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Created June 4, 2018 18:57
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Pandas_basics.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#import pandas and os\n",
"import pandas as pd\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory\n",
"os.chdir(\"C:/Users/rohan/Desktop/Virginia Tech/Semester 2/Library/Seminar\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#import data from csv\n",
"A = pd.read_csv('RealEstate.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>MLS</th>\n",
" <th>Location</th>\n",
" <th>Price</th>\n",
" <th>Bedrooms</th>\n",
" <th>Bathrooms</th>\n",
" <th>Size</th>\n",
" <th>Price/SQ.Ft</th>\n",
" <th>Status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>132842</td>\n",
" <td>Arroyo Grande</td>\n",
" <td>795000.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2371</td>\n",
" <td>335.30</td>\n",
" <td>Short Sale</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>134364</td>\n",
" <td>Paso Robles</td>\n",
" <td>399000.0</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2818</td>\n",
" <td>141.59</td>\n",
" <td>Short Sale</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>135141</td>\n",
" <td>Paso Robles</td>\n",
" <td>545000.0</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3032</td>\n",
" <td>179.75</td>\n",
" <td>Short Sale</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>135712</td>\n",
" <td>Morro Bay</td>\n",
" <td>909000.0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3540</td>\n",
" <td>256.78</td>\n",
" <td>Short Sale</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>136282</td>\n",
" <td>Santa Maria-Orcutt</td>\n",
" <td>109900.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1249</td>\n",
" <td>87.99</td>\n",
" <td>Short Sale</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MLS Location Price Bedrooms Bathrooms Size \\\n",
"0 132842 Arroyo Grande 795000.0 3 3 2371 \n",
"1 134364 Paso Robles 399000.0 4 3 2818 \n",
"2 135141 Paso Robles 545000.0 4 3 3032 \n",
"3 135712 Morro Bay 909000.0 4 4 3540 \n",
"4 136282 Santa Maria-Orcutt 109900.0 3 1 1249 \n",
"\n",
" Price/SQ.Ft Status \n",
"0 335.30 Short Sale \n",
"1 141.59 Short Sale \n",
"2 179.75 Short Sale \n",
"3 256.78 Short Sale \n",
"4 87.99 Short Sale "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#View top 5 rows\n",
"A.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>MLS</th>\n",
" <th>Location</th>\n",
" <th>Price</th>\n",
" <th>Bedrooms</th>\n",
" <th>Bathrooms</th>\n",
" <th>Size</th>\n",
" <th>Price/SQ.Ft</th>\n",
" <th>Status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>776</th>\n",
" <td>154562</td>\n",
" <td>Paso Robles</td>\n",
" <td>319900.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1605</td>\n",
" <td>199.31</td>\n",
" <td>Regular</td>\n",
" </tr>\n",
" <tr>\n",
" <th>777</th>\n",
" <td>154565</td>\n",
" <td>Paso Robles</td>\n",
" <td>495000.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1877</td>\n",
" <td>263.72</td>\n",
" <td>Regular</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778</th>\n",
" <td>154566</td>\n",
" <td>San Luis Obispo</td>\n",
" <td>372000.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1104</td>\n",
" <td>336.96</td>\n",
" <td>Foreclosure</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779</th>\n",
" <td>154575</td>\n",
" <td>Arroyo Grande</td>\n",
" <td>589000.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1975</td>\n",
" <td>298.23</td>\n",
" <td>Regular</td>\n",
" </tr>\n",
" <tr>\n",
" <th>780</th>\n",
" <td>154580</td>\n",
" <td>Cambria</td>\n",
" <td>1100000.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2392</td>\n",
" <td>459.87</td>\n",
" <td>Regular</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MLS Location Price Bedrooms Bathrooms Size \\\n",
"776 154562 Paso Robles 319900.0 3 3 1605 \n",
"777 154565 Paso Robles 495000.0 3 2 1877 \n",
"778 154566 San Luis Obispo 372000.0 3 2 1104 \n",
"779 154575 Arroyo Grande 589000.0 3 2 1975 \n",
"780 154580 Cambria 1100000.0 3 3 2392 \n",
"\n",
" Price/SQ.Ft Status \n",
"776 199.31 Regular \n",
"777 263.72 Regular \n",
"778 336.96 Foreclosure \n",
"779 298.23 Regular \n",
"780 459.87 Regular "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#View bottom 5 rows from imported data\n",
"A.tail()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>Price</th>\n",
" <th>Bedrooms</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>350000.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>249000.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>299000.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>235900.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>348000.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price Bedrooms\n",
"10 350000.0 3\n",
"11 249000.0 3\n",
"12 299000.0 2\n",
"13 235900.0 3\n",
"14 348000.0 3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#View a particular set of rows and columns using iloc function\n",
"A.iloc[10:15,2:4]"
]
},
{
"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>MLS</th>\n",
" <th>Price</th>\n",
" <th>Bedrooms</th>\n",
" <th>Bathrooms</th>\n",
" <th>Size</th>\n",
" <th>Price/SQ.Ft</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>781.000000</td>\n",
" <td>7.810000e+02</td>\n",
" <td>781.000000</td>\n",
" <td>781.000000</td>\n",
" <td>781.000000</td>\n",
" <td>781.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>151224.550576</td>\n",
" <td>3.833291e+05</td>\n",
" <td>3.142125</td>\n",
" <td>2.355954</td>\n",
" <td>1755.058899</td>\n",
" <td>213.131293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3936.122042</td>\n",
" <td>3.490381e+05</td>\n",
" <td>0.855768</td>\n",
" <td>0.846596</td>\n",
" <td>819.577603</td>\n",
" <td>115.082146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>132842.000000</td>\n",
" <td>2.650000e+04</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>120.000000</td>\n",
" <td>19.330000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>149922.000000</td>\n",
" <td>1.990000e+05</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1218.000000</td>\n",
" <td>142.140000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>152581.000000</td>\n",
" <td>2.950000e+05</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1550.000000</td>\n",
" <td>188.360000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>154167.000000</td>\n",
" <td>4.290000e+05</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2032.000000</td>\n",
" <td>245.420000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>154580.000000</td>\n",
" <td>5.499000e+06</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>6800.000000</td>\n",
" <td>1144.640000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MLS Price Bedrooms Bathrooms Size \\\n",
"count 781.000000 7.810000e+02 781.000000 781.000000 781.000000 \n",
"mean 151224.550576 3.833291e+05 3.142125 2.355954 1755.058899 \n",
"std 3936.122042 3.490381e+05 0.855768 0.846596 819.577603 \n",
"min 132842.000000 2.650000e+04 0.000000 1.000000 120.000000 \n",
"25% 149922.000000 1.990000e+05 3.000000 2.000000 1218.000000 \n",
"50% 152581.000000 2.950000e+05 3.000000 2.000000 1550.000000 \n",
"75% 154167.000000 4.290000e+05 4.000000 3.000000 2032.000000 \n",
"max 154580.000000 5.499000e+06 10.000000 11.000000 6800.000000 \n",
"\n",
" Price/SQ.Ft \n",
"count 781.000000 \n",
"mean 213.131293 \n",
"std 115.082146 \n",
"min 19.330000 \n",
"25% 142.140000 \n",
"50% 188.360000 \n",
"75% 245.420000 \n",
"max 1144.640000 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Summary of the data\n",
"A.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Sort using a particular column\n",
"A = A.sort_values('Location')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#Sort the data using 2 columns\n",
"A = A.sort_values(['Location','Price'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#Subset data where number of bedrooms = 3\n",
"B = A[A['Bedrooms']==3]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Subset data where Location is Oceano\n",
"C = A[A['Location']=='Oceano']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>Size</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Location</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Arroyo Grande</th>\n",
" <td>2202.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Atascadero</th>\n",
" <td>1913.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bradley</th>\n",
" <td>2640.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambria</th>\n",
" <td>2140.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cayucos</th>\n",
" <td>1634.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Size\n",
"Location \n",
" Arroyo Grande 2202.250000\n",
" Atascadero 1913.600000\n",
" Bradley 2640.000000\n",
" Cambria 2140.916667\n",
" Cayucos 1634.500000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Aggregate the data to understand what is the average House Size in each location\n",
"D = A.groupby(['Location'])[['Size']].mean()\n",
"D.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>MLS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Location</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Arroyo Grande</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Atascadero</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bradley</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambria</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cayucos</th>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MLS\n",
"Location \n",
" Arroyo Grande 12\n",
" Atascadero 10\n",
" Bradley 1\n",
" Cambria 12\n",
" Cayucos 2"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Aggregate the data to understand the number of houses in each location\n",
"E = A.groupby(['Location'])[['MLS']].count()\n",
"E.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
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"\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>Size</th>\n",
" <th>MLS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Location</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Arroyo Grande</th>\n",
" <td>2202.250000</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Atascadero</th>\n",
" <td>1913.600000</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bradley</th>\n",
" <td>2640.000000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambria</th>\n",
" <td>2140.916667</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cayucos</th>\n",
" <td>1634.500000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Size MLS\n",
"Location \n",
" Arroyo Grande 2202.250000 12\n",
" Atascadero 1913.600000 10\n",
" Bradley 2640.000000 1\n",
" Cambria 2140.916667 12\n",
" Cayucos 1634.500000 2"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Merge the above two data sets (B and C) together using inner join based on “Location” column\n",
"F = pd.merge(D,E,on='Location',how='inner')\n",
"F.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#Write the last file (F) into your directory\n",
"F.to_csv('F.csv')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001DBF3724BA8>]], dtype=object)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1dbf1bc3278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Histogram to view distribution of bedrooms\n",
"A.hist('Bedrooms')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1dbf405fe80>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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cA/ceOFtB2jhw7/Es/sHKAa2y3LUun49ZTiHOuKY0XVtep7cvW+Jy1/IyHtUd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\nCP8fqUm9HViOVWwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1dbf405f7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Create a scatter plot for Size v/s Price\n",
"A.plot(kind='scatter',x='Size',y='Price')"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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