{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare datasets\n", "Compares NASS county totals with values seen in the EWG dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DEQ Data (pulled from online)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Get the DEQ datset (online) and pivot so that animals are all in one column\n", "dfDEQ_raw = pd.read_excel('https://files.nc.gov/ncdeq/List_Permitted_Animal_Facilities2-12-2019.xls',skiprows=2)\n", "dfDEQ = dfDEQ_raw.pivot_table(index='County Name',\n", " columns='Regulated Operation',\n", " values='Allowable Count',\n", " aggfunc = 'sum').fillna(0)\n", "#Combine poultry\n", "dfDEQ['Poultry'] = dfDEQ['Dry Poultry'] + dfDEQ['Wet Poultry']\n", "#Drop unused columns\n", "dfDEQ.drop(['Horses','Other Animals ','Dry Poultry','Wet Poultry'],axis=1,inplace=True)\n", "#Rename columns\n", "dfDEQ.columns = ['DEQ_Cattle','DEQ_Hogs','DEQ_Poultry']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EWG Data (from local scraped copies)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Get the EWG Datasets\n", "dfEWG_Poultry = pd.read_csv('../data/raw/ScrapedEWG/EWG_Poultry.csv',usecols=['BIRD_COUNT','COUNTYNAME'])\n", "dfEWG_Swine = pd.read_csv('../data/raw/ScrapedEWG/EWG_Swine.csv',usecols=['ANIMAL_COUNT','COUNTYNAME'])\n", "dfEWG_Cattle = pd.read_csv('../data/raw/ScrapedEWG/EWG_Cattle.csv',usecols=['ANIMAL_COUNT','COUNTYNAME'])\n", "\n", "#Pivot each dataset on county\n", "dfEWG_P1 = dfEWG_Poultry.groupby('COUNTYNAME').agg({'BIRD_COUNT':'sum'})\n", "dfEWG_S1 = dfEWG_Swine.groupby('COUNTYNAME').agg({'ANIMAL_COUNT':'sum'})\n", "dfEWG_C1 = dfEWG_Cattle.groupby('COUNTYNAME').agg({'ANIMAL_COUNT':'sum'})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EWG_PoultryEWG_HogsEWG_Cattle
COUNTYNAME
Alamance929430.0600.01590.0
Alexander3694536.0NaN650.0
Alleghany14239.0NaN2117.0
Anson4284486.046158.0700.0
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" ], "text/plain": [ " EWG_Poultry EWG_Hogs EWG_Cattle\n", "COUNTYNAME \n", "Alamance 929430.0 600.0 1590.0\n", "Alexander 3694536.0 NaN 650.0\n", "Alleghany 14239.0 NaN 2117.0\n", "Anson 4284486.0 46158.0 700.0\n", "Ashe 28478.0 NaN NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Merge into one\n", "dfEWG = pd.merge(pd.merge(dfEWG_P1,dfEWG_S1,left_index=True,right_index=True,how='outer'),\n", " dfEWG_C1,left_index=True,right_index=True,how='outer')\n", "\n", "dfEWG.columns = ['EWG_Poultry','EWG_Hogs','EWG_Cattle']\n", "dfEWG.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### NASS data (from local processed copies)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Get the NASS Datasets\n", "dfNASS_Poultry = pd.read_csv('../data/processed/CHICKENS_2017.csv',\n", " usecols=['county_name',\n", " 'CHICKENS, BROILERS - INVENTORY',\n", " 'CHICKENS, LAYERS - INVENTORY',\n", " 'CHICKENS, PULLETS, REPLACEMENT - INVENTORY',\n", " 'CHICKENS, ROOSTERS - INVENTORY'],\n", " index_col='county_name')\n", "dfNASS_Hogs = pd.read_csv('../data/processed/HOGS_2017.csv',\n", " usecols=('county_name','HOGS - INVENTORY'),\n", " index_col='county_name')\n", "dfNASS_Cattle = pd.read_csv('../data/processed/CATTLE_2017.csv',\n", " usecols=('county_name','CATTLE, INCL CALVES - INVENTORY'),\n", " index_col='county_name')\n", "\n", "#Sum all poultry columns\n", "dfNASS_Poultry['NASS_Poultry'] = dfNASS_Poultry.sum(axis=1)\n", "dfNASS_Poultry.drop(dfNASS_Poultry.columns[:-1],axis=1,inplace=True)\n", "\n", "#Rename the Hog and Cattle fields\n", "dfNASS_Hogs.columns = ['NASS_Hogs']\n", "dfNASS_Cattle.columns = ['NASS_Cattle']\n", "\n", "#Merge all tables together\n", "dfNASS = pd.merge(pd.merge(dfNASS_Poultry,dfNASS_Hogs,left_index=True,right_index=True,how='outer'),\n", " dfNASS_Cattle,left_index=True,right_index=True,how='outer')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Merge all the datasets together" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#Fix indexes \n", "dfDEQ.set_index(dfDEQ.index.str.upper(),inplace=True)\n", "dfEWG.set_index(dfEWG.index.str.upper(),inplace=True)\n", "\n", "#Merge\n", "dfAll = pd.merge(pd.merge(dfDEQ,dfEWG,left_index=True,right_index=True,how='outer'),\n", " dfNASS,left_index=True,right_index=True,how='outer')\n", "dfAll.to_csv('../data/processed/DataComparison.csv',index_label='COUNTY')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Plot\n", "ax = dfAll.plot(kind='scatter',\n", " x='NASS_Hogs',\n", " y='DEQ_Hogs',\n", " alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dfAll.plot(kind='scatter',\n", " x='NASS_Hogs',\n", " y='EWG_Hogs',\n", " color='red',\n", " alpha=0.5);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }