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go-tablib | Go Module for Tabular Datasets in CSV, JSON, YAML, etc. | Dataset library

 by   agrison Go Version: v1.0 License: MIT

 by   agrison Go Version: v1.0 License: MIT

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kandi X-RAY | go-tablib Summary

go-tablib is a Go library typically used in Artificial Intelligence, Dataset applications. go-tablib has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
Go Module for Tabular Datasets in CSV, JSON, YAML, etc.
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • go-tablib has a low active ecosystem.
  • It has 140 star(s) with 14 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 3 have been closed. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of go-tablib is v1.0
go-tablib Support
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go-tablib Support
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quality kandi Quality

  • go-tablib has 0 bugs and 0 code smells.
go-tablib Quality
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Average in #Dataset
go-tablib Quality
Best in #Dataset
Average in #Dataset

securitySecurity

  • go-tablib has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • go-tablib code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
go-tablib Security
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Average in #Dataset
go-tablib Security
Best in #Dataset
Average in #Dataset

license License

  • go-tablib is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
go-tablib License
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go-tablib License
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buildReuse

  • go-tablib releases are available to install and integrate.
  • Installation instructions, examples and code snippets are available.
  • It has 1768 lines of code, 149 functions and 14 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
go-tablib Reuse
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go-tablib Reuse
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go-tablib Key Features

Go Module for Tabular Datasets in CSV, JSON, YAML, etc.

Usage

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ds := NewDataset([]string{"firstName", "lastName"})

Filtering

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ds := NewDataset([]string{"Maker", "Model"})
ds.AppendTagged([]interface{}{"Porsche", "911"}, "fast", "luxury")
ds.AppendTagged([]interface{}{"Skoda", "Octavia"}, "family")
ds.AppendTagged([]interface{}{"Ferrari", "458"}, "fast", "luxury")
ds.AppendValues("Citroen", "Picasso")
ds.AppendValues("Bentley", "Continental")
ds.Tag(4, "luxury") // Bentley
ds.AppendValuesTagged("Aston Martin", "DB9", /* these are tags */ "fast", "luxury")

Sorting

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ds := NewDataset([]string{"Maker", "Model", "Year"})
ds.AppendValues("Porsche", "991", 2012)
ds.AppendValues("Skoda", "Octavia", 2011)
ds.AppendValues("Ferrari", "458", 2009)
ds.AppendValues("Citroen", "Picasso II", 2013)
ds.AppendValues("Bentley", "Continental GT", 2003)

sorted, err := ds.Sort("Year").CSV()
fmt.Println(sorted)
// >>
// Maker, Model, Year
// Bentley, Continental GT, 2003
// Ferrari, 458, 2009
// Skoda, Octavia, 2011
// Porsche, 991, 2012
// Citroen, Picasso II, 2013

Constraining

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ds := NewDataset([]string{"Maker", "Model", "Year"})
ds.AppendValues("Porsche", "991", 2012)
ds.AppendValues("Skoda", "Octavia", 2011)
ds.AppendValues("Ferrari", "458", 2009)
ds.AppendValues("Citroen", "Picasso II", 2013)
ds.AppendValues("Bentley", "Continental GT", 2003)

ds.ConstrainColumn("Year", func(val interface{}) bool { return val.(int) > 2008 })
ds.ValidFailFast() // false
if !ds.Valid() { // validate the whole dataset, errors are retrieved in Dataset.ValidationErrors
	ds.ValidationErrors[0] // Row: 4, Column: 2
}

JSON

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ds, _ := LoadJSON([]byte(`[
  {"age":90,"firstName":"John","lastName":"Adams"},
  {"age":67,"firstName":"George","lastName":"Washington"},
  {"age":83,"firstName":"Henry","lastName":"Ford"}
]`))

YAML

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ds, _ := LoadYAML([]byte(`- age: 90
  firstName: John
  lastName: Adams
- age: 67
  firstName: George
  lastName: Washington
- age: 83
  firstName: Henry
  lastName: Ford`))

JSON

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json, _ := ds.JSON()
fmt.Println(json)

XML

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xml, _ := ds.XML()
fmt.Println(xml)

CSV

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csv, _ := ds.CSV()
fmt.Println(csv)

TSV

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tsv, _ := ds.TSV()
fmt.Println(tsv)

YAML

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yaml, _ := ds.YAML()
fmt.Println(yaml)

HTML

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html := ds.HTML()
fmt.Println(html)

XLSX

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xlsx, _ := ds.XLSX()
fmt.Println(xlsx)
// >>>
// binary content
xlsx.WriteTo(...)

ASCII

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ascii := ds.Tabular("grid" /* tablib.TabularGrid */)
fmt.Println(ascii)

Markdown

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mkd := ds.Markdown() // or
mkd := ds.Tabular("markdown" /* tablib.TabularMarkdown */)
fmt.Println(mkd)

MySQL

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sql := ds.MySQL()
fmt.Println(sql)

Postgres

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sql := ds.Postgres()
fmt.Println(sql)

Databooks

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db := NewDatabook()
// or loading a JSON content
db, err := LoadDatabookJSON([]byte(`...`))
// or a YAML content
db, err := LoadDatabookYAML([]byte(`...`))

// a dataset of presidents
presidents, _ := LoadJSON([]byte(`[
  {"Age":90,"First name":"John","Last name":"Adams"},
  {"Age":67,"First name":"George","Last name":"Washington"},
  {"Age":83,"First name":"Henry","Last name":"Ford"}
]`))

// a dataset of cars
cars := NewDataset([]string{"Maker", "Model", "Year"})
cars.AppendValues("Porsche", "991", 2012)
cars.AppendValues("Skoda", "Octavia", 2011)
cars.AppendValues("Ferrari", "458", 2009)
cars.AppendValues("Citroen", "Picasso II", 2013)
cars.AppendValues("Bentley", "Continental GT", 2003)

// add the sheets to the Databook
db.AddSheet("Cars", cars.Sort("Year"))
db.AddSheet("Presidents", presidents.SortReverse("Age"))

fmt.Println(db.JSON())

Installation

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go get github.com/agrison/go-tablib

Community Discussions

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QUESTION

Replacing dataframe value given multiple condition from another dataframe with R

Asked 2022-Apr-14 at 16:16

I have two dataframes one with the dates (converted in months) of multiple survey replicates for a given grid cell and the other one with snow data for each month for the same grid cell, they have a matching ID column to identify the cells. What I would like to do is to replace in the first dataframe, the one with months of survey replicates, the month value with the snow value for that month considering the grid cell ID. Thank you

CellID <- c(1,2,3,4,5,6)
sampl1 <- c("oct", "oct", "oct", "nov", NA, NA)
sampl2 <- c("nov", "nov", "jan", NA, NA, NA)
sampl3 <- c("dec", "dec", "jan", NA, NA, NA)
df1 <- data.frame(CellID, sampl1, sampl2, sampl3)
print(df1)

CellID <- c(1,2,3,4,5,6)
oct <- c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1)
nov <- c(0.4, 0.5, 0.4, 0.5, 0.6, 0.5)
dec <- c(0.6, 0.7, 0.8, 0.7, 0.6, 0.8)
df2 <- data.frame(CellID, oct, nov, dec)
print(df2)

CellID <- c(1,2,3,4,5,6)
sampl1_snow <- c(0.1, 0.1, 0.1, 0.5, NA, NA)
sampl2_snow <- c(0.4, 0.5, 0.9, NA, NA, NA)
sampl3_snow <- c(0.6, 0.7, 1, NA, NA, NA)
df3 <- data.frame(CellID, sampl1_snow, sampl2_snow, sampl3_snow)
print(df3)

ANSWER

Answered 2022-Apr-14 at 14:50
df3 <- df1
df3[!is.na(df1)] <- df2[!is.na(df1)]
#   CellID sampl1 sampl2 sampl3
# 1      1    0.1    0.4    0.6
# 2      2    0.1    0.5    0.7
# 3      3    0.1    0.4    0.8
# 4      4    0.1   <NA>   <NA>
# 5      5   <NA>   <NA>   <NA>
# 6      6   <NA>   <NA>   <NA>

Source https://stackoverflow.com/questions/71873315

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install go-tablib

For those wanting the v1 version where export methods returned a string and not an Exportable:.

Support

It is a work in progress, so it may exist some bugs and edge cases not covered by the test suite. But we're on Github and this is Open Source, pull requests are more than welcomed, come and have some fun :).

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