Hadrien Picq's repositories
Breathing-Between-the-Lines
Final Project for ParsonsTKO & TechSoup's Summer 2020 Data Strategy Mentorship Program
airQualityComparison_COVID19
A tentative analysis of changes in PM2.5 concentrations due to COVID-19 shelter-at-home guidelines in San Francisco
caravanStudios_webScraping4LocalKnowledge
Scraping Wykop, and pulling data from Google Trends and Twitter Analytics to observe the online discourse about air pollution in Poland
Air-Quality-Mapping
Using leaflet.js to map the difference in AQI between major fires in 2017 and 2018
airQualityInTheBayArea_aComparisonBetweenOctober2017-2018
A comparison of the San Francisco Bay Area's air quality index between the October 2017's Sonoma Fires and October 2018
Medium_codeRepo
Sample notebooks for Medium posts
Plotly-Dashboard_PoolTallies2019_Public
Made with plotly-dash, this dashboard summarizes pool patronage over the course of a year, by temporal trends and aquatic programming activities.
SMCapstone_GColab
SharpestMinds Capstone Project: Data Analysis of Lyft’s Bay Wheels in San Francisco. The ReadMe includes a directory of all Google Colab notebooks found in the Jupyter Book, in addition to listing all of the raw data source used throughout.
airQualityInTheBayArea_October2017
How has Air Quality changed over 2017 in the San Francisco Bay Area, and how was it affected by the October Sonoma Fires? Using Python libaries such as matplotlib.pyplot, seaborn, numpy, json and requests; and the AIR NOW API, we charted distributions over time and space of pollutant particulates across the Bay Area's counties.
caravanStudios_luftdatenDataExploration
Exploring Luftdaten's sensor data for air quality (https://luftdaten.info/)
caravanStudios_presentationApril.12.2019
Summary of Citizen Science's internship projects to the Caravan Studios' staff
caravanStudios_SensorAssemblyTutorial
A project for Caravan Studios' Citizen Science program
caravanStudios_SensorAssemblyTutorial2
Deployment backup to Sensor Assembly Tutorial
Charting-global-weather-with-OpenWeatherMap-API
An analysis of weather globally, randomly sampling 800 cities and charting climatic patterns at latitudinal lines; made using Python libraries including matplotlib.pyplot, plotly, citipy, request, json, numpy and sklearn; and the OpenWeatherMap API.
cheatSheets
Tired of going back-and-forth between my different notebooks; so I'm centralizing all my iterative issues and solutions into one repo
data-science-notes
Open-source project hosted at https://makeuseofdata.com to crowdsource a robust collection of notes related to data science (math, visualization, modeling, etc)
dataStrategyMentorship_airQAproj
An analysis of air pollution under the scope of the Data Strategy Mentorship Program by TechSoup & ParsonsTKO
geojson_test
Calling the geojson to MapBox
HP-Nunes.github.io
Portfolio/Blog website made with jekyll and hosted on Github Pages.
Mapping-Assaults-Vandalism-and-Thefts-in-San-Francisco-for-2018
Using data from datasf.org, we mapped the distribution of the three most prevalent reported crimes in the city of San Francisco over the course of 2018. We used a combination of D3.js, Python Flask, and deployment via Heroku to generate a crime map of the city.
markdown-magic
💫 Automatically format markdown files using comment blocks. Update contents via custom transforms, external data sources & your source code.
pokemonData
Repository containing generations 1 - 8 of basic Pokémon stats, courtesy of pokemondb.net.
SMCapstone_JupyterBook
JupyterBook's assets for the Bay Wheels Data Analytics project
SMCapstone_RawData
Repo for the raw data of my SM Capstone project
spatialpandas
Pandas extension arrays for spatial/geometric operations
Viability-of-Restaurants-in-San-Francisco--An-analysis-with-Machine-Learning
What makes or breaks a restaurant in the city of San Francisco? That's the question we set out to answer (and the answers, well, were inconclusive...). This final project at Berkeley's Extension data analysis program was instructive rather in the pitfalls of data and acknowledging the limits of relying on Machine Learning models to derive conclusions about complex, multi-layered inquiries.
workshop_introduction_to_version_control
Reader for the workshop, Introduction to Version Control