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DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

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Name		Name	Last commit message	Last commit date
Latest commit History 628 Commits
.github		.github
assets		assets
exports		exports
forecast		forecast
imports		imports
math		math
pandas		pandas
plot		plot
utils		utils
xseries		xseries
CHANGELOG		CHANGELOG
LICENSE		LICENSE
README.md		README.md
apply.go		apply.go
conversion.go		conversion.go
dataframe.go		dataframe.go
dataframe_fmt.go		dataframe_fmt.go
dataframe_test.go		dataframe_test.go
error_collection.go		error_collection.go
error_collection_test.go		error_collection_test.go
errors.go		errors.go
filter.go		filter.go
go.mod		go.mod
helpers.go		helpers.go
ordered_map.go		ordered_map.go
range.go		range.go
range_test.go		range_test.go
safe.go		safe.go
series.go		series.go
series_defaults.go		series_defaults.go
series_float64.go		series_float64.go
series_generic.go		series_generic.go
series_init.go		series_init.go
series_int64.go		series_int64.go
series_mixed.go		series_mixed.go
series_stats.go		series_stats.go
series_string.go		series_string.go
series_test.go		series_test.go
series_time.go		series_time.go
series_validation.go		series_validation.go
sort.go		sort.go
unsafe.go		unsafe.go

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⭐ the project to show your appreciation.↗️

Dataframes are used for statistics, machine-learning, and data manipulation/exploration. You can think of a Dataframe as an excel spreadsheet.This package is designed to be light-weight and intuitive.

⚠️ The package is production ready but the API is not stable yet. Once Go 1.18 (Generics) is introduced, theENTIRE package will be rewritten. For example, there will only be 1 generic Series type. After that, version1.0.0 will be tagged.

It is recommended your package manager locks to a commit id instead of the master branch directly.⚠️

Features

Importing from CSV, JSONL, Parquet, MySQL & PostgreSQL
Exporting to CSV, JSONL, Excel, Parquet, MySQL & PostgreSQL
Developer Friendly
Flexible - Create custom Series (custom data types)
Performant
Interoperability withgonum package.
pandas sub-package
Fake data generation
Interpolation (ForwardFill, BackwardFill, Linear, Spline, Lagrange)
Time-series Forecasting (SES, Holt-Winters)
Math functions
Plotting (cross-platform)

SeeTutorial here.

Installation

go get -u github.com/rocketlaunchr/dataframe-go

import dataframe"github.com/rocketlaunchr/dataframe-go"

DataFrames

Creating a DataFrame

s1:=dataframe.NewSeriesInt64("day",nil,1,2,3,4,5,6,7,8)s2:=dataframe.NewSeriesFloat64("sales",nil,50.3,23.4,56.2,nil,nil,84.2,72,89)df:=dataframe.NewDataFrame(s1,s2)fmt.Print(df.Table())OUTPUT:+-----+-------+---------+||DAY|SALES|+-----+-------+---------+|0:|1|50.3||1:|2|23.4||2:|3|56.2||3:|4|NaN||4:|5|NaN||5:|6|84.2||6:|7|72||7:|8|89|+-----+-------+---------+|8X2|INT64|FLOAT64|+-----+-------+---------+

Insert and Remove Row

df.Append(nil,9,123.6)df.Append(nil,map[string]interface{}{"day":10,"sales":nil,})df.Remove(0)OUTPUT:+-----+-------+---------+||DAY|SALES|+-----+-------+---------+|0:|2|23.4||1:|3|56.2||2:|4|NaN||3:|5|NaN||4:|6|84.2||5:|7|72||6:|8|89||7:|9|123.6||8:|10|NaN|+-----+-------+---------+|9X2|INT64|FLOAT64|+-----+-------+---------+

Update Row

df.UpdateRow(0,nil,map[string]interface{}{"day":3,"sales":45,})

Sorting

sks:= []dataframe.SortKey{{Key:"sales",Desc:true},{Key:"day",Desc:true},}df.Sort(ctx,sks)OUTPUT:+-----+-------+---------+||DAY|SALES|+-----+-------+---------+|0:|9|123.6||1:|8|89||2:|6|84.2||3:|7|72||4:|3|56.2||5:|2|23.4||6:|10|NaN||7:|5|NaN||8:|4|NaN|+-----+-------+---------+|9X2|INT64|FLOAT64|+-----+-------+---------+

Iterating

You can change the step and starting row. It may be wise to lock the DataFrame before iterating.

The returned value is a map containing the name of the series (string) and the index of the series (int) as keys.

iterator:=df.ValuesIterator(dataframe.ValuesOptions{0,1,true})// Don't apply read lock because we are write locking from outside.df.Lock()for {row,vals,_:=iterator()ifrow==nil {break}fmt.Println(*row,vals)}df.Unlock()OUTPUT:0map[day:10:1sales:50.31:50.3]1map[sales:23.41:23.4day:20:2]2map[day:30:3sales:56.21:56.2]3map[1:<nil>day:40:4sales:<nil>]4map[day:50:5sales:<nil>1:<nil>]5map[sales:84.21:84.2day:60:6]6map[day:70:7sales:721:72]7map[day:80:8sales:891:89]

Statistics

You can easily calculate statistics for a Series using thegonum ormontanaflynn/stats package.

SeriesFloat64 andSeriesTime provide access to the exportedValues field to seamlessly interoperate with external math-based packages.

Example

Some series provide easy conversion using theToSeriesFloat64 method.

import"gonum.org/v1/gonum/stat"s:=dataframe.NewSeriesInt64("random",nil,1,2,3,4,5,6,7,8)sf,_:=s.ToSeriesFloat64(ctx)

Mean

mean:=stat.Mean(sf.Values,nil)

Median

import"github.com/montanaflynn/stats"median,_:=stats.Median(sf.Values)

Standard Deviation

std:=stat.StdDev(sf.Values,nil)

Plotting (cross-platform)

import (chart"github.com/wcharczuk/go-chart""github.com/rocketlaunchr/dataframe-go/plot"wc"github.com/rocketlaunchr/dataframe-go/plot/wcharczuk/go-chart")sales:=dataframe.NewSeriesFloat64("sales",nil,50.3,nil,23.4,56.2,89,32,84.2,72,89)cs,_:=wc.S(ctx,sales,nil,nil)graph:= chart.Chart{Series: []chart.Series{cs}}plt,_:=plot.Open("Monthly sales",450,300)graph.Render(chart.SVG,plt)plt.Display(plot.None)<-plt.Closed

Output:

Math Functions

import"github.com/rocketlaunchr/dataframe-go/math/funcs"res:=24sx:=dataframe.NewSeriesFloat64("x",nil,utils.Float64Seq(1,float64(res),1))sy:=dataframe.NewSeriesFloat64("y",&dataframe.SeriesInit{Size:res})df:=dataframe.NewDataFrame(sx,sy)fn:=funcs.RegFunc("sin(2*𝜋*x/24)")funcs.Evaluate(ctx,df,fn,1)

Output:

Importing Data

Theimports sub-package has support for importing csv, jsonl, parquet, and directly from a SQL database. TheDictateDataType option can be set to specify the true underlying data type. Alternatively,InferDataTypes option can be set.

CSV

csvStr:=`Country,Date,Age,Amount,Id"United States",2012-02-01,50,112.1,01234"United States",2012-02-01,32,321.31,54320"United Kingdom",2012-02-01,17,18.2,12345"United States",2012-02-01,32,321.31,54320"United Kingdom",2012-05-07,NA,18.2,12345"United States",2012-02-01,32,321.31,54320"United States",2012-02-01,32,321.31,54320Spain,2012-02-01,66,555.42,00241`df,err:=imports.LoadFromCSV(ctx,strings.NewReader(csvStr))OUTPUT:+-----+----------------+------------+-------+---------+-------+||COUNTRY|DATE|AGE|AMOUNT|ID|+-----+----------------+------------+-------+---------+-------+|0:|UnitedStates|2012-02-01|50|112.1|1234||1:|UnitedStates|2012-02-01|32|321.31|54320||2:|UnitedKingdom|2012-02-01|17|18.2|12345||3:|UnitedStates|2012-02-01|32|321.31|54320||4:|UnitedKingdom|2015-05-07|NaN|18.2|12345||5:|UnitedStates|2012-02-01|32|321.31|54320||6:|UnitedStates|2012-02-01|32|321.31|54320||7:|Spain|2012-02-01|66|555.42|241|+-----+----------------+------------+-------+---------+-------+|8X5|STRING|TIME|INT64|FLOAT64|INT64|+-----+----------------+------------+-------+---------+-------+

Exporting Data

Theexports sub-package has support for exporting to csv, jsonl, parquet, Excel and directly to a SQL database.

Optimizations

If you know the number of rows in advance, you can set the capacity of the underlying slice of a series usingSeriesInit{}. This will preallocate memory and provide speed improvements.

Generic Series

Out of the box, there is support forstring,time.Time,float64 andint64. Automatic support exists forfloat32 and all types of integers. There is a convenience function provided for dealing withbool. There is also support forcomplex128 inside thexseries subpackage.

There may be times that you want to use your own custom data types. You can either implement your ownSeries type (more performant) or use theGeneric Series (more convenient).

civil.Date

import"time"import"cloud.google.com/go/civil"sg:=dataframe.NewSeriesGeneric("date", civil.Date{},nil, civil.Date{2018,time.May,01}, civil.Date{2018,time.May,02}, civil.Date{2018,time.May,03})s2:=dataframe.NewSeriesFloat64("sales",nil,50.3,23.4,56.2)df:=dataframe.NewDataFrame(sg,s2)OUTPUT:+-----+------------+---------+||DATE|SALES|+-----+------------+---------+|0:|2018-05-01|50.3||1:|2018-05-02|23.4||2:|2018-05-03|56.2|+-----+------------+---------+|3X2|CIVILDATE|FLOAT64|+-----+------------+---------+

Tutorial

Create some fake data

Let's create a list of 8 "fake" employees with a name, title and base hourly wage rate.

import"golang.org/x/exp/rand"import"rocketlaunchr/dataframe-go/utils/faker"src:=rand.NewSource(uint64(time.Now().UTC().UnixNano()))df:=faker.NewDataFrame(8,src,faker.S("name",0,"Name"),faker.S("title",0.5,"JobTitle"),faker.S("base rate",0,"Number",15,50))

+-----+----------------+----------------+-----------+||NAME|TITLE|BASERATE|+-----+----------------+----------------+-----------+|0:|CordiaJacobi|Consultant|42||1:|NickolasEmard|NaN|22||2:|HollisDickens|Representative|22||3:|StacyDietrich|NaN|43||4:|AleenLegros|Officer|21||5:|AdeliaMetz|Architect|18||6:|SunnyGerlach|NaN|28||7:|AustinHackett|NaN|39|+-----+----------------+----------------+-----------+|8X3|STRING|STRING|INT64|+-----+----------------+----------------+-----------+

Apply Function

Let's give a promotion to everyone by doubling their salary.

s:=df.Series[2]applyFn:=dataframe.ApplySeriesFn(func(valinterface{},row,nRowsint)interface{} {return2*val.(int64)})dataframe.Apply(ctx,s,applyFn, dataframe.FilterOptions{InPlace:true})

+-----+----------------+----------------+-----------+||NAME|TITLE|BASERATE|+-----+----------------+----------------+-----------+|0:|CordiaJacobi|Consultant|84||1:|NickolasEmard|NaN|44||2:|HollisDickens|Representative|44||3:|StacyDietrich|NaN|86||4:|AleenLegros|Officer|42||5:|AdeliaMetz|Architect|36||6:|SunnyGerlach|NaN|56||7:|AustinHackett|NaN|78|+-----+----------------+----------------+-----------+|8X3|STRING|STRING|INT64|+-----+----------------+----------------+-----------+

Create a Time series

Let's inform all employees separately on sequential days.

import"rocketlaunchr/dataframe-go/utils/utime"mts,_:=utime.NewSeriesTime(ctx,"meeting time","1D",time.Now().UTC(),false, utime.NewSeriesTimeOptions{Size:&[]int{8}[0]})df.AddSeries(mts,nil)

+-----+----------------+----------------+-----------+--------------------------------+||NAME|TITLE|BASERATE|MEETINGTIME|+-----+----------------+----------------+-----------+--------------------------------+|0:|CordiaJacobi|Consultant|84|2020-02-0223:13:53.015324||||||+0000UTC||1:|NickolasEmard|NaN|44|2020-02-0323:13:53.015324||||||+0000UTC||2:|HollisDickens|Representative|44|2020-02-0423:13:53.015324||||||+0000UTC||3:|StacyDietrich|NaN|86|2020-02-0523:13:53.015324||||||+0000UTC||4:|AleenLegros|Officer|42|2020-02-0623:13:53.015324||||||+0000UTC||5:|AdeliaMetz|Architect|36|2020-02-0723:13:53.015324||||||+0000UTC||6:|SunnyGerlach|NaN|56|2020-02-0823:13:53.015324||||||+0000UTC||7:|AustinHackett|NaN|78|2020-02-0923:13:53.015324||||||+0000UTC|+-----+----------------+----------------+-----------+--------------------------------+|8X4|STRING|STRING|INT64|TIME|+-----+----------------+----------------+-----------+--------------------------------+

Filtering

Let's filter out our senior employees (they have titles) for no reason.

filterFn:=dataframe.FilterDataFrameFn(func(valsmap[interface{}]interface{},row,nRowsint) (dataframe.FilterAction,error) {ifvals["title"]==nil {returndataframe.DROP,nil}returndataframe.KEEP,nil})seniors,_:=dataframe.Filter(ctx,df,filterFn)

+-----+----------------+----------------+-----------+--------------------------------+||NAME|TITLE|BASERATE|MEETINGTIME|+-----+----------------+----------------+-----------+--------------------------------+|0:|CordiaJacobi|Consultant|84|2020-02-0223:13:53.015324||||||+0000UTC||1:|HollisDickens|Representative|44|2020-02-0423:13:53.015324||||||+0000UTC||2:|AleenLegros|Officer|42|2020-02-0623:13:53.015324||||||+0000UTC||3:|AdeliaMetz|Architect|36|2020-02-0723:13:53.015324||||||+0000UTC|+-----+----------------+----------------+-----------+--------------------------------+|4X4|STRING|STRING|INT64|TIME|+-----+----------------+----------------+-----------+--------------------------------+

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