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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Importing required packages\n",
"import pandas as pd\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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Notes</th>\n",
" <th>State</th>\n",
" <th>State Code</th>\n",
" <th>County</th>\n",
" <th>County Code</th>\n",
" <th>Yearly July 1st Estimates</th>\n",
" <th>Yearly July 1st Estimates Code</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16408</th>\n",
" <td>NaN</td>\n",
" <td>Michigan</td>\n",
" <td>26.0</td>\n",
" <td>Gogebic County, MI</td>\n",
" <td>26053.0</td>\n",
" <td>2005.0</td>\n",
" <td>2005.0</td>\n",
" <td>16811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38823</th>\n",
" <td>NaN</td>\n",
" <td>Washington</td>\n",
" <td>53.0</td>\n",
" <td>Pierce County, WA</td>\n",
" <td>53053.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>785400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40791</th>\n",
" <td>NaN</td>\n",
" <td>Wyoming</td>\n",
" <td>56.0</td>\n",
" <td>Lincoln County, WY</td>\n",
" <td>56023.0</td>\n",
" <td>2013.0</td>\n",
" <td>2013.0</td>\n",
" <td>18342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39130</th>\n",
" <td>NaN</td>\n",
" <td>West Virginia</td>\n",
" <td>54.0</td>\n",
" <td>Grant County, WV</td>\n",
" <td>54023.0</td>\n",
" <td>2003.0</td>\n",
" <td>2003.0</td>\n",
" <td>11406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22278</th>\n",
" <td>NaN</td>\n",
" <td>Nebraska</td>\n",
" <td>31.0</td>\n",
" <td>Lancaster County, NE</td>\n",
" <td>31109.0</td>\n",
" <td>2012.0</td>\n",
" <td>2012.0</td>\n",
" <td>293515</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Notes State State Code County County Code \\\n",
"16408 NaN Michigan 26.0 Gogebic County, MI 26053.0 \n",
"38823 NaN Washington 53.0 Pierce County, WA 53053.0 \n",
"40791 NaN Wyoming 56.0 Lincoln County, WY 56023.0 \n",
"39130 NaN West Virginia 54.0 Grant County, WV 54023.0 \n",
"22278 NaN Nebraska 31.0 Lancaster County, NE 31109.0 \n",
"\n",
" Yearly July 1st Estimates Yearly July 1st Estimates Code Population \n",
"16408 2005.0 2005.0 16811 \n",
"38823 2008.0 2008.0 785400 \n",
"40791 2013.0 2013.0 18342 \n",
"39130 2003.0 2003.0 11406 \n",
"22278 2012.0 2012.0 293515 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load Raw Data File\n",
"df = pd.read_csv(\"../.01_Data/01_Raw/raw_population.txt\", sep=\"\\t\")\n",
"df.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 41037 entries, 0 to 41036\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Notes 100 non-null object \n",
" 1 State 40937 non-null object \n",
" 2 State Code 40937 non-null float64\n",
" 3 County 40937 non-null object \n",
" 4 County Code 40937 non-null float64\n",
" 5 Yearly July 1st Estimates 40937 non-null float64\n",
" 6 Yearly July 1st Estimates Code 40937 non-null float64\n",
" 7 Population 40937 non-null object \n",
"dtypes: float64(4), object(4)\n",
"memory usage: 2.5+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Notes</th>\n",
" <th>State</th>\n",
" <th>State Code</th>\n",
" <th>County</th>\n",
" <th>County Code</th>\n",
" <th>Yearly July 1st Estimates</th>\n",
" <th>Yearly July 1st Estimates Code</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>40937</th>\n",
" <td>---</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40938</th>\n",
" <td>Dataset: Bridged-Race Population Estimates 199...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40939</th>\n",
" <td>Query Parameters:</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40940</th>\n",
" <td>Yearly July 1st Estimates: 2003; 2004; 2005; 2...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40941</th>\n",
" <td>Group By: State; County; Yearly July 1st Estim...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41032</th>\n",
" <td>City are available only for the years prior to...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41033</th>\n",
" <td>1999 and 2000 due to the addition of population.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41034</th>\n",
" <td>20. South Boston City, Virginia (FIPS code 517...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41035</th>\n",
" <td>June 30, 1995. This change was made retroactiv...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41036</th>\n",
" <td>have been reported with Halifax County since y...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Notes State State Code \\\n",
"40937 --- NaN NaN \n",
"40938 Dataset: Bridged-Race Population Estimates 199... NaN NaN \n",
"40939 Query Parameters: NaN NaN \n",
"40940 Yearly July 1st Estimates: 2003; 2004; 2005; 2... NaN NaN \n",
"40941 Group By: State; County; Yearly July 1st Estim... NaN NaN \n",
"... ... ... ... \n",
"41032 City are available only for the years prior to... NaN NaN \n",
"41033 1999 and 2000 due to the addition of population. NaN NaN \n",
"41034 20. South Boston City, Virginia (FIPS code 517... NaN NaN \n",
"41035 June 30, 1995. This change was made retroactiv... NaN NaN \n",
"41036 have been reported with Halifax County since y... NaN NaN \n",
"\n",
" County County Code Yearly July 1st Estimates \\\n",
"40937 NaN NaN NaN \n",
"40938 NaN NaN NaN \n",
"40939 NaN NaN NaN \n",
"40940 NaN NaN NaN \n",
"40941 NaN NaN NaN \n",
"... ... ... ... \n",
"41032 NaN NaN NaN \n",
"41033 NaN NaN NaN \n",
"41034 NaN NaN NaN \n",
"41035 NaN NaN NaN \n",
"41036 NaN NaN NaN \n",
"\n",
" Yearly July 1st Estimates Code Population \n",
"40937 NaN NaN \n",
"40938 NaN NaN \n",
"40939 NaN NaN \n",
"40940 NaN NaN \n",
"40941 NaN NaN \n",
"... ... ... \n",
"41032 NaN NaN \n",
"41033 NaN NaN \n",
"41034 NaN NaN \n",
"41035 NaN NaN \n",
"41036 NaN NaN \n",
"\n",
"[100 rows x 8 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# View the rows which have text values in notes column\n",
"df[df[\"Notes\"].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# droping notes column\n",
"df1 = df.drop(columns=[\"Notes\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# removing the rows with na values generated due to Notes, using state column for reference\n",
"df1 = df1.dropna(subset=[\"State\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# dropping alaska\n",
"df1 = df1[df1[\"State\"] != \"Alaska\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>State Code</th>\n",
" <th>County</th>\n",
" <th>County Code</th>\n",
" <th>Yearly July 1st Estimates</th>\n",
" <th>Yearly July 1st Estimates Code</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26545</th>\n",
" <td>North Dakota</td>\n",
" <td>38.0</td>\n",
" <td>Stutsman County, ND</td>\n",
" <td>38093.0</td>\n",
" <td>2015.0</td>\n",
" <td>2015.0</td>\n",
" <td>21090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23363</th>\n",
" <td>New Jersey</td>\n",
" <td>34.0</td>\n",
" <td>Sussex County, NJ</td>\n",
" <td>34037.0</td>\n",
" <td>2005.0</td>\n",
" <td>2005.0</td>\n",
" <td>150192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35046</th>\n",
" <td>Texas</td>\n",
" <td>48.0</td>\n",
" <td>Mitchell County, TX</td>\n",
" <td>48335.0</td>\n",
" <td>2014.0</td>\n",
" <td>2014.0</td>\n",
" <td>9075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13607</th>\n",
" <td>Kentucky</td>\n",
" <td>21.0</td>\n",
" <td>Harrison County, KY</td>\n",
" <td>21097.0</td>\n",
" <td>2012.0</td>\n",
" <td>2012.0</td>\n",
" <td>18612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12325</th>\n",
" <td>Kansas</td>\n",
" <td>20.0</td>\n",
" <td>Lyon County, KS</td>\n",
" <td>20111.0</td>\n",
" <td>2004.0</td>\n",
" <td>2004.0</td>\n",
" <td>36034</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State State Code County County Code \\\n",
"26545 North Dakota 38.0 Stutsman County, ND 38093.0 \n",
"23363 New Jersey 34.0 Sussex County, NJ 34037.0 \n",
"35046 Texas 48.0 Mitchell County, TX 48335.0 \n",
"13607 Kentucky 21.0 Harrison County, KY 21097.0 \n",
"12325 Kansas 20.0 Lyon County, KS 20111.0 \n",
"\n",
" Yearly July 1st Estimates Yearly July 1st Estimates Code Population \n",
"26545 2015.0 2015.0 21090 \n",
"23363 2005.0 2005.0 150192 \n",
"35046 2014.0 2014.0 9075 \n",
"13607 2012.0 2012.0 18612 \n",
"12325 2004.0 2004.0 36034 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 40495 entries, 0 to 40936\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 State 40495 non-null object \n",
" 1 State Code 40495 non-null float64\n",
" 2 County 40495 non-null object \n",
" 3 County Code 40495 non-null float64\n",
" 4 Yearly July 1st Estimates 40495 non-null float64\n",
" 5 Yearly July 1st Estimates Code 40495 non-null float64\n",
" 6 Population 40495 non-null object \n",
"dtypes: float64(4), object(3)\n",
"memory usage: 2.5+ MB\n"
]
}
],
"source": [
"df1.info()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# validate if yealry estimate and estimate code are same\n",
"df1[\"Yearly July 1st Estimates\"].equals(df1[\"Yearly July 1st Estimates Code\"])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Correcting Data Types\n",
"df2 = df1.copy()\n",
"\n",
"# Saving state code as padded string\n",
"df2[\"State Code\"] = df2[\"State Code\"].astype(int).astype(str).str.zfill(2)\n",
"\n",
"# Saving county code as padded string\n",
"df2[\"County Code\"] = df2[\"County Code\"].astype(int).astype(str).str.zfill(5)\n",
"\n",
"# Converting Year to Integer\n",
"df2[\"Yearly July 1st Estimates\"] = df2[\"Yearly July 1st Estimates\"].astype(int)\n",
"\n",
"# Converting Population to Integer\n",
"# replacing the missing values with 0 for now\n",
"df2[\"Population\"] = df2[\"Population\"].replace(\"Missing\", 0)\n",
"df2[\"Population\"] = df2[\"Population\"].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>State Code</th>\n",
" <th>County</th>\n",
" <th>County Code</th>\n",
" <th>Yearly July 1st Estimates</th>\n",
" <th>Yearly July 1st Estimates Code</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18532</th>\n",
" <td>Mississippi</td>\n",
" <td>28</td>\n",
" <td>George County, MS</td>\n",
" <td>28039</td>\n",
" <td>2010</td>\n",
" <td>2010.0</td>\n",
" <td>22653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20169</th>\n",
" <td>Missouri</td>\n",
" <td>29</td>\n",
" <td>Marion County, MO</td>\n",
" <td>29127</td>\n",
" <td>2009</td>\n",
" <td>2009.0</td>\n",
" <td>28720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17948</th>\n",
" <td>Minnesota</td>\n",
" <td>27</td>\n",
" <td>Ramsey County, MN</td>\n",
" <td>27123</td>\n",
" <td>2011</td>\n",
" <td>2011.0</td>\n",
" <td>515856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37444</th>\n",
" <td>Virginia</td>\n",
" <td>51</td>\n",
" <td>Madison County, VA</td>\n",
" <td>51113</td>\n",
" <td>2007</td>\n",
" <td>2007.0</td>\n",
" <td>13429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24046</th>\n",
" <td>New York</td>\n",
" <td>36</td>\n",
" <td>Franklin County, NY</td>\n",
" <td>36033</td>\n",
" <td>2012</td>\n",
" <td>2012.0</td>\n",
" <td>51791</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State State Code County County Code \\\n",
"18532 Mississippi 28 George County, MS 28039 \n",
"20169 Missouri 29 Marion County, MO 29127 \n",
"17948 Minnesota 27 Ramsey County, MN 27123 \n",
"37444 Virginia 51 Madison County, VA 51113 \n",
"24046 New York 36 Franklin County, NY 36033 \n",
"\n",
" Yearly July 1st Estimates Yearly July 1st Estimates Code Population \n",
"18532 2010 2010.0 22653 \n",
"20169 2009 2009.0 28720 \n",
"17948 2011 2011.0 515856 \n",
"37444 2007 2007.0 13429 \n",
"24046 2012 2012.0 51791 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 40495 entries, 0 to 40936\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 State 40495 non-null object \n",
" 1 State Code 40495 non-null object \n",
" 2 County 40495 non-null object \n",
" 3 County Code 40495 non-null object \n",
" 4 Yearly July 1st Estimates 40495 non-null int64 \n",
" 5 Yearly July 1st Estimates Code 40495 non-null float64\n",
" 6 Population 40495 non-null int64 \n",
"dtypes: float64(1), int64(2), object(4)\n",
"memory usage: 2.5+ MB\n"
]
}
],
"source": [
"df2.info()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df3 = df2.copy()\n",
"\n",
"# rename columns\n",
"df3 = df3.rename(\n",
" columns={\n",
" \"Yearly July 1st Estimates\": \"Year\",\n",
" \"State Code\": \"State_Code\",\n",
" \"County Code\": \"County_Code\",\n",
" }\n",
")\n",
"\n",
"# reorder columns\n",
"df3 = df3[\n",
" [\n",
" \"State\",\n",
" \"State_Code\",\n",
" \"County\",\n",
" \"County_Code\",\n",
" \"Year\",\n",
" \"Population\",\n",
" ]\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>State_Code</th>\n",
" <th>County</th>\n",
" <th>County_Code</th>\n",
" <th>Year</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19741</th>\n",
" <td>Missouri</td>\n",
" <td>29</td>\n",
" <td>Daviess County, MO</td>\n",
" <td>29061</td>\n",
" <td>2010</td>\n",
" <td>8444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27063</th>\n",
" <td>Ohio</td>\n",
" <td>39</td>\n",
" <td>Harrison County, OH</td>\n",
" <td>39067</td>\n",
" <td>2013</td>\n",
" <td>15598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>646</th>\n",
" <td>Alabama</td>\n",
" <td>01</td>\n",
" <td>Monroe County, AL</td>\n",
" <td>01099</td>\n",
" <td>2012</td>\n",
" <td>22582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15374</th>\n",
" <td>Maine</td>\n",
" <td>23</td>\n",
" <td>Androscoggin County, ME</td>\n",
" <td>23001</td>\n",
" <td>2011</td>\n",
" <td>107458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1705</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Craighead County, AR</td>\n",
" <td>05031</td>\n",
" <td>2005</td>\n",
" <td>87512</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State State_Code County County_Code Year \\\n",
"19741 Missouri 29 Daviess County, MO 29061 2010 \n",
"27063 Ohio 39 Harrison County, OH 39067 2013 \n",
"646 Alabama 01 Monroe County, AL 01099 2012 \n",
"15374 Maine 23 Androscoggin County, ME 23001 2011 \n",
"1705 Arkansas 05 Craighead County, AR 05031 2005 \n",
"\n",
" Population \n",
"19741 8444 \n",
"27063 15598 \n",
"646 22582 \n",
"15374 107458 \n",
"1705 87512 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BUYER_COUNTY</th>\n",
" <th>BUYER_STATE</th>\n",
" <th>countyfips</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2151</th>\n",
" <td>GARVIN</td>\n",
" <td>OK</td>\n",
" <td>40049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>LAKE</td>\n",
" <td>CO</td>\n",
" <td>8065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>780</th>\n",
" <td>WASHINGTON</td>\n",
" <td>IN</td>\n",
" <td>18175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>485</th>\n",
" <td>MONROE</td>\n",
" <td>GA</td>\n",
" <td>13207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2114</th>\n",
" <td>STARK</td>\n",
" <td>OH</td>\n",
" <td>39151</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BUYER_COUNTY BUYER_STATE countyfips\n",
"2151 GARVIN OK 40049\n",
"274 LAKE CO 8065\n",
"780 WASHINGTON IN 18175\n",
"485 MONROE GA 13207\n",
"2114 STARK OH 39151"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# maps with fips for proper county names\n",
"fips = pd.read_csv(\"../.01_Data/01_Raw/county_fips.csv\")\n",
"fips.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# padding fips to have consistency\n",
"fips[\"countyfips\"] = fips[\"countyfips\"].astype(str).str.zfill(5)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# performing left join to get the county names\n",
"df4 = pd.merge(\n",
" df3,\n",
" fips,\n",
" how=\"left\",\n",
" left_on=\"County_Code\",\n",
" right_on=\"countyfips\",\n",
" validate=\"m:1\",\n",
" indicator=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_merge\n",
"both 40456\n",
"left_only 39\n",
"right_only 0\n",
"Name: count, dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# validate match for all rows\n",
"df4[\"_merge\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>State_Code</th>\n",
" <th>County</th>\n",
" <th>County_Code</th>\n",
" <th>Year</th>\n",
" <th>Population</th>\n",
" <th>BUYER_COUNTY</th>\n",
" <th>BUYER_STATE</th>\n",
" <th>countyfips</th>\n",
" <th>_merge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1690</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2003</td>\n",
" <td>9239</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1691</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2004</td>\n",
" <td>9334</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1692</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2005</td>\n",
" <td>9358</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1693</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2006</td>\n",
" <td>9437</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1694</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2007</td>\n",
" <td>9478</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1695</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2008</td>\n",
" <td>9573</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1696</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2009</td>\n",
" <td>9490</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1697</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2010</td>\n",
" <td>9515</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1698</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2011</td>\n",
" <td>9404</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1699</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2012</td>\n",
" <td>9344</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1700</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2013</td>\n",
" <td>9254</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1701</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2014</td>\n",
" <td>9163</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1702</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Montgomery County, AR</td>\n",
" <td>05097</td>\n",
" <td>2015</td>\n",
" <td>9029</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6747</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2003</td>\n",
" <td>127</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6748</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2004</td>\n",
" <td>117</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6749</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2005</td>\n",
" <td>114</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6750</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2006</td>\n",
" <td>109</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6751</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2007</td>\n",
" <td>105</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6752</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2008</td>\n",
" <td>99</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6753</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2009</td>\n",
" <td>93</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6754</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2010</td>\n",
" <td>90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6755</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2011</td>\n",
" <td>90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6756</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2012</td>\n",
" <td>89</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6757</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2013</td>\n",
" <td>89</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6758</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2014</td>\n",
" <td>89</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6759</th>\n",
" <td>Hawaii</td>\n",
" <td>15</td>\n",
" <td>Kalawao County, HI</td>\n",
" <td>15005</td>\n",
" <td>2015</td>\n",
" <td>88</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30979</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2003</td>\n",
" <td>12993</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30980</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2004</td>\n",
" <td>12983</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30981</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2005</td>\n",
" <td>13150</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30982</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2006</td>\n",
" <td>13404</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30983</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2007</td>\n",
" <td>13345</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30984</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2008</td>\n",
" <td>13368</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30985</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2009</td>\n",
" <td>13425</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30986</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2010</td>\n",
" <td>13636</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30987</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2011</td>\n",
" <td>13897</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30988</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2012</td>\n",
" <td>14041</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30989</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2013</td>\n",
" <td>14130</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30990</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2014</td>\n",
" <td>14217</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30991</th>\n",
" <td>South Dakota</td>\n",
" <td>46</td>\n",
" <td>Oglala Lakota County, SD</td>\n",
" <td>46102</td>\n",
" <td>2015</td>\n",
" <td>14364</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State State_Code County County_Code Year \\\n",
"1690 Arkansas 05 Montgomery County, AR 05097 2003 \n",
"1691 Arkansas 05 Montgomery County, AR 05097 2004 \n",
"1692 Arkansas 05 Montgomery County, AR 05097 2005 \n",
"1693 Arkansas 05 Montgomery County, AR 05097 2006 \n",
"1694 Arkansas 05 Montgomery County, AR 05097 2007 \n",
"1695 Arkansas 05 Montgomery County, AR 05097 2008 \n",
"1696 Arkansas 05 Montgomery County, AR 05097 2009 \n",
"1697 Arkansas 05 Montgomery County, AR 05097 2010 \n",
"1698 Arkansas 05 Montgomery County, AR 05097 2011 \n",
"1699 Arkansas 05 Montgomery County, AR 05097 2012 \n",
"1700 Arkansas 05 Montgomery County, AR 05097 2013 \n",
"1701 Arkansas 05 Montgomery County, AR 05097 2014 \n",
"1702 Arkansas 05 Montgomery County, AR 05097 2015 \n",
"6747 Hawaii 15 Kalawao County, HI 15005 2003 \n",
"6748 Hawaii 15 Kalawao County, HI 15005 2004 \n",
"6749 Hawaii 15 Kalawao County, HI 15005 2005 \n",
"6750 Hawaii 15 Kalawao County, HI 15005 2006 \n",
"6751 Hawaii 15 Kalawao County, HI 15005 2007 \n",
"6752 Hawaii 15 Kalawao County, HI 15005 2008 \n",
"6753 Hawaii 15 Kalawao County, HI 15005 2009 \n",
"6754 Hawaii 15 Kalawao County, HI 15005 2010 \n",
"6755 Hawaii 15 Kalawao County, HI 15005 2011 \n",
"6756 Hawaii 15 Kalawao County, HI 15005 2012 \n",
"6757 Hawaii 15 Kalawao County, HI 15005 2013 \n",
"6758 Hawaii 15 Kalawao County, HI 15005 2014 \n",
"6759 Hawaii 15 Kalawao County, HI 15005 2015 \n",
"30979 South Dakota 46 Oglala Lakota County, SD 46102 2003 \n",
"30980 South Dakota 46 Oglala Lakota County, SD 46102 2004 \n",
"30981 South Dakota 46 Oglala Lakota County, SD 46102 2005 \n",
"30982 South Dakota 46 Oglala Lakota County, SD 46102 2006 \n",
"30983 South Dakota 46 Oglala Lakota County, SD 46102 2007 \n",
"30984 South Dakota 46 Oglala Lakota County, SD 46102 2008 \n",
"30985 South Dakota 46 Oglala Lakota County, SD 46102 2009 \n",
"30986 South Dakota 46 Oglala Lakota County, SD 46102 2010 \n",
"30987 South Dakota 46 Oglala Lakota County, SD 46102 2011 \n",
"30988 South Dakota 46 Oglala Lakota County, SD 46102 2012 \n",
"30989 South Dakota 46 Oglala Lakota County, SD 46102 2013 \n",
"30990 South Dakota 46 Oglala Lakota County, SD 46102 2014 \n",
"30991 South Dakota 46 Oglala Lakota County, SD 46102 2015 \n",
"\n",
" Population BUYER_COUNTY BUYER_STATE countyfips _merge \n",
"1690 9239 NaN NaN NaN left_only \n",
"1691 9334 NaN NaN NaN left_only \n",
"1692 9358 NaN NaN NaN left_only \n",
"1693 9437 NaN NaN NaN left_only \n",
"1694 9478 NaN NaN NaN left_only \n",
"1695 9573 NaN NaN NaN left_only \n",
"1696 9490 NaN NaN NaN left_only \n",
"1697 9515 NaN NaN NaN left_only \n",
"1698 9404 NaN NaN NaN left_only \n",
"1699 9344 NaN NaN NaN left_only \n",
"1700 9254 NaN NaN NaN left_only \n",
"1701 9163 NaN NaN NaN left_only \n",
"1702 9029 NaN NaN NaN left_only \n",
"6747 127 NaN NaN NaN left_only \n",
"6748 117 NaN NaN NaN left_only \n",
"6749 114 NaN NaN NaN left_only \n",
"6750 109 NaN NaN NaN left_only \n",
"6751 105 NaN NaN NaN left_only \n",
"6752 99 NaN NaN NaN left_only \n",
"6753 93 NaN NaN NaN left_only \n",
"6754 90 NaN NaN NaN left_only \n",
"6755 90 NaN NaN NaN left_only \n",
"6756 89 NaN NaN NaN left_only \n",
"6757 89 NaN NaN NaN left_only \n",
"6758 89 NaN NaN NaN left_only \n",
"6759 88 NaN NaN NaN left_only \n",
"30979 12993 NaN NaN NaN left_only \n",
"30980 12983 NaN NaN NaN left_only \n",
"30981 13150 NaN NaN NaN left_only \n",
"30982 13404 NaN NaN NaN left_only \n",
"30983 13345 NaN NaN NaN left_only \n",
"30984 13368 NaN NaN NaN left_only \n",
"30985 13425 NaN NaN NaN left_only \n",
"30986 13636 NaN NaN NaN left_only \n",
"30987 13897 NaN NaN NaN left_only \n",
"30988 14041 NaN NaN NaN left_only \n",
"30989 14130 NaN NaN NaN left_only \n",
"30990 14217 NaN NaN NaN left_only \n",
"30991 14364 NaN NaN NaN left_only "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check for unmatched rows\n",
"df4[(df4[\"_merge\"] == \"left_only\")]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Montgomery County, AR', 'Kalawao County, HI',\n",
" 'Oglala Lakota County, SD'], dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4[(df4[\"_merge\"] == \"left_only\")][\"County\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Manual Correction\n",
"df4.loc[df4[\"County\"] == \"Montgomery County, AR\", \"BUYER_COUNTY\"] = \"MONTGOMERY\"\n",
"df4.loc[df4[\"County\"] == \"Kalawao County, HI\", \"BUYER_COUNTY\"] = \"KALAWAO\"\n",
"df4.loc[df4[\"County\"] == \"Oglala Lakota County, SD\", \"BUYER_COUNTY\"] = \"OGLALA LAKOTA\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>State_Code</th>\n",
" <th>County</th>\n",
" <th>County_Code</th>\n",
" <th>Year</th>\n",
" <th>Population</th>\n",
" <th>BUYER_COUNTY</th>\n",
" <th>BUYER_STATE</th>\n",
" <th>countyfips</th>\n",
" <th>_merge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27865</th>\n",
" <td>Oklahoma</td>\n",
" <td>40</td>\n",
" <td>Logan County, OK</td>\n",
" <td>40083</td>\n",
" <td>2009</td>\n",
" <td>41116</td>\n",
" <td>LOGAN</td>\n",
" <td>OK</td>\n",
" <td>40083</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38717</th>\n",
" <td>West Virginia</td>\n",
" <td>54</td>\n",
" <td>Hampshire County, WV</td>\n",
" <td>54027</td>\n",
" <td>2006</td>\n",
" <td>23016</td>\n",
" <td>HAMPSHIRE</td>\n",
" <td>WV</td>\n",
" <td>54027</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1637</th>\n",
" <td>Arkansas</td>\n",
" <td>05</td>\n",
" <td>Madison County, AR</td>\n",
" <td>05087</td>\n",
" <td>2015</td>\n",
" <td>15719</td>\n",
" <td>MADISON</td>\n",
" <td>AR</td>\n",
" <td>05087</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28355</th>\n",
" <td>Oregon</td>\n",
" <td>41</td>\n",
" <td>Clackamas County, OR</td>\n",
" <td>41005</td>\n",
" <td>2005</td>\n",
" <td>359308</td>\n",
" <td>CLACKAMAS</td>\n",
" <td>OR</td>\n",
" <td>41005</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34988</th>\n",
" <td>Texas</td>\n",
" <td>48</td>\n",
" <td>Robertson County, TX</td>\n",
" <td>48395</td>\n",
" <td>2008</td>\n",
" <td>16535</td>\n",
" <td>ROBERTSON</td>\n",
" <td>TX</td>\n",
" <td>48395</td>\n",
" <td>both</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State State_Code County County_Code Year \\\n",
"27865 Oklahoma 40 Logan County, OK 40083 2009 \n",
"38717 West Virginia 54 Hampshire County, WV 54027 2006 \n",
"1637 Arkansas 05 Madison County, AR 05087 2015 \n",
"28355 Oregon 41 Clackamas County, OR 41005 2005 \n",
"34988 Texas 48 Robertson County, TX 48395 2008 \n",
"\n",
" Population BUYER_COUNTY BUYER_STATE countyfips _merge \n",
"27865 41116 LOGAN OK 40083 both \n",
"38717 23016 HAMPSHIRE WV 54027 both \n",
"1637 15719 MADISON AR 05087 both \n",
"28355 359308 CLACKAMAS OR 41005 both \n",
"34988 16535 ROBERTSON TX 48395 both "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 40495 entries, 0 to 40494\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 State 40495 non-null object \n",
" 1 State_Code 40495 non-null object \n",
" 2 County 40495 non-null object \n",
" 3 County_Code 40495 non-null object \n",
" 4 Year 40495 non-null int64 \n",
" 5 Population 40495 non-null int64 \n",
" 6 BUYER_COUNTY 40495 non-null object \n",
" 7 BUYER_STATE 40456 non-null object \n",
" 8 countyfips 40456 non-null object \n",
" 9 _merge 40495 non-null category\n",
"dtypes: category(1), int64(2), object(7)\n",
"memory usage: 2.8+ MB\n"
]
}
],
"source": [
"# Final Verification\n",
"df4.info()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>abbrev</th>\n",
" <th>code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Arkansas</td>\n",
" <td>Ark.</td>\n",
" <td>AR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Georgia</td>\n",
" <td>Ga.</td>\n",
" <td>GA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Rhode Island</td>\n",
" <td>R.I.</td>\n",
" <td>RI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Ohio</td>\n",
" <td>Ohio</td>\n",
" <td>OH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Texas</td>\n",
" <td>Tex.</td>\n",
" <td>TX</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state abbrev code\n",
"3 Arkansas Ark. AR\n",
"10 Georgia Ga. GA\n",
"39 Rhode Island R.I. RI\n",
"35 Ohio Ohio OH\n",
"43 Texas Tex. TX"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abbreviations = pd.read_csv(\"../.01_Data/01_Raw/state_abbreviations.csv\")\n",
"abbreviations.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# rename colums to match with the main dataframe\n",
"abbreviations = abbreviations.rename(\n",
" columns={\n",
" \"state\": \"State\",\n",
" \"code\": \"State_Code\",\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>BUYER_COUNTY</th>\n",
" <th>County_Code</th>\n",
" <th>Year</th>\n",
" <th>Population</th>\n",
" <th>State_Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32238</th>\n",
" <td>Tennessee</td>\n",
" <td>STEWART</td>\n",
" <td>47161</td>\n",
" <td>2014</td>\n",
" <td>13211</td>\n",
" <td>TN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2461</th>\n",
" <td>California</td>\n",
" <td>RIVERSIDE</td>\n",
" <td>06065</td>\n",
" <td>2007</td>\n",
" <td>2075183</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36672</th>\n",
" <td>Virginia</td>\n",
" <td>FAUQUIER</td>\n",
" <td>51061</td>\n",
" <td>2015</td>\n",
" <td>68449</td>\n",
" <td>VA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1370</th>\n",
" <td>Arkansas</td>\n",
" <td>FRANKLIN</td>\n",
" <td>05047</td>\n",
" <td>2008</td>\n",
" <td>18229</td>\n",
" <td>AR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2130</th>\n",
" <td>California</td>\n",
" <td>CONTRA COSTA</td>\n",
" <td>06013</td>\n",
" <td>2014</td>\n",
" <td>1108665</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State BUYER_COUNTY County_Code Year Population State_Code\n",
"32238 Tennessee STEWART 47161 2014 13211 TN\n",
"2461 California RIVERSIDE 06065 2007 2075183 CA\n",
"36672 Virginia FAUQUIER 51061 2015 68449 VA\n",
"1370 Arkansas FRANKLIN 05047 2008 18229 AR\n",
"2130 California CONTRA COSTA 06013 2014 1108665 CA"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select required columns\n",
"df5 = pd.merge(\n",
" df4[[\"State\", \"BUYER_COUNTY\", \"County_Code\", \"Year\", \"Population\"]],\n",
" abbreviations[[\"State\", \"State_Code\"]],\n",
" how=\"left\",\n",
" left_on=\"State\",\n",
" right_on=\"State\",\n",
" validate=\"m:1\",\n",
")\n",
"df5.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>State_Code</th>\n",
" <th>County</th>\n",
" <th>County_Code</th>\n",
" <th>Year</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>34793</th>\n",
" <td>Texas</td>\n",
" <td>TX</td>\n",
" <td>PANOLA</td>\n",
" <td>48365</td>\n",
" <td>2008</td>\n",
" <td>23537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37853</th>\n",
" <td>Virginia</td>\n",
" <td>VA</td>\n",
" <td>NORFOLK CITY</td>\n",
" <td>51710</td>\n",
" <td>2013</td>\n",
" <td>245598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2350</th>\n",
" <td>California</td>\n",
" <td>CA</td>\n",
" <td>MERCED</td>\n",
" <td>06047</td>\n",
" <td>2013</td>\n",
" <td>261888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13345</th>\n",
" <td>Kentucky</td>\n",
" <td>KY</td>\n",
" <td>LAUREL</td>\n",
" <td>21125</td>\n",
" <td>2010</td>\n",
" <td>58993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28032</th>\n",
" <td>Oklahoma</td>\n",
" <td>OK</td>\n",
" <td>OKLAHOMA</td>\n",
" <td>40109</td>\n",
" <td>2007</td>\n",
" <td>695706</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State State_Code County County_Code Year Population\n",
"34793 Texas TX PANOLA 48365 2008 23537\n",
"37853 Virginia VA NORFOLK CITY 51710 2013 245598\n",
"2350 California CA MERCED 06047 2013 261888\n",
"13345 Kentucky KY LAUREL 21125 2010 58993\n",
"28032 Oklahoma OK OKLAHOMA 40109 2007 695706"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rename columns\n",
"df5 = df5.rename(\n",
" columns={\n",
" \"BUYER_COUNTY\": \"County\",\n",
" }\n",
")\n",
"\n",
"# reorder columns\n",
"df5 = df5[\n",
" [\n",
" \"State\",\n",
" \"State_Code\",\n",
" \"County\",\n",
" \"County_Code\",\n",
" \"Year\",\n",
" \"Population\",\n",
" ]\n",
"]\n",
"\n",
"df5.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 40495 entries, 0 to 40494\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 State 40495 non-null object\n",
" 1 State_Code 40495 non-null object\n",
" 2 County 40495 non-null object\n",
" 3 County_Code 40495 non-null object\n",
" 4 Year 40495 non-null int64 \n",
" 5 Population 40495 non-null int64 \n",
"dtypes: int64(2), object(4)\n",
"memory usage: 1.9+ MB\n"
]
}
],
"source": [
"df5.info()"
]
}
],
"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"
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},
"nbformat": 4,
"nbformat_minor": 2
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|