From 09c49cbca6700646c712e0f2ed6637dbfac43755 Mon Sep 17 00:00:00 2001 From: aeharman Date: Fri, 19 Sep 2025 16:13:40 -0400 Subject: [PATCH] Created some more graphs --- Exploration.ipynb | 10 -- explore.ipynb | 401 +++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 400 insertions(+), 11 deletions(-) delete mode 100644 Exploration.ipynb diff --git a/Exploration.ipynb b/Exploration.ipynb deleted file mode 100644 index 21c2679..0000000 --- a/Exploration.ipynb +++ /dev/null @@ -1,10 +0,0 @@ -{ - "cells": [], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/explore.ipynb b/explore.ipynb index d896530..e1017f5 100644 --- a/explore.ipynb +++ b/explore.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "4b71df88", "metadata": {}, "outputs": [], @@ -164,6 +164,405 @@ "plt.ylabel(\"Frequency\")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11d43cb9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\aharm\\AppData\\Local\\Temp\\ipykernel_15964\\2309007603.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " pending_ratio.loc[pending_ratio == 0] = 0.00001\n" + ] + }, + { + "data": { + "text/plain": [ + "np.float64(1e-05)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pending_ratio = df[\"pending_ratio\"]\n", + "\n", + "pending_ratio.loc[pending_ratio == 0] = 0.00001" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "da7431f6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(np.log10(pending_ratio), bins=100)\n", + "plt.title(\"Log Pending Ratio\")\n", + "plt.xlabel(\"Pending Ratio\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0e821afa", + "metadata": {}, + "source": [ + "Now we examine the second data set of scraped data from Kaggle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bdb4163d", + "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", + "
brokered_bystatuspricebedbathacre_lotstreetcitystatezip_codehouse_sizeprev_sold_date
0103378.0for_sale105000.03.02.00.121962661.0AdjuntasPuerto Rico601.0920.0NaN
152707.0for_sale80000.04.02.00.081902874.0AdjuntasPuerto Rico601.01527.0NaN
2103379.0for_sale67000.02.01.00.151404990.0Juana DiazPuerto Rico795.0748.0NaN
331239.0for_sale145000.04.02.00.101947675.0PoncePuerto Rico731.01800.0NaN
434632.0for_sale65000.06.02.00.05331151.0MayaguezPuerto Rico680.0NaNNaN
.......................................
222637723009.0sold359900.04.02.00.33353094.0RichlandWashington99354.03600.02022-03-25
222637818208.0sold350000.03.02.00.101062149.0RichlandWashington99354.01616.02022-03-25
222637976856.0sold440000.06.03.00.50405677.0RichlandWashington99354.03200.02022-03-24
222638053618.0sold179900.02.01.00.09761379.0RichlandWashington99354.0933.02022-03-24
2226381108243.0sold580000.05.03.00.31307704.0RichlandWashington99354.03615.02022-03-23
\n", + "

2226382 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " brokered_by status price bed bath acre_lot street \\\n", + "0 103378.0 for_sale 105000.0 3.0 2.0 0.12 1962661.0 \n", + "1 52707.0 for_sale 80000.0 4.0 2.0 0.08 1902874.0 \n", + "2 103379.0 for_sale 67000.0 2.0 1.0 0.15 1404990.0 \n", + "3 31239.0 for_sale 145000.0 4.0 2.0 0.10 1947675.0 \n", + "4 34632.0 for_sale 65000.0 6.0 2.0 0.05 331151.0 \n", + "... ... ... ... ... ... ... ... \n", + "2226377 23009.0 sold 359900.0 4.0 2.0 0.33 353094.0 \n", + "2226378 18208.0 sold 350000.0 3.0 2.0 0.10 1062149.0 \n", + "2226379 76856.0 sold 440000.0 6.0 3.0 0.50 405677.0 \n", + "2226380 53618.0 sold 179900.0 2.0 1.0 0.09 761379.0 \n", + "2226381 108243.0 sold 580000.0 5.0 3.0 0.31 307704.0 \n", + "\n", + " city state zip_code house_size prev_sold_date \n", + "0 Adjuntas Puerto Rico 601.0 920.0 NaN \n", + "1 Adjuntas Puerto Rico 601.0 1527.0 NaN \n", + "2 Juana Diaz Puerto Rico 795.0 748.0 NaN \n", + "3 Ponce Puerto Rico 731.0 1800.0 NaN \n", + "4 Mayaguez Puerto Rico 680.0 NaN NaN \n", + "... ... ... ... ... ... \n", + "2226377 Richland Washington 99354.0 3600.0 2022-03-25 \n", + "2226378 Richland Washington 99354.0 1616.0 2022-03-25 \n", + "2226379 Richland Washington 99354.0 3200.0 2022-03-24 \n", + "2226380 Richland Washington 99354.0 933.0 2022-03-24 \n", + "2226381 Richland Washington 99354.0 3615.0 2022-03-23 \n", + "\n", + "[2226382 rows x 12 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data = pd.read_csv(\"data/realtor_scrape.csv\")\n", + "\n", + "house_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "609d52c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 105000.0\n", + "1 80000.0\n", + "2 67000.0\n", + "3 145000.0\n", + "4 65000.0\n", + " ... \n", + "2226377 359900.0\n", + "2226378 350000.0\n", + "2226379 440000.0\n", + "2226380 179900.0\n", + "2226381 580000.0\n", + "Name: price, Length: 2226382, dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "price = house_data[\"price\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2fe68db0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\aharm\\AppData\\Local\\Temp\\ipykernel_12508\\1966681322.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " price.loc[price == 0] = 0.1\n" + ] + } + ], + "source": [ + "price.loc[price == 0] = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fbc2b58c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(np.log(price), bins=100)\n", + "plt.title(\"Log Price\")\n", + "plt.xlabel(\"Price\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()" + ] } ], "metadata": {