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‎_toc.yml‎

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format: jb-book
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root: index.md
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chapters:
4-
- file: lecture_16_example.ipynb
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- file: lecture_16_example.ipynb
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- file: project 1.ipynb

‎project 1.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "87fd6398",
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"metadata": {},
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"source": [
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"# Project 1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ecf460cf",
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"metadata": {},
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"source": [
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"We are going to analyze the sales of real estates in Manhattan in New York from November 2024 to December. \n",
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"Using a rolling sales data from https://www.nyc.gov/site/finance/property/property-rolling-sales-data.page, we are going to compute the mean, median and mode of the sale price. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "2aac3287",
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"metadata": {},
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"source": [
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"At first, we are going to download a data file. The NYC Department of Finance provides a only excel data, so you should convert it into csv file before you read the file in Python."
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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": 36,
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"id": "94ff853a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.series.Series'>\n",
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"RangeIndex: 18491 entries, 0 to 18490\n",
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"Series name: SALE PRICE\n",
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"Non-Null Count Dtype \n",
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"-------------- ----- \n",
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"18491 non-null object\n",
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"dtypes: object(1)\n",
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"memory usage: 144.6+ KB\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"df = pd.read_csv(\"rollingsales_manhattan.csv\", skiprows= 4)\n",
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"\n",
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"# clean your data.\n",
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"df.columns = df.columns.str.strip()\n",
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"\n",
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"df[\"SALE PRICE\"].info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3d23dbd5",
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"metadata": {},
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"source": [
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"Then, we are going to compute the mean, median and mode of all prices listed. Before moving on, we should remove 0 in the list."
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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": "a8166908",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4255161.156563907\n",
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"1260000.0\n",
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"0 550000\n",
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"Name: SALE PRICE, dtype: int64\n"
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]
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}
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],
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"source": [
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"# clean your data(remove \",\" in the column and change data type)\n",
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"df[\"SALE PRICE\"] = pd.to_numeric(df[\"SALE PRICE\"].str.replace(\",\",\"\"))\n",
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"\n",
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"# remove 0\n",
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"df_above_0 = df[df[\"SALE PRICE\"] > 0]\n",
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"\n",
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"average_sale_price = df_above_0[\"SALE PRICE\"].mean()\n",
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"median_sale_price = df_above_0[\"SALE PRICE\"].median()\n",
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"mode_sale_price = df_above_0[\"SALE PRICE\"].mode()\n",
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"\n",
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"print(average_sale_price)\n",
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"print(median_sale_price)\n",
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"print(mode_sale_price)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac8b5943",
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"metadata": {},
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"source": [
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"Let's do it again in \"hard\" way. You may not use pandas, the statistics module, a spreadsheet program, etc. You should be using the same dataset from the first step, but not accessing the DataFrame/Series.\n",
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"In other words, if put the code for this step in a totally separate notebook, it should still work. You should be calculating the mean, median, and mode yourself, not using functions with those names (or equivalent)."
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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": "a40a4c2f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4255161.156563907\n",
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"1260000\n",
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"550000\n"
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]
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}
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],
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"source": [
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"import csv\n",
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"with open('rollingsales_manhattan.csv', mode='r',encoding=\"utf-8\") as f:\n",
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" reader = csv.reader(f)\n",
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" \n",
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" price_list = []\n",
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" price_counts = {}\n",
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" \n",
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" # skip first 5 rows\n",
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" for _ in range(5):\n",
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" next(reader)\n",
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" \n",
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" # clean each figure in the SALE PRICE column and add it to the list\n",
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" for row in reader:\n",
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" price = int(row[19].replace(\",\",\"\"))\n",
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" if price > 0:\n",
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" price_list.append(price)\n",
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" if price in price_counts:\n",
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" price_counts[price] = price_counts[price] + 1\n",
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" else:\n",
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" price_counts[price] = 1\n",
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" \n",
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" # compute the mean\n",
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" total_sum = 0\n",
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" total_count = 0\n",
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" for p in price_list:\n",
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" total_sum += p\n",
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" total_count += 1\n",
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" mean = total_sum/total_count\n",
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" \n",
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" # compute the median\n",
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" price_list.sort()\n",
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" mid_index = total_count//2\n",
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" if total_count % 2 == 1:\n",
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" median = price_list[mid_index]\n",
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" else:\n",
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" value_1 = price_list[mid_index-1]\n",
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" value_2 = price_list[mid_index]\n",
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" median = (value_1 + value_2) / 2\n",
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" \n",
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" # compute the mode\n",
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" max_count = 0\n",
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" mode_list = []\n",
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" for price in price_counts:\n",
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" if price_counts[price] > max_count:\n",
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" max_count = price_counts[price]\n",
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" mode_list = price\n",
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" elif price_counts[price] == max_count:\n",
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" mode_list.append(price)\n",
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" mode = mode_list\n",
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" print(mean)\n",
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" print(median)\n",
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" print(mode)\n",
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" \n",
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"\n",
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" \n",
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" \n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c1d823c",
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"metadata": {},
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"source": [
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"Next, we are going to visualize the data we got before. Requirements is as follows:\n",
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"The data/calculations can come through pandas, but the drawing code should only use the Python standard library.\n",
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"In other words, don’t use plot(), plotly, or any other external packages.\n",
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"The visualization should be visual, using shape, size, symbols, etc. to represent the values. — Printing the numbers (as is) isn’t sufficient."
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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": 59,
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"id": "e04fb443",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The comparison between average and median\n",
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"average:##################################################################################### 4255161.156563907\n",
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"median:######################### 1260000.0\n"
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]
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}
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],
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"source": [
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"scale = 50000\n",
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"\n",
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"stats_data = {\"average\": average_sale_price, \"median\":median_sale_price}\n",
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"\n",
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"print(\"The comparison between average and median\") \n",
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"for key in stats_data:\n",
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" bar_height = int(stats_data[key]/scale)\n",
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" bar = \"#\" * bar_height\n",
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" print(f\"{key}:\" f\"{bar}\", f\"{stats_data[key]}\")\n",
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"\n"
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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": "venv",
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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": "3.13.9"
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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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