{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas: Groupby\n", "\n", "`groupby` is an amazingly powerful function in pandas. But it is also complicated to use and understand.\n", "The point of this notebook is to make you feel confident in using `groupby` and its cousins, `resample` and `rolling`. \n", "\n", "These notes are loosely based on the [Pandas GroupBy Documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html).\n", "\n", "\n", ":::{seealso}\n", "The \"split/apply/combine\" concept was first introduced in a paper by Hadley Wickham: .\n", ":::" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "from matplotlib import pyplot as plt\n", "# %config InlineBackend.figure_format = 'retina'\n", "plt.rcParams['figure.figsize'] = (12,7)\n", "plt.ion() # To trigger the interactive inline mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the following code to load a CSV file of the [NOAA IBTrACS cyclones](https://www.ncdc.noaa.gov/ibtracs/index.php?name=ibtracs-data) dataset:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SEASONNUMBERBASINSUBBASINNAMEISO_TIMENATURELATLONWMO_WINDWMO_PRES
SID
2018004S0809520181SIWAIRVING2018-01-03 18:00:00NR-8.2700095.330025.01004.0
2018004S0809520181SIWAIRVING2018-01-03 21:00:00NR-8.3681595.7494NaNNaN
2018004S0809520181SIWAIRVING2018-01-04 00:00:00NR-8.6000096.000025.01004.0
2018004S0809520181SIWAIRVING2018-01-04 03:00:00NR-9.0372195.9729NaNNaN
2018004S0809520181SIWAIRVING2018-01-04 06:00:00NR-9.5000095.800025.01004.0
\n", "
" ], "text/plain": [ " SEASON NUMBER BASIN SUBBASIN NAME ISO_TIME \\\n", "SID \n", "2018004S08095 2018 1 SI WA IRVING 2018-01-03 18:00:00 \n", "2018004S08095 2018 1 SI WA IRVING 2018-01-03 21:00:00 \n", "2018004S08095 2018 1 SI WA IRVING 2018-01-04 00:00:00 \n", "2018004S08095 2018 1 SI WA IRVING 2018-01-04 03:00:00 \n", "2018004S08095 2018 1 SI WA IRVING 2018-01-04 06:00:00 \n", "\n", " NATURE LAT LON WMO_WIND WMO_PRES \n", "SID \n", "2018004S08095 NR -8.27000 95.3300 25.0 1004.0 \n", "2018004S08095 NR -8.36815 95.7494 NaN NaN \n", "2018004S08095 NR -8.60000 96.0000 25.0 1004.0 \n", "2018004S08095 NR -9.03721 95.9729 NaN NaN \n", "2018004S08095 NR -9.50000 95.8000 25.0 1004.0 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#url = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.ALL.list.v04r00.csv'\n", "url = 'ibtracs.last3years.list.v04r00.csv'\n", "df = pd.read_csv(url, parse_dates=['ISO_TIME'], usecols=range(12), index_col='SID',\n", " skiprows=[1], na_values=[' ', 'NOT_NAMED'],\n", " keep_default_na=False, dtype={'NAME': str})\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Basin Key:** \n", "+ NA - North Atlantic\n", "+ EP - Eastern North Pacific \n", "+ WP - Western North Pacific \n", "+ NI - North Indian\n", "+ SI - South Indian\n", "+ SP - Southern Pacific\n", "+ SA - South Atlantic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## An Example\n", "\n", "This is an example of a \"one-liner\" that you can accomplish with groupby." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.groupby('BASIN').WMO_WIND.count().nlargest(20).plot(kind='bar', figsize=(12,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What Happened?\n", "\n", "Let's break apart this operation a bit. The workflow with `groubpy` can be divided into three general steps:\n", "\n", "1. **Split**: Partition the data into different groups based on some criterion.\n", "1. **Apply**: Do some caclulation within each group. Different types of \"apply\" steps might be\n", " 1. *Aggregation*: Get the mean or max within the group.\n", " 1. *Transformation*: Normalize all the values within a group\n", " 1. *Filtration*: Eliminate some groups based on a criterion.\n", "1. **Combine**: Put the results back together into a single object.\n", "\n", "![split-apply-combine](https://miro.medium.com/max/1840/1*JbF6nhrQsn4f-TaSF6IR9g.png)\n", "\n", "### The `groupby` method\n", "\n", "Both `Series` and `DataFrame` objects have a groupby method. It accepts a variety of arguments, but the simplest way to think about it is that you pass another series, whose unique values are used to split the original object into different groups." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(df.BASIN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a shortcut for doing this with dataframes: you just pass the column name:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('BASIN')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `GroubBy` object\n", "\n", "When we call, `groupby` we get back a `GroupBy` object:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = df.groupby('BASIN')\n", "gb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The length tells us how many groups were found:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(gb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the groups are available as a dictionary via the `.groups` attribute:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "groups = gb.groups\n", "len(groups)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['EP', 'NA', 'NI', 'SI', 'SP', 'WP']" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(groups.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iterating and selecting groups\n", "\n", "You can loop through the groups if you want." ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SEASONNUMBERBASINSUBBASINNAMEISO_TIMENATURELATLONWMO_WINDWMO_PRES
SID
2018131N12235201823EPMMNaN2018-05-10 12:00:00TS12.1000-125.40025.01008.0
2018131N12235201823EPMMNaN2018-05-10 15:00:00TS12.2101-125.790NaNNaN
2018131N12235201823EPMMNaN2018-05-10 18:00:00TS12.3000-126.20030.01007.0
2018131N12235201823EPMMNaN2018-05-10 21:00:00TS12.3575-126.643NaNNaN
2018131N12235201823EPMMNaN2018-05-11 00:00:00TS12.4000-127.10030.01007.0
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" ], "text/plain": [ " SEASON NUMBER BASIN SUBBASIN NAME ISO_TIME NATURE \\\n", "SID \n", "2018131N12235 2018 23 EP MM NaN 2018-05-10 12:00:00 TS \n", "2018131N12235 2018 23 EP MM NaN 2018-05-10 15:00:00 TS \n", "2018131N12235 2018 23 EP MM NaN 2018-05-10 18:00:00 TS \n", "2018131N12235 2018 23 EP MM NaN 2018-05-10 21:00:00 TS \n", "2018131N12235 2018 23 EP MM NaN 2018-05-11 00:00:00 TS \n", "\n", " LAT LON WMO_WIND WMO_PRES \n", "SID \n", "2018131N12235 12.1000 -125.400 25.0 1008.0 \n", "2018131N12235 12.2101 -125.790 NaN NaN \n", "2018131N12235 12.3000 -126.200 30.0 1007.0 \n", "2018131N12235 12.3575 -126.643 NaN NaN \n", "2018131N12235 12.4000 -127.100 30.0 1007.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The key is \"EP\"\n" ] } ], "source": [ "for key, group in gb:\n", " display(group.head())\n", " print(f'The key is \"{key}\"')\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can get a specific group by key." ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SEASONNUMBERBASINSUBBASINNAMEISO_TIMENATURELATLONWMO_WINDWMO_PRES
SID
2018027S1115620185SPEAFEHI2018-01-26 12:00:00DS-11.4000156.100NaNNaN
2018027S1115620185SPEAFEHI2018-01-26 15:00:00DS-11.5675156.525NaNNaN
2018027S1115620185SPEAFEHI2018-01-26 18:00:00DS-11.8000157.000NaNNaN
2018027S1115620185SPEAFEHI2018-01-26 21:00:00DS-12.1477157.537NaNNaN
2018027S1115620185SPEAFEHI2018-01-27 00:00:00TS-12.6000158.000NaNNaN
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" ], "text/plain": [ " SEASON NUMBER BASIN SUBBASIN NAME ISO_TIME NATURE \\\n", "SID \n", "2018027S11156 2018 5 SP EA FEHI 2018-01-26 12:00:00 DS \n", "2018027S11156 2018 5 SP EA FEHI 2018-01-26 15:00:00 DS \n", "2018027S11156 2018 5 SP EA FEHI 2018-01-26 18:00:00 DS \n", "2018027S11156 2018 5 SP EA FEHI 2018-01-26 21:00:00 DS \n", "2018027S11156 2018 5 SP EA FEHI 2018-01-27 00:00:00 TS \n", "\n", " LAT LON WMO_WIND WMO_PRES \n", "SID \n", "2018027S11156 -11.4000 156.100 NaN NaN \n", "2018027S11156 -11.5675 156.525 NaN NaN \n", "2018027S11156 -11.8000 157.000 NaN NaN \n", "2018027S11156 -12.1477 157.537 NaN NaN \n", "2018027S11156 -12.6000 158.000 NaN NaN " ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.get_group('SP').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregation\n", "\n", "Now that we know how to create a `GroupBy` object, let's learn how to do aggregation on it.\n", "\n", "One way us to use the `.aggregate` method, which accepts another function as its argument. The result is automatically combined into a new dataframe with the group key as the index." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use both `.` or `[]` syntax to select a specific column to operate on. Then we get back a series." ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BASIN\n", "EP 140.0\n", "NA 160.0\n", "NI 95.0\n", "SI 125.0\n", "SP 110.0\n", "Name: WMO_WIND, dtype: float64" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.aggregate(np.max).head()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BASIN\n", "NA 160.0\n", "EP 140.0\n", "SI 125.0\n", "WP 115.0\n", "SP 110.0\n", "NI 95.0\n", "Name: WMO_WIND, dtype: float64" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.aggregate(np.max).nlargest(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are shortcuts for common aggregation functions:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BASIN\n", "NA 160.0\n", "EP 140.0\n", "SI 125.0\n", "WP 115.0\n", "SP 110.0\n", "NI 95.0\n", "Name: WMO_WIND, dtype: float64" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.max().nlargest(10)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BASIN\n", "SI 10.0\n", "EP 15.0\n", "NA 15.0\n", "SP 15.0\n", "NI 20.0\n", "WP 35.0\n", "Name: WMO_WIND, dtype: float64" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.min().nsmallest(10)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BASIN\n", "WP 61.534009\n", "EP 51.912698\n", "NA 50.308810\n", "SP 47.766520\n", "SI 46.231362\n", "NI 41.353846\n", "Name: WMO_WIND, dtype: float64" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.mean().nlargest(10)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BASIN\n", "EP 30.176084\n", "NA 24.634510\n", "SP 23.220435\n", "WP 22.915217\n", "SI 22.771114\n", "NI 17.673680\n", "Name: WMO_WIND, dtype: float64" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.std().nlargest(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also apply multiple functions at once:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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aminamaxmean
BASIN
EP15.0140.051.912698
NA15.0160.050.308810
NI20.095.041.353846
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\n", "
" ], "text/plain": [ " amin amax mean\n", "BASIN \n", "EP 15.0 140.0 51.912698\n", "NA 15.0 160.0 50.308810\n", "NI 20.0 95.0 41.353846\n", "SI 10.0 125.0 46.231362\n", "SP 15.0 110.0 47.766520" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.WMO_WIND.aggregate([np.min, np.max, np.mean]).head()" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gb.WMO_WIND.aggregate([np.min, np.max, np.mean]).nlargest(10, 'mean').plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transformation\n", "\n", "The key difference between aggregation and transformation is that aggregation returns a *smaller* object than the original, indexed by the group keys, while *transformation* returns an object with the same index (and same size) as the original object. Groupby + transformation is used when applying an operation that requires information about the whole group.\n", "\n", "In this example, we standardize the earthquakes in each country so that the distribution has zero mean and unit variance. We do this by first defining a function called `standardize` and then passing it to the `transform` method.\n", "\n", "I admit that I don't know why you would want to do this. `transform` makes more sense to me in the context of time grouping operation. See below for another example." ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SID\n", "2018004S08095 -0.932381\n", "2018004S08095 NaN\n", "2018004S08095 -0.932381\n", "2018004S08095 NaN\n", "2018004S08095 -0.932381\n", " ... \n", "2021067S24058 NaN\n", "2021067S24058 NaN\n", "2021067S24058 NaN\n", "2021067S24058 NaN\n", "2021067S24058 NaN\n", "Name: WMO_WIND, Length: 17783, dtype: float64" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def standardize(x):\n", " return (x - x.mean())/x.std()\n", "\n", "wind_standardized_by_basin = gb.WMO_WIND.transform(standardize)\n", "wind_standardized_by_basin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time Grouping\n", "\n", "We already saw how pandas has a strong built-in understanding of time. This capability is even more powerful in the context of `groupby`. With datasets indexed by a pandas `DateTimeIndex`, we can easily group and resample the data using common time units.\n", "\n", "To get started, let's load a NOAA timeseries data:" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "import urllib\n", "\n", "header_url = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/HEADERS.txt'\n", "with urllib.request.urlopen(header_url) as response:\n", " data = response.read().decode('utf-8')\n", "lines = data.split('\\n')\n", "headers = lines[1].split(' ')\n", "\n", "ftp_base = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/'\n", "dframes = []\n", "for year in range(2016, 2019):\n", " data_url = f'{year}/CRND0103-{year}-NY_Millbrook_3_W.txt' \n", " df = pd.read_csv(ftp_base + data_url, parse_dates=[1],\n", " names=headers, header=None, sep='\\s+',\n", " na_values=[-9999.0, -99.0])\n", " dframes.append(df)\n", "\n", "df = pd.concat(dframes)\n", "df = df.set_index('LST_DATE')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WBANNOCRX_VNLONGITUDELATITUDET_DAILY_MAXT_DAILY_MINT_DAILY_MEANT_DAILY_AVGP_DAILY_CALCSOLARAD_DAILY...SOIL_MOISTURE_10_DAILYSOIL_MOISTURE_20_DAILYSOIL_MOISTURE_50_DAILYSOIL_MOISTURE_100_DAILYSOIL_TEMP_5_DAILYSOIL_TEMP_10_DAILYSOIL_TEMP_20_DAILYSOIL_TEMP_50_DAILYSOIL_TEMP_100_DAILY
LST_DATE
2016-01-01647562.422-73.7441.793.4-0.51.51.30.01.69...0.2330.2040.1550.1474.24.45.16.07.6NaN
2016-01-02647562.422-73.7441.792.9-3.6-0.4-0.30.06.25...0.2270.1990.1520.1442.83.14.25.77.4NaN
2016-01-03647562.422-73.7441.795.1-1.81.61.10.05.69...0.2230.1960.1510.1412.62.83.85.27.2NaN
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2016-01-05647562.422-73.7441.79-5.2-15.5-10.3-11.70.09.34...0.2130.1910.1480.1380.40.92.44.36.6NaN
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5 rows × 28 columns

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" ], "text/plain": [ " WBANNO CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN \\\n", "LST_DATE \n", "2016-01-01 64756 2.422 -73.74 41.79 3.4 -0.5 \n", "2016-01-02 64756 2.422 -73.74 41.79 2.9 -3.6 \n", "2016-01-03 64756 2.422 -73.74 41.79 5.1 -1.8 \n", "2016-01-04 64756 2.422 -73.74 41.79 0.5 -14.4 \n", "2016-01-05 64756 2.422 -73.74 41.79 -5.2 -15.5 \n", "\n", " T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY ... \\\n", "LST_DATE ... \n", "2016-01-01 1.5 1.3 0.0 1.69 ... \n", "2016-01-02 -0.4 -0.3 0.0 6.25 ... \n", "2016-01-03 1.6 1.1 0.0 5.69 ... \n", "2016-01-04 -6.9 -7.5 0.0 9.17 ... \n", "2016-01-05 -10.3 -11.7 0.0 9.34 ... \n", "\n", " SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY \\\n", "LST_DATE \n", "2016-01-01 0.233 0.204 \n", "2016-01-02 0.227 0.199 \n", "2016-01-03 0.223 0.196 \n", "2016-01-04 0.220 0.194 \n", "2016-01-05 0.213 0.191 \n", "\n", " SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY \\\n", "LST_DATE \n", "2016-01-01 0.155 0.147 \n", "2016-01-02 0.152 0.144 \n", "2016-01-03 0.151 0.141 \n", "2016-01-04 0.148 0.139 \n", "2016-01-05 0.148 0.138 \n", "\n", " SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY \\\n", "LST_DATE \n", "2016-01-01 4.2 4.4 5.1 \n", "2016-01-02 2.8 3.1 4.2 \n", "2016-01-03 2.6 2.8 3.8 \n", "2016-01-04 1.7 2.1 3.4 \n", "2016-01-05 0.4 0.9 2.4 \n", "\n", " SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n", "LST_DATE \n", "2016-01-01 6.0 7.6 NaN \n", "2016-01-02 5.7 7.4 NaN \n", "2016-01-03 5.2 7.2 NaN \n", "2016-01-04 4.9 6.9 NaN \n", "2016-01-05 4.3 6.6 NaN \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This timeseries has daily resolution, and the daily plots are somewhat noisy." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, 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12 rows × 27 columns

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" ], "text/plain": [ " WBANNO CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN \\\n", "LST_DATE \n", "1 64756 2.488667 -73.74 41.79 2.924731 -7.122581 \n", "2 64756 2.487882 -73.74 41.79 6.431765 -5.015294 \n", "3 64756 2.488667 -73.74 41.79 7.953763 -3.035484 \n", "4 64756 2.488667 -73.74 41.79 14.793333 1.816667 \n", "5 64756 2.488667 -73.74 41.79 21.235484 8.460215 \n", "6 64756 2.488667 -73.74 41.79 25.627778 11.837778 \n", "7 64756 2.488667 -73.74 41.79 28.568817 15.536559 \n", "8 64756 2.488667 -73.74 41.79 27.473118 15.351613 \n", "9 64756 2.488667 -73.74 41.79 24.084444 12.032222 \n", "10 64756 2.548882 -73.74 41.79 18.127473 5.757143 \n", "11 64756 2.555333 -73.74 41.79 9.586667 -1.375556 \n", "12 64756 2.555333 -73.74 41.79 3.569892 -5.704301 \n", "\n", " T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY ... \\\n", "LST_DATE ... \n", "1 -2.100000 -1.905376 2.478495 5.812258 ... \n", "2 0.712941 1.022353 4.077647 8.495882 ... \n", "3 2.455914 2.643011 2.788172 13.211290 ... \n", "4 8.302222 8.574444 2.396667 15.295889 ... \n", "5 14.850538 15.121505 3.015054 17.288602 ... \n", "6 18.733333 19.026667 3.053333 21.913333 ... \n", "7 22.054839 22.012903 3.865591 21.570645 ... \n", "8 21.410753 21.378495 4.480645 18.493333 ... \n", "9 18.057778 17.866667 3.730000 13.625667 ... \n", "10 11.938462 11.952747 3.228261 9.442527 ... \n", "11 4.097778 4.277778 3.991111 6.350111 ... \n", "12 -1.069892 -0.850538 2.791398 4.708602 ... \n", "\n", " SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY \\\n", "LST_DATE \n", "1 0.240250 0.199528 \n", "2 0.244229 0.203089 \n", "3 0.224155 0.190753 \n", "4 0.208611 0.183922 \n", "5 0.198925 0.175441 \n", "6 0.132867 0.127067 \n", "7 0.100871 0.084978 \n", "8 0.150914 0.121323 \n", "9 0.131133 0.113678 \n", "10 0.144363 0.103879 \n", "11 0.213856 0.174800 \n", "12 0.231729 0.188398 \n", "\n", " SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY \\\n", "LST_DATE \n", "1 0.150387 0.162554 0.168817 \n", "2 0.154294 0.165494 1.217647 \n", "3 0.153108 0.161742 3.469892 \n", "4 0.150022 0.159956 9.402222 \n", "5 0.146011 0.157710 16.889247 \n", "6 0.128344 0.154989 22.372222 \n", "7 0.112516 0.153264 25.453763 \n", "8 0.125398 0.162667 24.784946 \n", "9 0.117722 0.157660 21.061111 \n", "10 0.112703 0.147246 14.901099 \n", "11 0.149300 0.159456 6.845556 \n", "12 0.156817 0.172882 1.896774 \n", "\n", " SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY \\\n", "LST_DATE \n", "1 0.232258 0.788172 1.749462 \n", "2 1.183529 1.281176 1.602353 \n", "3 3.407527 3.379570 3.473118 \n", "4 9.140000 8.438889 7.600000 \n", "5 16.693548 15.570968 14.193548 \n", "6 22.198889 20.917778 19.421111 \n", "7 25.408602 24.141935 22.725806 \n", "8 24.897849 24.117204 23.311828 \n", "9 21.220000 20.947778 20.823333 \n", "10 15.050549 15.360440 16.113187 \n", "11 6.918889 7.663333 8.987778 \n", "12 1.964516 2.658065 3.869892 \n", "\n", " SOIL_TEMP_100_DAILY \n", "LST_DATE \n", "1 3.395699 NaN \n", "2 2.460000 NaN \n", "3 3.792473 NaN \n", "4 6.633333 NaN \n", "5 12.344086 NaN \n", "6 17.453333 NaN \n", "7 20.994624 NaN \n", "8 22.278495 NaN \n", "9 20.717778 NaN \n", "10 17.113187 NaN \n", "11 11.007778 NaN \n", "12 5.846237 NaN \n", "\n", "[12 rows x 27 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthly_climatology = df.groupby(df.index.month).mean()\n", "monthly_climatology" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each row in this new dataframe respresents the average values for the months (1=January, 2=February, etc.)\n", "\n", "We can apply more customized aggregations, as with any groupby operation. Below we keep the mean of the mean, max of the max, and min of the min for the temperature measurements." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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T_DAILY_MEANT_DAILY_MAXT_DAILY_MIN
LST_DATE
1-2.10000016.9-26.0
20.71294124.9-24.7
32.45591426.8-16.5
48.30222230.6-11.3
514.85053833.4-1.6
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" ], "text/plain": [ " T_DAILY_MEAN T_DAILY_MAX T_DAILY_MIN\n", "LST_DATE \n", "1 -2.100000 16.9 -26.0\n", "2 0.712941 24.9 -24.7\n", "3 2.455914 26.8 -16.5\n", "4 8.302222 30.6 -11.3\n", "5 14.850538 33.4 -1.6" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthly_T_climatology = df.groupby(df.index.month).aggregate({'T_DAILY_MEAN': 'mean',\n", " 'T_DAILY_MAX': 'max',\n", " 'T_DAILY_MIN': 'min'})\n", "monthly_T_climatology.head()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4MGj2KHR7C0qUMzWs3EoN27yevFJqOTA3/aOz1jo8/R/BX1rrOrk9tkiSfJrF6J7Z9TW0fBrumwEu5v6RCvt0bRD1WpIMKOvNAy2qMbBlAP5lvM0OL4vcZtckplj4/UAEi7eH8O/JaFwUdK5TiQdbVaNL3Uq4u8qayHxLumIsntryCXiWNhJ904dNWy1bZEleKRUI/A00BM5qrctkuu+S1jrXDu9CT/KpScYA6+FfoOMYuGti7jsziWInJj6Z5XvOsXh7SMYg6r0NqzCoZTXa1nT87o4z0VdZsiOEpTtDOR+XRIWSngxo7s+gVtW4rWJJs8NzPOcPwcrRcPZfqNbG6MKp3KDIwyiSJK+UKglsAN7RWv+olIrJS5JXSg0FhgJUr169xZkzZ2wSTzZJV2Dxw3DyL+jxP2g7vHCeRzica4OoS3aEsvpgBMmpxiDqgy2r0aeJv1MOXKZa0thwLIrF20P480gkqWmaVoFlGdSyGj0b+1HCQ+Zk5FlaGuz5DtZOhsRYaPuCUQPHs+j+aRZ6kldKuQO/Aqu11h+kHzuKvXTXxF+E7x4wNtHuOxeaPmT75xB278YujafaBxKXkJplELV/M/9sg6jOLvJyIj/uCmPJ9hBOXrhKSU83ejepyoOtqtEkwBcl73bzJv6ikeh3fwOlA+DeaVC3V5H0FhRqklfGb8AijEHWkZmOzwCiMw28ltNaj83tWoWS5OPOGWUKLp6CBxZA3Z62vb5wCNYGJ6/pUMt+BlHNpLVm++lLLN4ewsr950hMSaNulVIMalmN/s38KesjkxPy5OwW+HU0RB6EWj3gvvegbGChPmVhJ/n2wEZgP8YUSoDXgK3AEqA6cBZ4QGt9Mbdr2TzJR/8HX/eDhEsw5P8gqKPtri0cSpt31xFhZRVq5dKebH1NCtDdKC4xhRV7z7Fkewh7Q2PxcHWhe4PKDG5VnXa3leeXveekjEJuLCmw9TNY/y7oNOg0Btq+VGiz+Ip0dk1B2DTJh++Db+83vsGPLIOqzWxzXeFQTkRe5suNp/h+u7VVjKCAU9Pk3V1uDofHsXh7CD/tDiM2IYWyJdy5nJiaZZWtt7sr797fSBL9jWJDjbn1R36FCnWg5/sQ1MHmT1P8kvyZzfB/g42Bj0d/hoq1C35N4TC01vz7XzTzNp5k/dEoPN1ccHVRxCdn76rxL+PNpvFdTIjS8SSmWFhz6DxjfthLUmpatvvle5mLY6vht1ch5iw0Hgzdpxp1smwktyTvfBNkj602+uBLVoKnVkuCL0ZSLGn8tDuUnnP+4aEvt7I/LJbR3Wrz74Su/K9/I7xv6G/3dndlTI9c5wKITLzcXenTpCrJVhI8wLkcVt0KjO0GX9gKHV6BA8tgbgvYPt+YmVPInGOe1L4l6cuQ09+S+1aDp343dn0RTi82IYXgbWdZuOk0EXGJ3F6pJNPSuw6uDaTaqsyAgKplvK2WUSjp6UaKJU0WV+XEo0R6iYQHYeUrxvz6Pd9Bnftg58LsZRRsxPG7a/YtyV5QyM0b+sgG284u5GI8X206xZLtIVxNttDutvI826EmnWpXdPhFS/bM2kwlV6WwaE2TamWY/WBTAitIHahcaQ37FsOvr0DKlaz3uXtD7/zlL+fuk5/V0HppUN9qMOqAbQITdmVPSAzzNp5k1f5wXJSid5OqPN0+iIb+xWduu9mslVFwd3XhtZ/2k2JJ443e9RnUsprMsb+ZD+obNXBulM/8lVuSd/zuGics8i+ys6Rp/jh8ni83nmT76UuU8nLj2Y41eaJdIH6+9lVHpjjo18zfaldX8xplGL14L+OW7Wf9kSjevb+RzK/PTdw568dtmL8cP8nnVOTfwQr7C+sSki0s3RnC/H9OcTo6Hv8y3kzuVZ9BrapR0tPxf32djZ+vN989cwfzNp5k5pqj3DP7Eu8/0JT2tWR8zKoiyF+OP0LSdbLRh5WZu7dxXDisyMuJzFx9lLbT1vH68oP4lvBg7kPN2DCmM0+1D5IEb8dcXBTPdbqNn164k5KebjwyfytTfz1EUmr2KazFXhHkL8f/S8mtyL+wa9b6dev5lebLjSdZvuccKWlpdKtXmWc71qRljbLSv+tgGvr78utLHfjfb4f58p9T/HPiAnOGNKN25bzvjOX0iiB/Of7Aq3BI1mZouChI08am1g+0qMZT7YMIklkaTmHd4fOMXbqPK0mpTLi3Lo+3C5R/2jbk3AOvwiHNWH00W7GwNA2lvNz4e8xdMljnZLrWq8zvIzsydulepqw4xF/HonhvYGMqlZIdqgqb4/fJC4eU0+rIK4mpkuCdVMVSnnz1RCve6tuAf/+L5p4PN/LHofNmh+X0JMmLIhd1OQkPN+u/elXtbFs9YVtKKR5rG8ivL7WnSmkvnvl6BxN/2k+ClbpCwjYkyYsiteVkNPfN2UiqJQ1316x9slJLpvioVbkUPw1vx9CONflu61l6frSRA2GxZofllCTJiyKRlqb5eP0JHpq3hVKebvz6cgdmDGyCfxlvFEYFQylVW7x4urny2n31+O6ZO4hPstD/k018+td/WNLsZzKIM5DZNaLQXbqazKgle/jraBS9m1Tl3fsbyTx3kUVMfDKv/bSf3/ZHcEdQOWY92FS67vKheJUaFnZl55lL9Jyzkc0nonm7X0PmDG4qCV5kU6aEBx8/1JwZAxtzICyWez78mxV7c1jyL/JF/tpEodBaM/+fU0xbdQS/Ml4se74djQKkgJjImVKKB1pWo3VQOUZ8v4eXgnez/kgkb/ZtwLrDkVIm+hZJd42wudiEFMYu3cvqg+fpXr8yMx5ogq+3u9lhCQeSYknjoz9PMPfP45Qp4c7VJEuW3ahku8GspLtGFJn9obH0+mgj6w5HMqlnPT5/tIUkeJFv7q4ujO5Wmx+GtSU2ITXbdoMJKRZmrD5qUnSORbprhE1orflu61neWnGI8iU9WPxcG1rUKGd2WMLBtahRjrQcZtvIdoN5I0leFNiVpFRe+3E/v+w9R6faFZn1YFPKyapVYSM5bTcos2/yRrprRIEciYijz9x/+HXfOcb0qMOCJ1pJghc2NaZHnWybsCtgaMcgcwJyMDZJ8kqpr5RSkUqpA5mOlVNKrVVKHU//XNYWzyXsxw87Quj38SYuJ6by3TNtGH7X7bK3qrC5fs38eff+RhkL5yqU9MDdVfHF36f4L+rKTR9f3Nlkdo1SqiNwBfhaa90w/dh7wEWt9TSl1HigrNZ6XG7Xkdk1jiEh2cLk5Qf4YWcobWuWZ/aQplJNUBSpA2GxPLFgG5Y0zYInW9O0WhmzQzJVoc+u0Vr/DVy84XBfYFH614uAfrZ4LmGu/6Ku0P+TTSzdFcrLXW7n22fukAQvilxDf1+WDmtHKS93Hpq3hQ3HoswOyW4VZp98Za11OED650qF+FyiCPyy9xx9PvqHyMtJLHyyNaO718FVumeESQIr+LD0+bbUKO/D0wu38/PuMLNDskumD7wqpYYqpXYopXZERcl/Y3uUlGph0s/7eTl4N/X8SrPy5fZ0ql3R7LCEoFIpLxY/14aWgWUZuXgP8/85ZXZIdqcwk/x5pZQfQPrnSGsnaa2/0Fq31Fq3rFhREoe9ORsdz8BP/+XbLWcZ2rEmwUPb4OcrU9eE/Sjt5c7CJ1tzb8MqvP3rIaatOoI9reQ3W2HOk/8FeByYlv55eSE+l7CRzJtrl/Xx4GpSCp5urnzxaAu6N6hidngOZeXJlczeNZuIqxFU8anCiOYj6Fmzp9lhOSUvd1fmPtScycsP8NmG/7hwJYlp9zfCzdX0zgrT2STJK6WCgc5ABaVUKPAGRnJfopR6GjgLPGCL5xKF58bNtS9eTUYpGHtPLUnw+bTy5EqmbJ5CoiURgPCr4UzZPAVAEn0hcXVRTO3XkIqlPPnwj+NcuprM3Iea4+3hevMHOzFbza4ZorX201q7a60DtNbztdbRWuuuWuta6Z9vnH0j7Iy1zbW1hq/+OW1OQA5s9q7ZGQn+mkRLIrN3zTYpouJBKcXIu2sztV9D/jwaySPztxITn2x2WKaS9zICMGrPWFs6DlIj5FaEXw3P8fjFRGnvFLZH2tTgk4easz80lgc++7dY/w5Lkheci0ngsa+25Xi/1AjJu2OXjjFs7bBcz+m0uBP9l/dn2rZprDu7jtgk2du0MNzbyI9FT7UmIjaRAZ9u5kTkZbNDMoXUky/GtNb8sDOUt1ccwqI1PRv78evecySkSN3u/IqMj+TjPR/z84mfKelekg7+HVh3dl2WLhsvVy+ebPgkHq4ebAvfxu7I3SRaElEo6parS+sqrWnt15rmlZpT0qOkia/GuRw8F8vjX20nNS2N+Y+3okUN56uwktuKV0nyxVRkXCITftzPuiORtA4qx8yBTahevkSW2TWyA8/NxafEs+jgIhYcXEBKWgpD6g7hucbP4evpe9PZNcmWZPZf2M+2iG1sj9jO3si9JKcl46pcqV++Pq2qtKJ1ldY0q9SMEu4lTHyVju9sdDyPfrWV83GJfPJwc7rUrWx2SDYlSV5k0Frzy95zTF5+kMQUC2PvqcuT7QKlsFg+WdIsLP9vOXN3zyUqIYpuNboxqvkoqpWudsvXTExNZG/U3oykvz9qP6k6FTcXNxpVaJSR9JtUbIKXW9ZSEjJd8+aiLifx5MJtHA6/zHsDGjOgRYDZIdmMJHkBQPSVJF5ffoDf9kfQrHoZZj7QhNsqSrdAfm0O28zMnTM5fuk4jSs2ZkzLMTSt1NTmzxOfEs+eyD1sjdjK9ojtHIw+SJpOw8PFgyaVmmQk/dDLoUzdMjVb19CUdlMk0d/gcmIKw77dyaYT0Uy4ty7PdbrN7JBsQpK84PcDEUz8aT+XE1MZ2a0WQzvUlIUi+XTs0jE+2PEBm85twr+kP6NajKJ7je4oVTTvgq4kX2FX5C62hW9jW8Q2jlw8gibnv18/Hz/WDFxTJLE5kqRUC6OX7GXlvnCe7RDEhHvrOfw72dySvOwM5eRi41N445cD/LznHA2qlub/nm1KnSqlzA7LoUTFR/Hxno/56cRP+Lj78GrLVxlSdwgerkW7OUpJj5J0DOhIx4COAMQmxbLj/A5Grh9p9fyIqxFFGJ3j8HRz5aPBzajg48G8jae4cCWZ9wY2xt1JGz2S5J3Y+iORjFu2j4tXkxl5dy2G33W70/4iF4YbB1UfrvdwxqCqPfD19KVr9a74+fhZnZdfzkv22M2Ji4tiSp8GVCzlycw1x7h4NZlPH2lOCQ/nS4nyF++ELiemMG7pPp5cuJ0yJdz5efidjLy7tiT4PLKkWfjp+E/0+qkXn+z9hPb+7VnedzljW421mwSf2YjmI/ByzToQq1BEJ0bz9r9vczXlqkmR2TelFC92qcW79zdi4/EohszbysWrzrc61vn+bRVzm05cYOzSfYTHJvB859sYeXctPN2Kd+2O/Nh8bjPv73ifY5eO0bhiYz7o/EGhDKra0rXB1cyza55v8jz/xfzHN4e/YWPYRqa0nUI7/3YmR2qfhrSuTjkfD14K3s3Azzbz9VOtCSjrPFNWZeDVScQnpzJt1RG+/vcMNSv4MHNQE5pXd75FH4Xl+KXjvL/zfTaFGYOqI1uMpEeNHkU2qFpY9kbt5fVNr3Mq9hT9b+/Pq61epbRHabPDsktbT0bzzNc7UBhVLaMuJznMWhGZXePktp++yKs/7OXsxXiebBdk7G5fzCvv5dWNg6rPNX7OlEHVwpRkSeLTPZ+y8OBCynuX5422b2QM3oqsPl5/ghmrj2Y55girviXJO6nEFAvvrznKl/+cIqCsNzMHNuGOmuXNDstuZV4wVLlEZRpWaMimc5tISUthcJ3BPNf4Ocp4lTE7zEJz8MJBJm2axImYE/Su2ZtxrcfZ5RiDme6c9qfVQn3+ZbzZNL6LCRHljUyhdEJ7QmJ4Zcke/ou6ysN3VOe1++rh4yk/zpzcWN89Ij6CiLMRNCzfkOkdp1O9dHWTIyx8DSo0YEmvJXyx/wu+3Pcl/4b/y6Q2k+havavZodmNnKpVhsUkoLV2yO47mW7hYJJT05i5+igDPt1MfLKFr59qzTv9G0mCvwlr9d0BohOji0WCv8bd1Z3hTYcT3CuYCt4VGLl+JGM3jOVS4iWzQ7MLuVVcfWjeVo6dd7xKltJdY+cyFwyrUMoTNwXhcUkMbBHA673q4+vtbnaIdi/sShj3LLvH6n0Kxb7H9xVxRPYhJS2F+fvn8/m+zyntUZrX7niNHoE9zA7LVDfujgbg7e5Cz8ZVWXvoPFeTUnnyzkBG3F2bknbUsMqtu0Za8nbs2i9cWEwCGqPAUnhcEs+0D2LmA00kwd9EfEo8c3fPpe/PfXM8p4pP8d3W0N3FnWFNhrG412L8fPx4dcOrjP5rNBcSLpgdmmn6NfPn3fsb4V/GG4XRF//u/Y2Z+UAT/nylEwNbBDBv4ym6zPyL5XvCHGLDcGnJ26ErSakcDItl6Dc7iE1IzXa/vQ8CmU1rzW+nfuODnR8QGR/JvUH30qRiEz7c+aEU8cpBaloqiw4u4pM9n+Dt7s341uPpGdTTIfugC9vus5eYvPwg+8NiuSOoHG/1bWh6qRCZXWPHrialcig8jn2hsRwIi2VfaAwnL1wltx+LAk5Nk8RkzcELB5m2bRp7ovZQr1w9xrceT/PKzQEpx5sXJ2NO8vrm19kXtY/OAZ15ve3rVCpRyeyw7I4lTfP99rPMWH2Uy4mpPNEukJF316KUlznvriXJ24mEZAuHwmPZHxrLvjDj839RV0hL/xFULu1JI39fGvmXoXGALxN+3E9EXPbBQmnJZ3ch4QKzd81m+YnllPUqy8jmI+l7e19clPRI5pclzcK3h7/lo90f4eHqwbhW4+hzWx9p1Vtx6Woy760+yvfbz1KhpCcT76tH36ZVi/x7JUnehvK6c1JiioXD4XHsD4vNaKUfO385I6FXKOlJ4wDf9KTuS6MAXyqX9sr2XNkHgex/YUZRSrYk893h7/h83+ckWZJ4pN4jPNf4Odk+zwbOxJ1h8qbJ7IrcRXv/9rzR9o1iPYaRm70hMUxefoC9obG0DirHW30bULdK0a0sliRvIzkl3bf7NqBW5VLsC4vlQHor/dj5y1jSM3p5Hw8aZUrojQPKULm0Z57+28t2fNZprdkQuoEZ22dw9vJZOgV04tWWrxLoG2h2aE4lTacRfCSY2btm46pceaXlKwyoNUBa9VakpWkW7wjhvd+PEJeYymNtazCqW21KF0EXjqlJXil1DzAbcAW+1FpPy+lce0/yOa2Gy6xsCXca+vumt9LL0CjAl6q+XvJHYUMnY07y3vb32HRuE0G+QYxtNZb2/u3NDsuphVwOYcrmKWyL2EYbvzZ0DOjIN4e+kfENKy5dTWbmmqP837azlPfx5LX76tK/mX+h5gDTkrxSyhU4BnQDQoHtwBCt9SFr59tzkj8ReYW7P9iQ4/2fPNycRv6+BJT1loReSGKTYvls72cEHwmmhFsJXmj6Ag/WfRB3F5lKWhTSdBpLjy1l2tZppOiULPfJTKXs9oXGMHn5QfaExNAqsCxv9mlI/aqF04VjZpJvC0zRWvdIvz0BQGv9rrXz7SnJa605ev4yv+2P4PcD4Rw7fyXHc2UgtHBZ0iwsO76Mj3Z/RGxSLANrD+TFZi/Kphgm6bqkK5EJkdmOy3aD2aWlaX7YGcL0348SE5/MY20DGdWtts3XuJhZu8YfCMl0OxS4o5Cf85ZprTkQFsdvB8L5/UAEpy5cRSloHViOKb3rg4Lpq45m65Mf06OOiVE7t+0R25m2bRrHLh2jZeWWjGs9jrrl6podVrEWlRBl9bi13amKOxcXxYOtqtOjQRXeX3OMRf+e5td95xh/bz3ub+ZfJHvLFnaSt/YKsrx1UEoNBYYCVK9e9DVE0tI0u0NiWLU/nN8PRhB6KQFXF0W728rzTIcgutevQsVSnhnnl/H2kIHQIhB2JYz3d7zP2jNrqepTlfc7vU+3Gt2kK8wOVPGpkmNCf2r1UwxvOpwWlVsUcVT2rUwJD97u15AHW1Xj9eUHePWHvQRvO8tbfRtw/PyVQs0pxbK7xpKm2X76Ir8fiOD3AxFExCXi7qpof3sF7m3kR7d6lSnr4zz1xB1JfEo88w/MZ+GBhbgoF55u9DRPNHgCLzevmz9YFIkbK3qC0SffrUY3/g3/lwsJF2jj14bhTYfb/a5aZkhL0yzdFcr0VUeIvpqMq4vKmIkHtzZN2sw+eTeMgdeuQBjGwOtDWuuD1s4vzCSfYkljy8loVh2IYM3BCC5cScbTzYVOtStyXyM/utSrVCRTncR1N65A7RjQkfUh64mMj+S+oPsY1WKUzMu2UzmtHk5MTWTx0cV8deArLiZepL1/e4Y3HU7DCg3NDtnuxMan0G7aOq4mW7Ldl99xPrOnUN4HfIgxhfIrrfU7OZ17q0k+p7nkSakWNp24wKr9Eaw9fJ6Y+BRKeLhyV91K3NfQj851KkqJXpNYaw0CVPWpyrSO02hWqZlJkQlbiE+JJ/hIMAsOLiA2KZbO1TozvOlwGU+5QdD4lVjLwPktXeLUi6GsLVDycFU08vfl2PkrXE5KpZSnG3fXr8w9DavQqXZFvNxlazyzdV/a3Wq/rszQcC5Xkq/w3eHvWHRoEZeTL9OtRjeeb/I8tcrWMjs0u2CrnaicemeoGauzznYBSLZodoXE8ECLAO5t6Ee728vj6SaJ3Z5EXI3I13HhmEp6lOS5Js8xpN4Qvj30Ld8c+oY/zvxBj8AePN/0eWr61jQ7RFON6VHH6ip6W87Yc/gkn9N2XWh4b2CTog1G5FlZr7JcTLyY7bj0wTun0h6leaHpCzxc72EWHVzEt4e/Zc2ZNfQM6smwJsOK1e5cmV0bXC3M2TUOn+SrlvG2+nYnt228hLkOXDjA5aTLKBQ6U4+kl6sXI5qPMDEyUdh8PX15ufnLPFL/ERYeWEjwkWB+O/UbvW/rzXONnyOgVIDZIRa5fs38C3UatsPXYR3Tow7eN/SxywIl+xUSF8LwdcOp5FOJ1+54DT8fPxQKPx8/WRZfjJTzKsfolqNZNWAVQ+oO4beTv9H7p968+e+bhF+RRVW25PADryCVGh1FdEI0j656lMvJl/n63q8J8g0yOyRhJ85fPc+X+79k6fGlKBQDag3gmUbPUNmnstmhOQSnnl0jHEN8SjxPr36aEzEn+LLHlzSpKOMlIrvwK+F8sf8Lfj7+My7KhUF1BvF0o6fZGr5VdvXKhSR5YarUtFRe/vNlNp3bxIedP+Su6neZHZKwc6GXQ/l83+es+G9FxtiNRV+fgSJVL7PKLck7fJ+8sG9aa6ZumcrGsI1MvGOiJHiRJwGlAnj7zrdZ3m857q7uWRI8QKIlkdm7ZpsUnWORJC8K1Wd7P2PZ8WU81/g5BtUZZHY4wsHUKF2DxNTs+xyDrKnIK0nyotAsO7aMT/Z+Qr/b+zG86XCzwxEOKqe1E7KmIm8kyYtCsSFkA29veZs7/e9kctvJUiJY3LIRzUfg5Zq1Cqmbi5usqcgjh18MJezPvqh9vLrhVeqUq8MHnT6Q7flEgVwbXL02u8bD1YNkS7JMwc0jmV0jbOpM3Bke/e1RfNx9+Oa+b6jgXcHskISTiUmMYeCKgXi5ebG412J83H3MDsl0MrtGFIkLCRcYtnYYAJ91+0wSvCgUZbzKMK3DNEIuh/DOlhwrl4t0kuSFTcSnxDN83XCiE6P5uOvH1Chdw+yQhBNrWaUlwxoPY8XJFaz4b4XZ4dg1SfKiwFLSUhi9YTRHLx5lZqeZNKrYyOyQRDEwtPFQWlRuwdtb3uZ07Gmzw7FbkuRFgWiteXPzm2wK28TrbV6nY0BHs0MSxYSriyvTOkzDw9WDsX+PJdmSbHZIdkmSvCiQuXvmsvy/5bzQ5AUG1B5gdjiimKniU4W3273N4YuHmbVzltnh2CVJ8uKWLTm6hC/2fcGAWgMY1mSY2eGIYuqu6nfxcL2H+fbwt2wI2WB2OHZHkry4JX+e/ZN3tr5DB/8OTGozSRY7CVONbjGauuXqMmnTJM5fPW92OHZFkrzItz2Rexj791jql6vPzE4zcXORNXXCXB6uHrzX8T2SLElM+GcCljTLzR9UTEiSF/lyKvYUL/35EpVLVGZu17mUcC9hdkhCABDkG8TEOyayPWI78/bPMzscuyFJXuTZhYQLPP/H87goFz67+zPKe5c3OyQhsuhzWx961uzJp3s/Zdf5XWaHYxcKlOSVUg8opQ4qpdKUUi1vuG+CUuqEUuqoUqpHwcIUZruacpUX/niBi4kX+aTrJ1QrXc3skITIRinF621ex7+kP+M2jiM2KdbskExX0Jb8AeB+4O/MB5VS9YHBQAPgHuATpZRr9ocLR5BiSWH0X6M5dukY73d6nwYVGpgdkhA58nH3YUbHGVxIuMDkTZOxp/pcZihQktdaH9ZaH7VyV1/ge611ktb6FHACaF2Q5xLm0FrzxuY32HxuM2+0fYMOAR3MDkmIm2pQoQEjm4/kz5A/WXx0sdnhmKqw+uT9gZBMt0PTjwkHM2f3HFacXMGLTV+kf63+ZocjRJ49Wv9ROvh3YMb2GRy9aK0tWjzcNMkrpf5QSh2w8tE3t4dZOWb1PZNSaqhSaodSakdUVFRe4xZFIPhIMF/u/5KBtQcytPFQs8MRIl9clAtT20/F19OXMX+PIT4l3uyQTHHTCc5a67tv4bqhQOaRuQDgXA7X/wL4Aox68rfwXMKGVp5cyexdswm/Gg5AvbL1mHjHRFnsJBxSOa9yvNvhXZ5d8yzTt0/nzXZvmh1SkSus7ppfgMFKKU+lVBBQC9hWSM8lbGTlyZVM2TwlI8EDnIo7xerTq02MSoiCucPvDp5p9Aw/Hv+RVadWmR1OkSvoFMr+SqlQoC2wUim1GkBrfRBYAhwCfgeGa61lCZqdm71rNomWxCzHEi2JzN4126SIhLCNF5q+QNOKTXnz3zcJuRxy8wc4kYLOrvlJax2gtfbUWlfWWvfIdN87WuvbtNZ1tNbF79+ngzkVeypLCz6ziKsRRRyNELbl5uLG9I7TcVEujPt7HCmWFLNDKjJSdKSYi0+JZ97+eSw8uBCFQlsZH6/iUyXP10tJSSE0NJTExMSbnyxM5+XlRUBAAO7uzr/ZetWSVXmr3VuM+msUH+3+iNEtR5sdUpGQJF9Maa1Zd3Yd07dPJ+JqBH1u60OjCo14f8f7WbpsvFy9GNF8RJ6vGxoaSqlSpQgMDJTBWjuntSY6OprQ0FCCgoLMDqdI3F3jbgbVHsSCgwu4w+8O7vS/0+yQCp0k+WLoTNwZ3t36LpvObaJ22dpM7zCd5pWbA1DKoxSzd80m4moEVXyqMKL5CHrW7JnnaycmJkqCdxBKKcqXL09xm7o8ptUYdkXu4rV/XmNZn2VOv+G8JPliJCE1gS/3f8mCAwvwdPVkfOvxPFjnwSylgnvW7JmvpG6NJHjHURx/Vl5uXszsNJPBvw5mwsYJfN7tc1yU89ZqdN5XJjJorfnz7J/0+7kfX+z7gh6BPfil3y88XO9hqQUviqXbytzGuNbj2BK+hQUHFpgdTqGSv3AnFxIXwrvb3mVj2EZuL3M7C3osoGWVljd/oBBObkCtAWwJ38JHuz+iZZWWNKnYxOyQCoW05J1UYmoiH+/5mH7L+7Hz/E7GtBzDkt5L7C7B/7w7jDun/UnQ+JXcOe1Pft4dVqDrRUdH07RpU5o2bUqVKlXw9/fPuJ2cnJztfFdXV5o2bUqDBg1o0qQJH3zwAWlpaVnO6du3L23bts1ybMqUKcycOROAJ554gqVLlwKwZs0a2rZtm1H50GKx0LRpUzZv3mw13ilTpqCU4sSJExnHZs2ahVKKHTt2ABAYGEijRo0yXsfLL7+ccW5qaioVKlRgwoQJWa7buXNnWra8/rPesWMHnTt3zvV7V9wopXij7RtU8anCuL/HEZccZ3ZIhUKSvBPaELKBfsv78dnez+haoysr+q/gsQaP4e5iX9Pkft4dxoQf9xMWk4AGwmISmPDj/gIl+vLly7Nnzx727NnDsGHDGDVqVMZtDw+PbOd7e3uzZ88eDh48yNq1a/ntt994883rS99jYmLYtWsXMTExnDp16qbP3717d2rUqMH8+fMB+Oijj2jVqhXt2rXL8TGNGjXi+++/z7i9dOlS6tevn+Wc9evXZ7yOOXPmZBxfs2YNderUYcmSJdlK6kZGRrJqlSxRyU0pj1JM7zid81fP8+bmN52yLLF01ziR0MuhTN82nb9C/6Kmb03md59Paz/zKjy/ueIgh87l3DrafTaGZEvWVnNCioWxS/cRvO2s1cfUr1qaN3oXTj37SpUq8cUXX9CqVauMFvayZcvo3bs3lStX5vvvv8/WYrZm1qxZtG/fnrZt2zJ37ly2bcu9oke/fv1Yvnw5kyZN4uTJk/j6+uZ53npwcDAjRozg008/ZcuWLVnecYwZM4apU6dy77335ulaxVWTik14sdmLfLjrQ5YdX8bA2gPNDsmmpCXvBJIsSXy691P6Le/H1oitvNLiFZb2WWpqgs+LGxP8zY4XhZo1a5KWlkZkZCRgJNEhQ4YwZMgQgoOD83QNPz8/Ro4cSdu2bZk0aRLlypXL9fzSpUtTrVo1Dhw4QHBwMA8++GC2c+66666M7ppZs2YBkJCQwLp16+jVq5fV+Nq2bYunpyfr16/PU9zF2ZMNn6StX1umb5vOiUsnbv4AByIteQe3MXQj7257l5DLIdwTeA+vtHwlXytUC9PNWtx3TvuTsJiEbMf9y3iz+Lm2Vh5RNK69ZT9//jwnTpygffv2KKVwc3PjwIEDNGzY8KbXGD58OOPHj+eJJ57I03MOHjyY77//ntWrV7Nu3ToWLMg642P9+vVUqJB1Pvevv/7KXXfdRYkSJRgwYABvv/02s2bNwtX1+iZskyZNYurUqUyfPj1PcRRXLsqF/3X4HwN+GcCYv8cQ3DMYLzcvs8OyCWnJO6iwK2GM+HMEL6x7ATcXN+Z1n8eMTjPsJsHnxZgedfB2z7orpLe7K2N61DEpIjh58iSurq5UqlSJxYsXc+nSJYKCgggMDOT06dNZ+s5z4+Likq856L179+abb76hevXqlC5dOk+PCQ4O5o8//iAwMJAWLVoQHR2drdXepUsXEhMT2bJlS55jKa4qeFfg3fbvciLmBJ0Wd6LxosZ0X9qdlSdXmh1agUhL3sEkW5JZeHAh8/bNQynFyOYjeaz+Y7i72tegal70a2ZsFjZj9VHOxSRQtYw3Y3rUyThe1KKiohg2bBgvvvgiSimCg4P5/fffM/q5T506Rbdu3Zg6darNn9vb25vp06dTu3btPJ0fFxfHP//8Q0hICJ6engAsWLCA4OBg7r476xYQEydOZNiwYdSsWdPmcTubS0mXcFWuxKcaG4yEXw1nyuYpAAVeJGgWSfJ27tomHhFXIyjrVRaFIjoxmm41ujG21ViHarlb06+Zv2lJHYx+7aZNm5KSkoKbmxuPPvooo0eP5vTp05w9e5Y2bdpknBsUFETp0qXZunVrtus899xzjBw5EoBq1arx77//5juWwYMH53jfXXfdldEN07hxY7p06UKXLl0yEjwYUz3Hjh1LUlJSlsfed999VKxYMd/xFEezd83GckNV9Gvlth01ySt7mjLUsmVLfW1usLi+iUfmgmEKxZMNnmRUy1EmRpazw4cPU69ePbPDEPkgP7PrGi9qbLUSq0Kx7/F9JkSUN0qpnVprq4tgpE/ejlnbxEOjWXVa5j4LURhye2fsqLtKSZK3Y7KJh21kXgWb+SM6OrrIYnjnnXeyPf8777xTZM8v8mZE8xF4uWadVePp6klAqQDG/j2WVze8yqXESyZFd2ukT95ObQrblON9jt4PX9SurYI108SJE5k4caKpMYibu9bvfmO57R6BPVh4cCEf7/mYHRE7eLPdm3Sq1snkaPNGkrwd+uPMH4z5ewx+Pn5cTLxIkuX6QFp+N/EQQuRPTuW2n2n0DB38O/DaP6/x4p8v0v/2/oxtNZaSHiVNiDLvpLvGzqz4bwWvbniVBuUbsLTPUt5s9yZ+Pn4oFH4+fkxpN8VhR/mFcHR1ytUhuGcwzzR6huX/LWfALwPYFp572QqzSUvejiw5uoSpW6bSukpr5nSZQwn3EjbZxEMIYTserh6MaD6CTgGdmLRpEk+veZpH6j1i9Ofb4SpZacnbiQUHFvD2lrfpGNCRj+/+mBLuJcwOSQiRi6aVmrKk1xKG1B3Ct4e/5YEVD7Avyv6mWUqSN5nWmo/3fMwHOz+gR2APZt01C09Xz5s/0FnsWwKzGsKUMsbnfUsKdDlnrCcPsHv3bpRSrF69OuNYSEgIQUFBXLx4ESCjBMOZM2fy/P0SBVPCvQSv3fEa87rPI9GSyKOrHmXOrjmkWFLMDi2DJHkTaa2ZsWMGn+39jP6392d6h+l2V/O9UO1bAitehtgQQBufV7xcoETvrPXkg4ODad++fZZKk9WqVeP5559n/PjxAIwfP56hQ4dSo0aNm8YpbKuNXxt+7PMjvWv2Zt7+eTz020Mcu3TM7LCAAvbJK6VmAL2BZOA/4EmtdUz6fROApwEL8LLWenVO1ymOLGkW3t7yNsuOL+Pheg8zttVY59tMeNV4iNif8/2h28GSdQk+KQmw/EXYucj6Y6o0gnun2S7GTOy1nrzWmqVLl7J27Vo6dOhAYmIiXl5G3++oUaNo0aIFH374If/88w8fffRRwb4J4paV8ijF1PZT6VK9C2/++yaDfx3M8KbDeaLBE7i6uN78AoWkoFllLdBQa90YOAZMAFBK1QcGAw2Ae4BPlFLmvUo7k5KWwmv/vMay48t4ttGzjGs1zvkSfF7cmOBvdrwI2GM9+U2bNhEUFMRtt91G586d+e233zLuc3d3Z8aMGYwaNYoPP/zQ6rsVUbS6VO/CT31/onO1zny460Oe+P0JzsZZ3wSnKBSoJa+1XpPp5hbg2pYqfYHvtdZJwCml1AmgNZD/qk1OJsmSxJgNY1gfsp4RzUfwTKNnzA6p8NysxT2rYXpXzQ18q8GT5pV3tbd68sHBwRnFywYPHsw333zD/fffn3H/qlWr8PPz48CBA3Tr1i0fr1QUlnJe5Xi/0/usPLWS/239HwNXDGR0i9E8WOfBfJWgtgVbNh+fAq4Vd/AHMv/1hqYfy0YpNVQptUMptSMqKsqG4dif+JR4Xlz3IutD1vPaHa85d4LPi66Twd076zF3b+O4SeytnrzFYmHZsmW89dZbBAYG8tJLL7Fq1SouX74MwJ49e1i7di1btmxh1qxZhIdbL4Uhip5Sil41e/Fjnx9pXqk572x9h+fWPlfkZUlumuSVUn8opQ5Y+eib6ZyJQCrw3bVDVi5ltdyl1voLrXVLrXVLZy6Hejn5MsP+GMa2iG1MvXMqQ+oOMTsk8zUeBL3nGC13lPG59xzjuAlyqid/+vRpTp8+zc6dO/Oc5PPrWj35G0sf/PHHHzRp0oSQkBBOnz7NmTNnGDBgAD///DNaa55//nk+/PBDqlevzpgxY3j11VcLJT5x66r4VOHTuz/l9TavsydqD/cvv59f/vulyDYNv2l3jdb67tzuV0o9DvQCuurrUYcC1TKdFgCcu9UgHd2lxEs8t/Y5jsccZ0bHGXQP7G52SPaj8SDTkjrYfz354OBg+vfvn+XYgAED+PTTT0lISKB69eoZXTQvvPACCxcuZMOGDXTq5Bh1VYoLpRSD6gyirV9bJm2axMR/JrLuzDomt51Mee/yhfvcBflvopS6B/gA6KS1jsp0vAHwfxj98FWBdUAtrW+oxn8DZ6wnHxkfydA1Qwm9EsoHnT+gY0BHs0MqVFKb3PHIz6xoWdIsfHPoG+bsnkMpj1LcF3Qf686uy1IQLb+r3AuznvxcoBSwVim1Ryn1GYDW+iCwBDgE/A4Mv1mCd0ZhV8J44vcnCL8azqd3f+r0CV4IcXOuLq480fAJlvRagperF98e/pbwq+FodMZ2g7bcV7ags2tuz+W+d4BiWzD7VOwpnl3zLPGp8czrPo/GFRubHVKxFR0dTdeuXbMdX7duHeXLF+5b5WveeecdfvjhhyzHHnjgASk/XIzdXvZ2q7tQ2Xq7QSlQVgiOXjzK0LVDAVjQYwF1ytUxOaLiTerJC3uV00wbW87AKYYrcArXvqh9PLn6Sdxd3Fl4z0JJ8EKIHOW0AZAtNwaSJG9D2yO28+yaZ/H18GXRvYsI8g0yOyQhhB2ztt2grTcGku4aG9kYupFRf40ioGQAX3T/gkolKpkdkhDCzuW03aAt95CQJG8Da06vYdzGcdQqU4vPu31OWa+yZofkMFaeXFmov+BC2LvC3hhIumsKaPmJ5Yz5ewwNyzdkfo/5kuDzYeXJlUzZPMWm08ecsZ58YGAgFy5cAIxFNa+88krGuTNnzmTKlCn5+RaJYkZa8vmUueVZyqMUcclx3OF3B3PumiO7Od1g+rbpHLl4JMf790XtIzkta+JNtCQyedNklh5bavUxdcvVZVzrcTleM/NMmilTplCyZMlcl/pfqycPEBkZyUMPPURsbGxGTflr9eRLlizJqVOnCArKfZyle/fufPXVV8yfP59nnnkmX/XkJ02aBFivJ3+Np6cnP/74IxMmTKBChQq5xiIESEs+X25secYlx+GiXOhVs5ck+FtwY4K/2fHCdq2e/Ny5czNa4tfqyV+rEpkXs2bN4t133+XgwYPMnTuX6dOn53r+tXryQEY9+ZzqOLm5uTF06FBmzZqVj1cmijNpyd/EleQrnI47zem407yz5R0SLYlZ7k/TaXyy5xP63d7PnADtWG4tboDuS7sTfjV71UQ/Hz8W3LPAyiMKX+Z68pUrVyY4OJg33niDypUrM3DgwDxtGpK5nvycOXPyVU9++fLlPPjgg1lKDd9o+PDhNG7cmLFjx+b79YniR5I8xiYeoZdDOR17mjNxZzKS+unY00QnRt/08UVdOtRZjGg+gimbp2T5x2nr6WO3wt7qyd+odOnSPPbYY8yZMwdvb+8czxMCnCTJ52WGhtaaqIQozsSd4VTsqYxkfibuDKGXQ7FkKq1TzqscgaUD6RjQkUDfQGqUrkFQ6SCG/THMasvTlgsXipOimD6WX5nryX/00UcZ9eQB4uLi+P7775k6depNr3Mr9eTHjBlDy5Yts9STz8nIkSNp3rw5Tz75ZJ6fQxRPDp/kr/WTX2sNhl8NZ/LmyeyO3E05r3IZLfIzcWeIT43PeJynqyc1StegdtnadK/RnSDfIGqUrkGN0jXw9fS1+lz22vJ0ZIU9fSw/cqonf21mzalTp+jWrVueknx+XasnX7t27TydX65cOQYNGsT8+fN56qmnbB6PcB4On+Rn75qdrZ882ZLM4qOLUSiqlqxKYOlAmlVqlqVVXtmncr73VbXHlqcoGHuvJ5+bV155hblz5+b7eUTxUqB68rZ2K/XkGy9qbLWSm0Kx/ZHteLp62io8kQdSm9zxyM/M8RVmPXnT5VbgRxK8EKK4c/juGuknFzcj9eRFcebw3TUg9U/syeHDh6lbt26+ZpYI82itOXLkiHTXOLjcumscviUP9jVDo7jz8vIiOjqa8uXLS6K3c1proqOj8fLyuvnJwmE5RZIX9iMgIIDQ0FCioqJufrIwnZeXFwEBAWaHIQqRJHlhU+7u7jct4iWEKDoOP7tGCCFEziTJCyGEE5MkL4QQTsyuplAqpaKAM2bHkUcVgAtmB1GInPn1yWtzXM78+gry2mpora1uQmBXSd6RKKV25DQv1Rk48+uT1+a4nPn1FdZrk+4aIYRwYpLkhRDCiUmSv3VfmB1AIXPm1yevzXE58+srlNcmffJCCOHEpCUvhBBOTJK8EEI4MUny+aSUqqaUWq+UOqyUOqiUcrrC9UopV6XUbqXUr2bHYktKqTJKqaVKqSPpP7+2ZsdkS0qpUem/kweUUsFKKYctL6mU+kopFamUOpDpWDml1Fql1PH0z2XNjLEgcnh9M9J/N/cppX5SSpWxxXNJks+/VOAVrXU9oA0wXClV3+SYbG0EcNjsIArBbOB3rXVdoAlO9BqVUv7Ay0BLrXVDwBXI36ax9mUhcM8Nx8YD67TWtYB16bcd1UKyv761QEOtdWPgGDDBFk8kST6ftNbhWutd6V9fxkgU/uZGZTtKqQCgJ/Cl2bHYklKqNNARmA+gtU7WWseYGpTtuQHeSik3oARwzuR4bpnW+m/g4g2H+wKL0r9eBPQryphsydrr01qv0Vqnpt/cAtikBrQk+QJQSgUCzYCtJodiSx8CY4E0k+OwtZpAFLAgvSvqS6WUj9lB2YrWOgyYCZwFwoFYrfUac6Oyucpa63AwGltAJZPjKUxPAatscSFJ8rdIKVUSWAaM1FrHmR2PLSilegGRWuudZsdSCNyA5sCnWutmwFUc++1+Fun9032BIKAq4KOUesTcqMStUEpNxOgW/s4W15MkfwuUUu4YCf47rfWPZsdjQ3cCfZRSp4HvgS5KqW/NDclmQoFQrfW1d11LMZK+s7gbOKW1jtJapwA/Au1MjsnWziul/ADSP0eaHI/NKaUeB3oBD2sbLWKSJJ9Pyti4dD5wWGv9gdnx2JLWeoLWOkBrHYgxaPen1topWoNa6wggRClVJ/1QV+CQiSHZ2lmgjVKqRPrvaFecaGA53S/A4+lfPw4sNzEWm1NK3QOMA/poreNtdV1J8vl3J/AoRit3T/rHfWYHJfLkJeA7pdQ+oCnwP3PDsZ30dyhLgV3Afoy/bYctAaCUCgb+BeoopUKVUk8D04BuSqnjQLf02w4ph9c3FygFrE3PK5/Z5LmkrIEQQjgvackLIYQTkyQvhBBOTJK8EEI4MUnyQgjhxCTJCyGEE5MkL4QQTkySvHAaSqkrVo7VUUr9lT7v+LBS6gulVI9MaxyuKKWOpn/9dQ7X7ayUik2veXNUKfV3egmIzOe4KaUuKKXeTb89MdNzWDJ9/bJSaopSKizTsT22KisrxI1knrxwGkqpK1rrkjccWw18orVenn67kdZ6f6b7/wJe1VrvyOW6ndPP6ZV+uynwM/C01npd+rH7gIlAFeD2zEvSb4xLKTUFuKK1nlmAlytEnkhLXjg7P4y6NQBkTvC3Smu9B3gLeDHT4SEY9erPYuwzIIRdkCQvnN0s4E+l1Kr0nZPK2Oi6u4C6AEopb4xaMb8CwRgJ/2ZGZeqqWW+jmITIRpK8cGpa6wVAPeAHoDOwRSnlaYNLq0xf9wLWpxeVWgb0V0q53uTxs7TWTdM/7rJBPEJYJUleOD2t9Tmt9Vda674Ydbob2uCyzbhe5XEIcHd6ieadQHlAErewC5LkhVNTSt2TXv8fpVQVjAQcVsBrNgZeBz5O31awPVBdax2YXqZ5OHnrshGi0LmZHYAQNlRCKRWa6fYHGPtkzlZKJaYfG5NeWz6/OiildmPsnRoJvKy1XqeUegKj7n5SpnOXA+8ppTxvOJ7ZqBt2buqntT59C3EJkSuZQimEEE5MumuEEMKJSXeNEOmUUj2A6TccPqW17m9GPELYgnTXCCGEE5PuGiGEcGKS5IUQwolJkhdCCCcmSV4IIZzY/wN7v/WjWJAHSwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "monthly_T_climatology.plot(marker='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to do it on a finer scale, we can group by day of year." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "daily_T_climatology = df.groupby(df.index.dayofyear).aggregate({'T_DAILY_MEAN': 'mean',\n", " 'T_DAILY_MAX': 'max',\n", " 'T_DAILY_MIN': 'min'})\n", "daily_T_climatology.plot(marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating anomalies\n", "\n", "A common mode of analysis in climate science is to remove the climatology from a signal to focus only on the \"anomaly\" values. This can be accomplished with transformation." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def standardize(x):\n", " return (x - x.mean())/x.std()\n", "\n", "anomaly = df.groupby(df.index.month).transform(standardize)\n", "anomaly.plot(y='T_DAILY_MEAN')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Resampling\n", "\n", "Another common operation is to change the resolution of a dataset by resampling in time. Pandas exposes this through the [resample](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling) function. The resample periods are specified using pandas [offset index](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) syntax.\n", "\n", "Below we resample the dataset by taking the mean over each month." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.resample('M').mean().plot(y='T_DAILY_MEAN', marker='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like with `groupby`, we can apply any aggregation function to our `resample` operation." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IPE+4b8yNd4anVQmfyFy6oRZmo45T6xPk8Tcd8AVC+GRH87L7lpqNCAQXP9nkQhyTmY+0NvUsLvrx8/AFQjh/1fLtClMl0SYrci3/Wpsk8y2zGHOiGXnBGvBYE2c26vDxjfU4PjKNv//Va/jIf7yIx7sdeGxfHz76sxcBAPf85YRqHlup2YhL1tfhj/sH4QuEVDlHPvHg633Y0GBLuBxvNnqS5qL0LJeZW5uS5koAuP2po6r93aKbrCyV1Snv+8pNF8NuNeL/Oasu68YbKGAZ4bZNjZieDeCWzoMApImT5UO+QAid3Q78nxeO41sPdIMgfZEAYHhqVlX50JWbGvHEmwN44e0RXLK+VvHj5wsHHRM4NDCJ2z6+IeHPxMvYFAC+dv8+fPl9q7Cx2a5YZcNclZ7lMkutTan1N1uY7LMURIQNDTa8NTipyliSpWA9cABYWyd1w/nN5zfjpe1bI5NYZNDhUx3N2H3D+1FRXISFD95qPnZvWVOFyuIi9tSW4eG9fSgy6LAtifIDMZ+6DDp8cF0N/vrOKC7/z5fwwZ88j+8+sl+RODkX2UoeLdS1WV9vw5HTU/AHs/+UXNAG/PiwpLmOl5ar0xHGZ3wx31PrC2XU6/CxjQ3YfXgIk16/KufQOl5/EJ3dA/jwhjqUWRNfUI71uHz7Ve/C//3cZvxt+1bcfNk6nBidgW/BhZmq0dWCMco14q1NZVvtEc2GhjL4AiGcGJnJ9lAKN4QCAMdHpmEy6JbsaZmNQknlxUb4AiG869an54V2GIldh05jwuPHpxJYvFxIvMflUrMRX3zfSvz7k4djfm7A5UEoJKDTLZ/AdaB/Ar984fiiJzeZXDJGucaNl67Fdx95E76oBedcUHtEsz6chX1oYCLyFJ8tCtyAz2BldcmSF+WNl66N2X9PrS9UZ5cDv3z+eOQ1x00X8/DefjSVW/DeVcoXq1oqTv6+Hc/hyk2NuPLdTWitKl4QKzfjYxsbsL9/An877kSp2YCL19XgpXdG4Y1akM41Y5RrbNvUiBeODuMP3QNSXZscdGBWVhXDZNDhrYFJXPnu7I6lwA34NM5eRsGwsCSl2l8oKW4a+xE+l77E2aJvzI0Xj43ihg+ekZA3nCyxbthmow6fOKcJp5xu/Oy5Y/iPPcfQVmlFv8sDf9hTdLi8+OULJ2Az6/G9y87Etee2oNRsjBh5h8sDs1GXE9KzXKeixASLUY+3vn9pTlYKNOh1OLOuNCcWMgvWgHv9QfSNuRNaBEtmlTpdOG66NA/vkwpXfSJG6rwSLHfDHpzw4A9dDtz59NsIxKhiV2Iy4kvvWzXveNs2NeL6B7rwyokxNt4J4Bj3oLHckpPGW2Z9gw1PHjgNIURWx1mwBrzHOYOQAFbVJFZXOFNwc4L4BEMCj+ztw5bVVUuuW6TLUjfs+jILvnbRauz4c+xFzcE4WvONzXZ0dg/g9IQXdWWxezoyEg6XR9X5VYL1DWXY+VofBia8WR1rwapQjg9LK8irqouzPJL5xJK66Yk4bgrgpWOjGJjw4urNyS9eKk2yaomNzXYAwJv9LpVGlD/0j7vRWJ7jBrw+vJDpWFwMK5MUrgEPl21dWZVbHvhCqVuxSY+gEHhXU2LZhvnMg3v7YLcacyLBKdEUbJn19TYY9YTuPlcGRqdd3L4Axt3+nPfA19WXgghZj4MXbAjl+Mg0Gu0WWIriF0HKFtGP8KPTs7jwjufw02ffwU+v2ZTlkWWP8Rkfdh8awqff0wKTIftzluzittmox7p6G95kA74kjnEpfNiU4x64tciAtqpiHBpgA54Vjo9M51z8OxZVJSZcd0ErfvnCcXztotVZ151mmmgVBwBUl+ZOz9BkF7c3Ntnxhy5HwnryQqQ/PM+57oEDUkLPG6eyWzm0IEMooZDA8eGZnIt/x+PL71uJkiID7tr9draHklGim27I/HzPMc2WGdjYbMf0bAAnRrnrUjxkDzzXY+CAFBZzuDxwuWNna2eCgjTgpye98PiDCXe2zjZ2axH+YUsb/nzoNA5medEkk+RbLZH2ZmkdI1YXGEbC4fLAoCPUlOa+Ukfui5vNOHhBGnB5AVMrBhwAvnBhG8osRtxZQF54vmniV1aVoNRk4Dj4EjjGPai3m6HXQIhJTql/K4tx8MI04HIRqxpthFAAwGY24kvvW4k9R4YLpmOPFgobJYNOR3hXcxkrUZZACxpwmaoSE2ptptw24ERkJqLXiOhNIjpERLeFt1cQ0W4ieif8rzLtojPA8ZEZlJoNqC7JnQWxRLjuva2oLC7CnU8XhheerFRPC2xssuPw4CS8C0JDjIRj3INGuzXbw0iY9fW2rCpREvHAZwFsFUJsBNAO4MNEdB6A7QCeFUKsAfBs+LUmOD4yjVXVJTmdqhuLYpMBX71oFV48NopXTjizPRzVkTXx1rDUc6luKVphY7MdgZDIun44F/EFQhia8mpiAVNmQ0MZjo1MZ+2GvKwBFxLysrkx/CMAXA7gt+HtvwWwTY0BqoFswLXIZ85bgZpSE+58+u2C6F6/bVMjzl9ZiXX1tnlNN7RKu5yRyWGURZye8EIIoEkjIRRAioMHQwJvD01l5fwJxcCJSE9E3QCGAewWQrwKoFYIMQgA4X9jdvsloi8R0V4i2jsyMqLQsFNnyuvH0OSspuLf0ZiNenx962q81jOGF4+NZns4GcE540NVSVG2h6EItTYz6mxmNuAx6He5AWhDQiizIcsLmQkZcCFEUAjRDqAJwLlEdFaiJxBC3COE6BBCdFRXV6c4TOWQu2ho1QMHgKs3N6PRbsGPC8QLH5vxoaI4Pww4AGzkhcyYRDTgGvLAm8utKDEZshYHT0qFIoRwAXgewIcBDBFRPQCE/x1WenBqoEUJ4UJMBj2+sXU13uxzYc8RTfzZ08I5PZtnBtyOHqc7qwkguYicsFVvz30NuIxOR1hfn70mx4moUKqJyB7+3QLggwCOAPgfAJ8L7/Y5AI+rNEZFOT4yDYOOsKJSOyvdsbjqnCZUFhvx5d/vQ9v2P+GC2/doNkNxKbz+IGZ8QVTmkQGPxMH7OaEnGse4BzWlppyodZMM6xtsODw4iWCM+vBqk4gHXg/gOSLaD+B1SDHwPwK4HcAlRPQOgEvCr3Oe48MzWFFphVGvbQn8n/YPYtIbQCAk0u6ensuMhZtKV2pM8rkUZzeWgYgXMhficHk0Ff+WWd9gg9sXxCln5pscL1vMSgixH8CiMnhCCCeAi9UYlJpoWYESzY5dRyPtvGTysfWabMDzKYRSajZidXUJG/AFOFyeZVsc5iKR2uADk1iZYduibTc0SQLBEHqcM5qoQrgc+ZZmHo/R6VkAyKsQCiDFwd/sdxXEInQihEICgy4vmsq1F9pcU1sCg46yEgcvKAPeNy41oc0HDzzf0szjkY8eOCAZ8NFpH/rH8+uGmyoj07PwBUOaDKGYDHqsqS3NipSwoAx4pAaKRsrILkU+ppnHIh9j4ADQ3mQHwC3WZOQbmZaSeKLJVkp9QRnwY3IbtTzwwOU081qbZNjsVqPm08xj4Zzxwagn2Mz51XvkzPpSFBl0HAcPI0sIteiBA1JCz+j0LIanYje1VouCMuDHh6dRXWpCmcWY7aEowrZNjXjpn7fCoCNce25L3hlvQNKAl1uLNFe3ZjmMeh3OarDhTa4NDkCbSTzRyKVlM+2FF5YBH5nOi/BJNAa9Di2VVpwcybyEKRPkWxZmNBub7TjgmEAgGMr2ULKOw+WG3WpEsUmbT1rZqg1eMAZcCIHjIzN5sYC5kLbKYvRkQYOaCaQ6KPkV/5Zpb7bD4w/i7SFusSaVkdWm9w1I9fqbKyxswNXCOePDhMefnwa8qhgnR2cQykImmNrktQfOC5kR+jVuwAFgQ31ZxqWEBWPA57rw5KEBry7GbCCEwcnMLqBkAud0/hrwFZVW2K3Ggl/IFEJoNgszmvUNNpwcncH0bCBj5ywcAx6pQphfMXBA8sAB5F0cfDYQxPRsIO+SeGSICBub7AVfmdDl9sPtC2rfAw/HwY9k0AsvIAM+DbNRh4YybX9JYrGySnqqODmaX7HUfNWAR7Ox2Y63h6bg9mXOa8s1ZAlhUx544EBmlSgFZcBXVpVAp4Fu18lSazPBYtTjxGh+eeDO6fzMwoymvbkMIQEcdBRui7X+iIRQe2n00dTZzKgoLsroQmZBGfB8jH8D0qN4a3ghM59wRjzw/DXg7wovZHb3jWd3IFlE60k8MkRSbfBDg5nT9heEAff6g+gf9+Rl/FtmZVUxevLMgI/NSIWs8tkDryoxoancUtAJPY5xDyxGPcqt2k+w29Bgw9unp+HPkLa/IAz4ydEZCKHtLjzL0VZVjL5xD3yB/EkKkUMoVcX5GwMHpDh4IS9kOlxuNJZb8iLbdn2DDb5gCMeGM7MeVRAGPB/aqC1HW1UxgiGBvnF3toeiGGMzPhh0BJtFm9l5idLeZIfD5cHI1Gy2h5IVHC7ta8BlMt3kuDAM+PAMiObkdvlIW3X+SQmd0z6UF+dfHZSFtLfYAQD7CzShxzGufQ24TFtVCcxGXcaUKIVhwEem0Wi3wFKkrV57ybBS1oLnURzcOePLWw14NBsabNDrqCDDKG5fAONuf9544Hod4cw6G97K0EJmwRjwfA6fAIDdWoRyqzGvpIRjM7N5rUCRsRYZcEZtaUEacLkKodY14NFYinR49cRYRpqN570BD4UETozMYHWeSgijac0zJYpUByW/FzBl2pvL8GZf4bVY63dpu4zsQjq7HNjbMw4BZKTZuOYMeGeXAxfcvifhu9vgpBcefzDvPXBgrqhVvuCcLowQCiA5GpPeAFbe9KTqXlsuEakDnice+FLNxtVAUwa8s8uBmx47AIfLk/DdLZ/aqC3HyqpinJ70YiaDxXTUYjYQxNRsIK814DKdXQ50dg8AyIzXlks4XB4YdISaUnO2h6IImW42rikDvmPXUXj8wXnblru7RSSEBRBCaQvXRMmH2uDjM34A+Z2FKbNj11HMLtDvq+m15RKOcQ/q7Wbo86TERaabjWvKgKdydzs2PI0yi7EgHsXb8kiJ4gxnYRbCvGXaa8slHC4PmjReAyWaTDcb15QBj3cXqy+L//glt1HLdy0xALRWSRdCPmjB5wpZ5f8iZqa9tlwinzTgwFyzcWtYstxot6jabFxTBvzGS9fCbFw85JpSE4JxutHkaxu1WFiLDKizmXEyD0IoYwVQyEom015bruALhDA05c0bBYrMtk2N+NbFawAAT37zQlWbjWvKgG/b1DjvS91ot+DyjQ3o7p/A9584tEiCNeHxY2RqtiDi3zL5okSJVCIsgBCK7LXJT5KlJoOqXluucHrCCyHyR4ESTSScqbIzpbkiE2c1lAEAfvcP5+J9Z1QDAKpLTfi/L55EbZkZX7todWTfEwVQA2UhbdXFePLAYLaHkTZjM7PQ6wg2s/Yr1CXCtk2N2LapER/92V9RajLmvfEGgH6XVLenKc88cABYKZe2GJ1Ge7NdtfNoygMHgIEJaWEnOj74vcvW4fL2Bvzoz0fx8N6+yPZ8bqMWj5VVxXC5/RgPe7BaxTntQ7m1KC8bcCxFx4oKdPWNZ6wcaTbJNw14NM0VVuhI/fUo7Rlwl9S4t8E+t3Cp0xF2fGIjtqyuwvbHDuC5I8MApAVMo57QXJE/q9zLIT+6aT2l3jnjQ1UBxL8Xsrm1Al5/KKNtubKFw+UBEVCfh20OTQY9msqtOOlUtzqoBg24B+VWI6xF86M/RQYdfvnZc7CuvhRfu/8N3P3M2/j1iyfhDwpctOP5gkiKAPJHSiil0ReeAe9oLQcA7O0Zy/JI1Kd/3IOaUhOKDJozQwkhrUepWxdcc3+5AZcn7h27xGTAr687F5YiHe5+5h14w8kRhZTZ1lxhhV5Hmm9wXKgGvNZmRkuFFa8XgAF3jOdPHfBYtFUV4+TIjKr1bTRnwAcnvEvqY6tLTTDqF/+3CiWzzajXobncgp5RbTd2GJ2eLQgFSiw6Wsulgkh5XtjK4fKgsTx/w5ttVcWY8QVVbdShOQMude9Yum7C8GTsP1ghZLYB0hdHyzFwXyCEKW8AlSX5n8QTi82tFXDO+DQfBluKUEhgcCL/PXBA3fUoTRnwSa8fU94A6peZ9ELObAOkmig9ozMIxUluynXG3XIWZmF64JtbKwAAe3vyt1P98NQs/EGRlwoUmUysRy1rwImomYieI6LDRHSIiL4V3n4rETmIqDv8c5lqowwzGFGgLD3phZrZJtNWXQyPP4ihKW+2h5ISchp9oYZQVlUXo9xqzOs4uCOPNeAyDXYLigw6VWv0J5LIEwDwT0KIN4ioFMA+Itodfu8uIcSPVRvdAmQN+HIhFDkJYseuoxhwedBgt+DGS9cWRHIEENVebWRGkxItuZBVoXrgRISO1grsPZV9D7yzy6HKddSfxxpwGb2O0FppVTWEsqwBF0IMAhgM/z5FRIcBZMUSyjHsREIhcmZbIRIde3vv6qosjyZ55uqgFGYMHAA2t5Zj91tDGJmaRXVpdv4Ocv19uYSzrOYCkPa15cizTjzxaKsqjiQUqkFSMXAiagWwCcCr4U1fJ6L9RPQrIipXenALGXB5oM+j4u9qUWczw2xU99FNTQo9hAIAHeE4+L5T2QujpFJ/P1Ec4x7YrUYUmzRXzSMpWquKcco5E7fYXrokbMCJqATAowCuF0JMAvgFgFUA2iF56D+J87kvEdFeIto7MjKS1mAHXV7U2fKn+Lta6HSE1krtFrUam/FBryOUWQqjDkoszmoog8mgw+tZXMhUs065pCbLb+8bkMKZ/qCIlA1QmoQMOBEZIRnv+4UQjwGAEGJICBEUQoQA3Avg3FifFULcI4ToEEJ0VFdXpzVYh8szL4WeiY+WqxI6Z2ZRbjUWXB2UaIoMOrQ327Oakammmivfk3hk5C5ZJ1RKrEtEhUIA7gNwWAhxZ9T2+qjdrgBwUPnhzWdgIn4WJjOftqpi9I65NVkUSWpmXLjxb5nNrRU4ODAJty87PU4lNdd8E6GEmksIEU7iyf9rWV6PUiucmYgHfgGAzwLYukAy+CMiOkBE+wF8AMANqowwTCgkcHqZLExmjraqYgRCIrLaryUKNY1+IR2t5QiGBLp6XVk5/7ZNjbj5I+sjr2tKTYrUKXe5/XD7ggXhgVeVFKHUZFDtaTgRFcqLAGI9yz6p/HDiMzodFv5zCCUhousRy16AVhib8WFdgy3bw8g6715RDiLg9Z4xXJAlNdFZjWWR3+/8VDu2rEl/HLICpakAPHAiQlu1epnRmsnElCedQyiJIcfeTmqwJkoh10GJxmY24sw6W1YzMk9FdZQZmlQmMSyiAc+jZsZLoaagQDMGfCDBLExGotxqhM1s0FxVQn8whElvgGPgYTa3luON3nEEsrSW0Tc25wCcVsiAF5IHDkjhTIfLA+8CSaYSaMaAD04UhvBfKaRHtxLNKVHkTkIVBdjMIRYdrRVw+4I4PDiVlfOfcrpRXWqCzWxQzAN3jHtgLdLDbi0MmejK6mIIAfSOKf80rBkD7nB5UFykh82S38J/JVkZrkesJQqpmXEibA43eMhWXZTeMTdWVFhRazMrZ8BdbjTaLZAEbvmPmkWtNGPAB1we1BfQpCtBW1UxBia88PiUf3RTCzkLk1UoEvVlFjTaLdibpYzM3jE3WiqsqCsz43ScMs3JUigSQplWFQ24ZtzZ5Ro5MIuJaFCdM1hXnzlVRzoFkORCVoXYDzMem1vL8dJxJ4QQGXVgvP4gTk960VJpBRHh2PCoIsd1jHuwscmuyLG0gM1sRFWJSZWnYU154CwhTA61kwhiIRdAcrg8EEi+nZ1cyKqCFzEjdLRWYGRqVpUY6lL0j3sgBLCi0oq6MhOGp2bTrunh9gUw7vYXlAcOAG1V1sINoXj9QYxO+1hCmCTZ6FCfbgGksRkfdATYC7gOykLkBg+ZrovSOyZ9b1oqrKizmREMicgTUqo4xgtTjKBWlyxNGPDBCZYQpkKxyYCaUlNGlSjpFkAanfah3FpU0HVQFrKmpgRlFmPG66L0OiWPv6WiGDU26el3aCJ1A97Z5cA197wCAPjBH98qiCbjMm1VJRidnsWk16/ocbVhwCN1wDmEkiyZLmqVbgGksZlZVHL8ex46HaFjRXnGlSinxtywFulRVVKEurABT1ULLofWZJXR6LQvqdCa1lErnKkJA14oxd/VYGV1Zg14uu3suA5KbDpaK3B8ZAbOafU6nC+kL6xAISLUyh54igZczdriWmCutEUBGnA5C7OujD3wZGmrKsbYjA+ucKNgtdm2qRE/vPJs6MNqCZNBl1QBJOcMVyKMhawH35fBNmunnG40V0jp7lUlRdBR6gZczdriWkC6ERaoAR+c8KCqxASTQb/8zsw85mqiZM4L/+i76iPlz0rNhqSq1zmn2QOPxdlNZSgy6DLWJ1MIEUniAQCDXoeqElPKBlzN2uJawGzUo6HMUpgG3MESwpSJ1oJnCllutrqmBKPTPgxPJXbR+4MhTHj8HAOPgcmgx8amsozFwYenZjEbCKGlcq7gVDrJPOmG1vIBNcKZmjDgAy5u5JAqLRVW6AgZTamXH4svXlcDAAnX8Rh3cxr9UnS0VuCgYyIjmbWy5rylYs6A19rMGE7RA9+2qRHfv3xD5HWj3aJIbXEt0RYubSGEcv0xc96ACyE4CzMNigw6NJVbM6oFlxedP7iuFgBweHAyoc9xEs/SbG4thz8o8Ga/S/VznXLGMuCmtCoSnrNCiuPf+amNeGn71oIy3oBkwKdmAxidVm49KucN+IRH6t7BEsLUybSUUF50XldvQ32ZOWEDznVQluZ0OB/imntewQW371FVgtc75gYR0FQeFUKxmeFy+1MuiyrXAY8+ZiGhRjgz5w04SwjTJyQE3hqYRNv2P6l+4QNSCKXMYkSJyYB19bbEDXjYA+c6KIvp7HLgB388HHmdbImCZOl1zqChzIIiw5yJkJN5hlOMg0eu5QJLo5dZKQsKFAxn5rwBl725ejbgKdHZ5cArJ5wQQEq1SVJBLmIFAOvqS3F8ZAazgeW9trGwxpk98MVkWkctVyGMJt1knv5xNww6Qm1pYYbIGuxmGPWkaDgz5w243MiBQyipsWPXUfiD8xdN1E6giFYNnVlnQzAk8M7Q8p2BxmZ8IALsVjbgC8m0jrp3zI0VlfMNeLrJPP3jHtTbzTDoc97sqIJBr0NLhVXRLlk5/5d0uDwo0utQxQtbKZGNBAqpcqTsgUtlbBMJo4zOSHVQ9FwHZRGZ1FHPhBfamuN44OkY8KYC6YMZj7YqZbtk5bwBH3B5UVdm5uJGKZLpBIoprx+T3kDk+G1VxTAbdQlJCcemfSwhjEMmddSyhHChB26zGGA26lI24I7xwmrkEIuV1cXocboRSrMsr0zOG/BBl4fDJ2mQ6QSKhZUj9TrC2trShDxwroMSH7lEgRyaKjbpVdNRx9KAA4jUREklmWc2EMTQlLdgGhnHo62qGL5ACAMTyjwB57wBj14QY5JHvvCLwnFHtRMoHJHKkXNztq7ehiOnJ5dNYHByJcIl2bapES9tvxjr6m04t7VCtTmUy8iuqChe9F6qvTEHXV4IUbgSQhml+2PmtAEPBEM4PelFA2dhpsW2TY346MZ6NJSZVU+gGIgh+1xXb8O424+hZTw3J3vgCbGiwqpqd57eMTdsZgPKYnSNT9WAz2nAC/taXllIBnx4ahYhUTgFb9SkqdyK05Ne+AIhVc8z4PLAoCNUR0nFzqwrBbD0QmYgGILL7edKhAnQUmlF37hHsTjqQk6NubGicrH3DQB1NhNOT3iTTgd3uKQbTqEb8OpSE4qL9DihkBY8pw34ADdyUIwmuwUhMZfNpxbyonO0kuTMsBLlrSUM+Lhb6lTCIZTlaamwwhcIYSjBImHJ0hdDAy5TazNjNhDCpCeQ1DH7xz3Q6yiiZClUiAitCmZG57QB5yxM5ZA9n/5xdRvjOmKsWZRZjGi0W5b0wOfqoLABXw7ZuMr1SpQkGBLoH3fPq0IYTW2KyTz94x7U2QpXAx6NkqUtcvqvyVmYyiEvHvWrXEA/WgMejbSQGV9K6OQszISR5X1qxMEHXB74gyKuBy43VUk2Dt4/7i748InMyqpi9I+7FQln5rQBH5zwwGY2oMRkyPZQNE9dmRk6mltMUoNgSOD0hDdmyGt9fSlOjEzHLYQ0VweFY+DL0WC3QK+jiFpESfpkDXi8EEppah64Y9xT8AoUmbbqYoSEMjfgnDbgLCFUjiKDDrU2s6ohlJGpWQRCIuacrau3ISSAt4die+EcQkkco16HBrtZFQ/8VPiYC7MwZWps0g02mbrgvoCkJiv0JB4ZJbtk5bQBd7i4DriSNJVbVPXAY2nAZc5cJqXeGa6DUs51UBKipcIaMbZK0jvmhlFPca87s1EPu9WYlAd+esKLkGAFikxbpSwlTL8mSk4b8MEJzsJUkqZyKxwqGvBYGnCZFRVWWIv0cVPqndOzsFuMXAclQVoqiiPhDiXpdbrRVG5dch7qbGacnkg8G1N+6mMDLlFmNaKiuCi/PfCZ2QBcbj974ArSVG7B6UkvAkF1tOCyAa8vW3zT1ekIa+tK40oJx2Z8qOT4d8K0VFgxNuPDlNev6HF7x9xxwycyNTZzwn1OgbmF82aOgUdoqypWRAueswZcLiPLEkLlaLRbEAyJSL0SpXG4pEXnUvPiDD4grEQZjJ1Sz1mYyaGWEuWUcybuAqaMnMyTKP3jHuhoTsHCSAZcic48yxpwImomoueI6DARHSKib4W3VxDRbiJ6J/xvedqjicIhSwg5jV4xIlJClcIoyy06r6u3YdIbwECMi39shisRJoMs81NSiTLhlipJxpMQytTazBidnk34Sa5/3I06mxlG1oBHaKsqxtDkLGZmk0uIWkgif9EAgH8SQqwDcB6AfySi9QC2A3hWCLEGwLPh14oxyFmYiiPHIB0qacEdLu+ST0zr68Mp9QOLwyjOaS5klQxyoo2SC5mnxmbmHTsetTYzQgIJN+ftZwnhIpSqibKsARdCDAoh3gj/PgXgMIBGAJcD+G14t98C2JbWSBYw4JIeu2oLPPVWSertZhCpl425nAe+ti62EiUYEnB5/NyNPglsZiPKrUZFQyjxysguJNnGDpIGnJ+ko2nNlAGPhohaAWwC8CqAWiHEICAZeQA1aY1kAQ6XFzWl/NilJCaDHrWlZlVCKNOzAUx4ll50LjEZ0FJhxeHT8w34uNsHIcAhlCRpqbAqGkKRU/MTCaEAiSXzyBVFWQM+n9bKDBtwIioB8CiA64UQibUZlz73JSLaS0R7R0ZGEh4YSwjVobHcoooHnmjIa119KY4skBJyEk9qtFQWK+qB9425UVVShOJlMp9ryxJP5hmc8CIYEuyBL2DXodPQE3Dn7rdxwe17Um4ynpABJyIjJON9vxDisfDmISKqD79fD2A41meFEPcIITqEEB3V1dUJD4yzMNWhqdyiSgw80cJj6+ptOOmcgds3t3gzGq6DwjHw5GipkObSr5As9JQzfhXCaCqLTdDrKCEPfK4OOMfAZTq7HLjpsQOQe407XB7c9NiBlIx4IioUAnAfgMNCiDuj3vofAJ8L//45AI8nffY4CCEwMLH0ghiTGk3lFgy6lNeCy4XHlrvprqu3QQjgaFRhK9kD51rgybGioliShbqUkYX2LlFGNhq9jlBTakoomUe+sbMHPseOXUfhWVATyOMPYseuo0kfKxEP/AIAnwWwlYi6wz+XAbgdwCVE9A6AS8KvFcE544MvEIqZEMKkR1O5FYGQwNBU8n0Nl2LA5Ylc2EuxPpJSv9iAcwglOeSEG1k9kg6+QAiDEx60xGnksJBEk3n6x90gYjlwNANxnoDjbV+KZcv8CSFeBBAvr/bipM+YAANL1NRg0kN+qukfcyv6hDPgSqzec6PdghKTYZ4SxRmWo5XHaOHFxEfJZB6Hy4OQWH4BU6bOZkook7B/3IPaUjOKDCxGkGmwxw5jpmLvcvKvygZcPdTSgkuNHJZ/YtLpCGfWleJIlBLFOTOLcquRi/0nSa3NjCK9ThElyqlwVuCKZTTgMnUJ9sbkOuCLufHStbAY9fO2mQw63Hjp2qSPlZNXjCPBeCqTPPLfVGkp4cBE4ovOUkr9VCSlfozT6FNCryM0VVgU8cD7EtSAy9TYzJj0BuDxxa7vLuNwsQZ8Ids2NeKHV56NRrslEtq46IzqlJqN56QBH3R5YDbq+JFaBcxGPWpKTYpKCecaOSRuwKdmA5GbiHPaxwuYKdJSYVWktdoppxtmo27ZNQyZRJJ5AsEQBl2sAY/Ftk2NeGn7Vpy8/SO4cE0VDjgmEEyhSXVOGnDZm5MEMIzSNCpcF3x0ehb+YOxGDrFYF06plysTsgeeOisqrOgbcyfdJX4hsgIl0WsukWSeoXCDD5YQLs3Vm5sxMOHFS8dGk/5sThpwh8uLBl61Vo2mcquiMfA5DXhiqqG1daUgmkupd874WAOeIs0VVkzNBjDuTq+sbKISQpm6cDLPUh54/xjXAU+ES9bXwm414sG9fUl/NicN+GCCC2JMajSVWzDg8qT0yBaLZBedrUUGtFYW4/DgJIIhgXE3VyJMlRVh2V86cXAhRNiAJyYhBKQYOLC0AZ/TgLMHvhQmgx7b2hux+9AQxmcSKxAmk3MGfDYQxPDULC9gqkhTuQX+oEiqKP9SpKIaWldfiiOnp+AK10HhEEpqyKqRU2nUlh6d9sHtC6KlIvH5KzUZYC3SL5nMI4fpOJ9jea7e3AxfMITO7uSyMXPOgA+FvxBswNVD1n8r1V5twOVFqckAW5xGDrFYV2fDKac74jlWcDeelJC73KTTXq13TJYQJu6BE5EkJVzCCegfd6Om1ATzAskcs5h19Ta8q6kMD77el9R6Rs4Z8IFwJx6OgauH0o0dHCnUrVkXzsj823EnAKCKPfCUsBRJqqJ0lCi9y3Sij0eNzYShJTrz9HMZ2aT4ZEczjpyewgHHRMKfyT0Dzo0cVEe+qJSSEg6ksGaxrkEy4PLKewUvYqZMuh3qTzmldPdkje1yHrikAef4d6J8fGMDTAYdHkpiMTOHDTjfudXCbNSjqsSkmAeeSuXIhjIzbGYD9vaMA+AYeDq0VFrTDKFILc+SDXXU2swYmpyN+cgfDAkMuDysAU+CMosRl51dj8e7B+D1L50gJZNzBtzh8qKiuIjjZirTqFBZWbdPkrAla8CJCOvqbfCFqyKWW9mAp0pLhRWnJ70JX/QL6U2wjOxCam1m+AKhmBLG4Skv/EGuA54sn+xowpQ3gKcODia0f84ZcG7kkBmaFErmkcvIplIYS46D261G7ryUBisqrRAi9TWNZDXgMrVLSAm5DnhqnNdWiZYKKx56vT+h/XPuqhlweXgBMwM0lVvgGPcglKYWPJ2Ql8cvNXVwuf1pdSUpdCId6lMoK+vxSbLdRItYRSMn88TKxpTXV9gDTw6djvCpjia8fMKZkDQ0pwx4Z5cD7wxN4+m3hviCVpmmcit8wRBGptOrC57qonNnlwOdXQOR1+l0JSl05AScVKoS9o2npkABgJpSac5jtVaTJarclCV5rjqnCToCHt67vBeeMwa8s8uB7Y/th+wP8gWtLk0KVSUccHmgo7nH6UTZsesoZgPzuwKl2pWk0KkqKYK1SJ+SEkWWHyajAZeJ1EOJkczTP+5BVQlrwFOhvsyC951RjUf29S+bLZ0zBnzHrqPw+vmCzhRKSQkdLi9qbeakY9hKdiUpdIgILRWpKVFkDfiKFDzwIoMOlcVFMaWErAFPj6s7mnF60ou/vLN0I/icMeB8QWeWxnLlPPBU4t/xPsPy0dRoTrGsbK9zBqUmA+wplm6usZljJvNwI4f0uHhdLSqKi/DQ60trwnPGgMerRscXtDpYiwyoLC5K34An0cghmlhdSSxGfUpdSRjJg+5Noaxs75gbLZWJl5FdSJ3NtMgDD4UEBlxeVqCkQZFBhys2NeKZw0NwLrFOlRMGPBAMoSjGIzhf0OqSrhY8FO6Inorsc2FXkka7BT+88uyUupIwkpRwNhDCcBLNqju7HPjrO6M4NDCZsmig1mZeFAMfmZ6FLxjiJJ40+VRHM/xBga0/eR5FdavPibXPsk2NM8H9r/ZiYMKLz52/As8cHo48lt946Vq+oFWkqdyCI6enlt8xDqMz4Qs1xaekbZsaeX4VorlirsFxIgvKnV0O3PTYfgTCi2SyaABAUnNSazPDOTMLfzAUWQdhCaEyHB6cBBEw4QnE3SfrBnx0ehY/fvootqyuwq0f34DbLj8r20MqGJrKrXj28DCEECk9QstJPKzbzz6yiuSU043NrRXL7r9j11F44ogGkjXgQgAjUSWg5bBcMxvwtNix6yiWi4hlPYRyx1NH4PUHcevHN3ALtQzTVG7BbCB1LTjXrckdGu0W6Cjxxg5KiQZiJfP0RzTgHANPh0TmIqsGfN+pcTy8rx9f2LISq2tKsjmUgkR+xE21LviAi5M1coUigw71ZRb0JtjYQSkVkByuGV5gwCuLi2ApYg14OiQyF1kz4MGQwL90HkR9mRnf2Lo6W8MoaGQPKVUlSv+4B8VFetgsWY/EMZBS6hP1wD9/QeuibamIBuaSeaINOEsIlSCWUmshWTPg9796Cm8NTuKWj6xHsYkNQDZIVwsulwvl0FdusKIycQPunPGBINX0TkcFVGEtglFPGIpSvzjGuQ64EkQrteKRFcs5Oj2LH+86igtWV+Kys+uyMQQGQInJgHKrMeVszFQ14Iw6NFdYMTrtw/RsACVLOEW+QAgP7+3DB9fX4t6/70jrnDodoaZ0LplHCAGHy4NL1temdVxGQlZq0U3H9sV6Pyse+B1PHYHHH8RtHz+Lvbcs01RuTVkLPuDysgHPIeSKgsul1D9zeAij0z58+twWRc5bazNFFjFHpmcxG2ANeKbIuAGXFy7/YUsbL1zmAI321OqCe3xBjM34eAEzh5DLyi6XUr/ztV402qWCSUogdeaRDPhcHXD+XmSCjBvwf338IOpsZnxz65pMn5qJgdTYIfkU7EjzaW6+kTOsCJeVXcoD73W68dd3RnH15mbodco8/cqt1QBu5JBpMmrADzgmcGhgEpduqOWFyxyhqdwCrz8E54wvqc9FNOCcxJMzlFmNsJkNOLVEY4edr/dCR1KatlLUlZkxPRvA9GyA64BnmKzEwB/a28d1vnME2VNKVgvOSTy5yYrKYvSOxZ5LefFy65m1qCtT7smp1iYl8wxNetE/7ka51cgOWobIigH3+ENc5ztHSFVK6HB5QQRFDQGTPi2V1rjJPM+GFy//7j3KLF7KRPfG7GcJYUbJmg6c63znBo0pNnYYcHlQW5p8IwdGXVoqrOgf98Ts5PLfr/Wiocys2OKlzHwDzkk8mSRrVx8/eucGNrMRZRZj0h64VDGSve9cY0WFFYGQWOQgzS1etii2eCkjG/DBCS8cLu7Ek0myYsC5zndu0ZRCXfBUO/Ew6iJLCRcqUR4IL15evVm5xUuZEpMBJSYDDg9OwetPvbwwkzzLGnAi+hURDRPRwahttxKRg4i6wz+XJXpCLtyfe0ha8MRDKKGQwMCEly/UHKQlnMwT3eDYHwzhob39ii9eRlNrM2FfzxgAlhBmkkQ88N8A+HCM7XcJIdrDP08mcrKzG8vw0vatbLxzjKZyKW6aqBbcOeODLxBiDzwHqS+zwKineTVRnnlrCKPTs/j0e5T3vmVqbWYMhNPpmyr4e5EpljXgQoi/ABjLwFiYLNFUboHbF8S425/Q/iwhzF30OkJTuRW9UdmY8uLl+8+oUe28dVFdgPjJLHOkEwP/OhHtD4dYyuPtRERfIqK9RLR3ZGQkjdMxapFsXfA5A86LmLlIc1RZWTUXL6OpDYdmyixGlJpT63DPJE+qBvwXAFYBaAcwCOAn8XYUQtwjhOgQQnRUVysrX2KUIVkpoYMbOeQ0KyqsOBXWgsuLl5/a3KTqOWtLpWQeVqBklpQMuBBiSAgRFEKEANwL4Fxlh8VkEnnRKVEp4YDLC2uRHmUW9rRykZYKKya9AYxOz0YWL+tVLnkgp++n0+GeSZ6UDDgR1Ue9vALAwXj7MrmP9NhrSMgD7+xy4L9fOwW3L4gtdzzHF2oOIitRfvXiSdUXL4Hwd+LVvshrucM9fzfUJxEZ4U4ALwNYS0T9RPQFAD8iogNEtB/ABwDcoPI4GZVJpC54Z5cDNz12AN5wN3O+UHOTY8PTAID/ev449AS4ZhJbnE6VHbuOYjYQu8M9oy7LVpwRQlwbY/N9KoyFySKJaMF37DoKjz84b5t8obI0NDfo7HLgZ3veibwOCuDmzoPQ6Ui1OVKqwz2TPFzIggEg1wVfWgvOF2rus2PX0cgTkoza3rBSHe6Z5GEDzgCQDPj0bAATntiP225fACZj7K8LX6i5QzZusrG6p3O5jMzABpwBsLQSZcDlwSd+8TK8/hCM+vlaYr5Qc4tseMPR3dPT6XDPJA9XXWcAzOl3+8c9OKuxLLK9q3ccX/zdPnj9Qfz685sx4fZjx66jkWJWN166li/UHOLGS9fipscOzFuryMRNVu6ezmQWNuAMgGgDPreQ+Xi3Azc+sh+1NhP++4vvwRm1pQDAF2oOI88N32QLAzbgDADguSPDIAD/758O41cvncSGeht2Hx7GuW0V+OVnzkFFcVG2h8gkCHvDhQMbcAadXQ587w8HIetPBlxeDLi8OK+tAr/7wntQZOClEobJRfjKZGLquwGgb9zNxpthchi+OpklpGfeDI+EYZhkYAPOcCIGw2gUNuAMJ2IwjEbhRUyGpWcMo1HYgDMAWHrGMFqEQygMwzAahQ04wzCMRmEDzjAMo1HYgDMMw2gUNuAMwzAahZbqwKL4yYimACTSGqQMwIQC+/Cx1DtnFYDRHBxXrp4zV48FaHsuc/VYSp9zjRCibNFWIUTGfgDsTXC/e5TYh4+l6jmXncsC+lto9lhan8tcPVamzpmrIZQnFNqHj6XeOZU6X6L75fLfQsvHSpRcHX+uHisj58x0CGWvEKIjYydkVIPnMn/gudQumfbA78nw+Rj14LnMH3guNUpGPXCGYRhGOXI1Bq4ZiGh6mfefJyJ+PNUAPJf5QSHNIxtwhmEYjaKKAV/uDphvENFFRPTHqNc/J6LrsjgkxeC5zI+55HnMj3lcCHvgDMMwGkU1A05EJUT0LBG9QUQHiOjy8PZWIjpMRPcS0SEiepqIuHdXDsNzmR/wPOYfanrgXgBXCCHeDeADAH5CRBR+bw2A/xRCbADgAnCViuPIBAHM/1uaszUQleC5zA94HvMMNQ04Afh3ItoP4BkAjQBqw++dFEJ0h3/fB6BVxXFkglMA1hORiYjKAFyc7QEpDM9lfsDzmGeo2VLt7wBUAzhHCOEnoh7M3QVno/YLAtDk4xoRGQDMCiH6iOghAPsBvAOgK7sjUxyey/yA5zHPUNOAlwEYDn9RPgBghYrnyhYbABwHACHEdwF8d+EOQoiLMjwmNeC5RF7MJc8j8mIeIyhuwOU7IID7ATxBRHsBdAM4ovS5sgkRfQXANwFcn+WhqAbPZX7A85i/KJ5KT0QbAdwrhDhX0QMzGYfnMj/gecxfFF3EDN8BdwK4RcnjMpmH5zI/4HnMb7iYFcMwjEZJywMnomYiei6cBHCIiL4V3l5BRLuJ6J3wv+Xh7ZXh/aeJ6OcLjlVERPcQ0dtEdISItK5D1RRKzSURlRJRd9TPKBHdnaX/VkGi8HV5bTjpZz8R/ZmIqrLxf2Jik5YHTkT1AOqFEG8QUSkk/eg2ANcBGBNC3E5E2wGUCyH+mYiKAWwCcBaAs4QQX4861m0A9EKIW4hIB6BCCLFcnz5GIZScywXH3QfgBiHEXzLx/2CUm8vw4ucAgPVCiFEi+hEAtxDi1oz/p5iYpOWBCyEGhRBvhH+fAnAYUnLA5QB+G97tt5C+PBBCzAghXoSUEbaQfwDww/B+ITbemUXhuQQAENEaADUA/qreyJmFKDiXFP4pDmds2iAZdCZHUGwRk4haId3FXwVQK4QYBKQvE6SLeKnP2sO//oCkOg0PE1HtUp9h1COduVzAtQAeFLzQkjXSmUshhB/AVwEcQNgTB3CfmuNlkkMRA05EJQAeBXC9EGIyhUMYADQBeClcp+FlAD9WYmxMcigwl9FcA0kBwWSBdOeSiIyQDPgmAA2QshpvUnSQTFqkbcDDk/wogPuFEI+FNw+F43ByPG54mcM4AbgB/CH8+mEA7053bExyKDSX8rE2AjAIIfapMlhmSRSay3YAEEIcDz9FPQTgveqMmEmFdFUoBOmR6rAQ4s6ot/4HwOfCv38OwONLHSf85XgCwEXhTRcDeCudsTHJodRcRnEt2PvOCgrOpQNSQajq8OtLIMXTmRwhXRXKFkgLVAcAhMKbvwcp3vYQgBYAvQA+KYQYC3+mB9JiSBGkspUfEkK8RUQrAPwegB3ACIDPCyF6Ux4ckxRKzmX4vRMALhNC5FW6thZQ+Lr8CoBvAfBDqvB3nRDCmbH/DLMknMjDMAyjUbilGsMwjEZhA84wDKNR2IAzDMNoFDbgDMMwGoUNOMMwjEZhA84wDKNR2IAzmoGIpmNsW0tEz4dL1x4OlyS+NKqc7TQRHQ3//rs4x72IiCaIqCu871+I6KML9jGES+P+MPz65qhzBKN+/yYR3UpEjgVlde2q/FGYgoZ14IxmIKJpIUTJgm27APyXEOLx8OuzhRAHot5/HsB3hBB7lzjuReF9Php+3Q6gE8AXhBDPhrddBuBmAHUAVkcX6Fo4LiK6FcC0EILr+TCqwh44o3XqAfTLL6KNd6oIIboBfB9AdI3zawH8FFIG43npnoNhlIANOKN17gKwh4ieIqIbFAxVvAHgTAAgIguk+jx/hFTf5doEPn9DVPjkOYXGxDDzYAPOaBohxK8BrINUwfIiAK8QkUmBQ1PU7x8F8JwQwg2pwt8VRKRf5vN3CSHawz8fUGA8DLMINuCM5hFCDAghfiWEuBxAAFJrsHTZhLnKe9cC+GC44NM+AJUA2CgzWYcNOKNpiOjD4drXIKI6SMbVkeYx3wXgXwD8JxHZAGwB0CKEaBVCtAL4RyQWRmEYVTFkewAMkwRWIuqPen0npE5OPyUiuZ/jjUKI0ykc+0Ii6gJghdTo4JtCiGeJ6DoAe4QQs1H7Pg7gR0RkWrA9mhuI6DNRr7cJIXpSGBfDxIVlhAzDMBqFQygMwzAahUMoTMFARJcCuGPB5pNCiCuyMR6GSRcOoTAMw2gUDqEwDMNoFDbgDMMwGoUNOMMwjEZhA84wDKNR2IAzDMNolP8fBtAqA/FyymoAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.resample('M').max().plot(y='T_DAILY_MAX', marker='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rolling Operations\n", "\n", "The final category of operations applies to \"rolling windows\". (See [rolling](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html) documentation.) We specify a function to apply over a moving window along the index. We specify the size of the window and, optionally, the weights. We also use the keyword `centered` to tell pandas whether to center the operation around the midpoint of the window." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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