A tutorial on DataFrames.jl prepared for JuliaCon2021

Overview

JuliaCon2021 DataFrames.jl Tutorial

This is a tutorial on DataFrames.jl prepared for JuliaCon2021.

A video recording of the tutorial is available here.

In order to run the tutorial make sure that you have Julia executable installed. The tutorial was updated to Julia 1.7.0 and DataFrames.jl 1.3.0.

To check the version presented during JuliaCon 2021 please check out this commit from the repository.

Then the simplest way to run it is to proceed as follows:

  1. Clone the tutorial repository to a local folder on your computer.
  2. Start Julia in your local folder using the julia --project command.
  3. Run the following commands:
using Pkg
Pkg.instantiate()
Pkg.status()

The last command should produce the following output:

  [e28b5b4c] Bootstrap v2.3.3
  [336ed68f] CSV v0.9.11
  [324d7699] CategoricalArrays v0.10.2
  [8be319e6] Chain v0.4.10
  [a93c6f00] DataFrames v1.3.0
  [38e38edf] GLM v1.5.1
  [7073ff75] IJulia v1.23.2
  [91a5bcdd] Plots v1.25.1
  [f3b207a7] StatsPlots v0.14.29
  1. Start Jupyter Notebook with:
using IJulia
notebook(dir=pwd())
  1. In the Jupyter Notebook open the Tutorial.ipynb file and follow the tutorial.

Steps 3 and 4 need to be run only once. They are intended to make sure that you have the required packages properly instantiated.

You can find more tutorials on DataFrames.jl in its documentation and in my blog.

Owner
Bogumił Kamiński
Bogumił Kamiński
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

Adaptive Methods for Aggregated Domain Generalization (AdaClust) Official Pytorch Implementation of Adaptive Methods for Aggregated Domain Generalizat

Xavier Thomas 15 Sep 20, 2022
Transformers are Graph Neural Networks!

🚀 Gated Graph Transformers Gated Graph Transformers for graph-level property prediction, i.e. graph classification and regression. Associated article

Chaitanya Joshi 46 Jun 30, 2022
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

Contrastive Unpaired Translation (CUT) video (1m) | video (10m) | website | paper We provide our PyTorch implementation of unpaired image-to-image tra

1.7k Dec 27, 2022
ROS support for Velodyne 3D LIDARs

Overview Velodyne1 is a collection of ROS2 packages supporting Velodyne high definition 3D LIDARs3. Warning: The master branch normally contains code

ROS device drivers 543 Dec 30, 2022
Pytorch version of VidLanKD: Improving Language Understanding viaVideo-Distilled Knowledge Transfer

VidLanKD Implementation of VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer by Zineng Tang, Jaemin Cho, Hao Tan, Mohi

Zineng Tang 54 Dec 20, 2022
A PyTorch implementation of "CoAtNet: Marrying Convolution and Attention for All Data Sizes".

CoAtNet Overview This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021

Justin Wu 268 Jan 07, 2023
Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

ShICA Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging Install Move into the ShICA directory cd ShICA

8 Nov 07, 2022
Image based Human Fall Detection

Here I integrated the YOLOv5 object detection algorithm with my own created dataset which consists of human activity images to achieve low cost, high accuracy, and real-time computing requirements

UTTEJ KUMAR 12 Dec 11, 2022
Torchyolo - Yolov3 ve Yolov4 modellerin Pytorch uygulamasıdır

TORCHYOLO : Yolo Modellerin Pytorch Uygulaması Yapılacaklar: Yolov3 model.py ve

Kadir Nar 3 Aug 22, 2022
Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation Efficient Self-Ensemble Framework for Semantic Segmentation by Walid Bousselham

61 Dec 26, 2022
PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Xingdong Zuo 215 Dec 07, 2022
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Peter Lin 6.5k Jan 04, 2023
Fast and robust clustering of point clouds generated with a Velodyne sensor.

Depth Clustering This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velo

Photogrammetry & Robotics Bonn 957 Dec 21, 2022
MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet an

QIMP team 30 Jan 01, 2023
The implementation of the paper "A Deep Feature Aggregation Network for Accurate Indoor Camera Localization".

A Deep Feature Aggregation Network for Accurate Indoor Camera Localization This is the PyTorch implementation of our paper "A Deep Feature Aggregation

9 Dec 09, 2022
A list of all named GANs!

The GAN Zoo Every week, new GAN papers are coming out and it's hard to keep track of them all, not to mention the incredibly creative ways in which re

Avinash Hindupur 12.9k Jan 08, 2023
Dynamic Environments with Deformable Objects (DEDO)

DEDO - Dynamic Environments with Deformable Objects DEDO is a lightweight and customizable suite of environments with deformable objects. It is aimed

Rika 32 Dec 22, 2022
[ICCV 2021] Official PyTorch implementation for Deep Relational Metric Learning.

Ranking Models in Unlabeled New Environments Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch 1.7.0 + torchivision 0.8.1

Borui Zhang 39 Dec 10, 2022
A framework for annotating 3D meshes using the predictions of a 2D semantic segmentation model.

Semantic Meshes A framework for annotating 3D meshes using the predictions of a 2D semantic segmentation model. Paper If you find this framework usefu

Florian 40 Dec 09, 2022