From f03842e3ac7e1a05b70b64fdeedbcadfa1676eae Mon Sep 17 00:00:00 2001 From: Honey Date: Sun, 10 Nov 2024 14:25:41 +0530 Subject: [PATCH 01/10] Added Polars as a dev dependency. Modified Python version for Polars installation --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3f7bcef11..0b6a7a12e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ repository = "https://github.com/zauberzeug/nicegui" keywords = ["gui", "ui", "web", "interface", "live"] [tool.poetry.dependencies] -python = "^3.8" +python = ">=3.9,<4.0" typing-extensions = ">=4.0.0" markdown2 = ">=2.4.7,!=2.4.11" Pygments = ">=2.15.1,<3.0.0" @@ -63,6 +63,7 @@ pyecharts = "^2.0.4" ruff = ">=0.3.5" pre-commit = ">=3.5.0" isort = "^5.11" +polars = "^1.12.0" [tool.poetry.scripts] nicegui-pack = "nicegui.scripts.pack:main" From 80634e46bc3eba6d8a1be88526272c644a508fb2 Mon Sep 17 00:00:00 2001 From: Honey Date: Sun, 10 Nov 2024 14:25:58 +0530 Subject: [PATCH 02/10] Added Polars as optional Feature --- nicegui/optional_features.py | 1 + 1 file changed, 1 insertion(+) diff --git a/nicegui/optional_features.py b/nicegui/optional_features.py index da8c674bf..cb6326e50 100644 --- a/nicegui/optional_features.py +++ b/nicegui/optional_features.py @@ -6,6 +6,7 @@ 'highcharts', 'matplotlib', 'pandas', + 'polars', 'pillow', 'plotly', 'pyecharts', From d717448a3ac5f31ed9abe1fc8492a01625d71bfe Mon Sep 17 00:00:00 2001 From: Honey Date: Sun, 10 Nov 2024 14:49:04 +0530 Subject: [PATCH 03/10] Created from_polars and update_from_polars functions. Created test cases for the functions. Added function in documentation. --- nicegui/elements/table.py | 81 ++++++++++++++++++- tests/test_table.py | 76 +++++++++++++++++ .../content/table_documentation.py | 11 +++ 3 files changed, 165 insertions(+), 3 deletions(-) diff --git a/nicegui/elements/table.py b/nicegui/elements/table.py index 4cb96bea8..b36c5d4ff 100644 --- a/nicegui/elements/table.py +++ b/nicegui/elements/table.py @@ -19,6 +19,7 @@ optional_features.register('pandas') if TYPE_CHECKING: import pandas as pd + import polars as pl class Table(FilterElement, component='table.js'): @@ -105,6 +106,46 @@ def on_pagination_change(self, callback: Handler[ValueChangeEventArguments]) -> def _normalize_columns(self, columns: List[Dict]) -> List[Dict]: return [{**self._column_defaults, **column} for column in columns] if self._column_defaults else columns + @classmethod + def from_polars(cls, + df: 'pl.DataFrame', *, + columns: Optional[List[Dict]] = None, + column_defaults: Optional[Dict] = None, + row_key: str = 'id', + title: Optional[str] = None, + selection: Optional[Literal['single', 'multiple']] = None, + pagination: Optional[Union[int, dict]] = None, + on_select: Optional[Handler[TableSelectionEventArguments]] = None) -> Self: + """Create a table from a Polars DataFrame. + + Note: + If the DataFrame contains Non Utf-8 DataTypes, they will be converted to strings. + To use a different conversion, convert the DataFrame manually before passing it to this method. + + :param df: Polars DataFrame + :param columns: list of column objects (defaults to the columns of the dataframe) + :param column_defaults: optional default column properties + :param row_key: name of the column containing unique data identifying the row (default: "id") + :param title: title of the table + :param selection: selection type ("single" or "multiple"; default: `None`) + :param pagination: a dictionary correlating to a pagination object or number of rows per page (`None` hides the pagination, 0 means "infinite"; default: `None`). + :param on_select: callback which is invoked when the selection changes + :return: table element + """ + rows, columns_from_df = cls._polars_df_to_rows_and_columns(df) + table = cls( + rows=rows, + columns=columns or columns_from_df, + column_defaults=column_defaults, + row_key=row_key, + title=title, + selection=selection, + pagination=pagination, + on_select=on_select, + ) + table._use_columns_from_df = columns is None + return table + @classmethod def from_pandas(cls, df: 'pd.DataFrame', *, @@ -133,7 +174,7 @@ def from_pandas(cls, :param on_select: callback which is invoked when the selection changes :return: table element """ - rows, columns_from_df = cls._df_to_rows_and_columns(df) + rows, columns_from_df = cls._pandas_df_to_rows_and_columns(df) table = cls( rows=rows, columns=columns or columns_from_df, @@ -147,6 +188,21 @@ def from_pandas(cls, table._use_columns_from_df = columns is None return table + def update_from_polars(self, + df: 'pl.DataFrame', *, + clear_selection: bool = True, + columns: Optional[List[Dict]] = None, + column_defaults: Optional[Dict] = None) -> None: + """Update the table from a Polars DataFrame. + + :param df: Polars DataFrame + :param clear_selection: whether to clear the selection (default: True) + :param columns: list of column objects (defaults to the columns of the dataframe) + :param column_defaults: optional default column properties + """ + rows, columns_from_df = self._polars_df_to_rows_and_columns(df) + self._update_table(rows, columns_from_df, clear_selection, columns, column_defaults) + def update_from_pandas(self, df: 'pd.DataFrame', *, clear_selection: bool = True, @@ -164,7 +220,16 @@ def update_from_pandas(self, :param columns: list of column objects (defaults to the columns of the dataframe) :param column_defaults: optional default column properties """ - rows, columns_from_df = self._df_to_rows_and_columns(df) + rows, columns_from_df = self._pandas_df_to_rows_and_columns(df) + self._update_table(rows, columns_from_df, clear_selection, columns, column_defaults) + + def _update_table(self, + rows: List[Dict], + columns_from_df: List[Dict], + clear_selection: bool, + columns: Optional[List[Dict]], + column_defaults: Optional[Dict]) -> None: + """Helper function to update the table.""" self.rows[:] = rows if column_defaults is not None: self._column_defaults = column_defaults @@ -175,7 +240,17 @@ def update_from_pandas(self, self.update() @staticmethod - def _df_to_rows_and_columns(df: 'pd.DataFrame') -> Tuple[List[Dict], List[Dict]]: + def _polars_df_to_rows_and_columns(df: 'pl.DataFrame') -> Tuple[List[Dict], List[Dict]]: + import polars as pl # pylint: disable=import-outside-toplevel + + df = df.with_columns(pl.exclude(pl.String).cast(str)) + df = df.fill_null('') # fill null values with empty strings + + return df.to_dicts(), [{'name': col, 'label': col, 'field': col} for col in df.columns] + + + @staticmethod + def _pandas_df_to_rows_and_columns(df: 'pd.DataFrame') -> Tuple[List[Dict], List[Dict]]: import pandas as pd # pylint: disable=import-outside-toplevel def is_special_dtype(dtype): diff --git a/tests/test_table.py b/tests/test_table.py index ae7e806f5..bc11a6dcd 100644 --- a/tests/test_table.py +++ b/tests/test_table.py @@ -2,6 +2,8 @@ from typing import List import pandas as pd +import polars as pl + from selenium.webdriver.common.by import By from nicegui import ui @@ -190,6 +192,25 @@ def update(): screen.should_contain('Lionel') screen.should_contain('19') +def test_create_and_update_from_polars(screen: Screen): + df = pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]}) + table = ui.table.from_polars(df) + + def update(): + df = pl.DataFrame({'name': ['Lionel'], 'age': [19]}) + df = df.vstack(df) + table.update_from_polars(df) + ui.button('Update', on_click=update) + + screen.open('/') + screen.should_contain('Alice') + screen.should_contain('Bob') + screen.should_contain('18') + screen.should_contain('21') + + screen.click('Update') + screen.should_contain('Lionel') + screen.should_contain('19') def test_problematic_datatypes(screen: Screen): df = pd.DataFrame({ @@ -212,6 +233,18 @@ def test_problematic_datatypes(screen: Screen): screen.should_contain('(1+2j)') screen.should_contain('2021-01') +def test_problematic_datatypes_for_polars(screen: Screen): + df = pl.DataFrame({ + 'Datetime_col': [datetime(2020, 1, 1).date()], + 'Datetime_col_tz': [datetime(2020, 1, 1, tzinfo=timezone.utc)], + }) + ui.table.from_polars(df) + + screen.open('/') + screen.should_contain('Datetime_col') + screen.should_contain('Datetime_col_tz') + screen.should_contain('2020-01-01') + def test_table_computed_props(screen: Screen): all_rows = rows() @@ -318,3 +351,46 @@ def test_columns_from_df(screen: Screen): screen.click('Update cars with columns') # updated columns via parameter screen.should_contain('Hyundai') screen.should_contain('i30') + +def test_columns_from_polars_df(screen: Screen): + persons = ui.table.from_polars(pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]})) + cars = ui.table.from_polars(pl.DataFrame({'make': ['Ford', 'Toyota'], 'model': ['Focus', 'Corolla']}), + columns=[{'name': 'make', 'label': 'make', 'field': 'make'}]) + + ui.button('Update persons without columns', + on_click=lambda: persons.update_from_polars(pl.DataFrame({'name': ['Dan'], 'age': [5], 'sex': ['male']}))) + + ui.button('Update persons with columns', + on_click=lambda: persons.update_from_polars(pl.DataFrame({'name': ['Stephen'], 'age': [33]}), + columns=[{'name': 'name', 'label': 'Name', 'field': 'name'}])) + + ui.button('Update cars without columns', + on_click=lambda: cars.update_from_polars(pl.DataFrame({'make': ['Honda'], 'model': ['Civic']}))) + + ui.button('Update cars with columns', + on_click=lambda: cars.update_from_polars(pl.DataFrame({'make': ['Hyundai'], 'model': ['i30']}), + columns=[{'name': 'make', 'label': 'make', 'field': 'make'}, + {'name': 'model', 'label': 'model', 'field': 'model'}])) + + screen.open('/') + screen.should_contain('name') + screen.should_contain('age') + screen.should_contain('make') + screen.should_not_contain('model') + + screen.click('Update persons without columns') # infer columns (like during instantiation) + screen.should_contain('Dan') + screen.should_contain('5') + screen.should_contain('male') + + screen.click('Update persons with columns') # updated columns via parameter + screen.should_contain('Stephen') + screen.should_not_contain('32') + + screen.click('Update cars without columns') # don't change columns + screen.should_contain('Honda') + screen.should_not_contain('Civic') + + screen.click('Update cars with columns') # updated columns via parameter + screen.should_contain('Hyundai') + screen.should_contain('i30') \ No newline at end of file diff --git a/website/documentation/content/table_documentation.py b/website/documentation/content/table_documentation.py index 47e248560..bbf13ebd4 100644 --- a/website/documentation/content/table_documentation.py +++ b/website/documentation/content/table_documentation.py @@ -166,6 +166,17 @@ def table_from_pandas_demo(): ui.table.from_pandas(df).classes('max-h-40') +@doc.demo('Table from Polars DataFrame', ''' + You can create a table from a Pandas DataFrame using the `from_polars` method. + This method takes a Pandas DataFrame as input and returns a table. +''') +def table_from_polars_demo(): + import polars as pl + + df = pl.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]}) + ui.table.from_polars(df).classes('max-h-40') + + @doc.demo('Adding rows', ''' It's simple to add new rows with the `add_row(dict)` and `add_rows(list[dict])` methods. With the "virtual-scroll" prop set, the table can be programmatically scrolled with the `scrollTo` JavaScript function. From 74a10472211c013b9125402e5b50c45ba1a9bc24 Mon Sep 17 00:00:00 2001 From: Honey Date: Sun, 10 Nov 2024 20:04:45 +0530 Subject: [PATCH 04/10] Added from_polars method to aggrid --- nicegui/elements/aggrid.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/nicegui/elements/aggrid.py b/nicegui/elements/aggrid.py index 1e53f729b..735dfd5aa 100644 --- a/nicegui/elements/aggrid.py +++ b/nicegui/elements/aggrid.py @@ -12,6 +12,10 @@ if TYPE_CHECKING: import pandas as pd +if importlib.util.find_spec('polars'): + optional_features.register('polars') + if TYPE_CHECKING: + import polars as pl class AgGrid(Element, component='aggrid.js', @@ -41,6 +45,35 @@ def __init__(self, self._props['auto_size_columns'] = auto_size_columns self._classes.append(f'ag-theme-{theme}') + @classmethod + def from_polars(cls, + df: 'pl.DataFrame', *, + theme: str = 'balham', + auto_size_columns: bool = True, + options: Dict = {}) -> Self: + """Create an AG Grid from a Polars DataFrame. + + If the DataFrame contains Non Utf-8 DataTypes, they will be converted to strings. + To use a different conversion, convert the DataFrame manually before passing it to this method. + + :param df: Polars DataFrame + :param theme: AG Grid theme (default: 'balham') + :param auto_size_columns: whether to automatically resize columns to fit the grid width (default: `True`) + :param options: dictionary of additional AG Grid options + :return: AG Grid element + """ + import polars as pl # pylint: disable=import-outside-toplevel + + df = df.with_columns(pl.exclude(pl.String).cast(str)) + df = df.fill_null('') # fill null values with empty strings + + return cls({ + 'columnDefs': [{'field': str(col)} for col in df.columns], + 'rowData': df.to_dicts(), + 'suppressFieldDotNotation': True, + **options, + }, theme=theme, auto_size_columns=auto_size_columns) + @classmethod def from_pandas(cls, df: 'pd.DataFrame', *, From fd6e651dc95871861f3042eb7302edbb3c810759 Mon Sep 17 00:00:00 2001 From: Honey Date: Sun, 10 Nov 2024 20:05:01 +0530 Subject: [PATCH 05/10] Added test cases for from_polars --- tests/test_aggrid.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/tests/test_aggrid.py b/tests/test_aggrid.py index 6abb6bab2..0dfb6c31c 100644 --- a/tests/test_aggrid.py +++ b/tests/test_aggrid.py @@ -2,6 +2,7 @@ from typing import List import pandas as pd +import polars as pl from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.keys import Keys @@ -166,6 +167,22 @@ def test_create_from_pandas(screen: Screen): screen.should_contain('42') screen.should_contain('answer') +def test_create_from_polars(screen: Screen): + df = pl.DataFrame({ + 'name': ['Alice', 'Bob'], + 'age': [18, 21], + '42': 'answer', + }) + ui.aggrid.from_polars(df) + + screen.open('/') + screen.should_contain('Alice') + screen.should_contain('Bob') + screen.should_contain('18') + screen.should_contain('21') + screen.should_contain('42') + screen.should_contain('answer') + def test_create_dynamically(screen: Screen): ui.button('Create', on_click=lambda: ui.aggrid({'columnDefs': [{'field': 'name'}], 'rowData': [{'name': 'Alice'}]})) @@ -205,6 +222,18 @@ def test_problematic_datatypes(screen: Screen): screen.should_contain('(1+2j)') screen.should_contain('2021-01') +def test_problematic_datatypes_for_polars(screen: Screen): + df = pl.DataFrame({ + 'Datetime_col': [datetime(2020, 1, 1).date()], + 'Datetime_col_tz': [datetime(2020, 1, 1, tzinfo=timezone.utc)], + }) + ui.aggrid.from_polars(df) + + screen.open('/') + screen.should_contain('Datetime_col') + screen.should_contain('Datetime_col_tz') + screen.should_contain('2020-01-01') + def test_run_row_method(screen: Screen): grid = ui.aggrid({ From 5f036e52a0aed74b3393f1cdaa3bd75b454e4f95 Mon Sep 17 00:00:00 2001 From: Honey Date: Sun, 10 Nov 2024 20:05:14 +0530 Subject: [PATCH 06/10] Added Documentation for from_polars in ui.aggrid --- .../documentation/content/aggrid_documentation.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/website/documentation/content/aggrid_documentation.py b/website/documentation/content/aggrid_documentation.py index 6c11e596a..a5f3f295c 100644 --- a/website/documentation/content/aggrid_documentation.py +++ b/website/documentation/content/aggrid_documentation.py @@ -126,6 +126,18 @@ def aggrid_from_pandas(): df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]}) ui.aggrid.from_pandas(df).classes('max-h-40') +@doc.demo('Create Grid from Polars DataFrame', ''' + You can create an AG Grid from a Polars DataFrame using the `from_polars` method. + This method takes a Polars DataFrame as input and returns an AG Grid. +''') +def aggrid_from_polars(): + import polars as pl + + df = pl.DataFrame({ + 'col1': [1, 2], + 'col2': [3, 4], + }) + ui.aggrid.from_polars(df).classes('max-h-40') @doc.demo('Render columns as HTML', ''' You can render columns as HTML by passing a list of column indices to the `html_columns` argument. From 4e59630222bea98fcf08f485852ee346e3f5c45a Mon Sep 17 00:00:00 2001 From: Falko Schindler Date: Tue, 12 Nov 2024 09:38:06 +0100 Subject: [PATCH 07/10] code review --- nicegui/elements/aggrid.py | 59 +- nicegui/elements/table.py | 83 +-- nicegui/optional_features.py | 2 +- poetry.lock | 674 +++++++++++------- pyproject.toml | 7 +- tests/test_aggrid.py | 77 +- tests/test_table.py | 170 ++--- .../content/aggrid_documentation.py | 7 +- .../content/table_documentation.py | 4 +- 9 files changed, 595 insertions(+), 488 deletions(-) diff --git a/nicegui/elements/aggrid.py b/nicegui/elements/aggrid.py index 735dfd5aa..977d8b723 100644 --- a/nicegui/elements/aggrid.py +++ b/nicegui/elements/aggrid.py @@ -17,6 +17,7 @@ if TYPE_CHECKING: import polars as pl + class AgGrid(Element, component='aggrid.js', dependencies=['lib/aggrid/ag-grid-community.min.js'], @@ -45,35 +46,6 @@ def __init__(self, self._props['auto_size_columns'] = auto_size_columns self._classes.append(f'ag-theme-{theme}') - @classmethod - def from_polars(cls, - df: 'pl.DataFrame', *, - theme: str = 'balham', - auto_size_columns: bool = True, - options: Dict = {}) -> Self: - """Create an AG Grid from a Polars DataFrame. - - If the DataFrame contains Non Utf-8 DataTypes, they will be converted to strings. - To use a different conversion, convert the DataFrame manually before passing it to this method. - - :param df: Polars DataFrame - :param theme: AG Grid theme (default: 'balham') - :param auto_size_columns: whether to automatically resize columns to fit the grid width (default: `True`) - :param options: dictionary of additional AG Grid options - :return: AG Grid element - """ - import polars as pl # pylint: disable=import-outside-toplevel - - df = df.with_columns(pl.exclude(pl.String).cast(str)) - df = df.fill_null('') # fill null values with empty strings - - return cls({ - 'columnDefs': [{'field': str(col)} for col in df.columns], - 'rowData': df.to_dicts(), - 'suppressFieldDotNotation': True, - **options, - }, theme=theme, auto_size_columns=auto_size_columns) - @classmethod def from_pandas(cls, df: 'pd.DataFrame', *, @@ -118,6 +90,35 @@ def is_special_dtype(dtype): **options, }, theme=theme, auto_size_columns=auto_size_columns) + @classmethod + def from_polars(cls, + df: 'pl.DataFrame', *, + theme: str = 'balham', + auto_size_columns: bool = True, + options: Dict = {}) -> Self: # noqa: B006 + """Create an AG Grid from a Polars DataFrame. + + If the DataFrame contains non-UTF-8 datatypes, they will be converted to strings. + To use a different conversion, convert the DataFrame manually before passing it to this method. + + :param df: Polars DataFrame + :param theme: AG Grid theme (default: 'balham') + :param auto_size_columns: whether to automatically resize columns to fit the grid width (default: `True`) + :param options: dictionary of additional AG Grid options + :return: AG Grid element + """ + import polars as pl # pylint: disable=import-outside-toplevel + + df = df.with_columns(pl.exclude(pl.String).cast(str)) + df = df.fill_null('') # fill null values with empty strings + + return cls({ + 'columnDefs': [{'field': str(col)} for col in df.columns], + 'rowData': df.to_dicts(), + 'suppressFieldDotNotation': True, + **options, + }, theme=theme, auto_size_columns=auto_size_columns) + @property def options(self) -> Dict: """The options dictionary.""" diff --git a/nicegui/elements/table.py b/nicegui/elements/table.py index b36c5d4ff..bdeada19a 100644 --- a/nicegui/elements/table.py +++ b/nicegui/elements/table.py @@ -19,6 +19,10 @@ optional_features.register('pandas') if TYPE_CHECKING: import pandas as pd + +if importlib.util.find_spec('polars'): + optional_features.register('polars') + if TYPE_CHECKING: import polars as pl @@ -107,8 +111,8 @@ def _normalize_columns(self, columns: List[Dict]) -> List[Dict]: return [{**self._column_defaults, **column} for column in columns] if self._column_defaults else columns @classmethod - def from_polars(cls, - df: 'pl.DataFrame', *, + def from_pandas(cls, + df: 'pd.DataFrame', *, columns: Optional[List[Dict]] = None, column_defaults: Optional[Dict] = None, row_key: str = 'id', @@ -116,13 +120,15 @@ def from_polars(cls, selection: Optional[Literal['single', 'multiple']] = None, pagination: Optional[Union[int, dict]] = None, on_select: Optional[Handler[TableSelectionEventArguments]] = None) -> Self: - """Create a table from a Polars DataFrame. + """Create a table from a Pandas DataFrame. Note: - If the DataFrame contains Non Utf-8 DataTypes, they will be converted to strings. + If the DataFrame contains non-serializable columns of type `datetime64[ns]`, `timedelta64[ns]`, `complex128` or `period[M]`, + they will be converted to strings. To use a different conversion, convert the DataFrame manually before passing it to this method. + See `issue 1698 `_ for more information. - :param df: Polars DataFrame + :param df: Pandas DataFrame :param columns: list of column objects (defaults to the columns of the dataframe) :param column_defaults: optional default column properties :param row_key: name of the column containing unique data identifying the row (default: "id") @@ -132,7 +138,7 @@ def from_polars(cls, :param on_select: callback which is invoked when the selection changes :return: table element """ - rows, columns_from_df = cls._polars_df_to_rows_and_columns(df) + rows, columns_from_df = cls._pandas_df_to_rows_and_columns(df) table = cls( rows=rows, columns=columns or columns_from_df, @@ -147,8 +153,8 @@ def from_polars(cls, return table @classmethod - def from_pandas(cls, - df: 'pd.DataFrame', *, + def from_polars(cls, + df: 'pl.DataFrame', *, columns: Optional[List[Dict]] = None, column_defaults: Optional[Dict] = None, row_key: str = 'id', @@ -156,15 +162,13 @@ def from_pandas(cls, selection: Optional[Literal['single', 'multiple']] = None, pagination: Optional[Union[int, dict]] = None, on_select: Optional[Handler[TableSelectionEventArguments]] = None) -> Self: - """Create a table from a Pandas DataFrame. + """Create a table from a Polars DataFrame. Note: - If the DataFrame contains non-serializable columns of type `datetime64[ns]`, `timedelta64[ns]`, `complex128` or `period[M]`, - they will be converted to strings. + If the DataFrame contains non-UTF-8 datatypes, they will be converted to strings. To use a different conversion, convert the DataFrame manually before passing it to this method. - See `issue 1698 `_ for more information. - :param df: Pandas DataFrame + :param df: Polars DataFrame :param columns: list of column objects (defaults to the columns of the dataframe) :param column_defaults: optional default column properties :param row_key: name of the column containing unique data identifying the row (default: "id") @@ -174,7 +178,7 @@ def from_pandas(cls, :param on_select: callback which is invoked when the selection changes :return: table element """ - rows, columns_from_df = cls._pandas_df_to_rows_and_columns(df) + rows, columns_from_df = cls._polars_df_to_rows_and_columns(df) table = cls( rows=rows, columns=columns or columns_from_df, @@ -188,21 +192,6 @@ def from_pandas(cls, table._use_columns_from_df = columns is None return table - def update_from_polars(self, - df: 'pl.DataFrame', *, - clear_selection: bool = True, - columns: Optional[List[Dict]] = None, - column_defaults: Optional[Dict] = None) -> None: - """Update the table from a Polars DataFrame. - - :param df: Polars DataFrame - :param clear_selection: whether to clear the selection (default: True) - :param columns: list of column objects (defaults to the columns of the dataframe) - :param column_defaults: optional default column properties - """ - rows, columns_from_df = self._polars_df_to_rows_and_columns(df) - self._update_table(rows, columns_from_df, clear_selection, columns, column_defaults) - def update_from_pandas(self, df: 'pd.DataFrame', *, clear_selection: bool = True, @@ -222,7 +211,22 @@ def update_from_pandas(self, """ rows, columns_from_df = self._pandas_df_to_rows_and_columns(df) self._update_table(rows, columns_from_df, clear_selection, columns, column_defaults) - + + def update_from_polars(self, + df: 'pl.DataFrame', *, + clear_selection: bool = True, + columns: Optional[List[Dict]] = None, + column_defaults: Optional[Dict] = None) -> None: + """Update the table from a Polars DataFrame. + + :param df: Polars DataFrame + :param clear_selection: whether to clear the selection (default: True) + :param columns: list of column objects (defaults to the columns of the dataframe) + :param column_defaults: optional default column properties + """ + rows, columns_from_df = self._polars_df_to_rows_and_columns(df) + self._update_table(rows, columns_from_df, clear_selection, columns, column_defaults) + def _update_table(self, rows: List[Dict], columns_from_df: List[Dict], @@ -239,16 +243,6 @@ def _update_table(self, self.selected.clear() self.update() - @staticmethod - def _polars_df_to_rows_and_columns(df: 'pl.DataFrame') -> Tuple[List[Dict], List[Dict]]: - import polars as pl # pylint: disable=import-outside-toplevel - - df = df.with_columns(pl.exclude(pl.String).cast(str)) - df = df.fill_null('') # fill null values with empty strings - - return df.to_dicts(), [{'name': col, 'label': col, 'field': col} for col in df.columns] - - @staticmethod def 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[package.dependencies] @@ -3636,4 +3796,4 @@ sass = ["libsass"] [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "344ecf81ec9bf1c8e6863612c14401424543e648c932c47f4dc6a19c6ba55155" +content-hash = "ea3a5ab972a9cee38dcd7c456c2f0c755002f48f17e6c8ce4f4527c68e75495c" diff --git a/pyproject.toml b/pyproject.toml index 0b6a7a12e..4c6642807 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ repository = "https://github.com/zauberzeug/nicegui" keywords = ["gui", "ui", "web", "interface", "live"] [tool.poetry.dependencies] -python = ">=3.9,<4.0" +python = "^3.8" typing-extensions = ">=4.0.0" markdown2 = ">=2.4.7,!=2.4.11" Pygments = ">=2.15.1,<3.0.0" @@ -63,7 +63,10 @@ pyecharts = "^2.0.4" ruff = ">=0.3.5" pre-commit = ">=3.5.0" isort = "^5.11" -polars = "^1.12.0" +polars = [ + {version = "^1.8.0", python = "~3.8"}, + {version = "^1.12.0", python = ">=3.9,<3.13"} +] [tool.poetry.scripts] nicegui-pack = "nicegui.scripts.pack:main" diff --git a/tests/test_aggrid.py b/tests/test_aggrid.py index 0dfb6c31c..f306efb07 100644 --- a/tests/test_aggrid.py +++ b/tests/test_aggrid.py @@ -3,6 +3,7 @@ import pandas as pd import polars as pl +import pytest from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.keys import Keys @@ -155,8 +156,12 @@ def replace(): screen.should_not_contain('Alice') -def test_create_from_pandas(screen: Screen): - df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21], 42: 'answer'}) +@pytest.mark.parametrize('df_type', ['pandas', 'polars']) +def test_create_from_dataframe(screen: Screen, df_type: str): + if df_type == 'pandas': + df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21], 42: 'answer'}) + else: + df = pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21], 42: 'answer'}) ui.aggrid.from_pandas(df) screen.open('/') @@ -167,22 +172,6 @@ def test_create_from_pandas(screen: Screen): screen.should_contain('42') screen.should_contain('answer') -def test_create_from_polars(screen: Screen): - df = pl.DataFrame({ - 'name': ['Alice', 'Bob'], - 'age': [18, 21], - '42': 'answer', - }) - ui.aggrid.from_polars(df) - - screen.open('/') - screen.should_contain('Alice') - screen.should_contain('Bob') - screen.should_contain('18') - screen.should_contain('21') - screen.should_contain('42') - screen.should_contain('answer') - def test_create_dynamically(screen: Screen): ui.button('Create', on_click=lambda: ui.aggrid({'columnDefs': [{'field': 'name'}], 'rowData': [{'name': 'Alice'}]})) @@ -201,38 +190,36 @@ def test_api_method_after_creation(screen: Screen): assert screen.find_by_class('ag-row-selected') -def test_problematic_datatypes(screen: Screen): - df = pd.DataFrame({ - 'datetime_col': [datetime(2020, 1, 1)], - 'datetime_col_tz': [datetime(2020, 1, 1, tzinfo=timezone.utc)], - 'timedelta_col': [timedelta(days=5)], - 'complex_col': [1 + 2j], - 'period_col': pd.Series([pd.Period('2021-01')]), - }) - ui.aggrid.from_pandas(df) +@pytest.mark.parametrize('df_type', ['pandas', 'polars']) +def test_problematic_datatypes(screen: Screen, df_type: str): + if df_type == 'pandas': + df = pd.DataFrame({ + 'datetime_col': [datetime(2020, 1, 1)], + 'datetime_col_tz': [datetime(2020, 1, 2, tzinfo=timezone.utc)], + 'timedelta_col': [timedelta(days=5)], + 'complex_col': [1 + 2j], + 'period_col': pd.Series([pd.Period('2021-01')]), + }) + ui.aggrid.from_pandas(df) + else: + df = pl.DataFrame({ + 'datetime_col': [datetime(2020, 1, 1)], + 'datetime_col_tz': [datetime(2020, 1, 2, tzinfo=timezone.utc)], + }) + ui.aggrid.from_polars(df) screen.open('/') screen.should_contain('Datetime_col') - screen.should_contain('Datetime_col_tz') - screen.should_contain('Timedelta_col') - screen.should_contain('Complex_col') - screen.should_contain('Period_col') screen.should_contain('2020-01-01') - screen.should_contain('5 days') - screen.should_contain('(1+2j)') - screen.should_contain('2021-01') - -def test_problematic_datatypes_for_polars(screen: Screen): - df = pl.DataFrame({ - 'Datetime_col': [datetime(2020, 1, 1).date()], - 'Datetime_col_tz': [datetime(2020, 1, 1, tzinfo=timezone.utc)], - }) - ui.aggrid.from_polars(df) - - screen.open('/') - screen.should_contain('Datetime_col') screen.should_contain('Datetime_col_tz') - screen.should_contain('2020-01-01') + screen.should_contain('2020-01-02') + if df_type == 'pandas': + screen.should_contain('Timedelta_col') + screen.should_contain('5 days') + screen.should_contain('Complex_col') + screen.should_contain('(1+2j)') + screen.should_contain('Period_col') + screen.should_contain('2021-01') def test_run_row_method(screen: Screen): diff --git a/tests/test_table.py b/tests/test_table.py index bc11a6dcd..ce3eb4537 100644 --- a/tests/test_table.py +++ b/tests/test_table.py @@ -3,7 +3,7 @@ import pandas as pd import polars as pl - +import pytest from selenium.webdriver.common.by import By from nicegui import ui @@ -173,14 +173,20 @@ def replace_rows_with_daniel(): screen.should_contain('Daniel') -def test_create_and_update_from_pandas(screen: Screen): - df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]}) - table = ui.table.from_pandas(df) +@pytest.mark.parametrize('df_type', ['pandas', 'polars']) +def test_create_and_update_from_df(screen: Screen, df_type: str): + if df_type == 'pandas': + DataFrame = pd.DataFrame + df = DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]}) + table = ui.table.from_pandas(df) + update_from_df = table.update_from_pandas + else: + DataFrame = pl.DataFrame + df = DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]}) + table = ui.table.from_polars(df) + update_from_df = table.update_from_polars - def update(): - df.loc[2] = ['Lionel', 19] - table.update_from_pandas(df) - ui.button('Update', on_click=update) + ui.button('Update', on_click=lambda: update_from_df(DataFrame({'name': ['Lionel'], 'age': [19]}))) screen.open('/') screen.should_contain('Alice') @@ -192,58 +198,37 @@ def update(): screen.should_contain('Lionel') screen.should_contain('19') -def test_create_and_update_from_polars(screen: Screen): - df = pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]}) - table = ui.table.from_polars(df) - - def update(): - df = pl.DataFrame({'name': ['Lionel'], 'age': [19]}) - df = df.vstack(df) - table.update_from_polars(df) - ui.button('Update', on_click=update) - - screen.open('/') - screen.should_contain('Alice') - screen.should_contain('Bob') - screen.should_contain('18') - screen.should_contain('21') - - screen.click('Update') - screen.should_contain('Lionel') - screen.should_contain('19') -def test_problematic_datatypes(screen: Screen): - df = pd.DataFrame({ - 'Datetime_col': [datetime(2020, 1, 1)], - 'Datetime_col_tz': [datetime(2020, 1, 1, tzinfo=timezone.utc)], - 'Timedelta_col': [timedelta(days=5)], - 'Complex_col': [1 + 2j], - 'Period_col': pd.Series([pd.Period('2021-01')]), - }) - ui.table.from_pandas(df) +@pytest.mark.parametrize('df_type', ['pandas', 'polars']) +def test_problematic_datatypes(screen: Screen, df_type: str): + if df_type == 'pandas': + df = pd.DataFrame({ + 'Datetime_col': [datetime(2020, 1, 1)], + 'Datetime_col_tz': [datetime(2020, 1, 2, tzinfo=timezone.utc)], + 'Timedelta_col': [timedelta(days=5)], + 'Complex_col': [1 + 2j], + 'Period_col': pd.Series([pd.Period('2021-01')]), + }) + ui.table.from_pandas(df) + else: + df = pl.DataFrame({ + 'Datetime_col': [datetime(2020, 1, 1)], + 'Datetime_col_tz': [datetime(2020, 1, 2, tzinfo=timezone.utc)], + }) + ui.table.from_polars(df) screen.open('/') screen.should_contain('Datetime_col') - screen.should_contain('Datetime_col_tz') - screen.should_contain('Timedelta_col') - screen.should_contain('Complex_col') - screen.should_contain('Period_col') screen.should_contain('2020-01-01') - screen.should_contain('5 days') - screen.should_contain('(1+2j)') - screen.should_contain('2021-01') - -def test_problematic_datatypes_for_polars(screen: Screen): - df = pl.DataFrame({ - 'Datetime_col': [datetime(2020, 1, 1).date()], - 'Datetime_col_tz': [datetime(2020, 1, 1, tzinfo=timezone.utc)], - }) - ui.table.from_polars(df) - - screen.open('/') - screen.should_contain('Datetime_col') screen.should_contain('Datetime_col_tz') - screen.should_contain('2020-01-01') + screen.should_contain('2020-01-02') + if df_type == 'pandas': + screen.should_contain('Timedelta_col') + screen.should_contain('5 days') + screen.should_contain('Complex_col') + screen.should_contain('(1+2j)') + screen.should_contain('Period_col') + screen.should_contain('2021-01') def test_table_computed_props(screen: Screen): @@ -309,25 +294,37 @@ def test_default_column_parameters(screen: Screen): assert len(screen.find_all_by_class('sortable')) == 2 -def test_columns_from_df(screen: Screen): - persons = ui.table.from_pandas(pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]})) - cars = ui.table.from_pandas(pd.DataFrame({'make': ['Ford', 'Toyota'], 'model': ['Focus', 'Corolla']}), - columns=[{'name': 'make', 'label': 'make', 'field': 'make'}]) +@pytest.mark.parametrize('df_type', ['pandas', 'polars']) +def test_columns_from_df(screen: Screen, df_type: str): + if df_type == 'pandas': + persons = ui.table.from_pandas(pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]})) + cars = ui.table.from_pandas(pd.DataFrame({'make': ['Ford', 'Toyota'], 'model': ['Focus', 'Corolla']}), + columns=[{'name': 'make', 'label': 'make', 'field': 'make'}]) + DataFrame = pd.DataFrame + update_persons_from_df = persons.update_from_pandas + update_cars_from_df = cars.update_from_pandas + else: + persons = ui.table.from_polars(pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]})) + cars = ui.table.from_polars(pl.DataFrame({'make': ['Ford', 'Toyota'], 'model': ['Focus', 'Corolla']}), + columns=[{'name': 'make', 'label': 'make', 'field': 'make'}]) + DataFrame = pl.DataFrame + update_persons_from_df = persons.update_from_polars + update_cars_from_df = cars.update_from_polars ui.button('Update persons without columns', - on_click=lambda: persons.update_from_pandas(pd.DataFrame({'name': ['Dan'], 'age': [5], 'sex': ['male']}))) + on_click=lambda: update_persons_from_df(DataFrame({'name': ['Dan'], 'age': [5], 'sex': ['male']}))) ui.button('Update persons with columns', - on_click=lambda: persons.update_from_pandas(pd.DataFrame({'name': ['Stephen'], 'age': [33]}), - columns=[{'name': 'name', 'label': 'Name', 'field': 'name'}])) + on_click=lambda: update_persons_from_df(DataFrame({'name': ['Stephen'], 'age': [33]}), + columns=[{'name': 'name', 'label': 'Name', 'field': 'name'}])) ui.button('Update cars without columns', - on_click=lambda: cars.update_from_pandas(pd.DataFrame({'make': ['Honda'], 'model': ['Civic']}))) + on_click=lambda: update_cars_from_df(DataFrame({'make': ['Honda'], 'model': ['Civic']}))) ui.button('Update cars with columns', - on_click=lambda: cars.update_from_pandas(pd.DataFrame({'make': ['Hyundai'], 'model': ['i30']}), - columns=[{'name': 'make', 'label': 'make', 'field': 'make'}, - {'name': 'model', 'label': 'model', 'field': 'model'}])) + on_click=lambda: update_cars_from_df(DataFrame({'make': ['Hyundai'], 'model': ['i30']}), + columns=[{'name': 'make', 'label': 'make', 'field': 'make'}, + {'name': 'model', 'label': 'model', 'field': 'model'}])) screen.open('/') screen.should_contain('name') @@ -351,46 +348,3 @@ def test_columns_from_df(screen: Screen): screen.click('Update cars with columns') # updated columns via parameter screen.should_contain('Hyundai') screen.should_contain('i30') - -def test_columns_from_polars_df(screen: Screen): - persons = ui.table.from_polars(pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21]})) - cars = ui.table.from_polars(pl.DataFrame({'make': ['Ford', 'Toyota'], 'model': ['Focus', 'Corolla']}), - columns=[{'name': 'make', 'label': 'make', 'field': 'make'}]) - - ui.button('Update persons without columns', - on_click=lambda: persons.update_from_polars(pl.DataFrame({'name': ['Dan'], 'age': [5], 'sex': ['male']}))) - - ui.button('Update persons with columns', - on_click=lambda: persons.update_from_polars(pl.DataFrame({'name': ['Stephen'], 'age': [33]}), - columns=[{'name': 'name', 'label': 'Name', 'field': 'name'}])) - - ui.button('Update cars without columns', - on_click=lambda: cars.update_from_polars(pl.DataFrame({'make': ['Honda'], 'model': ['Civic']}))) - - ui.button('Update cars with columns', - on_click=lambda: cars.update_from_polars(pl.DataFrame({'make': ['Hyundai'], 'model': ['i30']}), - columns=[{'name': 'make', 'label': 'make', 'field': 'make'}, - {'name': 'model', 'label': 'model', 'field': 'model'}])) - - screen.open('/') - screen.should_contain('name') - screen.should_contain('age') - screen.should_contain('make') - screen.should_not_contain('model') - - screen.click('Update persons without columns') # infer columns (like during instantiation) - screen.should_contain('Dan') - screen.should_contain('5') - screen.should_contain('male') - - screen.click('Update persons with columns') # updated columns via parameter - screen.should_contain('Stephen') - screen.should_not_contain('32') - - screen.click('Update cars without columns') # don't change columns - screen.should_contain('Honda') - screen.should_not_contain('Civic') - - screen.click('Update cars with columns') # updated columns via parameter - screen.should_contain('Hyundai') - screen.should_contain('i30') \ No newline at end of file diff --git a/website/documentation/content/aggrid_documentation.py b/website/documentation/content/aggrid_documentation.py index a5f3f295c..b4bf9d9b3 100644 --- a/website/documentation/content/aggrid_documentation.py +++ b/website/documentation/content/aggrid_documentation.py @@ -126,6 +126,7 @@ def aggrid_from_pandas(): df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]}) ui.aggrid.from_pandas(df).classes('max-h-40') + @doc.demo('Create Grid from Polars DataFrame', ''' You can create an AG Grid from a Polars DataFrame using the `from_polars` method. This method takes a Polars DataFrame as input and returns an AG Grid. @@ -133,12 +134,10 @@ def aggrid_from_pandas(): def aggrid_from_polars(): import polars as pl - df = pl.DataFrame({ - 'col1': [1, 2], - 'col2': [3, 4], - }) + df = pl.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]}) ui.aggrid.from_polars(df).classes('max-h-40') + @doc.demo('Render columns as HTML', ''' You can render columns as HTML by passing a list of column indices to the `html_columns` argument. ''') diff --git a/website/documentation/content/table_documentation.py b/website/documentation/content/table_documentation.py index bbf13ebd4..e0865486a 100644 --- a/website/documentation/content/table_documentation.py +++ b/website/documentation/content/table_documentation.py @@ -167,8 +167,8 @@ def table_from_pandas_demo(): @doc.demo('Table from Polars DataFrame', ''' - You can create a table from a Pandas DataFrame using the `from_polars` method. - This method takes a Pandas DataFrame as input and returns a table. + You can create a table from a Polars DataFrame using the `from_polars` method. + This method takes a Polars DataFrame as input and returns a table. ''') def table_from_polars_demo(): import polars as pl From 165ea733ca28b9e80f49913fa5201cb813a371c2 Mon Sep 17 00:00:00 2001 From: Falko Schindler Date: Wed, 13 Nov 2024 10:53:06 +0100 Subject: [PATCH 08/10] remove casting and null replacement --- nicegui/elements/aggrid.py | 5 ----- nicegui/elements/table.py | 5 ----- 2 files changed, 10 deletions(-) diff --git a/nicegui/elements/aggrid.py b/nicegui/elements/aggrid.py index 977d8b723..bcf220bcd 100644 --- a/nicegui/elements/aggrid.py +++ b/nicegui/elements/aggrid.py @@ -107,11 +107,6 @@ def from_polars(cls, :param options: dictionary of additional AG Grid options :return: AG Grid element """ - import polars as pl # pylint: disable=import-outside-toplevel - - df = df.with_columns(pl.exclude(pl.String).cast(str)) - df = df.fill_null('') # fill null values with empty strings - return cls({ 'columnDefs': [{'field': str(col)} for col in df.columns], 'rowData': df.to_dicts(), diff --git a/nicegui/elements/table.py b/nicegui/elements/table.py index bdeada19a..d478ad6d2 100644 --- a/nicegui/elements/table.py +++ b/nicegui/elements/table.py @@ -266,11 +266,6 @@ def is_special_dtype(dtype): @staticmethod def _polars_df_to_rows_and_columns(df: 'pl.DataFrame') -> Tuple[List[Dict], List[Dict]]: - import polars as pl # pylint: disable=import-outside-toplevel - - df = df.with_columns(pl.exclude(pl.String).cast(str)) - df = df.fill_null('') # fill null values with empty strings - return df.to_dicts(), [{'name': col, 'label': col, 'field': col} for col in df.columns] @property From e4d57fac91b8f49099a57cb270021883258c70bb Mon Sep 17 00:00:00 2001 From: Falko Schindler Date: Wed, 13 Nov 2024 10:57:06 +0100 Subject: [PATCH 09/10] ignore mypy warning about polars import --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 4c6642807..5e0f16fbf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -93,6 +93,7 @@ module = [ "matplotlib.*", "nicegui_highcharts", "plotly.*", + "polars.*", "pyecharts.*", "sass", "socketio.*", From e4e43488779036a83e109299b10e7ca916ec1b3b Mon Sep 17 00:00:00 2001 From: Falko Schindler Date: Wed, 13 Nov 2024 11:03:39 +0100 Subject: [PATCH 10/10] fix pytest --- tests/test_aggrid.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/test_aggrid.py b/tests/test_aggrid.py index f306efb07..111aa9082 100644 --- a/tests/test_aggrid.py +++ b/tests/test_aggrid.py @@ -160,9 +160,10 @@ def replace(): def test_create_from_dataframe(screen: Screen, df_type: str): if df_type == 'pandas': df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21], 42: 'answer'}) + ui.aggrid.from_pandas(df) else: - df = pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21], 42: 'answer'}) - ui.aggrid.from_pandas(df) + df = pl.DataFrame({'name': ['Alice', 'Bob'], 'age': [18, 21], '42': 'answer'}) + ui.aggrid.from_polars(df) screen.open('/') screen.should_contain('Alice')