Golang application examples in data analysis and visualization
Go is widely used for data analysis and visualization. Examples include: Infrastructure Monitoring: Building monitoring applications using Go with Telegraf and Prometheus. Machine Learning: Build and train models using Go and TensorFlow or PyTorch. Data visualization: Create interactive charts using Plotly and Go-echarts libraries.
Examples of Go applications in data analysis and visualization
Go is a popular and efficient programming language that is Widely used in data analysis and visualization. This article explores some examples of using Go for data analysis and visualization, including infrastructure monitoring, machine learning, and data visualization.
Infrastructure Monitoring
Go is ideal for building applications that monitor infrastructure. Its concurrency and high performance enable it to handle large amounts of monitoring data. For example, you can use tools like Telegraf to collect system metrics, and then use Prometheus to store and visualize the data.
Code Example:
import "github.com/prometheus/client_golang/prometheus"
func main() {
const (
namespace = "my_app"
subsystem = "my_component"
)
guage := prometheus.NewGauge(
prometheus.GaugeOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "my_metric",
Help: "My metric",
},
)
prometheus.MustRegister(guage)
guage.Set(42)
}Machine Learning
Go can also be used to build machine learning models. It supports concurrency, allowing you to speed up the model training process. For example, you can use frameworks like TensorFlow or PyTorch to build and train machine learning models.
Code Example:
import (
"fmt"
"log"
"github.com/tensorflow/tensorflow/tensorflow/go"
)
func main() {
model, err := tensorflow.LoadSavedModel("my_model", []string{"serve"}, nil)
if err != nil {
log.Fatal(err)
}
defer model.Close()
t := tensorflow.MakeTensor([]float32{1, 2, 3, 4})
r, err := model.Predict(
[]tensorflow.Operation{model.Graph.Operation("my_input")},
[]tensorflow.Operation{model.Graph.Operation("my_output")},
map[tensorflow.Output]*tensorflow.Tensor{
model.Graph.Operation("my_input").Output(0): t,
},
)
if err != nil {
log.Fatal(err)
}
fmt.Println(r[0].Value().([]float32))
}Data Visualization
Finally, Go can be used to create interactive data visualizations. You can generate and render various types of charts using libraries like Plotly, Go-echarts, and more.
Code Example:
import (
"fmt"
"github.com/go-echarts/go-echarts/v2/charts"
"github.com/go-echarts/go-echarts/v2/opts"
)
func main() {
line := charts.NewLine()
line.SetGlobalOptions(charts.GlobalOptions{The above is the detailed content of Golang application examples in data analysis and visualization. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undress AI Tool
Undress images for free
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Read CSV files and perform data analysis using pandas
Jan 09, 2024 am 09:26 AM
Pandas is a powerful data analysis tool that can easily read and process various types of data files. Among them, CSV files are one of the most common and commonly used data file formats. This article will introduce how to use Pandas to read CSV files and perform data analysis, and provide specific code examples. 1. Import the necessary libraries First, we need to import the Pandas library and other related libraries that may be needed, as shown below: importpandasaspd 2. Read the CSV file using Pan
How to use php interface and ECharts to generate visual statistical charts
Dec 18, 2023 am 11:39 AM
In today's context where data visualization is becoming more and more important, many developers hope to use various tools to quickly generate various charts and reports so that they can better display data and help decision-makers make quick judgments. In this context, using the Php interface and ECharts library can help many developers quickly generate visual statistical charts. This article will introduce in detail how to use the Php interface and ECharts library to generate visual statistical charts. In the specific implementation, we will use MySQL
Introduction to data analysis methods
Jan 08, 2024 am 10:22 AM
Common data analysis methods: 1. Comparative analysis method; 2. Structural analysis method; 3. Cross analysis method; 4. Trend analysis method; 5. Cause and effect analysis method; 6. Association analysis method; 7. Cluster analysis method; 8 , Principal component analysis method; 9. Scatter analysis method; 10. Matrix analysis method. Detailed introduction: 1. Comparative analysis method: Comparative analysis of two or more data to find the differences and patterns; 2. Structural analysis method: A method of comparative analysis between each part of the whole and the whole. ; 3. Cross analysis method, etc.
11 basic distributions that data scientists use 95% of the time
Dec 15, 2023 am 08:21 AM
Following the last inventory of "11 Basic Charts Data Scientists Use 95% of the Time", today we will bring you 11 basic distributions that data scientists use 95% of the time. Mastering these distributions helps us understand the nature of the data more deeply and make more accurate inferences and predictions during data analysis and decision-making. 1. Normal Distribution Normal Distribution, also known as Gaussian Distribution, is a continuous probability distribution. It has a symmetrical bell-shaped curve with the mean (μ) as the center and the standard deviation (σ) as the width. The normal distribution has important application value in many fields such as statistics, probability theory, and engineering.
Five selections of visualization tools for exploring Kafka
Feb 01, 2024 am 08:03 AM
Five options for Kafka visualization tools ApacheKafka is a distributed stream processing platform capable of processing large amounts of real-time data. It is widely used to build real-time data pipelines, message queues, and event-driven applications. Kafka's visualization tools can help users monitor and manage Kafka clusters and better understand Kafka data flows. The following is an introduction to five popular Kafka visualization tools: ConfluentControlCenterConfluent
What are the recommended data analysis websites?
Mar 13, 2024 pm 05:44 PM
Recommended: 1. Business Data Analysis Forum; 2. National People’s Congress Economic Forum - Econometrics and Statistics Area; 3. China Statistics Forum; 4. Data Mining Learning and Exchange Forum; 5. Data Analysis Forum; 6. Website Data Analysis; 7. Data analysis; 8. Data Mining Research Institute; 9. S-PLUS, R Statistics Forum.
Machine learning and data analysis using Go language
Nov 30, 2023 am 08:44 AM
In today's intelligent society, machine learning and data analysis are indispensable tools that can help people better understand and utilize large amounts of data. In these fields, Go language has also become a programming language that has attracted much attention. Its speed and efficiency make it the choice of many programmers. This article introduces how to use Go language for machine learning and data analysis. 1. The ecosystem of machine learning Go language is not as rich as Python and R. However, as more and more people start to use it, some machine learning libraries and frameworks
How to use ECharts and php interfaces to implement data analysis and prediction of statistical charts
Dec 17, 2023 am 10:26 AM
How to use ECharts and PHP interfaces to implement data analysis and prediction of statistical charts. Data analysis and prediction play an important role in various fields. They can help us understand the trends and patterns of data and provide references for future decisions. ECharts is an open source data visualization library that provides rich and flexible chart components that can dynamically load and process data by using the PHP interface. This article will introduce the implementation method of statistical chart data analysis and prediction based on ECharts and php interface, and provide
