{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Impoting required packages\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# setting default option\n", "pd.set_option(\"mode.copy_on_write\", True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearCauseDeaths
5627South CarolinaSCSPARTANBURG450832010Drug poisonings (overdose) Unintentional (X40-...46
2482South CarolinaSCRICHLAND450792006All other drug-induced causes10
4771PennsylvaniaPALANCASTER420712009Drug poisonings (overdose) Unintentional (X40-...42
1132TexasTXORANGE483612004Drug poisonings (overdose) Unintentional (X40-...18
3524FloridaFLLEON120732008Drug poisonings (overdose) Unintentional (X40-...14
\n", "
" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "5627 South Carolina SC SPARTANBURG 45083 2010 \n", "2482 South Carolina SC RICHLAND 45079 2006 \n", "4771 Pennsylvania PA LANCASTER 42071 2009 \n", "1132 Texas TX ORANGE 48361 2004 \n", "3524 Florida FL LEON 12073 2008 \n", "\n", " Cause Deaths \n", "5627 Drug poisonings (overdose) Unintentional (X40-... 46 \n", "2482 All other drug-induced causes 10 \n", "4771 Drug poisonings (overdose) Unintentional (X40-... 42 \n", "1132 Drug poisonings (overdose) Unintentional (X40-... 18 \n", "3524 Drug poisonings (overdose) Unintentional (X40-... 14 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing datasets\n", "df = pd.read_parquet(\"../.01_Data/02_Processed/02_Mortality.parquet\")\n", "df.sample(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearPopulation
25528North DakotaNDBENSON3800520126771
27651OklahomaOKGRADY40051200347277
8696IndianaINADAMS18001201534962
11483KansasKSELK2004920072939
3378ColoradoCOMORGAN08087201428098
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" ], "text/plain": [ " State State_Code County County_Code Year Population\n", "25528 North Dakota ND BENSON 38005 2012 6771\n", "27651 Oklahoma OK GRADY 40051 2003 47277\n", "8696 Indiana IN ADAMS 18001 2015 34962\n", "11483 Kansas KS ELK 20049 2007 2939\n", "3378 Colorado CO MORGAN 08087 2014 28098" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population = pd.read_parquet(\"../.01_Data/02_Processed/01_Population.parquet\")\n", "population.sample(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Dropping Alaska\n", "df = df[df[\"State_Code\"] != \"AK\"]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Cause\n", "Drug poisonings (overdose) Unintentional (X40-X44) 7538\n", "Drug poisonings (overdose) Suicide (X60-X64) 1461\n", "Drug poisonings (overdose) Undetermined (Y10-Y14) 757\n", "All other drug-induced causes 625\n", "Drug poisonings (overdose) Homicide (X85) 2\n", "Name: count, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Viewing distribution of drug related deaths\n", "df[\"Cause\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# We will consider only unintentional drug related deaths since other have very few values\n", "df = df[df[\"Cause\"] == \"Drug poisonings (overdose) Unintentional (X40-X44)\"]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "State 0\n", "State_Code 0\n", "County 0\n", "County_Code 0\n", "Year 0\n", "Cause 0\n", "Deaths 2\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checking for missing values\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearCauseDeaths
10345VirginiaVABEDFORD CITY515152015Drug poisonings (overdose) Unintentional (X40-...<NA>
10351VirginiaVACLIFTON FORGE CITY515602015Drug poisonings (overdose) Unintentional (X40-...<NA>
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" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "10345 Virginia VA BEDFORD CITY 51515 2015 \n", "10351 Virginia VA CLIFTON FORGE CITY 51560 2015 \n", "\n", " Cause Deaths \n", "10345 Drug poisonings (overdose) Unintentional (X40-... \n", "10351 Drug poisonings (overdose) Unintentional (X40-... " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df[\"Deaths\"].isna()]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# drop these NAs for now since they are all in VA and in 2015\n", "df = df.dropna()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 7536.0\n", "mean 42.073248\n", "std 59.844214\n", "min 10.0\n", "25% 13.0\n", "50% 21.0\n", "75% 43.0\n", "max 705.0\n", "Name: Deaths, dtype: Float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Deaths\"].describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 4.049500e+04\n", "mean 9.803144e+04\n", "std 3.135551e+05\n", "min 0.000000e+00\n", "25% 1.126200e+04\n", "50% 2.577600e+04\n", "75% 6.644000e+04\n", "max 1.007726e+07\n", "Name: Population, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population[\"Population\"].describe()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateState_CodeCountyCounty_CodeYearCauseDeathsPopulation_merge
2870CaliforniaCALAKE060332009Drug poisonings (overdose) Unintentional (X40-...2164413both
757VirginiaVAPORTSMOUTH CITY517402004Drug poisonings (overdose) Unintentional (X40-...1697428both
2524MississippiMSHANCOCK280452008Drug poisonings (overdose) Unintentional (X40-...1042764both
126KentuckyKYJEFFERSON211112003Drug poisonings (overdose) Unintentional (X40-...72703970both
4976MichiganMIKALAMAZOO260772012Drug poisonings (overdose) Unintentional (X40-...19255413both
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" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "2870 California CA LAKE 06033 2009 \n", "757 Virginia VA PORTSMOUTH CITY 51740 2004 \n", "2524 Mississippi MS HANCOCK 28045 2008 \n", "126 Kentucky KY JEFFERSON 21111 2003 \n", "4976 Michigan MI KALAMAZOO 26077 2012 \n", "\n", " Cause Deaths Population \\\n", "2870 Drug poisonings (overdose) Unintentional (X40-... 21 64413 \n", "757 Drug poisonings (overdose) Unintentional (X40-... 16 97428 \n", "2524 Drug poisonings (overdose) Unintentional (X40-... 10 42764 \n", "126 Drug poisonings (overdose) Unintentional (X40-... 72 703970 \n", "4976 Drug poisonings (overdose) Unintentional (X40-... 19 255413 \n", "\n", " _merge \n", "2870 both \n", "757 both \n", "2524 both \n", "126 both \n", "4976 both " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Merging the datasets\n", "\n", "combined = pd.merge(\n", " df,\n", " population,\n", " on=[\"State\", \"State_Code\", \"County\", \"County_Code\", \"Year\"],\n", " how=\"left\",\n", " validate=\"1:1\",\n", " indicator=True,\n", ")\n", "combined.sample(5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "_merge\n", "both 7536\n", "left_only 0\n", "right_only 0\n", "Name: count, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking for merge errors\n", "combined[\"_merge\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 7.536000e+03\n", "mean 3.861557e+05\n", "std 6.451696e+05\n", "min 1.028200e+04\n", "25% 1.042135e+05\n", "50% 1.955340e+05\n", "75% 4.350700e+05\n", "max 1.007726e+07\n", "Name: Population, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check if all rows have non-0 population so that we don't divide by 0\n", "combined[\"Population\"].describe()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 7536.0\n", "mean 42.073248\n", "std 59.844214\n", "min 10.0\n", "25% 13.0\n", "50% 21.0\n", "75% 43.0\n", "max 705.0\n", "Name: Deaths, dtype: Float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking for Deaths, should be all non-0 and min 10\n", "combined[\"Deaths\"].describe()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# creating new df with only required columns\n", "df2 = combined[\n", " [\"State\", \"State_Code\", \"County\", \"County_Code\", \"Year\", \"Population\", \"Deaths\"]\n", "]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateState_CodeCountyCounty_CodeYearPopulationDeathsMortality_Rate
5539IndianaINHAMILTON180572013296970150.000051
755VirginiaVANEWPORT NEWS CITY517002004185204160.000086
6822CaliforniaCASAN LUIS OBISPO060792015280138430.000153
5027New JerseyNJBURLINGTON340052012450667690.000153
1518New YorkNYROCKLAND360872006299390100.000033
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" ], "text/plain": [ " State State_Code County County_Code Year Population \\\n", "5539 Indiana IN HAMILTON 18057 2013 296970 \n", "755 Virginia VA NEWPORT NEWS CITY 51700 2004 185204 \n", "6822 California CA SAN LUIS OBISPO 06079 2015 280138 \n", "5027 New Jersey NJ BURLINGTON 34005 2012 450667 \n", "1518 New York NY ROCKLAND 36087 2006 299390 \n", "\n", " Deaths Mortality_Rate \n", "5539 15 0.000051 \n", "755 16 0.000086 \n", "6822 43 0.000153 \n", "5027 69 0.000153 \n", "1518 10 0.000033 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculating mortality rate\n", "df3 = df2.copy()\n", "df3[\"Mortality_Rate\"] = df3[\"Deaths\"] / df3[\"Population\"]\n", "df3.sample(5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateYearPopulationDeaths
597Wisconsin20113075370412
33Arkansas20111434425135
38California2003335614832316
79Delaware200552334337
9Alabama20122858447352
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" ], "text/plain": [ " State Year Population Deaths\n", "597 Wisconsin 2011 3075370 412\n", "33 Arkansas 2011 1434425 135\n", "38 California 2003 33561483 2316\n", "79 Delaware 2005 523343 37\n", "9 Alabama 2012 2858447 352" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating mortality rate for each state-year\n", "df4 = (\n", " df3.groupby([\"State\", \"Year\"])\n", " .agg({\"Population\": \"sum\", \"Deaths\": \"sum\"})\n", " .reset_index()\n", ")\n", "df4.sample(5)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "State\n", "Alabama 13\n", "Wisconsin 13\n", "Missouri 13\n", "Arizona 13\n", "Nevada 13\n", "New Hampshire 13\n", "New Jersey 13\n", "New Mexico 13\n", "New York 13\n", "North Carolina 13\n", "Ohio 13\n", "Virginia 13\n", "Oklahoma 13\n", "Oregon 13\n", "Pennsylvania 13\n", "South Carolina 13\n", "West Virginia 13\n", "Tennessee 13\n", "Texas 13\n", "Utah 13\n", "Mississippi 13\n", "Minnesota 13\n", "Michigan 13\n", "Massachusetts 13\n", "California 13\n", "Colorado 13\n", "Connecticut 13\n", "Delaware 13\n", "District of Columbia 13\n", "Florida 13\n", "Georgia 13\n", "Hawaii 13\n", "Idaho 13\n", "Illinois 13\n", "Indiana 13\n", "Iowa 13\n", "Kansas 13\n", "Kentucky 13\n", "Louisiana 13\n", "Maine 13\n", "Washington 13\n", "Nebraska 12\n", "Maryland 12\n", "Arkansas 12\n", "Rhode Island 11\n", "Montana 10\n", "Vermont 7\n", "Wyoming 7\n", "South Dakota 4\n", "North Dakota 1\n", "Name: count, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check if we have data for all years in all states\n", "df4[[\"State\", \"Year\"]][\"State\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count\n", "13 41\n", "12 3\n", "7 2\n", "11 1\n", "10 1\n", "4 1\n", "1 1\n", "Name: count, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check if we have data for all years in all states\n", "df4[[\"State\", \"Year\"]][\"State\"].value_counts().value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some states have data for very few years, so be cautious when using the data for these states" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Calculating Mortality Rate at state level\n", "df4[\"State_Mortality_Rate\"] = df4[\"Deaths\"] / df4[\"Population\"]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYear
33173TexasTXDAWSON481152013
20776MontanaMTMEAGHER300592005
32909TexasTXCHILDRESS480752009
33829TexasTXHILL482172006
23359New MexicoNMTORRANCE350572014
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" ], "text/plain": [ " State State_Code County County_Code Year\n", "33173 Texas TX DAWSON 48115 2013\n", "20776 Montana MT MEAGHER 30059 2005\n", "32909 Texas TX CHILDRESS 48075 2009\n", "33829 Texas TX HILL 48217 2006\n", "23359 New Mexico NM TORRANCE 35057 2014" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# combinations of state and county from population data\n", "st_county = population[\n", " [\"State\", \"State_Code\", \"County\", \"County_Code\", \"Year\"]\n", "].drop_duplicates()\n", "st_county.sample(5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearPopulationDeathsState_Mortality_Rate_merge
7032IdahoIDCUSTER160372015775025.0870.000112both
2928ColoradoCOCLEAR CREEK0801920063570120.03660.000103both
21150NebraskaNEANTELOPE310032015855606.0590.000069both
30353South DakotaSDBEADLE460052014180925.0140.000077both
30737South DakotaSDHUGHES460652008NaN<NA><NA>left_only
\n", "
" ], "text/plain": [ " State State_Code County County_Code Year Population \\\n", "7032 Idaho ID CUSTER 16037 2015 775025.0 \n", "2928 Colorado CO CLEAR CREEK 08019 2006 3570120.0 \n", "21150 Nebraska NE ANTELOPE 31003 2015 855606.0 \n", "30353 South Dakota SD BEADLE 46005 2014 180925.0 \n", "30737 South Dakota SD HUGHES 46065 2008 NaN \n", "\n", " Deaths State_Mortality_Rate _merge \n", "7032 87 0.000112 both \n", "2928 366 0.000103 both \n", "21150 59 0.000069 both \n", "30353 14 0.000077 both \n", "30737 left_only " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creating a cross-join at state and county level\n", "master = pd.merge(st_county, df4, on=[\"State\", \"Year\"], how=\"left\", indicator=True)\n", "master.sample(5)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "_merge\n", "both 38673\n", "left_only 1822\n", "right_only 0\n", "Name: count, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master[\"_merge\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These 1822 combinations are not present in our actual data and hence will have to be imputed" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearPopulationDeathsState_Mortality_Rate_merge
1066ArkansasARARKANSAS050012003NaN<NA><NA>left_only
1079ArkansasARASHLEY050032003NaN<NA><NA>left_only
1092ArkansasARBAXTER050052003NaN<NA><NA>left_only
1105ArkansasARBENTON050072003NaN<NA><NA>left_only
1118ArkansasARBOONE050092003NaN<NA><NA>left_only
..............................
40483WyomingWYWESTON560452004NaN<NA><NA>left_only
40484WyomingWYWESTON560452005NaN<NA><NA>left_only
40485WyomingWYWESTON560452006NaN<NA><NA>left_only
40487WyomingWYWESTON560452008NaN<NA><NA>left_only
40490WyomingWYWESTON560452011NaN<NA><NA>left_only
\n", "

1822 rows × 9 columns

\n", "
" ], "text/plain": [ " State State_Code County County_Code Year Population Deaths \\\n", "1066 Arkansas AR ARKANSAS 05001 2003 NaN \n", "1079 Arkansas AR ASHLEY 05003 2003 NaN \n", "1092 Arkansas AR BAXTER 05005 2003 NaN \n", "1105 Arkansas AR BENTON 05007 2003 NaN \n", "1118 Arkansas AR BOONE 05009 2003 NaN \n", "... ... ... ... ... ... ... ... \n", "40483 Wyoming WY WESTON 56045 2004 NaN \n", "40484 Wyoming WY WESTON 56045 2005 NaN \n", "40485 Wyoming WY WESTON 56045 2006 NaN \n", "40487 Wyoming WY WESTON 56045 2008 NaN \n", "40490 Wyoming WY WESTON 56045 2011 NaN \n", "\n", " State_Mortality_Rate _merge \n", "1066 left_only \n", "1079 left_only \n", "1092 left_only \n", "1105 left_only \n", "1118 left_only \n", "... ... ... \n", "40483 left_only \n", "40484 left_only \n", "40485 left_only \n", "40487 left_only \n", "40490 left_only \n", "\n", "[1822 rows x 9 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master[master[\"_merge\"] == \"left_only\"]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateYear
1066Arkansas2003
15136Maryland2007
20398Montana2004
20400Montana2006
20405Montana2011
21125Nebraska2003
25493North Dakota2003
25494North Dakota2004
25495North Dakota2005
25496North Dakota2006
25497North Dakota2007
25498North Dakota2008
25499North Dakota2009
25500North Dakota2010
25501North Dakota2011
25502North Dakota2012
25503North Dakota2013
25505North Dakota2015
29666Rhode Island2003
29667Rhode Island2004
30329South Dakota2003
30330South Dakota2004
30331South Dakota2005
30332South Dakota2006
30333South Dakota2007
30334South Dakota2008
30336South Dakota2010
30337South Dakota2011
30338South Dakota2012
36101Vermont2003
36102Vermont2004
36103Vermont2005
36106Vermont2008
36107Vermont2009
36108Vermont2010
40196Wyoming2003
40197Wyoming2004
40198Wyoming2005
40199Wyoming2006
40201Wyoming2008
40204Wyoming2011
\n", "
" ], "text/plain": [ " State Year\n", "1066 Arkansas 2003\n", "15136 Maryland 2007\n", "20398 Montana 2004\n", "20400 Montana 2006\n", "20405 Montana 2011\n", "21125 Nebraska 2003\n", "25493 North Dakota 2003\n", "25494 North Dakota 2004\n", "25495 North Dakota 2005\n", "25496 North Dakota 2006\n", "25497 North Dakota 2007\n", "25498 North Dakota 2008\n", "25499 North Dakota 2009\n", "25500 North Dakota 2010\n", "25501 North Dakota 2011\n", "25502 North Dakota 2012\n", "25503 North Dakota 2013\n", "25505 North Dakota 2015\n", "29666 Rhode Island 2003\n", "29667 Rhode Island 2004\n", "30329 South Dakota 2003\n", "30330 South Dakota 2004\n", "30331 South Dakota 2005\n", "30332 South Dakota 2006\n", "30333 South Dakota 2007\n", "30334 South Dakota 2008\n", "30336 South Dakota 2010\n", "30337 South Dakota 2011\n", "30338 South Dakota 2012\n", "36101 Vermont 2003\n", "36102 Vermont 2004\n", "36103 Vermont 2005\n", "36106 Vermont 2008\n", "36107 Vermont 2009\n", "36108 Vermont 2010\n", "40196 Wyoming 2003\n", "40197 Wyoming 2004\n", "40198 Wyoming 2005\n", "40199 Wyoming 2006\n", "40201 Wyoming 2008\n", "40204 Wyoming 2011" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master[master[\"_merge\"] == \"left_only\"][[\"State\", \"Year\"]].drop_duplicates()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to drop these states since we don't have any data for them even at a state level so would not make sense to impute for them" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# dropping these rows since we have no state level data for them\n", "master = master[master[\"_merge\"] == \"both\"]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 38673.0\n", "mean 0.000114\n", "std 0.000054\n", "min 0.000012\n", "25% 0.00008\n", "50% 0.000101\n", "75% 0.000134\n", "max 0.000465\n", "Name: State_Mortality_Rate, dtype: Float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master[\"State_Mortality_Rate\"].describe()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# create a new df with only required columns\n", "master2 = master[\n", " [\"State\", \"State_Code\", \"County\", \"County_Code\", \"Year\", \"State_Mortality_Rate\"]\n", "]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearState_Mortality_RatePopulationDeathsMortality_Rate_merge
2680CaliforniaCATUOLUMNE0610920150.00010353599.0190.000354both
38133WisconsinWIOCONTO5508320140.000157NaN<NA><NA>left_only
31548TexasTXDALLAM4811120140.000084NaN<NA><NA>left_only
21399NebraskaNEHOOKER3109120050.000031NaN<NA><NA>left_only
32383TexasTXJASPER4824120040.000066NaN<NA><NA>left_only
\n", "
" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "2680 California CA TUOLUMNE 06109 2015 \n", "38133 Wisconsin WI OCONTO 55083 2014 \n", "31548 Texas TX DALLAM 48111 2014 \n", "21399 Nebraska NE HOOKER 31091 2005 \n", "32383 Texas TX JASPER 48241 2004 \n", "\n", " State_Mortality_Rate Population Deaths Mortality_Rate _merge \n", "2680 0.000103 53599.0 19 0.000354 both \n", "38133 0.000157 NaN left_only \n", "31548 0.000084 NaN left_only \n", "21399 0.000031 NaN left_only \n", "32383 0.000066 NaN left_only " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# merging this with the original Data\n", "df5 = pd.merge(\n", " master2,\n", " df3,\n", " on=[\"State\", \"State_Code\", \"County\", \"County_Code\", \"Year\"],\n", " how=\"left\",\n", " indicator=True,\n", " validate=\"1:1\",\n", ")\n", "df5.sample(5)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "_merge\n", "left_only 31137\n", "both 7536\n", "right_only 0\n", "Name: count, dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df5[\"_merge\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# adding a new flag to identify if original data or not\n", "df5[\"Original\"] = df5[\"_merge\"] == \"both\"" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearState_Mortality_RatePopulationDeathsMortality_Rate_mergeOriginal
20757MontanaMTSHERIDAN3009120150.0001NaN<NA><NA>left_onlyFalse
16066MichiganMIKEWEENAW2608320090.000113NaN<NA><NA>left_onlyFalse
33364TexasTXREFUGIO4839120100.000081NaN<NA><NA>left_onlyFalse
37500West VirginiaWVWAYNE5409920050.000111NaN<NA><NA>left_onlyFalse
12151KansasKSROOKS2016320090.000083NaN<NA><NA>left_onlyFalse
\n", "
" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "20757 Montana MT SHERIDAN 30091 2015 \n", "16066 Michigan MI KEWEENAW 26083 2009 \n", "33364 Texas TX REFUGIO 48391 2010 \n", "37500 West Virginia WV WAYNE 54099 2005 \n", "12151 Kansas KS ROOKS 20163 2009 \n", "\n", " State_Mortality_Rate Population Deaths Mortality_Rate _merge \\\n", "20757 0.0001 NaN left_only \n", "16066 0.000113 NaN left_only \n", "33364 0.000081 NaN left_only \n", "37500 0.000111 NaN left_only \n", "12151 0.000083 NaN left_only \n", "\n", " Original \n", "20757 False \n", "16066 False \n", "33364 False \n", "37500 False \n", "12151 False " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df5.sample(5)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "_merge Original\n", "left_only False 31137\n", "both True 7536\n", "Name: count, dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df5[[\"_merge\", \"Original\"]].value_counts()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StateState_CodeCountyCounty_CodeYearState_Mortality_RatePopulation_xDeathsMortality_Rate_mergeOriginalPopulation_ymerge2
29268South CarolinaSCSPARTANBURG4508320130.000125290545.0350.00012bothTrue290545both
5670GeorgiaGAJONES1316920150.000122NaN<NA><NA>left_onlyFalse28432both
13463KentuckyKYMARION2115520080.000193NaN<NA><NA>left_onlyFalse19647both
6463GeorgiaGAUPSON1329320150.000122NaN<NA><NA>left_onlyFalse26237both
31212TexasTXCAMERON4806120030.000068NaN<NA><NA>left_onlyFalse358492both
\n", "
" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "29268 South Carolina SC SPARTANBURG 45083 2013 \n", "5670 Georgia GA JONES 13169 2015 \n", "13463 Kentucky KY MARION 21155 2008 \n", "6463 Georgia GA UPSON 13293 2015 \n", "31212 Texas TX CAMERON 48061 2003 \n", "\n", " State_Mortality_Rate Population_x Deaths Mortality_Rate _merge \\\n", "29268 0.000125 290545.0 35 0.00012 both \n", "5670 0.000122 NaN left_only \n", "13463 0.000193 NaN left_only \n", "6463 0.000122 NaN left_only \n", "31212 0.000068 NaN left_only \n", "\n", " Original Population_y merge2 \n", "29268 True 290545 both \n", "5670 False 28432 both \n", "13463 False 19647 both \n", "6463 False 26237 both \n", "31212 False 358492 both " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Remapping with population data\n", "df6 = pd.merge(\n", " df5,\n", " population[[\"County_Code\", \"Year\", \"Population\"]],\n", " on=[\"County_Code\", \"Year\"],\n", " how=\"left\",\n", " indicator=\"merge2\",\n", " validate=\"1:1\",\n", ")\n", "df6.sample(5)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "merge2\n", "both 38673\n", "left_only 0\n", "right_only 0\n", "Name: count, dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df6[\"merge2\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 3.867300e+04\n", "mean 1.016857e+05\n", "std 3.201735e+05\n", "min 0.000000e+00\n", "25% 1.229900e+04\n", "50% 2.715300e+04\n", "75% 6.946000e+04\n", "max 1.007726e+07\n", "Name: Population_y, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check if we have population data for all counties\n", "df6[\"Population_y\"].describe()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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3l/vzzz8RFxeH6dOnA6ja8V2V3++9Pu+mYuh2Kqv9ypUr+Omnnyp8Rtu2bQsA8mfUmM+QoX/n7rQv73YMZGRkwNLSssI2Kjum6O4YfOqRsWPHIj09HUuWLJH7eBjK3t4es2bNwr///ovU1FR88cUX2L9/PwYPHiwv4+vri2XLliE1NRWnT5/Gyy+/jMWLF+P111+/79p1H+YrV65UmJeammrweoYNGwYnJycsX74cV69exZYtWzBy5Ej5f6rHjx/HP//8g48++ggvvfQSwsPD0bVr10q/YCtjY2NTaZ+j2/8X6Orqinbt2iEuLq7SxzvvvCMvO2TIEPz222/Izs7G7t274eXlhdGjR1cY3nu7vn37wtPTU+6LsGLFCgQHB1cYWffMM88gNjYW2dnZ+PnnnyGEwKBBg+76v+++ffsCKO/kfC9OTk5QKBRISUmpME/XsVPXb0HXp+b2fVgdLVE6kyZNwr59+3Do0CEsXLgQLVq0QEREhMGvb9++Pbp06YL27dtXOE5cXFxw5coVvY7DQHmrYmlpaYX+GpUdy7ppunXfzz5at24drKyssGXLFowYMQKhoaHo0qXLPV93N8b8fmuryjqLu7q6IjIy8o6f0VsvfVGVz1BldL/jO+3Lu+1HFxcXlJaWVghHxvx9pHIMPvVIkyZN8Prrr2Pw4MEYM2ZMldfj4eGBsWPHYtSoUTh9+jTy8/MrLNOiRQu8/fbbaNeunUkuZNeyZUtoNBr88MMPetMvXryI2NhYg9djY2OD0aNHY/v27Zg7dy5KSkr0TnPp/gDe3pHzyy+/NGj9fn5+OHr0qN60M2fOVGjqHzRoEM6fPw8XFxd06dKlwqOy0yxKpRJhYWGYO3cuAODw4cN3rUV3qmHTpk3Yu3cv/v7777ue3rS3t0f//v0xffp0FBcX48SJE3dcdsiQIWjXrh2io6Nx/PjxSpfZtm0b8vPzYW9vj+DgYGzYsEHvf9parRbffvstvLy85NMMuvd9+z786aef7vpe7+ZerQmPPPIIfHx88Oqrr2LHjh2Iiooy2fV3+vTpg7y8vAoBcfXq1fL8W/3222964b6srAzff/89mjVrJl9X6372kSRJsLS01DutXFBQgG+++abCsoa2nBnz+61LBg0ahOPHj6NZs2aVfkZ1rVm3MuYzVJkHH3wQAPDtt9/qTY+Li8OpU6cqHC+36t27NwBUuK7RmjVrjKqBOKqr3vnwww+r9Lrg4GAMGjQIQUFBcHJywqlTp/DNN98gJCQEdnZ2OHr0KCZOnIjHHnsMAQEBsLa2xs6dO3H06FG8+eab9123QqHArFmzMH78eAwfPhzjxo1DVlYWZs2ahcaNG0OhMDyjP/vss1i0aBHmz5+PVq1aITQ0VJ7XqlUrNGvWDG+++SaEEHB2dsZPP/10z1M/Ok899RSefPJJREVF4dFHH0ViYiLmzZsHNzc3veWmTJmC9evXo1evXnj55ZcRFBQErVaLixcvYvv27Xj11VcRHByMGTNmIDk5GX369IGXlxeysrLw2WefwcrKCmFhYfesZ9y4cZg7dy5Gjx4NW1vbCqNrnn/+edja2qJHjx5o3LgxUlNTER0dDbVaLfcvqoyFhQU2btyIyMhIhISE4IUXXkDv3r1hb2+PxMRE/Pjjj/jpp5+QmZkJAIiOjkZERAR69+6N1157DdbW1li8eDGOHz+OtWvXykFjwIABcHZ2xrPPPovZs2fD0tISK1eurHTIuaF0w+2XLl0KlUoFGxsb+Pv7y/+7trCwwIsvvog33ngD9vb2lV6OoKqefvppLFq0CGPGjEFCQgLatWuHP/74A3PmzMGAAQPw0EMP6S3v6uqKBx98EO+88448quvff//VG9J+P/to4MCBmD9/PkaPHo3//Oc/yMjIwMcff1zpiK127dph3bp1+P7779G0aVPY2NigXbt2la7X0N9vXTJ79mzExMQgNDQUkyZNQsuWLVFYWIiEhAT88ssvWLJkCby8vKr8GapMy5Yt8Z///Aeff/45FAoF+vfvL4/q8vb2xssvv3zH10ZGRqJXr16YOnUqrl+/ji5duuDPP/+sNNTSPZixYzXdp1tHdd2NIaO63nzzTdGlSxfh5OQklEqlaNq0qXj55ZdFenq6EEKIK1euiLFjx4pWrVoJe3t74eDgIIKCgsSnn36qN1rrTqO6Pvroowp13V6DEEIsXbpUNG/eXFhbW4sWLVqI5cuXiyFDhoiOHTsatlNu6NixowAg5s2bV2HeyZMnRUREhFCpVMLJyUk89thj4uLFixXqqWwUjVarFfPmzRNNmzYVNjY2okuXLmLnzp2Vjq7Jy8sTb7/9tmjZsqWwtrYWarVatGvXTrz88svyyJ4tW7aI/v37iyZNmghra2vh7u4uBgwYIPbu3Wvwew0NDRUAxBNPPFFh3qpVq0Tv3r2Fh4eHsLa2Fp6enmLEiBHi6NGjBq07KytLvPfee6JTp07CwcFBWFlZCR8fH/Hkk0+KP//8U2/ZvXv3igcffFDY29sLW1tb0b17d/HTTz9VWOeBAwdEaGiosLe3F02aNBHvvvuu+Prrrysd1TVw4MAKr69sXy9YsED4+/sLCwuLCiOShBAiISFBABATJkww6H0LcXN01dWrV++6XEZGhpgwYYJo3LixsLS0FL6+vmLatGkVRsQBEC+++KJYvHixaNasmbCyshKtWrUS3333XYV1GrqPKtsXy5cvFy1btpQ/x9HR0WLZsmUVXpuQkCAiIyOFSqUSAOQRRJWN6hLCsN/vnf4m6UadVjby7k4MGdVlyHbudBwJIcTVq1fFpEmThL+/v7CyshLOzs6ic+fOYvr06SIvL08IYdhnyJh6ysrKxNy5c0WLFi2ElZWVcHV1FU8++aRISkrSe+3to7qEKP88jhs3TjRq1EjY2dmJiIgI8e+//3JUl5EkIW47OU1Ui2RlZaFFixYYOnQoli5dau5yqI76/PPPMWnSJBw/flzuvFrTJEnCiy++iIULF5pl+0RUjqe6qNZITU3FBx98gN69e8PFxQWJiYn49NNPkZubi8mTJ5u7PKqDDh8+jPj4eMyePRtDhgwxW+ghotqDwYdqDaVSiYSEBERFReHatWuws7ND9+7dsWTJEn5hUZU88sgjSE1NxQMPPIAlS5aYuxwiqgV4qouIiIgaDA5nJyIiogaDwYeIiIgaDAYfIiIiajDqfedmrVaLy5cvQ6VS1cmLbBERETVEQgjk5ubC09PTqIvY3ku9Dz6XL1+Gt7e3ucsgIiKiKkhKSpJv6WIK9T74qFQqAOU7ztHR0czVEBERkSFycnLg7e0tf4+bSr0PPrrTW46Ojgw+REREdYypu6mwczMRERE1GAw+RERE1GAw+BAREVGDweBDREREDQaDDxERETUYDD5ERETUYDD4EBERUYPB4ENEREQNBoMPERERNRgMPkRERNRgMPgQERFRg8HgQ0RERA2GWYNPaWkp3n77bfj7+8PW1hZNmzbF7NmzodVq5WWEEJg5cyY8PT1ha2uL8PBwnDhxwoxV36zrYkY+LmUVmLsUIiIiMpBZg8/cuXOxZMkSLFy4EKdOncK8efPw0Ucf4fPPP5eXmTdvHubPn4+FCxciLi4OGo0GERERyM3NNWPlwJxfTqHXR7uw8s94s9ZBREREhjNr8Nm3bx+GDBmCgQMHws/PD8OHD0dkZCT+/vtvAOWtKgsWLMD06dMxbNgwBAYGYtWqVcjPz8eaNWvMWTpaN3YEABxMzDRrHURERGQ4swafnj174rfffsOZM2cAAP/88w/++OMPDBgwAAAQHx+P1NRUREZGyq9RKpUICwtDbGxspessKipCTk6O3qM6dPZ1AgAcv5SDwpKyatkGERERmZalOTf+xhtvIDs7G61atYKFhQXKysrwwQcfYNSoUQCA1NRUAICHh4fe6zw8PJCYmFjpOqOjozFr1qzqLRyAj7MdXB2skZ5XjOOXstHFz7nat0lERET3x6wtPt9//z2+/fZbrFmzBocOHcKqVavw8ccfY9WqVXrLSZKk91wIUWGazrRp05CdnS0/kpKSqqV2SZLkVh+e7iIiIqobzNri8/rrr+PNN9/E448/DgBo164dEhMTER0djTFjxkCj0QAob/lp3Lix/Lq0tLQKrUA6SqUSSqWy+otH+emubSeuMPgQERHVEWZt8cnPz4dCoV+ChYWFPJzd398fGo0GMTEx8vzi4mLs2bMHoaGhNVprZXQtPocuZkIIYeZqiIiI6F7M2uIzePBgfPDBB/Dx8UHbtm1x+PBhzJ8/H+PGjQNQfjppypQpmDNnDgICAhAQEIA5c+bAzs4Oo0ePNmfpAIDAJmpYWyiQnleMxIx8+Lnam7skIiIiuguzBp/PP/8c77zzDqKiopCWlgZPT0+MHz8eM2bMkJeZOnUqCgoKEBUVhczMTAQHB2P79u1QqVRmrLyc0tIC7bzUOJiYiYOJmQw+REREtZwk6vk5mpycHKjVamRnZ8PR0dHk65/zyyks/f0CRgf7YM4j7Uy+fiIiooaour6/ea+u+ySP7EpgB2ciIqLajsHnPnXyKQ8+Z9JykV1QYuZqiIiI6G4YfO6Tm0oJXxc7CAEcScoydzlERER0Fww+JtDZR3e665qZKyEiIqK7YfAxgc5+N4LPRfbzISIiqs0YfExA18H5yMUslJZpzVwNERER3QmDjwkEuKugUlrienEZTl/JNXc5REREdAcMPiZgoZDQkTcsJSIiqvUYfExE7uDM4ENERFRrMfiYSGe2+BAREdV6DD4m0sGnERQSkJxZgCs5heYuh4iIiCrB4GMiDkpLtNKU30uErT5ERES1E4OPCfF0FxERUe3G4GNCuuDzN4MPERFRrcTgY0K64HPiUjYKS8rMXA0RERHdjsHHhLycbOGuUqJUK3A0Odvc5RAREdFtGHxMSJIk9vMhIiKqxRh8TOxm8OGd2omIiGobBh8Tu7XFRwhh5mqIiIjoVgw+JtbWUw2lpQKZ+SWIT79u7nKIiIjoFgw+JmZtqUB7r0YAOKydiIiotmHwqQadbpzuOsTgQ0REVKsw+FQDjuwiIiKqnRh8qkEnn0YAgLNpecjKLzZvMURERCRj8KkGLg5K+LvaAwCOXeKFDImIiGoLBp9qEuDuAAA4n5Zn5kqIiIhIh8GnmjR1Kw8+FziknYiIqNZg8KkmTd3KT3VduMrgQ0REVFsw+FSTZnLw4akuIiKi2oLBp5o0dS0/1XU5uxD5xaVmroaIiIgABp9q42RvDSc7KwDgrSuIiIhqCbMGHz8/P0iSVOHx4osvAgCEEJg5cyY8PT1ha2uL8PBwnDhxwpwlG0Xu4Mx+PkRERLWCWYNPXFwcUlJS5EdMTAwA4LHHHgMAzJs3D/Pnz8fChQsRFxcHjUaDiIgI5ObmmrNsgzV1ZQdnIiKi2sSswcfNzQ0ajUZ+bNmyBc2aNUNYWBiEEFiwYAGmT5+OYcOGITAwEKtWrUJ+fj7WrFljzrINdnNIOzs4ExER1Qa1po9PcXExvv32W4wbNw6SJCE+Ph6pqamIjIyUl1EqlQgLC0NsbOwd11NUVIScnBy9h7lwSDsREVHtUmuCz6ZNm5CVlYWxY8cCAFJTUwEAHh4eest5eHjI8yoTHR0NtVotP7y9vaut5nu5dUi7EMJsdRAREVG5WhN8li1bhv79+8PT01NvuiRJes+FEBWm3WratGnIzs6WH0lJSdVSryF8nO1hoZBwvbgMablFZquDiIiIylmauwAASExMxI4dO7BhwwZ5mkajAVDe8tO4cWN5elpaWoVWoFsplUoolcrqK9YI1pYKeDvZIiEjH+ev5sHD0cbcJRERETVotaLFZ8WKFXB3d8fAgQPlaf7+/tBoNPJIL6C8H9CePXsQGhpqjjKrhEPaiYiIag+zt/hotVqsWLECY8aMgaXlzXIkScKUKVMwZ84cBAQEICAgAHPmzIGdnR1Gjx5txoqN09TVHjsBnOetK4iIiMzO7MFnx44duHjxIsaNG1dh3tSpU1FQUICoqChkZmYiODgY27dvh0qlMkOlVcMWHyIiotpDEvV8uFFOTg7UajWys7Ph6OhY49vffyEDjy/dD29nW+yd+mCNb5+IiKguqq7v71rRx6c+013LJzmzAIUlZWauhoiIqGFj8Klmbg5KqJSWEAJIzMg3dzlEREQNGoNPNZMk6ZYrOLODMxERkTkx+NSAm/fsYgdnIiIic2LwqQG6u7RzSDsREZF5MfjUAA5pJyIiqh0YfGpAU96slIiIqFZg8KkB/q72kCQgp7AUGdeLzV0OERFRg8XgUwNsrCzgqbYFwNNdRERE5sTgU0Oauev6+bCDMxERkbkw+NQQ3cguDmknIiIyHwafGtKMFzEkIiIyOwafGsIh7URERObH4FNDdEPaL17LR0mZ1szVEBERNUwMPjVE42gDO2sLlGoFLl7jzUqJiIjMgcGnhkiSBH9dB2ee7iIiIjILBp8adLOfDzs4ExERmQODTw1qyhYfIiIis2LwqUG6Ds68SzsREZF5MPjUoGa6U128iCEREZFZMPjUIF3n5mvXi5GVz5uVEhER1TQGnxpkr7SExtEGAHCe/XyIiIhqHINPDWvKW1cQERGZDYNPDZODD/v5EBER1TgGnxrW1JXX8iEiIjIXBp8advNUF1t8iIiIahqDTw3TDWlPzMhHmVaYuRoiIqKGhcGnhnk2soW1pQLFZVokZ/JmpURERDWJwaeGWSgk+LvwdBcREZE5MPiYAW9dQUREZB4MPmbAIe1ERETmYfbgc+nSJTz55JNwcXGBnZ0dOnTogIMHD8rzhRCYOXMmPD09YWtri/DwcJw4ccKMFd8/DmknIiIyD7MGn8zMTPTo0QNWVlbYunUrTp48iU8++QSNGjWSl5k3bx7mz5+PhQsXIi4uDhqNBhEREcjNzTVf4fepmXt58OFtK4iIiGqWpTk3PnfuXHh7e2PFihXyND8/P/lnIQQWLFiA6dOnY9iwYQCAVatWwcPDA2vWrMH48eNrumSTaHbjVNfV3CJk5RejkZ21mSsiIiJqGMza4rN582Z06dIFjz32GNzd3dGxY0d89dVX8vz4+HikpqYiMjJSnqZUKhEWFobY2NhK11lUVIScnBy9R22jsrFCk0a2AIAzV3i6i4iIqKaYNfhcuHABX3zxBQICArBt2zZMmDABkyZNwurVqwEAqampAAAPDw+913l4eMjzbhcdHQ21Wi0/vL29q/dNVFELj/LTXaev1N1TdkRERHWNWYOPVqtFp06dMGfOHHTs2BHjx4/H888/jy+++EJvOUmS9J4LISpM05k2bRqys7PlR1JSUrXVfz9aaFQAgLMMPkRERDXGrMGncePGaNOmjd601q1b4+LFiwAAjUYDABVad9LS0iq0AukolUo4OjrqPWqjFu7lwed0KoMPERFRTTFr8OnRowdOnz6tN+3MmTPw9fUFAPj7+0Oj0SAmJkaeX1xcjD179iA0NLRGazW1ljdafM5cyYUQvGcXERFRTTDrqK6XX34ZoaGhmDNnDkaMGIEDBw5g6dKlWLp0KYDyU1xTpkzBnDlzEBAQgICAAMyZMwd2dnYYPXq0OUu/b83cHCBJQGZ+CdLziuGmUpq7JCIionrPrMGna9eu2LhxI6ZNm4bZs2fD398fCxYswBNPPCEvM3XqVBQUFCAqKgqZmZkIDg7G9u3boVKpzFj5/bO1toCvsx0SMvJx5kougw8REVENkEQ9P8+Sk5MDtVqN7OzsWtff5/nVfyPm5BW8O7gNnunhb+5yiIiIao3q+v42+y0rGrKWHjf7+RAREVH1Y/Axo4Ab1/LhRQyJiIhqBoOPGckju1I5souIiKgmMPiYkb+rPSwUEnKLSpGaU2jucoiIiOo9Bh8zUlpawN+1/IalvJAhERFR9WPwMTNdB+ez7OdDRERU7Rh8zCyANyslIiKqMQw+ZnazxYfBh4iIqLox+JhZgHwtnzxotRzZRUREVJ2qHHxSUlIwfPhwuLm5wdnZGYMHD8aFCxdMWVuD4OdiB2sLBQpKynApq8Dc5RAREdVrVQ4+48aNQ2BgIPbs2YOdO3fCw8Ojzt841BwsLRRo6saRXURERDXB4OAzefJkXL9+XX5+7tw5vPHGG2jTpg06dOiAyZMn4/Tp09VSZH0nX8gwjcGHiIioOhl8d/YmTZqgc+fOmDdvHh5++GGMHDkSwcHBGDBgAEpKSrBhwwa9u6qT4Vp43LyCMxEREVUfo+7OHh8fj6ioKNja2uL//u//cPjwYezevRtlZWXo0aMHhg8fDkmSqrNeo9Xmu7PrxJy8gudX/402jR3xy+QHzF0OERGR2VXX97fBLT4A4O/vj61bt+Lbb79FeHg4Jk+ejI8//rjWhZ26psWNa/mcu5qHMq2AhYL7k4iIqDoY3bk5IyMDTz75JOLi4nDo0CGEhITg6NGj1VFbg+HtZAcbKwWKS7VIzLh+7xcQERFRlRgcfHbt2gWNRgM3Nzd4eXnh33//xYoVKzBnzhw8/vjjmDp1KgoKOBy7KhQK6WY/H17IkIiIqNoYHHyioqLw+uuvIz8/HwsXLsSUKVMAAA8++CAOHz4MS0tLdOjQoZrKrP8C3MuDz+lU3rOLiIiouhgcfC5fvoyBAwfCxsYG/fr1w9WrV+V5SqUSc+bMwYYNG6qlyIagpaa8nw+HtBMREVUfgzs3P/zwwxg+fDgefvhh/PHHHxgwYECFZdq2bWvS4hqSAA5pJyIiqnYGt/gsW7YM48ePR3Z2Np588kksWLCgGstqeHQ3K41Pv47iUq2ZqyEiIqqfDG7xsba2xksvvVSdtTRojdU2UCktkVtUivj06/LVnImIiMh0eHf2WkKSJATcuJ4PR3YRERFVDwafWoRD2omIiKoXg08twuBDRERUvRh8apGbwYfX8iEiIqoORt2r61bFxcVIS0uDVqs/AsnHx+e+i2qoWty4lk9ixnUUlpTBxsrCzBURERHVL0YHn7Nnz2LcuHGIjY3Vmy6EgCRJKCsrM1lxDY2bgxJOdlbIzC/BubQ8BDZRm7skIiKiesXo4DN27FhYWlpiy5YtaNy4Me/MbkLlI7tUOBB/DWfTchl8iIiITMzo4HPkyBEcPHgQrVq1qo56GryWN4IP79lFRERkekZ3bm7Tpg3S09NNsvGZM2dCkiS9h0ajkecLITBz5kx4enrC1tYW4eHhOHHihEm2XVu1uHEtn7Mc2UVERGRyRgefuXPnYurUqdi9ezcyMjKQk5Oj9zBW27ZtkZKSIj+OHTsmz5s3bx7mz5+PhQsXIi4uDhqNBhEREcjNrb+hQDey6zSDDxERkckZfarroYceAgD06dNHb3pVOzdbWlrqtfLcur4FCxZg+vTpGDZsGABg1apV8PDwwJo1azB+/HhjS68TdMEnObMA14tKYa+s8sA7IiIiuo3R36q7du0yaQFnz56Fp6cnlEolgoODMWfOHDRt2hTx8fFITU1FZGSkvKxSqURYWBhiY2PrbfBxsreGm0qJq7lFOJuWhw7ejcxdEhERUb1hdPAJCwsz2caDg4OxevVqtGjRAleuXMH777+P0NBQnDhxAqmpqQAADw8Pvdd4eHggMTHxjussKipCUVGR/Lwqp9/MrYWHA67mFuFMai6DDxERkQkZFHyOHj2KwMBAKBQKHD169K7LBgUFGbzx/v37yz+3a9cOISEhaNasGVatWoXu3bsDQIXh8rpTancSHR2NWbNmGVxDbdTCQ4U/z2Xg31T28yEiIjIlg4JPhw4dkJqaCnd3d3To0AGSJEEIUWG5+72Aob29Pdq1a4ezZ89i6NChAIDU1FQ0btxYXiYtLa1CK9Ctpk2bhldeeUV+npOTA29v7yrXZA6BnuXX7zl+KdvMlRAREdUvBgWf+Ph4uLm5yT9Xl6KiIpw6dQoPPPAA/P39odFoEBMTg44dOwIov03Gnj17MHfu3DuuQ6lUQqlUVluNNaG9943gczkbZVoBCwUvEklERGQKBgUfX1/fSn++X6+99hoGDx4MHx8fpKWl4f3330dOTg7GjBkDSZIwZcoUzJkzBwEBAQgICMCcOXNgZ2eH0aNHm6yG2sjf1QH21ha4XlyG81fz5JFeREREdH/MOlY6OTkZo0aNQnp6Otzc3NC9e3fs379fDldTp05FQUEBoqKikJmZieDgYGzfvh0qVf0OAhYKCW2bqHEg/hqOJmcz+BAREZmIJCrrrFOP5OTkQK1WIzs7G46OjuYux2DvbzmJr/+Ix5gQX8waEmjucoiIiGpUdX1/G33lZqoZ7bzK+/kcZQdnIiIik2HwqaWCvBoBAE5ezkFJmda8xRAREdUTRgefpKQkJCcny88PHDiAKVOmYOnSpSYtrKHzdbaDysYSRaVanOF9u4iIiEzC6OAzevRo+bYVqampiIiIwIEDB/DWW29h9uzZJi+woVIoJLRrUn6661gyT3cRERGZgtHB5/jx4+jWrRsA4IcffkBgYCBiY2OxZs0arFy50tT1NWi6013s50NERGQaRgefkpIS+QKBO3bswMMPPwwAaNWqFVJSUkxbXQMX5MUWHyIiIlMyOvi0bdsWS5Yswd69exETE4N+/foBAC5fvgwXFxeTF9iQ6U51/Zuag6LSqt8KhIiIiMoZHXzmzp2LL7/8EuHh4Rg1ahTat28PANi8ebN8CoxMw8vJFk52VigpEzjNG5YSERHdN6Ov3BweHo709HTk5OTAyclJnv6f//wHdnZ2Ji2uoZMkCe28GuH3M1fxT3K23OeHiIiIqqZK1/ERQuDgwYP48ssvkZtb3hJhbW3N4FMNguSRXVnmLYSIiKgeMLrFJzExEf369cPFixdRVFSEiIgIqFQqzJs3D4WFhViyZEl11NlgyVdwZgdnIiKi+2Z0i8/kyZPRpUsXZGZmwtbWVp7+yCOP4LfffjNpcQS0v3F662xaHgqK2cGZiIjofhjd4vPHH3/gzz//hLW1td50X19fXLp0yWSFUTkPRyXcVEpczS3CyZQcdPZ1uveLiIiIqFJGt/hotVqUlVVseUhOToZKpTJJUXSTJEns50NERGQiRgefiIgILFiwQH4uSRLy8vLw7rvvYsCAAaasjW7gndqJiIhMw+hTXZ9++il69+6NNm3aoLCwEKNHj8bZs2fh6uqKtWvXVkeNDV4QOzgTERGZhNHBx9PTE0eOHMHatWtx6NAhaLVaPPvss3jiiSf0OjuT6QTeONV1/moe8opK4aA0+tdGREREqELwAQBbW1uMGzcO48aNM3U9VAl3lQ0aq22Qkl2IE5eyEdyUtwYhIiKqCqODz+rVq+86/+mnn65yMXRnQV5qpGQX4hiDDxERUZUZHXwmT56s97ykpAT5+fnylZsZfKpHkFcjbDtxhf18iIiI7oPRo7oyMzP1Hnl5eTh9+jR69uzJzs3VSHen9mMc2UVERFRlVbpX1+0CAgLw4YcfVmgNItPRBZ/49OvILigxczVERER1k0mCDwBYWFjg8uXLplod3cbJ3hrezuWj5o6z1YeIiKhKjO7js3nzZr3nQgikpKRg4cKF6NGjh8kKo4qCmjRC0rUCHE3ORo/mruYuh4iIqM4xOvgMHTpU77kkSXBzc8ODDz6ITz75xFR1USXaeanx87EUHLuUZe5SiIiI6iSjg49Wq62OOsgAunt2cWQXERFR1Zisjw9Vv8Abt65IzizAtevFZq6GiIio7jGoxeeVV14xeIXz58+vcjF0d442Vmjqao8L6ddx7FI2wlq4mbskIiKiOsWg4HP48GGDViZJ0n0VQ/fWzkuNC+nXcTQpi8GHiIjISAYFn127dlV3HWSgdk3U+N+RyzjKIe1ERERGYx+fOibIqxEA4Bg7OBMRERmtSsEnLi4OU6dOxeOPP45hw4bpPaoqOjoakiRhypQp8jQhBGbOnAlPT0/Y2toiPDwcJ06cqPI26oO2no6QJCA1pxBpOYXmLoeIiKhOMTr4rFu3Dj169MDJkyexceNGlJSU4OTJk9i5cyfUanWVioiLi8PSpUsRFBSkN33evHmYP38+Fi5ciLi4OGg0GkRERCA3N7dK26kP7JWWaOmhAgAcTMw0czVERER1i9HBZ86cOfj000+xZcsWWFtb47PPPsOpU6cwYsQI+Pj4GF1AXl4ennjiCXz11VdwcnKSpwshsGDBAkyfPh3Dhg1DYGAgVq1ahfz8fKxZs8bo7dQn3fydAQB/xV8zcyVERER1i9HB5/z58xg4cCAAQKlU4vr165AkCS+//DKWLl1qdAEvvvgiBg4ciIceekhvenx8PFJTUxEZGSlPUyqVCAsLQ2xsrNHbqU8YfIiIiKrG6Cs3Ozs7y6eamjRpguPHj6Ndu3bIyspCfn6+Uetat24dDh06hLi4uArzUlNTAQAeHh560z08PJCYmHjHdRYVFaGoqEh+npOTY1RNdYEu+PybmoPs/BKo7azMXBEREVHdYHSLzwMPPICYmBgAwIgRIzB58mQ8//zzGDVqFPr06WPwepKSkjB58mR8++23sLGxueNyt18bSAhx1+sFRUdHQ61Wyw9vb2+Da6or3FU2aOpqDyGAuAS2+hARERnK4OBz5MgRAMDChQvx+OOPAwCmTZuG1157DVeuXMGwYcOwbNkygzd88OBBpKWloXPnzrC0tISlpSX27NmD//u//4OlpaXc0qNr+dFJS0ur0Ap0q2nTpiE7O1t+JCUlGVxTXRLctLzV5wCDDxERkcEMDj6dOnVC586d8f3338Pe3r78xQoFpk6dis2bN2P+/Pl6nZPvpU+fPjh27BiOHDkiP7p06YInnngCR44cQdOmTaHRaOTWJQAoLi7Gnj17EBoaesf1KpVKODo66j3qI7mfz4UMM1dCRERUdxgcfP7880906tQJb775Jho3bownn3zyvq7orFKpEBgYqPewt7eHi4sLAgMD5Wv6zJkzBxs3bsTx48cxduxY2NnZYfTo0VXebn3Rzd8FAHD8cg7yikrNXA0REVHdYHDwCQkJwVdffYXU1FR88cUXSE5OxkMPPYRmzZrhgw8+QHJyssmLmzp1KqZMmYKoqCh06dIFly5dwvbt26FSqUy+rbqmSSNbeDnZokwrcIjX8yEiIjKIJIQQVX3x+fPnsWLFCqxevRopKSmIiIjAL7/8Ysr67ltOTg7UajWys7Pr3WmvV344gg2HLuHF3s3wet9W5i6HiIjIZKrr+/u+7tXVrFkzvPnmm5g+fTocHR2xbds2U9VFBuh+43TXAV7Ph4iIyCBGX8dHZ8+ePVi+fDnWr18PCwsLjBgxAs8++6wpa6N70HVw/icpG4UlZbCxsjBzRURERLWbUcEnKSkJK1euxMqVKxEfH4/Q0FB8/vnnGDFihDzSi2qOr4sd3FVKpOUW4fDFLIQ0czF3SURERLWawcEnIiICu3btgpubG55++mmMGzcOLVu2rM7a6B4kSUJwUxf89M9lHIi/xuBDRER0DwYHH1tbW6xfvx6DBg2ChQVPqdQW3fyd8dM/l/FXfAaAAHOXQ0REVKsZHHw2b95cnXVQFQXf6Odz6GImiku1sLa8r/7qRERE9Rq/Jeu4AHcHONtbo7BEi2OXss1dDhERUa3G4FPHSZKErn7ltwopP91FREREd8LgUw8E83o+REREBmHwqQd01/P5OyETpWVaM1dDRERUexl9AcM7dXKWJAk2NjZo3rw5/P3977swMlzrxo5Q2Vgit7AUp1Jy0c5Lbe6SiIiIaiWjg8/QoUMhSRJuv8WXbpokSejZsyc2bdoEJycnkxVKd2ahkNDVzxk7/03DX/EZDD5ERER3YPSprpiYGHTt2hUxMTHIzs5GdnY2YmJi0K1bN2zZsgW///47MjIy8Nprr1VHvXQHutNdf7GfDxER0R0Z3eIzefJkLF26FKGhofK0Pn36wMbGBv/5z39w4sQJLFiwAOPGjTNpoXR3uuv5xCVcg1YroFBIZq6IiIio9jG6xef8+fOV3h7e0dERFy5cAAAEBAQgPT39/qsjgwU2UcPWygJZ+SU4m5Zn7nKIiIhqJaODT+fOnfH666/j6tWr8rSrV69i6tSp6Nq1KwDg7Nmz8PLyMl2VdE9WFgp09uX1fIiIiO7G6OCzbNkyxMfHw8vLC82bN0dAQAC8vLyQkJCAr7/+GgCQl5eHd955x+TF0t0Fs58PERHRXRndx6dly5Y4deoUtm3bhjNnzkAIgVatWiEiIgIKRXmOGjp0qKnrJAPIHZwvXJNH2BEREdFNRgcfoHzoer9+/dCvXz9T10P3ob13I1hbKpCeV4T49Oto6uZg7pKIiIhqlSoFn99++w2//fYb0tLSoNXqXyl4+fLlJimMjGdjZYEO3o1wIP4aDsRfY/AhIiK6jdF9fGbNmoXIyEj89ttvSE9PR2Zmpt6DzIv9fIiIiO7M6BafJUuWYOXKlXjqqaeqox66T92buuDzneew92w6yrQCFryeDxERkczoFp/i4mK9ixdS7dLVzxmONpZIzytCXAJbfYiIiG5ldPB57rnnsGbNmuqohUzA2lKByLYaAMAvx1LMXA0REVHtYvSprsLCQixduhQ7duxAUFAQrKys9ObPnz/fZMVR1QwMaowfDybjl2OpeHdwW57uIiIiusHo4HP06FF06NABAHD8+HG9ebxuTO3Qo5mr3umu7k1dzF0SERFRrWB08Nm1a1d11EEmpDvdVd7qk8LgQ0REdIPRfXyobhgY1BgA8MuxVJRphZmrISIiqh0MavEZNmwYVq5cCUdHRwwbNuyuy27YsMEkhdH94ekuIiKiigwKPmq1Wu6/o1arq7UgMg2e7iIiIqpIEkLU6/MgOTk5UKvVyM7OhqOjo7nLqVG7TqfhmRVxcHVQ4q+3+nB0FxER1RnV9f1tdB+fgoIC5Ofny88TExOxYMECbN++3eiNf/HFFwgKCoKjoyMcHR0REhKCrVu3yvOFEJg5cyY8PT1ha2uL8PBwnDhxwujtNFS3n+4iIiJq6IwOPkOGDMHq1asBAFlZWejWrRs++eQTDBkyBF988YVR6/Ly8sKHH36Iv//+G3///TcefPBBDBkyRA438+bNw/z587Fw4ULExcVBo9EgIiICubm5xpbdIPFihkRERPqMDj6HDh3CAw88AAD48ccfodFokJiYiNWrV+P//u//jFrX4MGDMWDAALRo0QItWrTABx98AAcHB+zfvx9CCCxYsADTp0/HsGHDEBgYiFWrViE/P59XjjbCwHblo7u2HufoLiIiIqODT35+PlQqFQBg+/btGDZsGBQKBbp3747ExMQqF1JWVoZ169bh+vXrCAkJQXx8PFJTUxEZGSkvo1QqERYWhtjY2Duup6ioCDk5OXqPhqxH8/LTXVdzebqLiIjI6ODTvHlzbNq0CUlJSdi2bZscTNLS0qrU+ejYsWNwcHCAUqnEhAkTsHHjRrRp0wapqakAAA8PD73lPTw85HmViY6Ohlqtlh/e3t5G11Sf8HQXERHRTUYHnxkzZuC1116Dn58fgoODERISAqC89adjx45GF9CyZUscOXIE+/fvxwsvvIAxY8bg5MmT8vzbb4MhhLjrrTGmTZuG7Oxs+ZGUlGR0TfUNT3cRERGVM/qWFcOHD0fPnj2RkpKC9u3by9P79OmDRx55xOgCrK2t0bx5cwBAly5dEBcXh88++wxvvPEGACA1NRWNGzeWl09LS6vQCnQrpVIJpVJpdB312e2nu3hNHyIiaqiqdMsKjUaDjh07QqFQICcnB5s2bYJKpUKrVq3uuyAhBIqKiuDv7w+NRoOYmBh5XnFxMfbs2YPQ0ND73k5DwtNdRERE5YwOPiNGjMDChQsBlF/Tp0uXLhgxYgSCgoKwfv16o9b11ltvYe/evUhISMCxY8cwffp07N69G0888QQkScKUKVMwZ84cbNy4EcePH8fYsWNhZ2eH0aNHG1t2g8fTXURERFU41fX7779j+vTpAICNGzdCCIGsrCysWrUK77//Ph599FGD13XlyhU89dRTSElJgVqtRlBQEH799VdEREQAAKZOnYqCggJERUUhMzMTwcHB2L59uzyqjAzH011ERERVuGWFra0tzpw5A29vbzz99NPw9PTEhx9+iIsXL6JNmzbIy8urrlqrpCHfsuJ2r/33H/x4MBlPh/hi9pBAc5dDRER0R7XmlhXe3t7Yt28frl+/jl9//VUezp6ZmQkbGxuTFUamx9NdRETU0BkdfKZMmYInnngCXl5e8PT0RHh4OIDyU2Dt2rUzdX1kQryYIRERNXRGB5+oqCjs27cPy5cvxx9//AGFonwVTZs2xfvvv2/yAsl0rC0V6HtjdNcPcby+ERERNTxG9/Gpa9jHR98/SVkYsuhPWFlI+PONB+HuyNOTRERU+1TX97dBo7peeeUVvPfee7C3t8crr7xy12Xnz59vksKoerT3boQuvk74OzET3+xPxKuRLc1dEhERUY0xKPgcPnwYJSUl8s93crdbSVDtMa6nP/5OzMR3f13Ei72bw8bKwtwlERER1QiDgs+uXbsq/Znqpsg2HmjSyBaXsgqw6fAlPN7Nx9wlERER1Ygq3bKC6jZLCwWe6eEHAFj+ZzzqeTcvIiIimcFXbh43bpxByy1fvrzKxVDNGdHVG5/GnMGZK3nYezYdvVq4mbskIiKiamdw8Fm5ciV8fX3RsWNHthDUA442VnisizdWxiZg+Z/xDD5ERNQgGBx8JkyYgHXr1uHChQsYN24cnnzySTg7O1dnbVTNnunhh1X7ErD79FWcS8tFc3feA42IiOo3g/v4LF68GCkpKXjjjTfw008/wdvbGyNGjMC2bdvYAlRH+brY46HWHgCAFX8mmLcYIiKiGmBU52alUolRo0YhJiYGJ0+eRNu2bREVFQVfX99ad3NSMsy4Hv4AgPWHkpF5vdjM1RAREVWvKo/qkiQJkiRBCAGtVmvKmqgGdW/qjDaNHVFYosXauIvmLoeIiKhaGRV8ioqKsHbtWkRERKBly5Y4duwYFi5ciIsXL8LBwaG6aqRqJEkSxvUsb/VZHZuIkjKGWCIiqr8MDj5RUVFo3Lgx5s6di0GDBiE5ORn//e9/MWDAAPlGpVQ3DW7fGK4OSqTmFOKXYynmLoeIiKjaGHyTUoVCAR8fH3Ts2PGut6bYsGGDyYozBd6k1DCf7TiLT3ecQZCXGv97sQdvP0JERGZl1puUAsDTTz/NL8N67InuPli0+xyOJmfjYGImuvjxUgVERFT/GHUBQ6q/XB2UGNrBEz/8nYzlf8Yz+BARUb3Ezjkk03Vy/vV4KpKu5Zu5GiIiItNj8CFZK40jejZ3hVbwgoZERFQ/MfiQnuceKG/1+T7uIrILSsxcDRERkWkx+JCesBZuaOHhgOvFZVh7gBc0JCKi+oXBh/RIkoTnejYFAKz8MwHFpbygIRER1R8MPlTBkI6e8gUNfz522dzlEBERmQyDD1WgtLTA2FBfAMBXv8fDwGtcEhER1XoMPlSpJ4J9YWOlwMmUHOw7n2HucoiIiEyCwYcq5WRvjcc6ewMAvtp7wczVEBERmQaDD93Rsz39IUnArtNXcS4t19zlEBER3TcGH7ojP1d7RLT2AAB8vTfezNUQERHdPwYfuqvne5UPbd9w+BKu5haZuRoiIqL7Y9bgEx0dja5du0KlUsHd3R1Dhw7F6dOn9ZYRQmDmzJnw9PSEra0twsPDceLECTNV3PB08XVCe+9GKC7V4pv9ieYuh4iI6L6YNfjs2bMHL774Ivbv34+YmBiUlpYiMjIS169fl5eZN28e5s+fj4ULFyIuLg4ajQYRERHIzWWfk5ogSRKev3Ebi2/3J6KwpMzMFREREVWdJGrRRVquXr0Kd3d37NmzB7169YIQAp6enpgyZQreeOMNAEBRURE8PDwwd+5cjB8//p7rzMnJgVqtRnZ2NhwdHav7LdRLpWVahH20G5eyCvDBI4F4ItjX3CUREVE9V13f37Wqj092djYAwNnZGQAQHx+P1NRUREZGyssolUqEhYUhNja20nUUFRUhJydH70H3x9JCgXE9y1t9lu2Nh1Zba7IyERGRUWpN8BFC4JVXXkHPnj0RGBgIAEhNTQUAeHh46C3r4eEhz7tddHQ01Gq1/PD29q7ewhuIkV29obKxxIX069j5b5q5yyEiIqqSWhN8Jk6ciKNHj2Lt2rUV5kmSpPdcCFFhms60adOQnZ0tP5KSkqql3obGQWmJ0cE+AIDPd51jqw8REdVJtSL4vPTSS9i8eTN27doFLy8vebpGowGACq07aWlpFVqBdJRKJRwdHfUeZBrP9vSHvbUF/knKwqYjl8xdDhERkdHMGnyEEJg4cSI2bNiAnTt3wt/fX2++v78/NBoNYmJi5GnFxcXYs2cPQkNDa7rcBs9dZYOJDwYAAD7c+i+uF5WauSIiIiLjmDX4vPjii/j222+xZs0aqFQqpKamIjU1FQUFBQDKT3FNmTIFc+bMwcaNG3H8+HGMHTsWdnZ2GD16tDlLb7DG9fSDr4sd0nKLsHj3OXOXQ0REZBSzDme/Uz+dFStWYOzYsQDKW4VmzZqFL7/8EpmZmQgODsaiRYvkDtD3wuHsprf9RCr+881BWFsqsOPlMPi42Jm7JCIiqmeq6/u7Vl3Hpzow+JieEAJPLz+AvWfT0betB758qou5SyIionqmQVzHh+oGSZLwzqA2sFBI2HbiCv48l27ukoiIiAzC4ENV0sJDhae6l1/BedZPJ1BapjVzRURERPfG4ENVNuWhADjZWeHMlTysOXDR3OUQERHdE4MPVVkjO2u8EtkSAPDJ9jPIvF5s5oqIiIjujsGH7suort5opVEhu6AEn+44Y+5yiIiI7orBh+6LpYUCMwa1AQB8uz8R/6byprBERFR7MfjQfQtt7op+bTXQCmDW5pOo51dIICKiOozBh0xi+sDWsLZUYN+FDPx8LMXc5RAREVWKwYdMwtvZDhPCmgEA3v3fCWTkFZm5IiIioooYfMhkXuzdDC09VMi4Xox3N58wdzlEREQVMPiQySgtLfDRY0GwUEjYcjQFW3nKi4iIahkGHzKpIK9GmBDWFADwzv+O4xqv7UNERLUIgw+Z3KQ+AQhwd0B6XjFm8pQXERHVIgw+ZHJKSwt8/Fh7KCRg8z+Xse1EqrlLIiIiAsDgQ9WkvXcjjL8xymv6xuPIyucpLyIiMj8GH6o2k/sEoLm7A9LzijDrp5PmLoeIiIjBh6qPjZUFPhoeBIUEbDx8CTEnr5i7JCIiauAYfKhadfRxwvO9ykd5vbXxGE95ERGRWTH4ULV7+aEWaOZmj6u5RZi5+QTv5UVERGbD4EPVzsbKAh/dGOW16chlfLz9NMMPERGZBYMP1YhOPk6Y9XBbAMCiXefxf7+dM3NFRETUEDH4UI15KsQPbw9sDQD4dMcZfLH7vJkrIiKihobBh2rUcw80xdR+LQEAc3/9F1/vvWDmioiIqCFh8KEaFxXeHFMeCgAAvP/zKazel2DegoiIqMFg8CGzmNwnAFHh5Vd2nvG/E1h74KKZKyIiooaAwYfMQpIkvN63JZ7r6Q+g/Bo/Px5MNnNVRERU3zH4kNlIkoTpA1tjTIgvhACm/vgPvo9jyw8REVUfBh8yK0mS8O7gthjVzQdaAbyx/himbzyGotIyc5dGRET1EIMPmZ1CIeGDoYF4+aEWkCTgu78uYsSX+3Epq8DcpRERUT3D4EO1gkIhYfJDAVgxtivUtlb4JykLgz//A3+cTTd3aUREVI8w+FCtEt7SHVte6onAJo64dr0YTy//C4t2nYNWy1tcEBHR/TNr8Pn9998xePBgeHp6QpIkbNq0SW++EAIzZ86Ep6cnbG1tER4ejhMnTpinWKox3s52+HFCKEZ28YZWAB9tO43/fHMQ2QUl5i6NiIjqOLMGn+vXr6N9+/ZYuHBhpfPnzZuH+fPnY+HChYiLi4NGo0FERARyc3NruFKqaTZWFpg7PAgfDmsHa0sFdpy6giEL/8CplBxzl0ZERHWYJGrJbbIlScLGjRsxdOhQAOWtPZ6enpgyZQreeOMNAEBRURE8PDwwd+5cjB8/3qD15uTkQK1WIzs7G46OjtVVPlWjY8nZmPDtQVzKKoCNlQLvD22H4Z29zF0WERFVo+r6/q61fXzi4+ORmpqKyMhIeZpSqURYWBhiY2Pv+LqioiLk5OToPahua+elxpaXeiKshRsKS7R47b//YNqGoygs4ZB3IiIyTq0NPqmpqQAADw8PvekeHh7yvMpER0dDrVbLD29v72qtk2qGk701Voztilciyoe8rz2QhOFLYpF0Ld/cpRERUR1Sa4OPjiRJes+FEBWm3WratGnIzs6WH0lJSdVdItUQhULCpD4BWPVMNzjZWeH4pRwM/L+92PnvFXOXRkREdUStDT4ajQYAKrTupKWlVWgFupVSqYSjo6Peg+qXXi3csGXSA+jg3Qg5haUYt/JvfLTtX5RxyDsREd1DrQ0+/v7+0Gg0iImJkacVFxdjz549CA0NNWNlVBs0aWSLH8aHYEyILwBg0a7zGPnlPhy+mGnmyoiIqDazNOfG8/LycO7cOfl5fHw8jhw5AmdnZ/j4+GDKlCmYM2cOAgICEBAQgDlz5sDOzg6jR482Y9VUW1hbKjBrSCA6+Tph2oZj+DsxE48sjsWAdhq8FtkSTd0czF0iERHVMmYdzr5792707t27wvQxY8Zg5cqVEEJg1qxZ+PLLL5GZmYng4GAsWrQIgYGBBm+Dw9kbhstZBZgfcwbrDyVDCMBCIeHxrt6Y3CcA7o425i6PiIiMVF3f37XmOj7VhcGnYfk3NQcf/Xoav/2bBgCwtbLAcw/44z+9mkJlY2Xm6oiIyFAMPlXE4NMw/XUhAx/++i8OX8wCADjbW+OFsGZ4srsvbK0tzFscERHdE4NPFTH4NFxCCGw7kYp5v57GhfTrAABXByUmhDXFk919YWPFAEREVFsx+FQRgw+Vlmmx4dAl/N/Os0jOLAAAuKmUeCGsGUYH+zAAERHVQgw+VcTgQzolZVqsP5iMz3eew6Ws8gDkrlLihfBmGNWNAYiIqDZh8KkiBh+6XXGpFusPJWPhLQHITaXEY529MKKLN/xc7c1cIRERMfhUEYMP3UlxqRb/PZiERTvP4XJ2oTw92N8ZI7t6o39gY3aEJiIyEwafKmLwoXspLtXit1NX8P3fSfj9zFXo7nyhsrHEw+09MbKrN9o1Ud/1HnFERGRaDD5VxOBDxkjJLsCPfyfjh4NJSLpWIE9vpVFhZFdvDO3QBE721maskIioYWDwqSIGH6oKrVZg/4UMfP93ErYeT0VxqRYAYG2hQEQbDzzWxQsPBLjBQsFWICKi6sDgU0UMPnS/svNLsPmfS/j+7yQcv5QjT2+stsGjnbzwWBcv+LqwQzQRkSkx+FQRgw+Z0onL2fjv38nYdOQSsvJL5OmBTRwR0VqDh9q4o01jR/YHIiK6Tww+VcTgQ9WhsKQMO05dwQ9/J2Pv2au49VPUpJEtHmrtjog2GnTzd4a1pcJ8hRIR1VEMPlXE4EPV7WpuEXb9m4aYU1ew9+xVFJZo5XkqG0v0bumOAe0aI7ylGy+SSERkIAafKmLwoZpUWFKGP86mY8epK9hxKg3peUXyPAelJR5q7Y6BQZ7o1cIVSkuGICKiO2HwqSIGHzIXrVbgSHIWth5Lwc9HU/QukqhSWiKijQcGtW+MHs0ZgoiIbsfgU0UMPlQb6ELQln9S8MuxFKTm3AxBSksFuvo5I6SZC0KbuaBdEzUsLdgviIgaNgafKmLwodpGqxU4dDETW46Wh6C03CK9+Q5KSwT7lwehkGYuaKVx5PWCiKjBYfCpIgYfqs2EEDiXlofY8xmIPZ+OfeczkFNYqreMnbUFAj3VaO+tRnvvRmjv1QheTrYcMk9E9RqDTxUx+FBdUqYVOJWSg9jz6Yg9n4G4+Gu4XlxWYTlne2u091Kjg7cTuvo5oYNPI9hZW5qhYiKi6sHgU0UMPlSXlWkFLlzNw5GkLBxNzsY/yVk4lZKDkjL9j62FQkKgpyM6+zqjq58TOvs5wV1lY6aqiYjuH4NPFTH4UH1TVFqGUym5+CcpC4cuZuLvhExcyiqosJyfix3aeqrR3N0BAR4OaO7uAH9Xe44gI6I6gcGnihh8qCG4lFWAvxOu4e+ETMQlXMPpK7mo7JNtoZDg62yHZu7lQcjPxQ4+zvbwcbFDY0cbKNiJmohqCQafKmLwoYYou6AER5KycPZKLs5eycPZtFycTctD7m0dp29lbaGAl7MtfJ3t4OtiDy8nW2jUNmistoFGbQt3lRJWHGZPRDWEwaeKGHyIygkhcDW3CGfT8nAuLQ/nr+YhMSMfF6/lIzkzv0K/odtJEuDqoCwPQo428HKyg7ezLbyd7ODjYgcvJ1t2sCYik6mu72/+lSJqICRJgrujDdwdbdCjuavevDKtwOWsAly8Vh6EEjPycTmrAKnZhUjJKf+3pKw8OF3NLcJRZFe6DVcHpRyGvJxs4SX/awvPRra8VxkRmR1bfIjonrRagWv5xeVBKLsQl7MKkJyZj6RrBUjKLA9LdzuNpuOuUsLLyRaN1bZwUynh6mB941+l/K+rg5J3tCcitvgQkfkoFJIcSgKbqCtdJju/BEmZ+Ui60Wp0KasAyZnlASk5swD5xWVIyy26caXqrLtuT2VjCVcHJZztreFibw0XB2u42Jc/d1Up4aFSwsPRBh6ONrC1ZisSERmOwYeITEJtZwW1nbrSYCSEQGZ+iRyCruQU4mpuEdLzim78Wyw/L9UK5BaWIrewFPHp1++5XZWNJTwcy/sduTsq4a6ygZuqvAXJzeHmv462lrzaNREx+BBR9ZMkCc721nC2t0aQV6M7LqfVCmQVlODa9SJk5BXj2vVipF8vxrW8Yly7XoT068VyP6PU7EIUlJTdCEnlHbbvxtpCcTMQqZRwV90MSe4qJdwdy1uUVEor2CsteKNYonqKwYeIag2F4mZAau5+92WFEMgrKsWVnEJcySnClZxCpN5oSbq1NelqbhFyCktRXKbFpayCSi/2WBlbKws42FjCQXnzYa+0hL3SAva659Y3n9tZW0BpqYC1pQLWFhawtlTAykKCtaWifLqFBawsJVhZKGBlUT7NykLBG9AS1TAGHyKqkyRJgsrGCiobKzR3V9112cKSMqTnlfcvupp789+ruYVIyynC1bwipOUU4Vp+MYpLtQCAgpIyFJSU4WpuUbW+D4UEWFkoYGNlATtrC9haWcDWuvznW6dZ3whK5cFKofdc969SN/+2ZZVW5UFLaVkezsqf3/jZUsFTgNSg1Ings3jxYnz00UdISUlB27ZtsWDBAjzwwAPmLouI6ggbK4sbQ+vt7rlscakW14tKkVdU3s/oenEp8gpLkVtUiuvyowzXi28+zysqQ35xKYpLtSgu0+r/e8vPJWXaCtdL0gqgqFSLolItsgtKqmsX3JVCAiwV5a1PlgoJFhYSLBWK8p8VEiwtJFhI5T/Lz3XzJQmSVH5VcIUkQaGQoJAAxY3lrXTrspBgpfvXovy1VpYKWCluPLcobyHTtYhZWkiwvvHzrdOtLMpfp5tnbakfBpU3fuZVyOlOan3w+f777zFlyhQsXrwYPXr0wJdffon+/fvj5MmT8PHxMXd5RFTPlH+RWsPJ3rpa1i+EQEmZuBGCboaiwpIyFBRrkV9cWt7aVFyG/OLyVqfCkjIU3QhOxbf8W1wm5GBVclvYKroxrai0DMVlWhSVaG8ErPJ13XohE60Aisu0QFm1vGWzkCTA8kYY0wU2C8XNAGepkGB5I2BZKsrDmZWFLtgp5MCmC1uWFuUhzdKifJ3SjcCnkAAJ5WHv5jQJEspP3eqe68KgJOlqACwUClgoboZES0X5fHl5hXTba29s48b7k3BjGnDjdTe2pYD+enSvvW35mz8DuG1e+RT97ejq1NVmoduGAlDZWEFta2WeX7aRav11fIKDg9GpUyd88cUX8rTWrVtj6NChiI6OvufreR0fIiJ9uvBVVFqG4lItyrQCpVpxy79alGoFSsvEbfPKl9U9SsoEhBAoE+XPhSi/GKZWlD906ygp062vvMWrVKu9MV03T4vi0pvTi2+EwtJbAmLJbT9X1qpG5vNCeDO80a+VSdfZIK/jU1xcjIMHD+LNN9/Umx4ZGYnY2NhKX1NUVISiopvn5HNycqq1RiKiukaSJFhbSvXqQpG6MKcLQ7eGNK0WN8KZFmVayPNKdMGuTIuSG//qwlqp9mbYujWw6cKeEOUtZQICWgFAlP+rFQIC5f/ixnPddK22/OeyGz/LIVLc/Fm3vLjldbpQqb2xXQHdv5C3IXDzNbe+Vve8THtzmRvl6v98Y5248fzW/apbpkx+D7cE3Rsh17oOjYKs1cEnPT0dZWVl8PDw0Jvu4eGB1NTUSl8THR2NWbNm1UR5RERUS+iFOaW5q6HarE5EtNtHHAgh7jgKYdq0acjOzpYfSUlJNVEiERER1QG1usXH1dUVFhYWFVp30tLSKrQC6SiVSiiVjPtERERUUa1u8bG2tkbnzp0RExOjNz0mJgahoaFmqoqIiIjqqlrd4gMAr7zyCp566il06dIFISEhWLp0KS5evIgJEyaYuzQiIiKqY2p98Bk5ciQyMjIwe/ZspKSkIDAwEL/88gt8fX3NXRoRERHVMbX+Oj73i9fxISIiqnuq6/u7VvfxISIiIjIlBh8iIiJqMBh8iIiIqMFg8CEiIqIGg8GHiIiIGgwGHyIiImowGHyIiIiowWDwISIiogaj1l+5+X7prs+Yk5Nj5kqIiIjIULrvbVNfZ7neB5/c3FwAgLe3t5krISIiImPl5uZCrVabbH31/pYVWq0Wly9fhkqlgiRJJl13Tk4OvL29kZSUxNthGID7yzjcX8bjPjMO95fxuM+Mcz/7SwiB3NxceHp6QqEwXc+cet/io1Ao4OXlVa3bcHR05AfACNxfxuH+Mh73mXG4v4zHfWacqu4vU7b06LBzMxERETUYDD5ERETUYDD43AelUol3330XSqXS3KXUCdxfxuH+Mh73mXG4v4zHfWac2ri/6n3nZiIiIiIdtvgQERFRg8HgQ0RERA0Ggw8RERE1GAw+RERE1GAw+FTR4sWL4e/vDxsbG3Tu3Bl79+41d0kmN3PmTEiSpPfQaDTyfCEEZs6cCU9PT9ja2iI8PBwnTpzQW0dRURFeeukluLq6wt7eHg8//DCSk5P1lsnMzMRTTz0FtVoNtVqNp556CllZWXrLXLx4EYMHD4a9vT1cXV0xadIkFBcXV9t7N8Tvv/+OwYMHw9PTE5IkYdOmTXrza9v+OXbsGMLCwmBra4smTZpg9uzZJr8Hzr3ca5+NHTu2wjHXvXt3vWUa0j6Ljo5G165doVKp4O7ujqFDh+L06dN6y/A4u8mQ/cVjTN8XX3yBoKAg+QKDISEh2Lp1qzy/Xh5fgoy2bt06YWVlJb766itx8uRJMXnyZGFvby8SExPNXZpJvfvuu6Jt27YiJSVFfqSlpcnzP/zwQ6FSqcT69evFsWPHxMiRI0Xjxo1FTk6OvMyECRNEkyZNRExMjDh06JDo3bu3aN++vSgtLZWX6devnwgMDBSxsbEiNjZWBAYGikGDBsnzS0tLRWBgoOjdu7c4dOiQiImJEZ6enmLixIk1syPu4JdffhHTp08X69evFwDExo0b9ebXpv2TnZ0tPDw8xOOPPy6OHTsm1q9fL1Qqlfj444+rbwdV4l77bMyYMaJfv356x1xGRobeMg1pn/Xt21esWLFCHD9+XBw5ckQMHDhQ+Pj4iLy8PHkZHmc3GbK/eIzp27x5s/j555/F6dOnxenTp8Vbb70lrKysxPHjx4UQ9fP4YvCpgm7duokJEyboTWvVqpV48803zVRR9Xj33XdF+/btK52n1WqFRqMRH374oTytsLBQqNVqsWTJEiGEEFlZWcLKykqsW7dOXubSpUtCoVCIX3/9VQghxMmTJwUAsX//fnmZffv2CQDi33//FUKUf1kqFApx6dIleZm1a9cKpVIpsrOzTfZ+78ftX+K1bf8sXrxYqNVqUVhYKC8THR0tPD09hVarNeGeMNydgs+QIUPu+JqGvs/S0tIEALFnzx4hBI+ze7l9fwnBY8wQTk5O4uuvv663xxdPdRmpuLgYBw8eRGRkpN70yMhIxMbGmqmq6nP27Fl4enrC398fjz/+OC5cuAAAiI+PR2pqqt5+UCqVCAsLk/fDwYMHUVJSoreMp6cnAgMD5WX27dsHtVqN4OBgeZnu3btDrVbrLRMYGAhPT095mb59+6KoqAgHDx6svjd/H2rb/tm3bx/CwsL0LiLWt29fXL58GQkJCabfAfdh9+7dcHd3R4sWLfD8888jLS1NntfQ91l2djYAwNnZGQCPs3u5fX/p8BirXFlZGdatW4fr168jJCSk3h5fDD5GSk9PR1lZGTw8PPSme3h4IDU11UxVVY/g4GCsXr0a27Ztw1dffYXU1FSEhoYiIyNDfq932w+pqamwtraGk5PTXZdxd3evsG13d3e9ZW7fjpOTE6ytrWvtPq9t+6eyZXTPa9M+7N+/P7777jvs3LkTn3zyCeLi4vDggw+iqKgIQMPeZ0IIvPLKK+jZsycCAwP16uBxVlFl+wvgMVaZY8eOwcHBAUqlEhMmTMDGjRvRpk2bent81fu7s1cXSZL0ngshKkyr6/r37y//3K5dO4SEhKBZs2ZYtWqV3BmwKvvh9mUqW74qy9RGtWn/VFbLnV5rLiNHjpR/DgwMRJcuXeDr64uff/4Zw4YNu+PrGsI+mzhxIo4ePYo//vijwjweZxXdaX/xGKuoZcuWOHLkCLKysrB+/XqMGTMGe/bsuWuNdfn4YouPkVxdXWFhYVEhXaalpVVIovWNvb092rVrh7Nnz8qju+62HzQaDYqLi5GZmXnXZa5cuVJhW1evXtVb5vbtZGZmoqSkpNbu89q2fypbRte8X1v3IQA0btwYvr6+OHv2LICGu89eeuklbN68Gbt27YKXl5c8ncdZ5e60vyrDYwywtrZG8+bN0aVLF0RHR6N9+/b47LPP6u3xxeBjJGtra3Tu3BkxMTF602NiYhAaGmqmqmpGUVERTp06hcaNG8Pf3x8ajUZvPxQXF2PPnj3yfujcuTOsrKz0lklJScHx48flZUJCQpCdnY0DBw7Iy/z111/Izs7WW+b48eNISUmRl9m+fTuUSiU6d+5cre+5qmrb/gkJCcHvv/+uNzR0+/bt8PT0hJ+fn+l3gIlkZGQgKSkJjRs3BtDw9pkQAhMnTsSGDRuwc+dO+Pv7683ncabvXvurMg39GKuMEAJFRUX19/gyuBs0yXTD2ZctWyZOnjwppkyZIuzt7UVCQoK5SzOpV199VezevVtcuHBB7N+/XwwaNEioVCr5fX744YdCrVaLDRs2iGPHjolRo0ZVOszRy8tL7NixQxw6dEg8+OCDlQ5zDAoKEvv27RP79u0T7dq1q3SYY58+fcShQ4fEjh07hJeXl9mHs+fm5orDhw+Lw4cPCwBi/vz54vDhw/JlDWrT/snKyhIeHh5i1KhR4tixY2LDhg3C0dGxxoez322f5ebmildffVXExsaK+Ph4sWvXLhESEiKaNGnSYPfZCy+8INRqtdi9e7fe8Ov8/Hx5GR5nN91rf/EYq2jatGni999/F/Hx8eLo0aPirbfeEgqFQmzfvl0IUT+PLwafKlq0aJHw9fUV1tbWolOnTnrDJesL3fUarKyshKenpxg2bJg4ceKEPF+r1Yp3331XaDQaoVQqRa9evcSxY8f01lFQUCAmTpwonJ2dha2trRg0aJC4ePGi3jIZGRniiSeeECqVSqhUKvHEE0+IzMxMvWUSExPFwIEDha2trXB2dhYTJ07UG9JoDrt27RIAKjzGjBkjhKh9++fo0aPigQceEEqlUmg0GjFz5swaHzJ7t32Wn58vIiMjhZubm7CyshI+Pj5izJgxFfZHQ9pnle0rAGLFihXyMjzObrrX/uIxVtG4cePk7zI3NzfRp08fOfQIUT+PL0mIGr50KxEREZGZsI8PERERNRgMPkRERNRgMPgQERFRg8HgQ0RERA0Ggw8RERE1GAw+RERE1GAw+BAREVGDweBD9Vp4eDimTJli8PIJCQmQJAlHjhyptpqo7jDV8VCbjitJkrBp06Ya3aap3r+fnx8WLFhw12XM8f6obmHwoTpl7NixkCQJEyZMqDAvKioKkiRh7Nix8rQNGzbgvffeM3j93t7eSElJQWBgoCnKrTG7du3CgAED4OLiAjs7O7Rp0wavvvoqLl26VKN1rFy5Eo0aNbrv9ei+KHUPJycn9OrVS++O0bXV2LFjMXToUL1pNXFc+fn56e2z2x/h4eHVtm2iuoTBh+ocb29vrFu3DgUFBfK0wsJCrF27Fj4+PnrLOjs7Q6VSGbxuCwsLaDQaWFpamqze6vbll1/ioYcegkajwfr163Hy5EksWbIE2dnZ+OSTT8xd3n3ZsWMHUlJSsGfPHjg6OmLAgAGIj483d1lGq4njKi4uDikpKUhJScH69esBAKdPn5anbdiwoUrrFUKgtLTUlKUSmRWDD9U5nTp1go+Pj94f8g0bNsDb2xsdO3bUW/b2U11+fn6YM2cOxo0bB5VKBR8fHyxdulSef3uT/O7duyFJErZt24aOHTvC1tYWDz74INLS0rB161a0bt0ajo6OGDVqFPLz8/W2c3uTfIcOHTBz5kz5uSRJ+PLLLzFo0CDY2dmhdevW2LdvH86dO4fw8HDY29sjJCQE58+fv+O+SE5OxqRJkzBp0iQsX74c4eHh8PPzQ69evfD1119jxowZ8rLr169H27ZtoVQq4efnVyEUVXaKoFGjRli5cqXevtmwYQN69+4NOzs7tG/fHvv27ZP31TPPPIPs7Gy5lWHmzJmYPXs22rVrV6H2zp0769VXGRcXF2g0GgQFBeHLL79Efn4+tm/fDgDYs2cPunXrBqVSicaNG+PNN9/U+4IODw/HxIkTMXHiRDRq1AguLi54++23cetdeu71nm9XVlaGZ599Fv7+/rC1tUXLli3x2WefyfNnzpyJVatW4X//+5+8D3bv3l3pqR5D6p80aRKmTp0KZ2dnaDQavePndm5ubtBoNNBoNHB2dgYAuLu7V5gGAOnp6XjkkUdgZ2eHgIAAbN68WZ536zHfpUsXKJVK7N27F0IIzJs3D02bNoWtrS3at2+PH3/8UX5dZmYmnnjiCbi5ucHW1hYBAQFYsWKFXo0XLlyo9NjRudcxeruzZ8+iV69esLGxQZs2bfTuEE50R0bd2YvIzMaMGSOGDBki5s+fL/r06SNP79Onj/j000/FkCFD5JuECiFEWFiYmDx5svzc19dXODs7i0WLFomzZ8+K6OhooVAoxKlTp4QQQsTHxwsA4vDhw0KImzfV7N69u/jjjz/EoUOHRPPmzUVYWJiIjIwUhw4dEr///rtwcXERH374od52Pv30U73a27dvL9599135OQDRpEkT8f3334vTp0+LoUOHCj8/P/Hggw+KX3/9VZw8eVJ0795d9OvX7477Y/78+QKAuHz58l33299//y0UCoWYPXu2OH36tFixYoWwtbXVu9klALFx40a916nVankZ3b5p1aqV2LJlizh9+rQYPny48PX1FSUlJaKoqEgsWLBAODo6ynfFzs3NFUlJSUKhUIgDBw7I6/3nn3+EJEni/PnzldZ7++9BCCGuXbsmAIjPP/9cJCcnCzs7OxEVFSVOnTolNm7cKFxdXfX2b1hYmHBwcBCTJ08W//77r/j222+FnZ2dWLp0qdHvWVdHcXGxmDFjhjhw4IC4cOGCvM7vv/9eCFF+9+8RI0aIfv36yfugqKiownoMrd/R0VHMnDlTnDlzRqxatUpIkqR3A8k70R23t98EUveevby8xJo1a8TZs2fFpEmThIODg8jIyNB7bVBQkNi+fbs4d+6cSE9PF2+99ZZo1aqV+PXXX8X58+fFihUrhFKpFLt37xZCCPHiiy+KDh06iLi4OBEfHy9iYmLE5s2b9fbjnY4dIQw7Rm/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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# graph to check missing in original vs population\n", "threshold = []\n", "missing = []\n", "for i in range(0, 300000, 5000):\n", " threshold.append(i)\n", " missing.append(\n", " (df6[df6[\"Population_y\"] >= i][\"Mortality_Rate\"].isna().sum()) / len(df6) * 100\n", " )\n", "\n", "# plotting this\n", "plt.plot(threshold, missing)\n", "plt.xlabel(\"Minimum County Population Threshold\")\n", "plt.ylabel(\"Missing Values in %\")\n", "plt.title(\"Missing Values vs County Population Threshold\")\n", "\n", "# Saving the plot\n", "plt.savefig(\"../.01_Data/Missing_vs_Population.png\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lower population counties will add a lot of noise to the data, hence it is important to consider this when we plan to use the imputed data for any analysis." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# function to Impute the deaths\n", "def new_death(row):\n", " if pd.isna(row[\"Deaths\"]):\n", " # calculate based on state mortality rate, with upper theshold of 9\n", " return min(int(row[\"Population_y\"] * row[\"State_Mortality_Rate\"]), 9)\n", " else:\n", " # has original data so return the original value\n", " return row[\"Deaths\"]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateState_CodeCountyCounty_CodeYearState_Mortality_RatePopulation_xDeathsMortality_Rate_mergeOriginalPopulation_ymerge2Deaths_2
17930MississippiMSCOPIAH2802920140.00015NaN<NA><NA>left_onlyFalse28976both4
12320KansasKSSTEVENS2018920090.000083NaN<NA><NA>left_onlyFalse5592both0
3895FloridaFLDUVAL1203120080.000135855437.0890.000104bothTrue855437both89
14815LouisianaLAWEST CARROLL2212320080.000145NaN<NA><NA>left_onlyFalse11670both1
28304PennsylvaniaPALEHIGH4207720140.000197357591.0550.000154bothTrue357591both55
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" ], "text/plain": [ " State State_Code County County_Code Year \\\n", "17930 Mississippi MS COPIAH 28029 2014 \n", "12320 Kansas KS STEVENS 20189 2009 \n", "3895 Florida FL DUVAL 12031 2008 \n", "14815 Louisiana LA WEST CARROLL 22123 2008 \n", "28304 Pennsylvania PA LEHIGH 42077 2014 \n", "\n", " State_Mortality_Rate Population_x Deaths Mortality_Rate _merge \\\n", "17930 0.00015 NaN left_only \n", "12320 0.000083 NaN left_only \n", "3895 0.000135 855437.0 89 0.000104 both \n", "14815 0.000145 NaN left_only \n", "28304 0.000197 357591.0 55 0.000154 both \n", "\n", " Original Population_y merge2 Deaths_2 \n", "17930 False 28976 both 4 \n", "12320 False 5592 both 0 \n", "3895 True 855437 both 89 \n", "14815 False 11670 both 1 \n", "28304 True 357591 both 55 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df7 = df6.copy()\n", "df7[\"Deaths_2\"] = df7.apply(new_death, axis=1)\n", "df7.sample(5)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateState_CodeCountyCounty_CodeYearPopulationDeathsOriginalState_Mortality_Rate
2566CaliforniaCASISKIYOU060932005446413False0.000071
6476GeorgiaGAWALKER132952015686048False0.000122
20797MontanaMTTETON30099201560640False0.0001
21154NebraskaNEDEUEL31049201219580False0.000091
24414North CarolinaNCHENDERSON37089201210781112True0.000108
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" ], "text/plain": [ " State State_Code County County_Code Year Population \\\n", "2566 California CA SISKIYOU 06093 2005 44641 \n", "6476 Georgia GA WALKER 13295 2015 68604 \n", "20797 Montana MT TETON 30099 2015 6064 \n", "21154 Nebraska NE DEUEL 31049 2012 1958 \n", "24414 North Carolina NC HENDERSON 37089 2012 107811 \n", "\n", " Deaths Original State_Mortality_Rate \n", "2566 3 False 0.000071 \n", "6476 8 False 0.000122 \n", "20797 0 False 0.0001 \n", "21154 0 False 0.000091 \n", "24414 12 True 0.000108 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creating a new df with only required columns\n", "df8 = df7[\n", " [\n", " \"State\",\n", " \"State_Code\",\n", " \"County\",\n", " \"County_Code\",\n", " \"Year\",\n", " \"Population_y\",\n", " \"Deaths_2\",\n", " \"Original\",\n", " \"State_Mortality_Rate\",\n", " ]\n", "]\n", "\n", "# Renaming columns\n", "df8 = df8.rename(columns={\"Population_y\": \"Population\", \"Deaths_2\": \"Deaths\"})\n", "\n", "df8.sample(5)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateState_CodeCountyCounty_CodeYearPopulationDeathsOriginalState_Mortality_RateCounty_Mortality_Rate
4773GeorgiaGABRYAN130292015348604False0.0001220.000115
9653IndianaINUNION18161200774670False0.0000850.000000
7188IdahoIDOWYHEE160732012114110False0.0000840.000000
13180KentuckyKYJEFFERSON211112011746458129True0.0002580.000173
17294MinnesotaMNNOBLES271052015217542False0.0000990.000092
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" ], "text/plain": [ " State State_Code County County_Code Year Population Deaths \\\n", "4773 Georgia GA BRYAN 13029 2015 34860 4 \n", "9653 Indiana IN UNION 18161 2007 7467 0 \n", "7188 Idaho ID OWYHEE 16073 2012 11411 0 \n", "13180 Kentucky KY JEFFERSON 21111 2011 746458 129 \n", "17294 Minnesota MN NOBLES 27105 2015 21754 2 \n", "\n", " Original State_Mortality_Rate County_Mortality_Rate \n", "4773 False 0.000122 0.000115 \n", "9653 False 0.000085 0.000000 \n", "7188 False 0.000084 0.000000 \n", "13180 True 0.000258 0.000173 \n", "17294 False 0.000099 0.000092 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating Mortality Rate for each county, if population is 0 then mortality rate is 0\n", "df8[\"County_Mortality_Rate\"] = np.where(\n", " df8[\"Population\"] == 0, 0, df8[\"Deaths\"] / df8[\"Population\"]\n", ")\n", "df8.sample(5)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateState_CodeCountyCounty_CodeYearPopulationDeathsOriginalState_Mortality_RateCounty_Mortality_Rate
23379New YorkNYMONROE3605520047410759False0.0000580.000012
33474TexasTXSAN PATRICIO484092003664404False0.0000680.000060
5794GeorgiaGAMACON131932009148421False0.0000870.000067
30023TennesseeTNHANCOCK47067201067960False0.0001470.000000
32951TexasTXMENARD48327201321380False0.000080.000000
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" ], "text/plain": [ " State State_Code County County_Code Year Population \\\n", "23379 New York NY MONROE 36055 2004 741075 \n", "33474 Texas TX SAN PATRICIO 48409 2003 66440 \n", "5794 Georgia GA MACON 13193 2009 14842 \n", "30023 Tennessee TN HANCOCK 47067 2010 6796 \n", "32951 Texas TX MENARD 48327 2013 2138 \n", "\n", " Deaths Original State_Mortality_Rate County_Mortality_Rate \n", "23379 9 False 0.000058 0.000012 \n", "33474 4 False 0.000068 0.000060 \n", "5794 1 False 0.000087 0.000067 \n", "30023 0 False 0.000147 0.000000 \n", "32951 0 False 0.00008 0.000000 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sorting the rows\n", "df9 = df8.sort_values(by=[\"State\", \"County\", \"Year\"]).reset_index(drop=True)\n", "df9.sample(5)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 38673 entries, 0 to 38672\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 State 38673 non-null object \n", " 1 State_Code 38673 non-null object \n", " 2 County 38673 non-null object \n", " 3 County_Code 38673 non-null object \n", " 4 Year 38673 non-null int64 \n", " 5 Population 38673 non-null int64 \n", " 6 Deaths 38673 non-null int64 \n", " 7 Original 38673 non-null bool \n", " 8 State_Mortality_Rate 38673 non-null Float64\n", " 9 County_Mortality_Rate 38673 non-null float64\n", "dtypes: Float64(1), bool(1), float64(1), int64(3), object(4)\n", "memory usage: 2.7+ MB\n" ] } ], "source": [ "df9.info()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearPopulationDeathsState_Mortality_RateCounty_Mortality_Rate
count38673.0000003.867300e+0438673.00000038673.038673.000000
mean2009.0854081.016857e+0510.2794460.0001140.000087
std3.7354773.201735e+0530.7972440.0000540.000079
min2003.0000000.000000e+000.0000000.0000120.000000
25%2006.0000001.229900e+041.0000000.000080.000043
50%2009.0000002.715300e+042.0000000.0001010.000079
75%2012.0000006.946000e+049.0000000.0001340.000119
max2015.0000001.007726e+07705.0000000.0004650.001266
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" ], "text/plain": [ " Year Population Deaths State_Mortality_Rate \\\n", "count 38673.000000 3.867300e+04 38673.000000 38673.0 \n", "mean 2009.085408 1.016857e+05 10.279446 0.000114 \n", "std 3.735477 3.201735e+05 30.797244 0.000054 \n", "min 2003.000000 0.000000e+00 0.000000 0.000012 \n", "25% 2006.000000 1.229900e+04 1.000000 0.00008 \n", "50% 2009.000000 2.715300e+04 2.000000 0.000101 \n", "75% 2012.000000 6.946000e+04 9.000000 0.000134 \n", "max 2015.000000 1.007726e+07 705.000000 0.000465 \n", "\n", " County_Mortality_Rate \n", "count 38673.000000 \n", "mean 0.000087 \n", "std 0.000079 \n", "min 0.000000 \n", "25% 0.000043 \n", "50% 0.000079 \n", "75% 0.000119 \n", "max 0.001266 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df9.describe()" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }