:00 0 0 \n", "11/12/2018 07:00 0 0 \n", "11/12/2018 08:00 0 0 \n", "11/12/2018 09:00 0 0 \n", "11/12/2018 10:00 0 0 \n", "11/12/2018 11:00 0 0 \n", "11/12/2018 12:00 0 0 \n", "11/12/2018 13:00 0 0 \n", "11/12/2018 14:00 0 0 \n", "11/12/2018 15:00 0 0 \n", "11/12/2018 16:00 0 0 \n", "11/12/2018 17:00 0 0 \n", "11/12/2018 18:00 0 0 \n", "11/12/2018 19:00 0 0 \n", "11/12/2018 20:00 0 0 \n", "11/12/2018 21:00 0 0 \n", "11/12/2018 22:00 0 0 \n", "11/12/2018 23:00 0 0 \n", "\n", "[20520 rows x 94 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices = pd.get_dummies(prices_shifted)\n", "prices.dropna()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pricePh-24Ph-25Ph-26Ph-27Ph-28Ph-29Ph-30Ph-31Ph-32...month_Decembermonth_Februarymonth_Januarymonth_Julymonth_Junemonth_Marchmonth_Maymonth_Novembermonth_Octobermonth_September
count20712.00000020688.00000020687.00000020686.00000020685.00000020684.00000020683.00000020682.00000020681.00000020680.000000...20712.00000020712.00000020712.00000020712.00000020712.00000020712.00000020712.00000020712.00000020712.00000020712.000000
mean71.83407871.81264871.81184371.81030271.80676971.80457871.80180171.80012771.79824971.795429...0.0764770.0706840.0776360.0776360.0753190.0776360.0776360.0961760.0996520.096176
std34.13794334.14481934.14544834.14555434.14259934.14197034.14045834.14043534.14019334.138609...0.2657670.2563020.2676050.2676050.2639110.2676050.2676050.2948400.2995430.294840
min18.70490018.70490018.70490018.70490018.70490018.70490018.70490018.70490018.70490018.704900...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%47.85337547.84097547.83775047.83452547.83130047.83122547.83115047.83107547.83100047.826850...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
50%64.74220064.70235064.70080064.70075064.70070064.69815064.69560064.69475064.69390064.688300...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%88.36625088.33050088.32065088.31212588.30500088.29712588.29200088.28807588.28380088.278400...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max329.440000329.440000329.440000329.440000329.440000329.440000329.440000329.440000329.440000329.440000...1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", "

8 rows × 94 columns

\n", "
" ], "text/plain": [ " price Ph-24 Ph-25 Ph-26 Ph-27 \\\n", "count 20712.000000 20688.000000 20687.000000 20686.000000 20685.000000 \n", "mean 71.834078 71.812648 71.811843 71.810302 71.806769 \n", "std 34.137943 34.144819 34.145448 34.145554 34.142599 \n", "min 18.704900 18.704900 18.704900 18.704900 18.704900 \n", "25% 47.853375 47.840975 47.837750 47.834525 47.831300 \n", "50% 64.742200 64.702350 64.700800 64.700750 64.700700 \n", "75% 88.366250 88.330500 88.320650 88.312125 88.305000 \n", "max 329.440000 329.440000 329.440000 329.440000 329.440000 \n", "\n", " Ph-28 Ph-29 Ph-30 Ph-31 Ph-32 \\\n", "count 20684.000000 20683.000000 20682.000000 20681.000000 20680.000000 \n", "mean 71.804578 71.801801 71.800127 71.798249 71.795429 \n", "std 34.141970 34.140458 34.140435 34.140193 34.138609 \n", "min 18.704900 18.704900 18.704900 18.704900 18.704900 \n", "25% 47.831225 47.831150 47.831075 47.831000 47.826850 \n", "50% 64.698150 64.695600 64.694750 64.693900 64.688300 \n", "75% 88.297125 88.292000 88.288075 88.283800 88.278400 \n", "max 329.440000 329.440000 329.440000 329.440000 329.440000 \n", "\n", " ... month_December month_February month_January \\\n", "count ... 20712.000000 20712.000000 20712.000000 \n", "mean ... 0.076477 0.070684 0.077636 \n", "std ... 0.265767 0.256302 0.267605 \n", "min ... 0.000000 0.000000 0.000000 \n", "25% ... 0.000000 0.000000 0.000000 \n", "50% ... 0.000000 0.000000 0.000000 \n", "75% ... 0.000000 0.000000 0.000000 \n", "max ... 1.000000 1.000000 1.000000 \n", "\n", " month_July month_June month_March month_May month_November \\\n", "count 20712.000000 20712.000000 20712.000000 20712.000000 20712.000000 \n", "mean 0.077636 0.075319 0.077636 0.077636 0.096176 \n", "std 0.267605 0.263911 0.267605 0.267605 0.294840 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " month_October month_September \n", "count 20712.000000 20712.000000 \n", "mean 0.099652 0.096176 \n", "std 0.299543 0.294840 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 0.000000 \n", "75% 0.000000 0.000000 \n", "max 1.000000 1.000000 \n", "\n", "[8 rows x 94 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices.describe()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prices.to_csv(\"lbrprices_transformed_v2.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }