Pages
- Biography Creation Form
- Clinical Decision Support System (CDSS)
- Collaborations
- Contact Us
- COVID-19
- First Workshop on Data-Centric Infrastructure for Big Data Science (DIBS)
- Forum
- Front Page (Beta)
- GF Update Test
- Kelsey Urgo
- Mission and Vision
- News Appearances (Beta)
- RENCI Blog
- RENCI Internal Dashboard
- RENCI Super Computing 2014 Booth Schedule
- Research
- Clinical Informatics & AI
- Data Science and Analytics
- DataBridge
- DH Press
- Duke Projects
- Earth Data Science Research
- EarthCube
- Evidence-Based Medicine
- Galapagos Science Center
- iRODS
- Knowledge-based Radiation Treatment Planning
- NC State Projects
- NCDS
- Network Research and Infrastructure
- ADAMANT
- ATPER
- Cyber-Physical Systems for Smart Grids
- GIMI
- ImPACT
- PANORAMA
- RADII
- SciDAC SUPER
- SciDAC: Nuclear Physics QCD
- Scientific Data Analysis at Scale (SciDAS)
- XPRESS
- R3SAR
- Blue Waters
- Global Environment for Network Innovations
- CI Evaluation Center
- Lattice Gauge Theory
- Claim Jumping
- MotifNetwork
- MAESTRO
- Open Science Grid
- QDACT
- REACH NC
- Water Science Software Institute
- ACL Injuries
- Bioportal
- Carolina Launch Pad
- Geoanalytics Platform
- NC Coastal Wind Demonstration Project
- Secure Research Space: Security for a world of distributed data
- Software Architecture
- Bot 2.0
- Orthophotography
- Sensor Data Bus
- The Big Board
- VSCSE Summer Courses
- Weather and Emergency Management Decision Support
- Cochlear Implants
- Informatics for Genetic Sequencing
- Innovation and Technology Award
- NARA TPAP
- 4K Video Production
- CCEGA
- DataNet Federation Consortium
- Lightning Detection
- Metadata Management
- The Docuverse
- Flexible Occlusion Rendering
- Meteorological Modeling
- TUCASI
- High-growth Companies
- Mobile Micro Rain Radar
- RENCI People
- Cochlear Implant Studies
- Hospital Monitor
- iBiblio 2D
- Brown Bag Lecture Series
- DataLab
- iBiblio 3D
- Improving Health and Economies
- NC-FIRST
- Distinguished Lecture Series
- Immersive Media
- NCEMA Collaborative Workspace
- SciDAC PERI
- USC Shoah Foundation Institute VHA
- Coastal Hazard and Risk Modeling
- Environmental Sensor Networks
- Injury Prevention
- Melanoma Image Analysis
- EPIC
- Immersive Media
- Manhattan Wind Visualization
- Public Health 2.0
- Gigapixel Imagery
- Melanoma 3D
- High-growth Companies
- NC Floodplain Mapping
- LACSI
- Open Cobalt
- LEAD
- Open Science Grid Jobflow
- MSI Network
- Open Science Grid WRF
- NARA
- Polymer Modeling
- NC-FIRST for Teachers
- ProteinViz
- NC Collaboratory for Bioinformatics
- Quantum Chromodynamics
- NCSA Melanoma Project
- RTP Startups
- OHMS
- Soil Science
- Pablo
- Storm Surge
- Supernovas
- The DESTINY Traveling Science Learning Program
- Precision Agriculture
- UNC Worldwide Grants
- Renaissance Bistro
- UNC Network Vis
- RENCI @ SC
- Voluminous
- RENCI Weather Web
- WRF with Google Earth
- RTP Start-up Studies
- Visualize Particulate Matter
- SCOOP
- Sequence Assembly
- Spectacular Justice
- Summer Institute
- Soundings Radio Show Archive
- TeraGrid Science Gateway
- VGrADS
- Research by Tag
- Room Reservation Request
- SC14 Demo Form
- SC15 RENCI Booth Schedule
- SC16 RENCI Booth Schedule
- SC17 Booth Schedule
- SC18 Booth Schedule
- SC19 Booth Schedule
- SC22 Booth Schedule
- SC23 Booth Schedule
- South Big Data Hub
- Spotlight
- Team Science at RENCI
- RENCI Tutorials
- Resources
- Publications
- News
- Degrees, Certificates, and Short Courses
Tag Archives: python
TriPython January 2020 Meeting: Integrating a database migration framework
This talk will cover key considerations for replacing a bespoke or completely manual process for handling database migrations with a new process built around Alembic or Django migrations. Jeff will also show a few specific tricks he has collected for Alembic to support his current project. The talk will not replace the Alembic or Django […]
Tagged Jeff Trawick, python, tripython
Comments Off on TriPython January 2020 Meeting: Integrating a database migration framework
Dplyr-Style Data Manipulation With Pandas
In this talk, Ian Cook will discuss how to apply the tenets of R’s dplyr package (immutability, chaining, consistency, parsimony) when working with Python’s pandas library. In the R community, dplyr is the most widely used data manipulation package. dplyr provides a small, consistent set set of “verbs” (functions) that you can use to perform most common operations on R data frames.
