# streamlit-rxdb-dataframe
Custom `streamlit` component connecting local browser `indexedDB` database as `RxDB` collection as `dataframe`
## Demo
[demo](https://st-rxdb-dataframe.streamlit.app/) - based on TodoMVC
## Usage
### Provide JSONSchema
<details>
<summary>JSONSchema</summary>
```json
{
"definitions": {},
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"title": "Todo",
"description": "Todo Schema",
"required": ["id", "title", "createdAt"],
"version": 0,
"properties": {
"id": {
"type": "string",
"format": "uuid",
"title": "Id",
"pattern": "^(.*)$",
"maxLength": 36,
"readOnly": true
},
"title": {
"type": "string",
"title": "Title",
"minLength": 3
},
"completed": {
"type": "boolean",
"title": "Done"
},
"createdAt": {
"type": "string",
"title": "Created Date",
"format": "date-time",
"readOnly": true
},
"last_modified": {
"type": "integer",
"format": "time",
"title": "Last Modified Date"
}
},
"__indexes": ["createdAt"],
"primaryKey": "id",
"attachments": {
"encrypted": false
}
}
```
</details>
### Provide Collection Config and use streamlit dataframe, data_editor or table
```python
todoSchema: Dict = json.load(open(os.path.join(data_dir, "todo.schema.json")))
# initial_docs: List = json.load(open(os.path.join(data_dir, "col.dump.json")))["docs"]
collection_config = {
"name": "todo",
"schema": todoSchema, # to auto load schema from remote url pass None
"localDocuments": True,
"options": {
# 'schemaUrl': 'assets/data/todo.schema.json',
# "initialDocs": initial_docs,
},
}
def on_change_dataframe(rxdb_state: RxDBSessionState):
print("RxDBDataframe component on_change call")
print("collection.info()", rxdb_state.info)
print("dataframe.head()", rxdb_state.dataframe.head())
df = rxdb_dataframe(
collection_config,
query=None,
with_rev=False,
on_change=on_change_dataframe,
)
st.dataframe(
df,
use_container_width=True,
hide_index=True,
column_config=st.session_state["rxdb"]["column_config"],
column_order=["title", "completed", "createdAt"],
)
```
## Run & Build
### Run
```bash
cd packages/streamlit-rxdb-dataframe/
poetry run streamlit run example.py
```
### Build
```bash
poetry build -o ../../dist/packages/streamlit-rxdb-dataframe
```
### Publish
```bash
poetry publish -r test-pypi --dist-dir ../../dist/packages/streamlit-rx
db-dataframe
```
<!--
# TODO: Add form to construct RxQuery
# query_container = st.container()
# with query_container:
# to_filter_columns = st.multiselect(
# "Filter dataframe on",
# df.columns,
# key=f"{prefix}_multiselect",
# )
# filters: Dict[str, Any] = dict()
# for column in to_filter_columns:
# left, right = st.columns((1, 20))
# # Treat columns with < 10 unique values as categorical
# if is_categorical_dtype(df[column]) or df[column].nunique() < 10:
# left.write("↳")
# filters[column] = right.multiselect(
# f"Values for {column}",
# df[column].unique(),
# default=list(df[column].unique()),
# key=f"{prefix}_{column}",
# )
# df = df[df[column].isin(filters[column])]
# elif is_numeric_dtype(df[column]):
# left.write("↳")
# _min = float(df[column].min())
# _max = float(df[column].max())
# step = (_max - _min) / 100
# filters[column] = right.slider(
# f"Values for {column}",
# _min,
# _max,
# (_min, _max),
# step=step,
# key=f"{prefix}_{column}",
# )
# df = df[df[column].between(*filters[column])]
# elif is_datetime64_any_dtype(df[column]):
# left.write("↳")
# filters[column] = right.date_input(
# f"Values for {column}",
# value=(
# df[column].min(),
# df[column].max(),
# ),
# key=f"{prefix}_{column}",
# )
# if len(filters[column]) == 2:
# filters[column] = tuple(map(pd.to_datetime, filters[column])) # noqa: E501
# start_date, end_date = filters[column]
# df = df.loc[df[column].between(start_date, end_date)]
# else:
# left.write("↳")
# filters[column] = right.text_input(
# f"Pattern in {column}",
# key=f"{prefix}_{column}",
# )
# if filters[column]:
# print(filters[column])
-->
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"description": "# streamlit-rxdb-dataframe\n\nCustom `streamlit` component connecting local browser `indexedDB` database as `RxDB` collection as `dataframe`\n\n## Demo\n\n[demo](https://st-rxdb-dataframe.streamlit.app/) - based on TodoMVC\n\n\n\n## Usage\n\n### Provide JSONSchema\n\n<details>\n<summary>JSONSchema</summary>\n\n```json\n{\n \"definitions\": {},\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"type\": \"object\",\n \"title\": \"Todo\",\n \"description\": \"Todo Schema\",\n \"required\": [\"id\", \"title\", \"createdAt\"],\n \"version\": 0,\n \"properties\": {\n \"id\": {\n \"type\": \"string\",\n \"format\": \"uuid\",\n \"title\": \"Id\",\n \"pattern\": \"^(.*)$\",\n \"maxLength\": 36,\n \"readOnly\": true\n },\n \"title\": {\n \"type\": \"string\",\n \"title\": \"Title\",\n \"minLength\": 3\n },\n \"completed\": {\n \"type\": \"boolean\",\n \"title\": \"Done\"\n },\n \"createdAt\": {\n \"type\": \"string\",\n \"title\": \"Created Date\",\n \"format\": \"date-time\",\n \"readOnly\": true\n },\n \"last_modified\": {\n \"type\": \"integer\",\n \"format\": \"time\",\n \"title\": \"Last Modified Date\"\n }\n },\n \"__indexes\": [\"createdAt\"],\n \"primaryKey\": \"id\",\n \"attachments\": {\n \"encrypted\": false\n }\n}\n```\n\n</details>\n\n### Provide Collection Config and use streamlit dataframe, data_editor or table\n\n```python\n\ntodoSchema: Dict = json.load(open(os.path.join(data_dir, \"todo.schema.json\")))\n# initial_docs: List = json.load(open(os.path.join(data_dir, \"col.dump.json\")))[\"docs\"]\ncollection_config = {\n \"name\": \"todo\",\n \"schema\": todoSchema, # to auto load schema from remote url pass None\n \"localDocuments\": True,\n \"options\": {\n # 'schemaUrl': 'assets/data/todo.schema.json',\n # \"initialDocs\": initial_docs,\n },\n}\n\ndef on_change_dataframe(rxdb_state: RxDBSessionState):\n print(\"RxDBDataframe component on_change call\")\n print(\"collection.info()\", rxdb_state.info)\n print(\"dataframe.head()\", rxdb_state.dataframe.head())\n\ndf = rxdb_dataframe(\n collection_config,\n query=None,\n with_rev=False,\n on_change=on_change_dataframe,\n)\nst.dataframe(\n df,\n use_container_width=True,\n hide_index=True,\n column_config=st.session_state[\"rxdb\"][\"column_config\"],\n column_order=[\"title\", \"completed\", \"createdAt\"],\n)\n```\n\n## Run & Build\n\n### Run\n\n```bash\ncd packages/streamlit-rxdb-dataframe/\npoetry run streamlit run example.py\n```\n\n### Build\n\n```bash\npoetry build -o ../../dist/packages/streamlit-rxdb-dataframe\n```\n\n### Publish\n\n```bash\npoetry publish -r test-pypi --dist-dir ../../dist/packages/streamlit-rx\ndb-dataframe\n```\n\n<!--\n# TODO: Add form to construct RxQuery\n # query_container = st.container()\n # with query_container:\n # to_filter_columns = st.multiselect(\n # \"Filter dataframe on\",\n # df.columns,\n # key=f\"{prefix}_multiselect\",\n # )\n # filters: Dict[str, Any] = dict()\n # for column in to_filter_columns:\n # left, right = st.columns((1, 20))\n # # Treat columns with < 10 unique values as categorical\n # if is_categorical_dtype(df[column]) or df[column].nunique() < 10:\n # left.write(\"\u21b3\")\n # filters[column] = right.multiselect(\n # f\"Values for {column}\",\n # df[column].unique(),\n # default=list(df[column].unique()),\n # key=f\"{prefix}_{column}\",\n # )\n # df = df[df[column].isin(filters[column])]\n # elif is_numeric_dtype(df[column]):\n # left.write(\"\u21b3\")\n # _min = float(df[column].min())\n # _max = float(df[column].max())\n # step = (_max - _min) / 100\n # filters[column] = right.slider(\n # f\"Values for {column}\",\n # _min,\n # _max,\n # (_min, _max),\n # step=step,\n # key=f\"{prefix}_{column}\",\n # )\n # df = df[df[column].between(*filters[column])]\n # elif is_datetime64_any_dtype(df[column]):\n # left.write(\"\u21b3\")\n # filters[column] = right.date_input(\n # f\"Values for {column}\",\n # value=(\n # df[column].min(),\n # df[column].max(),\n # ),\n # key=f\"{prefix}_{column}\",\n # )\n # if len(filters[column]) == 2:\n # filters[column] = tuple(map(pd.to_datetime, filters[column])) # noqa: E501\n # start_date, end_date = filters[column]\n # df = df.loc[df[column].between(start_date, end_date)]\n # else:\n # left.write(\"\u21b3\")\n # filters[column] = right.text_input(\n # f\"Pattern in {column}\",\n # key=f\"{prefix}_{column}\",\n # )\n # if filters[column]:\n # print(filters[column])\n -->\n",
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