remove other examples
Cullen Watson
parent
bf4ed201ee
commit
c2165d2ba8
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@ -1,141 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
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"metadata": {
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"from homeharvest import scrape_property\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "156488ce-0d5f-43c5-87f4-c33e9c427860",
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"metadata": {},
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"outputs": [],
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"source": [
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"pd.set_option('display.max_columns', None) # Show all columns\n",
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"pd.set_option('display.max_rows', None) # Show all rows\n",
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"pd.set_option('display.width', None) # Auto-adjust display width to fit console\n",
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"pd.set_option('display.max_colwidth', 50) # Limit max column width to 50 characters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c8b9744-8606-4e9b-8add-b90371a249a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# check for sale properties\n",
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"scrape_property(\n",
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" location=\"dallas\",\n",
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" listing_type=\"for_sale\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aaf86093",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"# search a specific address\n",
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"scrape_property(\n",
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" location=\"2530 Al Lipscomb Way\",\n",
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" listing_type=\"for_sale\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# check rentals\n",
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"scrape_property(\n",
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" location=\"chicago, illinois\",\n",
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" listing_type=\"for_rent\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "af280cd3",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"# check sold properties\n",
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"properties = scrape_property(\n",
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" location=\"90210\",\n",
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" listing_type=\"sold\",\n",
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" past_days=10\n",
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")\n",
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"display(properties)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "628c1ce2",
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"metadata": {
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"collapsed": false,
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"is_executing": true,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"# display clickable URLs\n",
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"from IPython.display import display, HTML\n",
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"properties['property_url'] = '<a href=\"' + properties['property_url'] + '\" target=\"_blank\">' + properties['property_url'] + '</a>'\n",
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"\n",
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"html = properties.to_html(escape=False)\n",
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"truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
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"display(HTML(truncate_width))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1,20 +0,0 @@
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from homeharvest import scrape_property
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from datetime import datetime
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# Generate filename based on current timestamp
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current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"HomeHarvest_{current_timestamp}.csv"
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properties = scrape_property(
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location="San Diego, CA",
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listing_type="sold", # or (for_sale, for_rent)
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past_days=30, # sold in last 30 days - listed in last x days if (for_sale, for_rent)
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# pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
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# mls_only=True, # only fetch MLS listings
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# proxy="http://user:pass@host:port" # use a proxy to change your IP address
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)
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print(f"Number of properties: {len(properties)}")
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# Export to csv
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properties.to_csv(filename, index=False)
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print(properties.head())
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