Dataframes are used for statistics and data manipulation. 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 stability is reached, version 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, MySQL & PostgreSQL
- Exporting to CSV, JSONL, Excel, MySQL & PostgreSQL
- Developer Friendly
- Flexible - Create custom Series (custom data types)
- Performant
- Interoperability with gonum package.
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(9, 123.6)
df.Append(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, map[string]interface{}{
"day": 3,
"sales": 45,
})sks := []dataframe.SortKey{
{Key: "sales", SortDesc: true},
{Key: "day", SortDesc: true},
}
df.Sort(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 values are a map containing the name of the series (string) and the order of the series (int).
iterator := df.Values(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]The imports sub-package has support for importing csv, jsonl and directly from a SQL database.
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",2012-02-01,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 | 01234 |
| 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 | 2012-02-01 | NA | 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 | 00241 |
+-----+----------------+------------+--------+--------+--------+
| 8X5 | STRING | STRING | STRING | STRING | STRING |
+-----+----------------+------------+--------+--------+--------+The exports sub-package has support for exporting to csv, jsonl, 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"
"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 |
+-----+------------+---------+- remember-go - Cache slow database queries
- mysql-go - Properly cancel slow MySQL queries
- react - Build front end applications using Go
- igo - A Go transpiler with cool new syntax such as
fordefer(defer for for-loops)
The license is a modified MIT license. Refer to LICENSE file for more details.
© 2018 PJ Engineering and Business Solutions Pty. Ltd.
Feel free to enhance features by issuing pull-requests.
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