Data Visualization on Ye Joo Park's Blog

Analyzing College Town Landlords at UIUC, BYU, and PSU

Thu, 30 Jan 2025 00:00:00 +0000

✨ Background¶

I first moved into an apartment at UIUC during the summer of 2007. The apartment was Gregory Place under a management company called JSM. Here is a photo of the apartment from 2007. The construction site shows the second building being built.

Back in 2007, JSM was regarded as one of the better management companies, along with Royse Brinkmeyer and Roland. Almost two decades later, JSM is still highly regraded. Prospective tenants lining up from the early morning in front of JSM's office to secure a lease was a sight to see in recent years.

Evaluating LLMs' DataViz Code Generation and Refinement Capabilities

Sun, 29 Sep 2024 00:00:00 +0000

Refining a visual often involves getting rid of unnecessary elements and directing a viewer's attention to specific elements. The process to enhance clearity, readability, and effectiveness is a iterative process that requires constant self-evaluations. This process requires many hours, although the output may look simple.

Can Large Language Models (LLMs) be used as a tool for generating visuals? While LLMs excel at text-based tasks, their ability to understand and generate complex concepts can be leveraged to assist in writing code to create visuals. But can LLMs be used to refine visuals? This post tests LLM's ability to refine Plotly visuals.

COVID-19 Exploratory Data Analysis

Mon, 01 Jun 2020 00:00:00 +0000

This is a notebook from the live coding session by Dr Hugo Bowne-Anderson on April 10, 2020 via DataCamp.

Imports and data¶

Let's import the necessary packages from the SciPy stack and get the data.

In [1]:

# Import packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Set style & figures inline
sns.set()
%matplotlib inline

In [2]:

# Data urls
base_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/'
confirmed_cases_data_url = base_url + 'time_series_covid19_confirmed_global.csv'
death_cases_data_url = base_url + 'time_series_covid19_deaths_global.csv'
recovery_cases_data_url = base_url+ 'time_series_covid19_recovered_global.csv'
# Import datasets as pandas dataframes
raw_data_confirmed = pd.read_csv(confirmed_cases_data_url)
raw_data_deaths = pd.read_csv(death_cases_data_url)
raw_data_recovered = pd.read_csv(recovery_cases_data_url)

Confirmed cases of COVID-19¶

We'll first check out the confirmed cases data by looking at the head of the dataframe: