Tags

• SymPy 19
• Matrices 7
• Uncertainty 1
• Stats 2
• scipy 131
• LogPy 3
• stats 1
• Theano 4
• Programming 127
• Functional 2
• Python 124
• SciPy 3
• Blaze 18
• dask 77
• blaze 11
• pangeo 1
• numba 1
• Pangeo 1
• GPU 1
• Pandas 1
• python 2

SymPy

• Using SymPy within Theano
• SymPy and Theano -- Matrix Expressions
• SymPy and Theano -- Scalar Simplification
• SymPy and Theano -- Code Generation
• Maximum a Posteriori Estimation
• Assuming assumptions
• Commutative Unification
• LogPy - Facts and Relations
• Introducing LogPy
• Statistical Simplification
• Characteristic Functions
• Computing the Kalman Filter
• Building Computations
• Computations
• Preliminary BLAS Results
• Branching Strategies
• Strategies
• Unification in SymPy
• Matrix Computations in SymPy

Matrices

• SymPy and Theano -- Matrix Expressions
• Operation Ordering in MatLab
• Computing the Kalman Filter
• Building Computations
• Preliminary BLAS Results
• Unification in SymPy
• Matrix Computations in SymPy

Uncertainty

• Computing the Kalman Filter

Stats

• Statistical Simplification
• Characteristic Functions

scipy

• HTML outputs in Jupyter
• Write Short Blogposts
• The Role of a Maintainer
• GPU Dask Arrays, first steps
• First Impressions of GPUs and PyData
• Support Python 2 with Cython
• Anatomy of an OSS Institutional Visit
• So you want to contribute to open source
• Dask Development Log
• Dask Release 0.19.0
• High level performance of Pandas, Dask, Spark, and Arrow
• Public Institutions and Open Source Software
• Cloud Lock-in and Open Standards
• Building SAGA optimization for Dask arrays
• Dask Development Log
• Pickle isn't slow, it's a protocol
• Dask Development Log, Scipy 2018
• Who uses Dask?
• Dask Development Log
• Dask Scaling Limits
• Dask Release 0.18.0
• Beyond Numpy Arrays in Python
• Dask Release 0.17.2
• Summer Student Projects 2018
• Craft Minimal Bug Reports
• Dask Release 0.17.0
• HDF in the Cloud
• The Case for Numba in Community Code
• Write Dumb Code
• Pangeo: JupyterHub, Dask, and XArray on the Cloud
• Dask Development Log
• Dask Release 0.16.0
• Optimizing Data Structure Access in Python
• Streaming Dataframes
• Notes on Kafka in Python
• Dask Release 0.15.3
• Fast GeoSpatial Analysis in Python
• Dask on HPC - Initial Work
• Dask Release 0.15.2
• Scikit-Image and Dask Performance
• Dask Benchmarks
• Use Apache Parquet
• Programmatic Bokeh Servers
• Dask Release 0.15.0
• Dask Release 0.14.3
• Dask Development Log
• Asynchronous Optimization Algorithms with Dask
• Streaming Python Prototype
• Dask and Pandas and XGBoost
• Dask Release 0.14.1
• Developing Convex Optimization Algorithms in Dask
• Biased Benchmarks
• Dask Release 0.14.0
• Dask Development Log
• Experiment with Dask and TensorFlow
• Two Easy Ways to Use Scikit Learn and Dask
• Dask Development Log
• Custom Parallel Algorithms on a Cluster with Dask
• Dask Development Log
• Distributed NumPy on a Cluster with Dask Arrays
• Distributed Pandas on a Cluster with Dask DataFrames
• Dask Release 0.13.0
• Dask Development Log
• Dask Development Log
• Dask Development Log
• Dask Development Log
• Dask Cluster Deployments
• Dask and Celery
• Where to Write Prose?
• Dask Distributed Release 1.13.0
• Supporting Users in Open Source
• Dask for Institutions
• Ad Hoc Distributed Random Forests
• Fast Message Serialization
• Distributed Dask Arrays
• Pandas on HDFS with Dask Dataframes
• Introducing Dask distributed
• Disk Bandwidth
• Data Bandwidth
• Distributed Prototype
• Efficient Tabular Storage
• A Weekend with Asyncio
• Caching
• Custom Parallel Workflows
• Write Complex Parallel Algorithms
• Distributed Scheduling
• Pandas Categoricals
• State of Dask
• Profiling Data Throughput
• Partition and Shuffle
• Efficiently Store Pandas DataFrames
• Towards Out-of-core DataFrames
• PyData and the GIL
• Ising models and Numba
• Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
• Towards Out-of-core ND-Arrays -- Slicing and Stacking
• Into and Remote Data
• ReIntroducing Into
• Towards Out-of-core ND-Arrays -- Spilling to Disk
• Towards Out-of-core ND-Arrays -- Benchmark MatMul
• Towards Out-of-core ND-Arrays -- Multi-core Scheduling
• Towards Out-of-core ND-Arrays -- Frontend
• Towards Out-of-core ND-Arrays
• Blaze Datasets
• Introducing Blaze - Migrations
• Introducing Blaze - Practice
• Introducing Blaze - Expressions
• Streaming Analytics
• Introducing CyToolz
• Python Data Structures are Fast
• Multiple Dispatch
• Python v. Clojure v. Julia
• Wordcounting and Verbosity
• Introducing PyToolz
• Thread First Pattern
• Using SymPy within Theano
• Packages Considered Slightly Harmful
• SymPy and Theano -- Matrix Expressions
• SymPy and Theano -- Scalar Simplification
• SymPy and Theano -- Code Generation
• LogPy - Facts and Relations
• Characteristic Functions

LogPy

• Commutative Unification
• LogPy - Facts and Relations
• Introducing LogPy

stats

• Maximum a Posteriori Estimation

Theano

• Using SymPy within Theano
• SymPy and Theano -- Matrix Expressions
• SymPy and Theano -- Scalar Simplification
• SymPy and Theano -- Code Generation

Programming

• Non-Technical roles in OSS
• Write Short Blogposts
• Avoid Indirection in Code
• GPU Dask Arrays, first steps
• First Impressions of GPUs and PyData
• Support Python 2 with Cython
• Anatomy of an OSS Institutional Visit
• So you want to contribute to open source
• Dask Development Log
• Dask Release 0.19.0
• High level performance of Pandas, Dask, Spark, and Arrow
• Public Institutions and Open Source Software
• Cloud Lock-in and Open Standards
• Building SAGA optimization for Dask arrays
• Dask Development Log
• Pickle isn't slow, it's a protocol
• Dask Development Log, Scipy 2018
• Who uses Dask?
• Dask Development Log
• Dask Scaling Limits
• Dask Release 0.18.0
• Beyond Numpy Arrays in Python
• Dask Release 0.17.2
• Summer Student Projects 2018
• Craft Minimal Bug Reports
• Dask Release 0.17.0
• Credit Modeling with Dask
• HDF in the Cloud
• The Case for Numba in Community Code
• Write Dumb Code
• Pangeo: JupyterHub, Dask, and XArray on the Cloud
• Dask Development Log
• Dask Release 0.16.0
• Optimizing Data Structure Access in Python
• Streaming Dataframes
• Notes on Kafka in Python
• Dask Release 0.15.3
• Fast GeoSpatial Analysis in Python
• Dask on HPC - Initial Work
• Dask Release 0.15.2
• Scikit-Image and Dask Performance
• Dask Benchmarks
• Use Apache Parquet
• Programmatic Bokeh Servers
• Dask Release 0.15.0
• Dask Release 0.14.3
• Dask Development Log
• Asynchronous Optimization Algorithms with Dask
• Streaming Python Prototype
• Dask and Pandas and XGBoost
• Dask Release 0.14.1
• Developing Convex Optimization Algorithms in Dask
• Biased Benchmarks
• Dask Release 0.14.0
• Dask Development Log
• Experiment with Dask and TensorFlow
• Two Easy Ways to Use Scikit Learn and Dask
• Dask Development Log
• Custom Parallel Algorithms on a Cluster with Dask
• Dask Development Log
• Distributed NumPy on a Cluster with Dask Arrays
• Distributed Pandas on a Cluster with Dask DataFrames
• Dask Release 0.13.0
• Dask Development Log
• Dask Development Log
• Dask Development Log
• Dask Development Log
• Dask Cluster Deployments
• Dask and Celery
• Where to Write Prose?
• Dask Distributed Release 1.13.0
• Supporting Users in Open Source
• Dask for Institutions
• Dask and Scikit-Learn -- Model Parallelism
• Ad Hoc Distributed Random Forests
• Fast Message Serialization
• Distributed Dask Arrays
• Pandas on HDFS with Dask Dataframes
• Introducing Dask distributed
• Write tests
• Disk Bandwidth
• Data Bandwidth
• Dask is one year old
• Distributed Prototype
• Efficient Tabular Storage
• A Weekend with Asyncio
• Caching
• Custom Parallel Workflows
• Write Complex Parallel Algorithms
• Distributed Scheduling
• Pandas Categoricals
• State of Dask
• Profiling Data Throughput
• Partition and Shuffle
• Efficiently Store Pandas DataFrames
• Towards Out-of-core DataFrames
• PyData and the GIL
• Ising models and Numba
• Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
• Towards Out-of-core ND-Arrays -- Slicing and Stacking
• Into and Remote Data
• ReIntroducing Into
• Towards Out-of-core ND-Arrays -- Spilling to Disk
• Towards Out-of-core ND-Arrays -- Benchmark MatMul
• Towards Out-of-core ND-Arrays -- Multi-core Scheduling
• Towards Out-of-core ND-Arrays -- Frontend
• Towards Out-of-core ND-Arrays
• Blaze Datasets
• Introducing Blaze - Migrations
• Introducing Blaze - Practice
• Introducing Blaze - Expressions
• Streaming Analytics
• Introducing CyToolz
• Python Data Structures are Fast
• Multiple Dispatch
• Python v. Clojure v. Julia
• Packages Considered Slightly Harmful
• GroupBy and Package Management

Functional

• Dictionaries v. Objects
• Thread First Pattern

Python

• Write Short Blogposts
• GPU Dask Arrays, first steps
• First Impressions of GPUs and PyData
• Support Python 2 with Cython
• Anatomy of an OSS Institutional Visit
• So you want to contribute to open source
• Dask Development Log
• Dask Release 0.19.0
• High level performance of Pandas, Dask, Spark, and Arrow
• Public Institutions and Open Source Software
• Cloud Lock-in and Open Standards
• Building SAGA optimization for Dask arrays
• Dask Development Log
• Pickle isn't slow, it's a protocol
• Dask Development Log, Scipy 2018
• Who uses Dask?
• Dask Development Log
• Dask Scaling Limits
• Dask Release 0.18.0
• Beyond Numpy Arrays in Python
• Dask Release 0.17.2
• Summer Student Projects 2018
• Craft Minimal Bug Reports
• Dask Release 0.17.0
• Credit Modeling with Dask
• HDF in the Cloud
• The Case for Numba in Community Code
• Write Dumb Code
• Pangeo: JupyterHub, Dask, and XArray on the Cloud
• Dask Development Log
• Dask Release 0.16.0
• Optimizing Data Structure Access in Python
• Streaming Dataframes
• Notes on Kafka in Python
• Dask Release 0.15.3
• Fast GeoSpatial Analysis in Python
• Dask on HPC - Initial Work
• Dask Release 0.15.2
• Scikit-Image and Dask Performance
• Dask Benchmarks
• Use Apache Parquet
• Programmatic Bokeh Servers
• Dask Release 0.15.0
• Dask Release 0.14.3
• Dask Development Log
• Asynchronous Optimization Algorithms with Dask
• Streaming Python Prototype
• Dask and Pandas and XGBoost
• Dask Release 0.14.1
• Developing Convex Optimization Algorithms in Dask
• Biased Benchmarks
• Dask Release 0.14.0
• Dask Development Log
• Experiment with Dask and TensorFlow
• Two Easy Ways to Use Scikit Learn and Dask
• Dask Development Log
• Custom Parallel Algorithms on a Cluster with Dask
• Dask Development Log
• Distributed NumPy on a Cluster with Dask Arrays
• Distributed Pandas on a Cluster with Dask DataFrames
• Dask Release 0.13.0
• Dask Development Log
• Dask Development Log
• Dask Development Log
• Dask Development Log
• Dask Cluster Deployments
• Dask and Celery
• Where to Write Prose?
• Dask Distributed Release 1.13.0
• Supporting Users in Open Source
• Dask for Institutions
• Ad Hoc Distributed Random Forests
• Fast Message Serialization
• Distributed Dask Arrays
• Pandas on HDFS with Dask Dataframes
• Introducing Dask distributed
• Disk Bandwidth
• Data Bandwidth
• Distributed Prototype
• Efficient Tabular Storage
• A Weekend with Asyncio
• Caching
• Custom Parallel Workflows
• Write Complex Parallel Algorithms
• Distributed Scheduling
• Pandas Categoricals
• Profiling Data Throughput
• Partition and Shuffle
• Efficiently Store Pandas DataFrames
• Towards Out-of-core DataFrames
• PyData and the GIL
• Ising models and Numba
• Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
• Towards Out-of-core ND-Arrays -- Slicing and Stacking
• Into and Remote Data
• ReIntroducing Into
• Towards Out-of-core ND-Arrays -- Spilling to Disk
• Towards Out-of-core ND-Arrays -- Benchmark MatMul
• Towards Out-of-core ND-Arrays -- Multi-core Scheduling
• Towards Out-of-core ND-Arrays -- Frontend
• Towards Out-of-core ND-Arrays
• Blaze Datasets
• Introducing Blaze - Migrations
• Introducing Blaze - Practice
• Introducing Blaze - Expressions
• Streaming Analytics
• Introducing CyToolz
• Python Data Structures are Fast
• Multiple Dispatch
• Python v. Clojure v. Julia
• Parallelism and Serialization
• Wordcounting and Verbosity
• Introducing PyToolz
• How I Test Python
• Thread First Pattern

SciPy

• Multiple Dispatch
• Python v. Clojure v. Julia
• Parallelism and Serialization

Blaze

• State of Dask
• Profiling Data Throughput
• Partition and Shuffle
• Towards Out-of-core DataFrames
• Ising models and Numba
• Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
• Towards Out-of-core ND-Arrays -- Slicing and Stacking
• Into and Remote Data
• ReIntroducing Into
• Towards Out-of-core ND-Arrays -- Spilling to Disk
• Towards Out-of-core ND-Arrays -- Benchmark MatMul
• Towards Out-of-core ND-Arrays -- Multi-core Scheduling
• Towards Out-of-core ND-Arrays -- Frontend
• Towards Out-of-core ND-Arrays
• Blaze Datasets
• Introducing Blaze - Migrations
• Introducing Blaze - Practice
• Introducing Blaze - Expressions

dask

• Hockey Stick growth and Github Stars
• I'm Founding a Dask Company
• Dask, Pandas, and GPUs: first steps
• GPU Dask Arrays, first steps
• First Impressions of GPUs and PyData
• Anatomy of an OSS Institutional Visit
• Dask Development Log
• Dask Release 0.19.0
• High level performance of Pandas, Dask, Spark, and Arrow
• Public Institutions and Open Source Software
• Cloud Lock-in and Open Standards
• Building SAGA optimization for Dask arrays
• Dask Development Log
• Pickle isn't slow, it's a protocol
• Dask Development Log, Scipy 2018
• Who uses Dask?
• Dask Development Log
• Dask Scaling Limits
• Dask Release 0.18.0
• Beyond Numpy Arrays in Python
• Dask Release 0.17.2
• Dask Release 0.17.0
• Write Dumb Code
• Pangeo: JupyterHub, Dask, and XArray on the Cloud
• Dask Development Log
• Dask Release 0.16.0
• Optimizing Data Structure Access in Python
• Streaming Dataframes
• Notes on Kafka in Python
• Dask Release 0.15.3
• Fast GeoSpatial Analysis in Python
• Dask on HPC - Initial Work
• Dask Release 0.15.2
• Scikit-Image and Dask Performance
• Dask Benchmarks
• Use Apache Parquet
• Programmatic Bokeh Servers
• Dask Release 0.15.0
• Dask Release 0.14.3
• Dask Release 0.14.1
• Dask Distributed Release 1.13.0
• Dask for Institutions
• Dask and Scikit-Learn -- Model Parallelism
• Ad Hoc Distributed Random Forests
• Fast Message Serialization
• Distributed Dask Arrays
• Pandas on HDFS with Dask Dataframes
• Introducing Dask distributed
• Disk Bandwidth
• Data Bandwidth
• Dask is one year old
• Distributed Prototype
• Efficient Tabular Storage
• A Weekend with Asyncio
• Caching
• Custom Parallel Workflows
• Write Complex Parallel Algorithms
• Distributed Scheduling
• State of Dask
• Profiling Data Throughput
• Partition and Shuffle
• Towards Out-of-core DataFrames
• Ising models and Numba
• Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
• Towards Out-of-core ND-Arrays -- Slicing and Stacking
• Towards Out-of-core ND-Arrays -- Spilling to Disk
• Towards Out-of-core ND-Arrays -- Benchmark MatMul
• Towards Out-of-core ND-Arrays -- Multi-core Scheduling
• Towards Out-of-core ND-Arrays -- Frontend
• Towards Out-of-core ND-Arrays

blaze

• Disk Bandwidth
• Data Bandwidth
• Dask is one year old
• Distributed Prototype
• Efficient Tabular Storage
• A Weekend with Asyncio
• Caching
• Custom Parallel Workflows
• Write Complex Parallel Algorithms
• Distributed Scheduling

pangeo

• Dask on HPC - Initial Work

numba

• The Case for Numba in Community Code

Pangeo

• HDF in the Cloud

GPU

• Dask, Pandas, and GPUs: first steps

Pandas

• Dask, Pandas, and GPUs: first steps

python

• HTML outputs in Jupyter
• The Role of a Maintainer