{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Welcome to the DataWeave Jupyter Notebook\n",
"\n",
"## How to use it\n",
"\n",
"Enter any DataWeave script and the result will be shown.\n",
"\n",
"### Example:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/dw": "[\n 2, \n 4, \n 6\n]",
"text/plain": [
"[\n",
" 2, \n",
" 4, \n",
" 6\n",
"]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1,2,3,4,5,6] filter isEven($)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Storing Variables\n",
"Use the `%var <variable name>` magic in order to store a value.\n",
"\n",
"### Example:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/dw": "[\n 1, \n 2, \n 3, \n 4, \n 5\n]",
"text/plain": [
"[\n",
" 1, \n",
" 2, \n",
" 3, \n",
" 4, \n",
" 5\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%var payload\n",
"[1,2,3,4,5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and that value van be reused later calling the `payload` value:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/dw": "[\n 1, \n 2, \n 3, \n 4, \n 5\n]",
"text/plain": [
"[\n",
" 1, \n",
" 2, \n",
" 3, \n",
" 4, \n",
" 5\n",
"]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"payload"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding static imputs\n",
"In order to add inputs to the execution context use the `%%input <input name> <content type>` magic cell. Example:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%input users application/json\n",
"[{ \"name\" : \"Ana\"} , { \"name\" : \"Mariano\"}]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And use it later:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"application/dw": "[\n {\n name: \"Ana\"\n }, \n {\n name: \"Mariano\"\n }\n]",
"text/plain": [
"[\n",
" {\n",
" name: \"Ana\"\n",
" }, \n",
" {\n",
" name: \"Mariano\"\n",
" }\n",
"]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Render to Markdown\n",
"When working with List of Objects you can use the `%mrkdwn` magic line in order to render tables to Markdown. Example:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|name|\n",
"|--|\n",
"|Ana|\n",
"|Mariano|"
],
"text/plain": [
"|name|\n",
"|--|\n",
"|Ana|\n",
"|Mariano|"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%mrkdwn\n",
"users"
]
}
],
"metadata": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
},
"kernelspec": {
"display_name": "DataWeave",
"language": "dw",
"name": "dataweave"
},
"language_info": {
"codemirror_mode": "dataweave",
"file_extension": "dw",
"mimetype": "application/dw",
"name": "DataWeave",
"nbconvert_exporter": "dataweave",
"pygments_lexer": "dataweave",
"version": "2.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}