# Creating a DataFrame
danfo.**DataFrame**(data, options)
| Parameters | Type | Description |
| ---------- | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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:
{% tabs %}
{% tab title="Node" %}
```javascript
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()
```
{% endtab %}
{% tab title="Browser" %}
```markup
Document
```
{% endtab %}
{% endtabs %}
### Creating a `DataFrame` from an array of array
{% tabs %}
{% tab title="Node" %}
```javascript
const 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()
```
{% endtab %}
{% tab title="Browser" %}
```markup
Document
```
{% endtab %}
{% endtabs %}
```
╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║ │ A │ B │ C │ D ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 12 │ 34 │ 2.2 │ 2 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 30 │ 30 │ 2.1 │ 7 ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
```
### Creating a `DataFrame` from a 2D tensor
{% tabs %}
{% tab title="Node" %}
```javascript
const 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()
```
{% endtab %}
{% tab title="Browser" %}
```markup
Document
```
{% endtab %}
{% endtabs %}
```
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║ │ A │ B │ C │ D ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 12 │ 34 │ 2.20000004768... │ 2 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 30 │ 30 │ 2.09999990463... │ 7 ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
╔═══╤══════════════════════╗
║ │ 0 ║
╟───┼──────────────────────╢
║ A │ int32 ║
╟───┼──────────────────────╢
║ B │ int32 ║
╟───┼──────────────────────╢
║ C │ float32 ║
╟───┼──────────────────────╢
║ D │ int32 ║
╚═══╧══════════════════════╝
```
### Creating a `DataFrame` from an object
{% tabs %}
{% tab title="Node" %}
```javascript
const 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()
```
{% endtab %}
{% tab title="Browser" %}
```markup
Document
```
{% endtab %}
{% endtabs %}
```
//output in console
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║ │ A │ B │ C │ D │ E ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 1/1/2017, 1:0... │ bval1 │ 10 │ 1.2 │ test ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 1/1/2018, 1:0... │ bval2 │ 20 │ 3.45 │ train ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ 1/1/2019, 1:0... │ bval3 │ 30 │ 60.1 │ test ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ 1/1/2020, 1:0... │ bval4 │ 40 │ 45 │ train ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
```
### Creating a `DataFrame` and specifying index, dtypes, columns
You 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.
{% tabs %}
{% tab title="Node" %}
```javascript
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()
```
{% endtab %}
{% endtabs %}
```
╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║ │ col1 │ col2 │ col3 │ col4 │ col5 │ col6 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ a │ 1 │ 2.3 │ 3 │ 4 │ 5 │ girl ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ b │ 30 │ 40.1 │ 39 │ 89 │ 78 │ boy ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
```
### Creating a `DataFrame` and specifying memory mode
To use less space on DataFrame creation, you can set the low memory mode as demonstrated below:
```javascript
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()
```
{% hint style="info" %}
**Note**: In low memory mode, less space is used by the DataFrame. The drawback is that some operations especially the ones involving column data become slightly slower.
{% endhint %}
For loading flat files like CSV, EXCEL and, JSON into DataFrames, see this [page](https://danfo.jsdata.org/api-reference/input-output)