Creating a DataFrame
Creates a DataFrame object from flat structure
danfo.DataFrame(data, options)
data
2D Array, 2D Tensor, JSON object.
Flat data structure to load into DataFrame
options
Object
Optional configuration object. Supported properties are:
index: Array of numeric or string names for subseting array. If not specified, indexes are auto-generated.
columns: Array of column names. If not specified, column names are auto generated.
dtypes: Array of data types for each the column. If not specified, dtypes are/is inferred.
config: General configuration object for extending or setting NDframe behavior. See full options here
In order to create a DataFrame, you need to call the new Keyword and pass in a flat data structure. In the following examples, we show you how to create DataFrames by specifying different config options.
Creating a DataFrame
from a JSON object:
DataFrame
from a JSON object:const dfd = require("danfojs-node")
json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
df = new dfd.DataFrame(json_data)
df.print()
Creating a DataFrame
from an array of array
DataFrame
from an array of arrayconst dfd = require("danfojs-node")
let arr = [[12, 34, 2.2, 2], [30, 30, 2.1, 7]]
let df = new dfd.DataFrame(arr, {columns: ["A", "B", "C", "D"]})
df.print()
ββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ
β β A β B β C β D β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 0 β 12 β 34 β 2.2 β 2 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 1 β 30 β 30 β 2.1 β 7 β
ββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ
Creating a DataFrame
from a 2D tensor
DataFrame
from a 2D tensorconst dfd = require("danfojs-node")
const tf = dfd.tensorflow
let tensor_arr = tf.tensor2d([[12, 34, 2.2, 2], [30, 30, 2.1, 7]])
let df = new dfd.DataFrame(tensor_arr, {columns: ["A", "B", "C", "D"]})
df.print()
df.ctypes.print()
βββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ
β β A β B β C β D β
βββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 0 β 12 β 34 β 2.20000004768... β 2 β
βββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 1 β 30 β 30 β 2.09999990463... β 7 β
βββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ
βββββ€βββββββββββββββββββββββ
β β 0 β
βββββΌβββββββββββββββββββββββ’
β A β int32 β
βββββΌβββββββββββββββββββββββ’
β B β int32 β
βββββΌβββββββββββββββββββββββ’
β C β float32 β
βββββΌβββββββββββββββββββββββ’
β D β int32 β
βββββ§βββββββββββββββββββββββ
Creating a DataFrame
from an object
DataFrame
from an objectconst dfd = require("danfojs-node")
dates = new dfd.dateRange({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })
console.log(dates);
obj_data = {'A': dates,
'B': ["bval1", "bval2", "bval3", "bval4"],
'C': [10, 20, 30, 40],
'D': [1.2, 3.45, 60.1, 45],
'E': ["test", "train", "test", "train"]
}
df = new dfd.DataFrame(obj_data)
df.print()
//output in console
βββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ
β β A β B β C β D β E β
βββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 0 β 1/1/2017, 1:0... β bval1 β 10 β 1.2 β test β
βββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 1 β 1/1/2018, 1:0... β bval2 β 20 β 3.45 β train β
βββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 2 β 1/1/2019, 1:0... β bval3 β 30 β 60.1 β test β
βββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 3 β 1/1/2020, 1:0... β bval4 β 40 β 45 β train β
βββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ
Creating a DataFrame
and specifying index, dtypes, columns
DataFrame
and specifying index, dtypes, columnsYou can create a DataFrame and specify options like index, column names, dtypes as well as configuration options like display, memory mode etc.
Note: Specifing dtypes, column names and index on DataFrame creation makes the process slightly faster.
import { DataFrame } from "danfojs"
let data1 = [[1, 2.3, 3, 4, 5, "girl"], [30, 40.1, 39, 89, 78, "boy"]];
let index = ["a", "b"];
let columns = ["col1", "col2", "col3", "col4", "col5", "col6"]
let dtypes = ["int32", "float32", "int32", "int32", "int32", "string"]
let df = new DataFrame(data1, { index, columns, dtypes });
df.print()
ββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ
β β col1 β col2 β col3 β col4 β col5 β col6 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β a β 1 β 2.3 β 3 β 4 β 5 β girl β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β b β 30 β 40.1 β 39 β 89 β 78 β boy β
ββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ
Creating a DataFrame
and specifying memory mode
DataFrame
and specifying memory modeTo use less space on DataFrame creation, you can set the low memory mode as demonstrated below:
import { DataFrame } from "danfojs"
let data1 = [[1, 2.3, 3, 4, 5, "girl"], [30, 40.1, 39, 89, 78, "boy"]];
let df = new DataFrame(data1, {
config: { lowMemoryMode: true }
});
df.print()
For loading flat files like CSV, EXCEL and, JSON into DataFrames, see this page
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