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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.
1.0.0
will be tagged.
It is recommended your package manager locks to a commit id instead of the master branch directly.
- 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 with gonum package.
- pandas sub-package
- Fake data generation
- Interpolation (ForwardFill, BackwardFill, Linear, Spline, Lagrange)
- Time-series Forecasting (SES, Holt-Winters)
- Math functions
- Plotting (cross-platform)
See Tutorial here.
go get -u github.com/rocketlaunchr/dataframe-go
import dataframe "github.com/rocketlaunchr/dataframe-go"
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 |
+-----+-------+---------+
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 |
+-----+-------+---------+
df.UpdateRow(0, nil, map[string]interface{}{
"day": 3,
"sales": 45,
})
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 |
+-----+-------+---------+
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()
if row == nil {
break
}
fmt.Println(*row, vals)
}
df.Unlock()
OUTPUT:
0 map[day:1 0:1 sales:50.3 1:50.3]
1 map[sales:23.4 1:23.4 day:2 0:2]
2 map[day:3 0:3 sales:56.2 1:56.2]
3 map[1:<nil> day:4 0:4 sales:<nil>]
4 map[day:5 0:5 sales:<nil> 1:<nil>]
5 map[sales:84.2 1:84.2 day:6 0:6]
6 map[day:7 0:7 sales:72 1:72]
7 map[day:8 0:8 sales:89 1:89]
You can easily calculate statistics for a Series using the gonum or montanaflynn/stats package.
SeriesFloat64
and SeriesTime
provide access to the exported Values
field to seamlessly interoperate with external math-based packages.
Some series provide easy conversion using the ToSeriesFloat64
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 := stat.Mean(sf.Values, nil)
import "github.com/montanaflynn/stats"
median, _ := stats.Median(sf.Values)
std := stat.StdDev(sf.Values, nil)
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:
import "github.com/rocketlaunchr/dataframe-go/math/funcs"
res := 24
sx := 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:
The imports
sub-package has support for importing csv, jsonl, parquet, and directly from a SQL database. The DictateDataType
option can be set to specify the true underlying data type. Alternatively, InferDataTypes
option can be set.
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",2012-02-01,32,321.31,54320
Spain,2012-02-01,66,555.42,00241
`
df, err := imports.LoadFromCSV(ctx, strings.NewReader(csvStr))
OUTPUT:
+-----+----------------+------------+-------+---------+-------+
| | COUNTRY | DATE | AGE | AMOUNT | ID |
+-----+----------------+------------+-------+---------+-------+
| 0: | United States | 2012-02-01 | 50 | 112.1 | 1234 |
| 1: | United States | 2012-02-01 | 32 | 321.31 | 54320 |
| 2: | United Kingdom | 2012-02-01 | 17 | 18.2 | 12345 |
| 3: | United States | 2012-02-01 | 32 | 321.31 | 54320 |
| 4: | United Kingdom | 2015-05-07 | NaN | 18.2 | 12345 |
| 5: | United States | 2012-02-01 | 32 | 321.31 | 54320 |
| 6: | United States | 2012-02-01 | 32 | 321.31 | 54320 |
| 7: | Spain | 2012-02-01 | 66 | 555.42 | 241 |
+-----+----------------+------------+-------+---------+-------+
| 8X5 | STRING | TIME | INT64 | FLOAT64 | INT64 |
+-----+----------------+------------+-------+---------+-------+
The exports
sub-package has support for exporting to csv, jsonl, parquet, Excel and directly to a SQL database.
- If you know the number of rows in advance, you can set the capacity of the underlying slice of a series using
SeriesInit{}
. This will preallocate memory and provide speed improvements.
Out of the box, there is support for string
, time.Time
, float64
and int64
. Automatic support exists for float32
and all types of integers. There is a convenience function provided for dealing with bool
. There is also support for complex128
inside the xseries
subpackage.
There may be times that you want to use your own custom data types. You can either implement your own Series
type (more performant) or use the Generic Series (more convenient).
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 | CIVIL DATE | FLOAT64 |
+-----+------------+---------+
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 | BASE RATE |
+-----+----------------+----------------+-----------+
| 0: | Cordia Jacobi | Consultant | 42 |
| 1: | Nickolas Emard | NaN | 22 |
| 2: | Hollis Dickens | Representative | 22 |
| 3: | Stacy Dietrich | NaN | 43 |
| 4: | Aleen Legros | Officer | 21 |
| 5: | Adelia Metz | Architect | 18 |
| 6: | Sunny Gerlach | NaN | 28 |
| 7: | Austin Hackett | NaN | 39 |
+-----+----------------+----------------+-----------+
| 8X3 | STRING | STRING | INT64 |
+-----+----------------+----------------+-----------+
Let's give a promotion to everyone by doubling their salary.
s := df.Series[2]
applyFn := dataframe.ApplySeriesFn(func(val interface{}, row, nRows int) interface{} {
return 2 * val.(int64)
})
dataframe.Apply(ctx, s, applyFn, dataframe.FilterOptions{InPlace: true})
+-----+----------------+----------------+-----------+
| | NAME | TITLE | BASE RATE |
+-----+----------------+----------------+-----------+
| 0: | Cordia Jacobi | Consultant | 84 |
| 1: | Nickolas Emard | NaN | 44 |
| 2: | Hollis Dickens | Representative | 44 |
| 3: | Stacy Dietrich | NaN | 86 |
| 4: | Aleen Legros | Officer | 42 |
| 5: | Adelia Metz | Architect | 36 |
| 6: | Sunny Gerlach | NaN | 56 |
| 7: | Austin Hackett | NaN | 78 |
+-----+----------------+----------------+-----------+
| 8X3 | STRING | STRING | INT64 |
+-----+----------------+----------------+-----------+
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 | BASE RATE | MEETING TIME |
+-----+----------------+----------------+-----------+--------------------------------+
| 0: | Cordia Jacobi | Consultant | 84 | 2020-02-02 23:13:53.015324 |
| | | | | +0000 UTC |
| 1: | Nickolas Emard | NaN | 44 | 2020-02-03 23:13:53.015324 |
| | | | | +0000 UTC |
| 2: | Hollis Dickens | Representative | 44 | 2020-02-04 23:13:53.015324 |
| | | | | +0000 UTC |
| 3: | Stacy Dietrich | NaN | 86 | 2020-02-05 23:13:53.015324 |
| | | | | +0000 UTC |
| 4: | Aleen Legros | Officer | 42 | 2020-02-06 23:13:53.015324 |
| | | | | +0000 UTC |
| 5: | Adelia Metz | Architect | 36 | 2020-02-07 23:13:53.015324 |
| | | | | +0000 UTC |
| 6: | Sunny Gerlach | NaN | 56 | 2020-02-08 23:13:53.015324 |
| | | | | +0000 UTC |
| 7: | Austin Hackett | NaN | 78 | 2020-02-09 23:13:53.015324 |
| | | | | +0000 UTC |
+-----+----------------+----------------+-----------+--------------------------------+
| 8X4 | STRING | STRING | INT64 | TIME |
+-----+----------------+----------------+-----------+--------------------------------+
Let's filter out our senior employees (they have titles) for no reason.
filterFn := dataframe.FilterDataFrameFn(func(vals map[interface{}]interface{}, row, nRows int) (dataframe.FilterAction, error) {
if vals["title"] == nil {
return dataframe.DROP, nil
}
return dataframe.KEEP, nil
})
seniors, _ := dataframe.Filter(ctx, df, filterFn)
+-----+----------------+----------------+-----------+--------------------------------+
| | NAME | TITLE | BASE RATE | MEETING TIME |
+-----+----------------+----------------+-----------+--------------------------------+
| 0: | Cordia Jacobi | Consultant | 84 | 2020-02-02 23:13:53.015324 |
| | | | | +0000 UTC |
| 1: | Hollis Dickens | Representative | 44 | 2020-02-04 23:13:53.015324 |
| | | | | +0000 UTC |
| 2: | Aleen Legros | Officer | 42 | 2020-02-06 23:13:53.015324 |
| | | | | +0000 UTC |
| 3: | Adelia Metz | Architect | 36 | 2020-02-07 23:13:53.015324 |
| | | | | +0000 UTC |
+-----+----------------+----------------+-----------+--------------------------------+
| 4X4 | STRING | STRING | INT64 | TIME |
+-----+----------------+----------------+-----------+--------------------------------+
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The license is a modified MIT license. Refer to LICENSE
file for more details.
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