{ "cells": [ { "cell_type": "markdown", "id": "respective-store", "metadata": {}, "source": [ "# In Class Notebook, Week 05" ] }, { "cell_type": "markdown", "id": "2a597611", "metadata": {}, "source": [ "You can click on the GitHub URL of this notebook to access the file in near-real time: https://github.com/UIUC-iSchool-DataViz/is445_bcubcg_fall2024/blob/master/week05/inClass_week05.ipynb \n", "\n", "Or you can copy-paste into the nbviewer interface for a plain-text rendering:\n", "\n", "https://kokes.github.io/nbviewer.js/viewer.html" ] }, { "cell_type": "code", "execution_count": 1, "id": "b227d3fe", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "9ee4fc54-7f99-49da-9a01-54f39208a2f1", "metadata": {}, "outputs": [], "source": [ "buildings = pd.read_csv('https://github.com/UIUC-iSchool-DataViz/is445_data/raw/main/building_inventory.csv',\n", " na_values={'Square Footage':0,\n", " 'Year Acquired':0,\n", " 'Year Constructed':0})" ] }, { "cell_type": "code", "execution_count": 5, "id": "14db9f42-0ee0-43ef-9dd6-50b718471711", "metadata": {}, "outputs": [], "source": [ "#buildings.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "459d1c64-13d7-469e-bbaf-734d52e3c26d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | count | \n", "mean | \n", "std | \n", "min | \n", "25% | \n", "50% | \n", "75% | \n", "max | \n", "
---|---|---|---|---|---|---|---|---|
Year Acquired | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
1753.0 | \n", "1.0 | \n", "1200.000000 | \n", "NaN | \n", "1200.0 | \n", "1200.0 | \n", "1200.0 | \n", "1200.00 | \n", "1200.0 | \n", "
1802.0 | \n", "2.0 | \n", "2220.000000 | \n", "1943.129435 | \n", "846.0 | \n", "1533.0 | \n", "2220.0 | \n", "2907.00 | \n", "3594.0 | \n", "
1810.0 | \n", "3.0 | \n", "1344.333333 | \n", "1809.945948 | \n", "216.0 | \n", "300.5 | \n", "385.0 | \n", "1908.50 | \n", "3432.0 | \n", "
1832.0 | \n", "1.0 | \n", "120000.000000 | \n", "NaN | \n", "120000.0 | \n", "120000.0 | \n", "120000.0 | \n", "120000.00 | \n", "120000.0 | \n", "
1837.0 | \n", "1.0 | \n", "10302.000000 | \n", "NaN | \n", "10302.0 | \n", "10302.0 | \n", "10302.0 | \n", "10302.00 | \n", "10302.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
2015.0 | \n", "20.0 | \n", "15254.650000 | \n", "29153.085290 | \n", "144.0 | \n", "696.0 | \n", "3152.0 | \n", "10590.25 | \n", "105000.0 | \n", "
2016.0 | \n", "10.0 | \n", "30483.900000 | \n", "61864.180491 | \n", "1152.0 | \n", "2464.0 | \n", "3352.5 | \n", "3793.00 | \n", "184000.0 | \n", "
2017.0 | \n", "1.0 | \n", "6720.000000 | \n", "NaN | \n", "6720.0 | \n", "6720.0 | \n", "6720.0 | \n", "6720.00 | \n", "6720.0 | \n", "
2018.0 | \n", "4.0 | \n", "4290.000000 | \n", "5153.644342 | \n", "1455.0 | \n", "1455.0 | \n", "1852.5 | \n", "4687.50 | \n", "12000.0 | \n", "
2019.0 | \n", "2.0 | \n", "760.000000 | \n", "0.000000 | \n", "760.0 | \n", "760.0 | \n", "760.0 | \n", "760.00 | \n", "760.0 | \n", "
171 rows × 8 columns
\n", "