> For the complete documentation index, see [llms.txt](https://docs.shimoku.com/dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.shimoku.com/dev/releases/2022/v.0.9.md).
# v.0.9
{% hint style="danger" %}
This version has been deprecated. If used with the current API version it can produce unexpected behaviour or errors.
{% endhint %}
{% hint style="danger" %}
The package shimoku-api-python is no longer maintained
{% endhint %}
{% hint style="info" %}
To get the new version 🎨🖌️
{% endhint %}
```
pip install --upgrade shimoku-api-python
pip install --upgrade shimoku-components-catalog
```
{% hint style="info" %}
This version is a **big leap forward** adding critical new features to keep giving tools to Python developers to interact with a Data Platform ready to create Data & AI Apps
{% endhint %}
### Fixes
* Fixed responsiveness of tables
* Responsiveness of components when the devices are turned solved
### Improvements
* Now `s.plt.set_apps_orders()` and `s.plt.set_sub_path_orders()` can get the URL to sort and order paths and subpaths.
Examples:
```python
s.plt.set_apps_orders(apps_order={'test': 1, 'caetsu': 2})
s.plt.set_sub_path_orders(
paths_order={
'test/funnel-test': 1,
'test/tree-test': 2,
}
)
```
### New
* **Free eCharts** a cornerstone feature. The catalog of Shimoku has now all the power of personalization of eCharts! The proposed usage is:
1. Visit and pick a chart you want to plot, for instance
2. Play in the eCharts console to tune it until you got precissely what you want. Add the data you want to the eCharts console to check out that everything works as you want.
3. Copy the eCharts console code and paste it into your Python code as a string and use the `raw_options` as input of `s.plt.free_echarts()`
```python
# https://echarts.apache.org/examples/en/editor.html?c=line-style
raw_options: str = """
{
xAxis: {
type: 'category',
data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
},
yAxis: {
type: 'value'tpyth
},
series: [
{
data: [120, 200, 150, 80, 70, 110, 130],
type: 'line',
symbol: 'triangle',
symbolSize: 20,
lineStyle: {
color: '#5470C6',
width: 4,
type: 'dashed'
},
itemStyle: {
borderWidth: 3,
borderColor: '#EE6666',
color: 'yellow'
}
}
]
};
"""
s.plt.free_echarts(
raw_options=raw_options,
menu_path='test/raw-free-echarts',
order=2, rows_size=2, cols_size=12,
)
```
4\. É Voila!
**Note** you cannot add eCharts functions such as from you must get rid of those for it to work!
```python
xAxis: {
data: data.map(function (item) {
return item[0];
})
},
```
You can also build it without copy pasting the data, just passing the rest of the `options` from eCharts. Use `options` and `data` as input of `s.plt.free_echarts()`
```python
data = [
{'product': 'Matcha Latte', '2015': 43.3, '2016': 85.8, '2017': 93.7},
{'product': 'Milk Tea', '2015': 83.1, '2016': 73.4, '2017': 55.1},
{'product': 'Cheese Cocoa', '2015': 86.4, '2016': 65.2, '2017': 82.5},
{'product': 'Walnut Brownie', '2015': 72.4, '2016': 53.9, '2017': 39.1}
]
options = {
'legend': {},
'tooltip': {},
'xAxis': {'type': 'category'},
'yAxis': {},
'series': [{'type': 'bar'}, {'type': 'bar'}, {'type': 'bar'}]
}
s.plt.free_echarts(
data=data,
options=options,
menu_path='test/free-echarts',
order=0, rows_size=2, cols_size=12,
)
```
* **Input Form.** A game changer! Shimoku users can now introduce further inputs besides the [filters](broken://pages/cXMrNfUPRJZUEG6M0w6x) and these can be linked to different logics to produce real time outcomes. The Input form allows a rich variety of input components. You can use a rich variety of inputs and combine them passing it to `s.plt.input_form()`
```python
report_dataset_properties: Dict = {
'fields': [
{
'title': 'Personal information',
'fields': [
{
'mapping': 'name',
'fieldName': 'name',
'inputType': 'text',
},
{
'mapping': 'surname',
'fieldName': 'surname',
'inputType': 'text',
},
{
'mapping': 'age',
'fieldName': 'apythge',
'inputType': 'number',
},
{
'mapping': 'tel',
'fieldName': 'phone',
'inputType': 'tel',
},
{
'mapping': 'gender',
'fieldName': 'Gender',
'inputType': 'radio',
'options': ['Male', 'Female', 'No-binary', 'Undefined'],
},
{
'mapping': 'email',
'fieldName': 'email',
'inputType': 'email',
},
],
},
{
'title': 'Other data',
'fields': [
{
'mapping': 'skills',
'fieldName': 'Skills',
'options': ['Backend', 'Frontend', 'UX/UI', 'Api Builder', 'DevOps'],
'inputType': 'checkbox',
},
{
'mapping': 'birthDay',
'fieldName': 'Birthday',
'inputType': 'date',
},
{
'mapping': 'onCompany',
'fieldName': 'Time on Shimoku',
'inputType': 'dateRange',
},
{
'mapping': 'hobbies',
'fieldName': 'Hobbies',
'inputType': 'select',
'options': ['Make Strong Api', 'Sailing to Canarias', 'Send Abracitos'],
},
{
'mapping': 'textField2',
'fieldName': 'Test Text',
'inputType': 'text',
},
],
},
],
}
s.plt.input_form(
menu_path='test/input-form', order=0,
report_dataset_properties=report_dataset_properties
)
```
That results in
* **File IO** for Feature Store. Now users can store and retrieve datasets and models in Shimoku as part of our service. Every file is assigned to a business and app.
*Example #1* You can store raw binary or string objects and retrieve them (they can be ML models or any other binary object)
```python
file_name = 'helloworld'
app_name = 'test'
object_data = b''
file_metadata: Dict = s.file.post_object(
business_id=business_id,
app_name=app_name,
file_name=file_name,
object_data=object_data
)
object: binary = s.file.get_object(
business_id=business_id,
app_name=app_name,
file_name=file_name,
)
```
Example #2 You can also store pandas dataframes (from any size) and retrieve them easily:
```python
file_name: str = 'df-test'
d = {'a': [1, 2, 3], 'b': [1, 4, 9]}
df_ = pd.DataFrame(d)
# Post dataframe to Shimoku
file: Dict = s.file.post_dataframe(
business_id=business_id,
app_name='test',
file_name=file_name,
df=df_,
)
# Retrieve it from Shimoku
df: pd.DataFrame = s.file.get_dataframe(
business_id=business_id,
app_name='test',
file_name=file_name,
)pyt
```
Example #3 You can retrieve binary objects and pandas dataframe by date:
```python
yesterday: dt.date = dt.date.today() - dt.timedelta(1)
file: Dict = s.file.get_file_by_date(
business_id=business_id,
date=yesterday,
file_name='helloworld',
app_name='test',
)
```
### Tricks
* Combine reports `hide` / `unhide` with `Input forms` this allow users to see the components they want to.
* Use eCharts console to prepare your chart and when you have what you want you can just copy paste it to the Free eCharts with raw input
---
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