From 2c32eede1db32e52434b0d03038d8afe2ae8111e Mon Sep 17 00:00:00 2001 From: GideonKoenig <44088734+GideonKoenig@users.noreply.github.com> Date: Fri, 27 Jan 2023 10:40:41 +0100 Subject: [PATCH] feat: __repr__ method to Schema, Row, Column, SupervisedDataset & Table; summary() to Column & Table (#333) Closes #319. ### Summary of Changes - Added `__str__()`, `__repr__()` and `_ipython_display_()` methods to the classes `Table`, `Column`, `Row`, `TableSchema`and `SupervisedDataset`. - Added the method `summary()` to the `Table` class - Changed the raised error in some of the ColumnStatistics methods from `TypeError` to `NonNumericColumnError` - Moved the methods `idness()` and `stability()` from the class `Column` to `ColumnStatistics` - Refactored the test accordingly ### Testing Instructions Create an instance of one of the mentioned classes and either call the print method on it or use the jupyter notebook interactive display method to visualize it in a prettier way. Co-authored-by: Marvin Walter Co-authored-by: GideonKoenig Co-authored-by: WinPlay02 Co-authored-by: SmiteDeluxe Co-authored-by: SmiteDeluxe --- Runtime/safe-ds/poetry.lock | 1432 ++++++++++------- Runtime/safe-ds/pyproject.toml | 1 + Runtime/safe-ds/safe_ds/data/_column.py | 135 +- Runtime/safe-ds/safe_ds/data/_row.py | 66 +- .../safe_ds/data/_supervised_dataset.py | 26 + Runtime/safe-ds/safe_ds/data/_table.py | 134 +- Runtime/safe-ds/safe_ds/data/_table_schema.py | 5 +- .../safe-ds/tests/data/_column/test_idness.py | 4 +- .../safe-ds/tests/data/_column/test_max.py | 3 +- .../safe-ds/tests/data/_column/test_mean.py | 3 +- .../safe-ds/tests/data/_column/test_median.py | 3 +- .../safe-ds/tests/data/_column/test_min.py | 3 +- .../tests/data/_column/test_stability.py | 8 +- .../tests/data/_row/test_get_column_names.py | 21 + .../data/_table/test_get_column_names.py | 12 + .../safe-ds/tests/data/_table/test_summary.py | 42 + .../tests/data/_table_schema/test__str__.py | 2 +- 17 files changed, 1282 insertions(+), 618 deletions(-) create mode 100644 Runtime/safe-ds/tests/data/_row/test_get_column_names.py create mode 100644 Runtime/safe-ds/tests/data/_table/test_get_column_names.py create mode 100644 Runtime/safe-ds/tests/data/_table/test_summary.py diff --git a/Runtime/safe-ds/poetry.lock b/Runtime/safe-ds/poetry.lock index 5dcf0f9e7..8b24053fc 100644 --- a/Runtime/safe-ds/poetry.lock +++ b/Runtime/safe-ds/poetry.lock @@ -1,183 +1,81 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +[[package]] +name = "appnope" +version = "0.1.3" +description = "Disable App Nap on macOS >= 10.9" +category = "main" +optional = false +python-versions = "*" + +[[package]] +name = "asttokens" +version = "2.2.1" +description = "Annotate AST trees with source code positions" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +six = "*" + +[package.extras] +test = ["astroid", "pytest"] [[package]] name = "attrs" 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a/Runtime/safe-ds/safe_ds/data/_column.py b/Runtime/safe-ds/safe_ds/data/_column.py index a79554820..b5e4c001e 100644 --- a/Runtime/safe-ds/safe_ds/data/_column.py +++ b/Runtime/safe-ds/safe_ds/data/_column.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd +from IPython.core.display_functions import DisplayHandle, display from safe_ds.exceptions import ( ColumnLengthMismatchError, ColumnSizeError, @@ -73,24 +74,6 @@ def get_value(self, index: int) -> Any: return self._data[index] - def idness(self) -> float: - """ - Calculates the idness of this column (number of unique values / number of rows). - - Returns - ------- - idness: float - The idness of the column - - Raises - ------ - ColumnSizeError - If this column is empty - """ - if self._data.size == 0: - raise ColumnSizeError("> 0", "0") - return self._data.nunique() / self._data.size - @property def statistics(self) -> ColumnStatistics: return ColumnStatistics(self) @@ -204,25 +187,6 @@ def has_missing_values(self) -> bool: or (isinstance(value, Number) and np.isnan(value)) ) - def stability(self) -> float: - """ - Calculates the stability of this column. - The value is calculated as the ratio between the number of mode values and the number of non-null-values. - - Returns - ------- - stability: float - Stability of this column - - Raises - ------ - ColumnSizeError - If this column is empty - """ - if self._data.size == 0: - raise ColumnSizeError("> 0", "0") - return self._data.value_counts()[self.statistics.mode()] / self._data.count() - def correlation_with(self, other_column: Column) -> float: """ Calculates Pearson correlation between this and another column, if both are numerical @@ -270,6 +234,33 @@ def __eq__(self, other: object) -> bool: def __hash__(self) -> int: return hash(self._data) + def __str__(self) -> str: + tmp = self._data.to_frame() + tmp.columns = [self.name] + return tmp.__str__() + + def __repr__(self) -> str: + tmp = self._data.to_frame() + tmp.columns = [self.name] + return tmp.__repr__() + + def _ipython_display_(self) -> DisplayHandle: + """ + Returns a pretty display object for the Column to be used in Jupyter Notebooks + + Returns + ------- + output: DisplayHandle + Output object + """ + tmp = self._data.to_frame() + tmp.columns = [self.name] + + with pd.option_context( + "display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1] + ): + return display(tmp) + class ColumnStatistics: def __init__(self, column: Column): @@ -286,11 +277,13 @@ def max(self) -> float: Raises ------ - TypeError + NonNumericColumnError If the data contains non-numerical data. """ if not self.column._type.is_numeric(): - raise TypeError("The column contains non numerical data.") + raise NonNumericColumnError( + f"{self.column.name} is of type {self.column._type}." + ) return self.column._data.max() def min(self) -> float: @@ -304,11 +297,13 @@ def min(self) -> float: Raises ------ - TypeError + NonNumericColumnError If the data contains non-numerical data. """ if not self.column._type.is_numeric(): - raise TypeError("The column contains non numerical data.") + raise NonNumericColumnError( + f"{self.column.name} is of type {self.column._type}." + ) return self.column._data.min() def mean(self) -> float: @@ -322,11 +317,13 @@ def mean(self) -> float: Raises ------ - TypeError + NonNumericColumnError If the data contains non-numerical data. """ if not self.column._type.is_numeric(): - raise TypeError("The column contains non numerical data.") + raise NonNumericColumnError( + f"{self.column.name} is of type {self.column._type}." + ) return self.column._data.mean() def mode(self) -> Any: @@ -351,11 +348,13 @@ def median(self) -> float: Raises ------ - TypeError + NonNumericColumnError If the data contains non-numerical data. """ if not self.column._type.is_numeric(): - raise TypeError("The column contains non numerical data.") + raise NonNumericColumnError( + f"{self.column.name} is of type {self.column._type}." + ) return self.column._data.median() def sum(self) -> float: @@ -369,7 +368,7 @@ def sum(self) -> float: Raises --- - NonNumericalColumnError + NonNumericColumnError If the data is non numerical """ @@ -391,7 +390,7 @@ def variance(self) -> float: Raises --- - NonNumericalColumnError + NonNumericColumnError If the data is non numerical """ @@ -414,7 +413,7 @@ def standard_deviation(self) -> float: Raises --- - NonNumericalColumnError + NonNumericColumnError If the data is non numerical """ @@ -423,3 +422,43 @@ def standard_deviation(self) -> float: f"{self.column.name} is of type {self.column._type}." ) return self.column._data.std() + + def stability(self) -> float: + """ + Calculates the stability of this column. + The value is calculated as the ratio between the number of mode values and the number of non-null-values. + + Returns + ------- + stability: float + Stability of this column + + Raises + ------ + ColumnSizeError + If this column is empty + """ + if self.column._data.size == 0: + raise ColumnSizeError("> 0", "0") + return ( + self.column._data.value_counts()[self.column.statistics.mode()] + / self.column._data.count() + ) + + def idness(self) -> float: + """ + Calculates the idness of this column (number of unique values / number of rows). + + Returns + ------- + idness: float + The idness of the column + + Raises + ------ + ColumnSizeError + If this column is empty + """ + if self.column._data.size == 0: + raise ColumnSizeError("> 0", "0") + return self.column._data.nunique() / self.column._data.size diff --git a/Runtime/safe-ds/safe_ds/data/_row.py b/Runtime/safe-ds/safe_ds/data/_row.py index 323065874..e64ab1980 100644 --- a/Runtime/safe-ds/safe_ds/data/_row.py +++ b/Runtime/safe-ds/safe_ds/data/_row.py @@ -2,8 +2,10 @@ from typing import Any import pandas as pd +from IPython.core.display_functions import DisplayHandle, display from safe_ds.exceptions import UnknownColumnNameError +from ._column_type import ColumnType from ._table_schema import TableSchema @@ -35,7 +37,8 @@ def get_value(self, column_name: str) -> Any: def has_column(self, column_name: str) -> bool: """ - Returns if the row contains a given column + Alias for self.schema.hasColumn(column_name: str) -> bool. + Returns if the row contains a given column. Parameters ---------- @@ -49,6 +52,40 @@ def has_column(self, column_name: str) -> bool: """ return self.schema.has_column(column_name) + def get_column_names(self) -> list[str]: + """ + Alias for self.schema.get_column_names() -> list[str]. + Returns a list of all column names saved in this schema + + Returns + ------- + column_names: list[str] + the column names + """ + return self.schema.get_column_names() + + def get_type_of_column(self, column_name: str) -> ColumnType: + """ + Alias for self.schema.get_type_of_column(column_name: str) -> ColumnType. + Returns the type of the given column. + + Parameters + ---------- + column_name : str + The name of the column you want the type of + + Returns + ------- + type: ColumnType + The type of the column + + Raises + ------ + ColumnNameError + If the specified target column name doesn't exist + """ + return self.schema.get_type_of_column(column_name) + def __eq__(self, other: typing.Any) -> bool: if not isinstance(other, Row): return NotImplemented @@ -58,3 +95,30 @@ def __eq__(self, other: typing.Any) -> bool: def __hash__(self) -> int: return hash(self._data) + + def __str__(self) -> str: + tmp = self._data.to_frame().T + tmp.columns = self.get_column_names() + return tmp.__str__() + + def __repr__(self) -> str: + tmp = self._data.to_frame().T + tmp.columns = self.get_column_names() + return tmp.__repr__() + + def _ipython_display_(self) -> DisplayHandle: + """ + Returns a pretty display object for the Row to be used in Jupyter Notebooks + + Returns + ------- + output: DisplayHandle + Output object + """ + tmp = self._data.to_frame().T + tmp.columns = self.get_column_names() + + with pd.option_context( + "display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1] + ): + return display(tmp) diff --git a/Runtime/safe-ds/safe_ds/data/_supervised_dataset.py b/Runtime/safe-ds/safe_ds/data/_supervised_dataset.py index c30a25539..6033db847 100644 --- a/Runtime/safe-ds/safe_ds/data/_supervised_dataset.py +++ b/Runtime/safe-ds/safe_ds/data/_supervised_dataset.py @@ -1,3 +1,5 @@ +from IPython.core.display_functions import DisplayHandle + from ._column import Column from ._table import Table @@ -26,3 +28,27 @@ def feature_vectors(self) -> Table: @property def target_values(self) -> Column: return self._y + + def __repr__(self) -> str: + tmp = self._X.add_column(self._y) + header_info = "Target Column is '" + self._y.name + "'\n" + return header_info + tmp.__repr__() + + def __str__(self) -> str: + tmp = self._X.add_column(self._y) + header_info = "Target Column is '" + self._y.name + "'\n" + return header_info + tmp.__str__() + + def _ipython_display_(self) -> DisplayHandle: + """ + Returns a pretty display object for the Table to be used in Jupyter Notebooks + + Returns + ------- + output: DisplayHandle + Output object + """ + tmp = self._X.add_column(self._y) + header_info = "Target Column is '" + self._y.name + "'\n" + print(header_info) + return tmp._ipython_display_() diff --git a/Runtime/safe-ds/safe_ds/data/_table.py b/Runtime/safe-ds/safe_ds/data/_table.py index 594aa1d1c..5c39e677c 100644 --- a/Runtime/safe-ds/safe_ds/data/_table.py +++ b/Runtime/safe-ds/safe_ds/data/_table.py @@ -7,6 +7,7 @@ from typing import Callable, Optional, Union import pandas as pd +from IPython.core.display_functions import DisplayHandle, display from pandas import DataFrame, Series from safe_ds.exceptions import ( ColumnLengthMismatchError, @@ -14,11 +15,13 @@ DuplicateColumnNameError, IndexOutOfBoundsError, MissingSchemaError, + NonNumericColumnError, SchemaMismatchError, UnknownColumnNameError, ) from ._column import Column +from ._column_type import ColumnType from ._row import Row from ._table_schema import TableSchema @@ -592,7 +595,8 @@ def add_rows(self, rows: Union[list[Row], Table]) -> Table: def has_column(self, column_name: str) -> bool: """ - Returns if the table contains a given column + Alias for self.schema.hasColumn(column_name: str) -> bool. + Returns if the table contains a given column. Parameters ---------- @@ -637,6 +641,40 @@ def list_columns_with_non_numerical_values(self) -> list[Column]: cols.append(self.get_column(column_name)) return cols + def get_column_names(self) -> list[str]: + """ + Alias for self.schema.get_column_names() -> list[str]. + Returns a list of all column names saved in this schema + + Returns + ------- + column_names: list[str] + the column names + """ + return self.schema.get_column_names() + + def get_type_of_column(self, column_name: str) -> ColumnType: + """ + Alias for self.schema.get_type_of_column(column_name: str) -> ColumnType. + Returns the type of the given column. + + Parameters + ---------- + column_name : str + The name of the column you want the type of + + Returns + ------- + type: ColumnType + The type of the column + + Raises + ------ + ColumnNameError + If the specified target column name doesn't exist + """ + return self.schema.get_type_of_column(column_name) + def sort_columns( self, query: Callable[[Column, Column], int] = lambda col1, col2: ( @@ -667,18 +705,6 @@ def sort_columns( columns.sort(key=functools.cmp_to_key(query)) return Table.from_columns(columns) - def __eq__(self, other: typing.Any) -> bool: - if not isinstance(other, Table): - return NotImplemented - if self is other: - return True - table1 = self.sort_columns() - table2 = other.sort_columns() - return table1._data.equals(table2._data) and table1.schema == table2.schema - - def __hash__(self) -> int: - return hash(self._data) - def transform_column( self, name: str, transformer: Callable[[Row], typing.Any] ) -> Table: @@ -702,6 +728,88 @@ def transform_column( return self.replace_column(name, result) raise UnknownColumnNameError([name]) + def summary(self) -> Table: + """ + Returns a Table with a number of statistical key values for a table + + Returns + ------- + result: Table + Table with statistics + """ + + columns = self.to_columns() + result = pd.DataFrame() + statistics = {} + + for column in columns: + statistics = { + "max": column.statistics.max, + "min": column.statistics.min, + "mean": column.statistics.mean, + "mode": column.statistics.mode, + "median": column.statistics.median, + "sum": column.statistics.sum, + "variance": column.statistics.variance, + "standard deviation": column.statistics.standard_deviation, + "idness": column.statistics.idness, + "stability": column.statistics.stability, + "row count": column.count, + } + values = [] + + for function in statistics.values(): + try: + values.append(function()) + except NonNumericColumnError: + values.append("-") + + result = pd.concat([result, pd.DataFrame(values)], axis=1) + + result = pd.concat([pd.DataFrame(list(statistics.keys())), result], axis=1) + result.columns = [""] + self.get_column_names() + + return Table(result) + + def __eq__(self, other: typing.Any) -> bool: + if not isinstance(other, Table): + return NotImplemented + if self is other: + return True + table1 = self.sort_columns() + table2 = other.sort_columns() + return table1._data.equals(table2._data) and table1.schema == table2.schema + + def __hash__(self) -> int: + return hash(self._data) + + def __repr__(self) -> str: + tmp = self._data.copy(deep=True) + tmp.columns = self.get_column_names() + return tmp.__repr__() + + def __str__(self) -> str: + tmp = self._data.copy(deep=True) + tmp.columns = self.get_column_names() + return tmp.__str__() + + def _ipython_display_(self) -> DisplayHandle: + """ + Returns a pretty display object for the Table to be used in Jupyter Notebooks + + Returns + ------- + output: DisplayHandle + Output object + """ + tmp = self._data.copy(deep=True) + tmp.columns = self.get_column_names() + + with pd.option_context( + "display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1] + ): + return display(tmp) + def slice( self, start: typing.Optional[int] = None, diff --git a/Runtime/safe-ds/safe_ds/data/_table_schema.py b/Runtime/safe-ds/safe_ds/data/_table_schema.py index 35f773f89..0511d7093 100644 --- a/Runtime/safe-ds/safe_ds/data/_table_schema.py +++ b/Runtime/safe-ds/safe_ds/data/_table_schema.py @@ -123,5 +123,8 @@ def __str__(self) -> str: column_count = len(self._schema) output_string = f"TableSchema:\nColumn Count: {column_count}\nColumns:\n" for column_name, data_type in self._schema.items(): - output_string += f" {column_name}, {data_type}\n" + output_string += f" {column_name}: {data_type}\n" return output_string + + def __repr__(self) -> str: + return self.__str__() diff --git a/Runtime/safe-ds/tests/data/_column/test_idness.py b/Runtime/safe-ds/tests/data/_column/test_idness.py index 884b4488c..f760bf7d0 100644 --- a/Runtime/safe-ds/tests/data/_column/test_idness.py +++ b/Runtime/safe-ds/tests/data/_column/test_idness.py @@ -10,11 +10,11 @@ ) def test_idness_valid(values: list[str], result: float) -> None: column: Column = Column(pd.Series(values), "test_idness_valid") - idness = column.idness() + idness = column.statistics.idness() assert idness == result def test_idness_invalid() -> None: column = Column(pd.Series([], dtype=int), "test_idness_invalid") with pytest.raises(ColumnSizeError): - column.idness() + column.statistics.idness() diff --git a/Runtime/safe-ds/tests/data/_column/test_max.py b/Runtime/safe-ds/tests/data/_column/test_max.py index d8b3b2953..e98a6d20d 100644 --- a/Runtime/safe-ds/tests/data/_column/test_max.py +++ b/Runtime/safe-ds/tests/data/_column/test_max.py @@ -1,10 +1,11 @@ import pandas as pd import pytest from safe_ds.data import Table +from safe_ds.exceptions import NonNumericColumnError def test_max_invalid() -> None: - with pytest.raises(TypeError): + with pytest.raises(NonNumericColumnError): table = Table(pd.DataFrame(data={"col1": ["col1_1", 2]})) column = table.get_column("col1") column.statistics.max() diff --git a/Runtime/safe-ds/tests/data/_column/test_mean.py b/Runtime/safe-ds/tests/data/_column/test_mean.py index c205b48bd..30f2a69bd 100644 --- a/Runtime/safe-ds/tests/data/_column/test_mean.py +++ b/Runtime/safe-ds/tests/data/_column/test_mean.py @@ -1,10 +1,11 @@ import pandas as pd import pytest from safe_ds.data import Table +from safe_ds.exceptions import NonNumericColumnError def test_mean_invalid() -> None: - with pytest.raises(TypeError): + with pytest.raises(NonNumericColumnError): table = Table(pd.DataFrame(data={"col1": ["col1_1", 2]})) column = table.get_column("col1") column.statistics.mean() diff --git a/Runtime/safe-ds/tests/data/_column/test_median.py b/Runtime/safe-ds/tests/data/_column/test_median.py index 8b1e5d5f1..ef0237010 100644 --- a/Runtime/safe-ds/tests/data/_column/test_median.py +++ b/Runtime/safe-ds/tests/data/_column/test_median.py @@ -1,10 +1,11 @@ import pandas as pd import pytest from safe_ds.data import Table +from safe_ds.exceptions import NonNumericColumnError def test_median_invalid() -> None: - with pytest.raises(TypeError): + with pytest.raises(NonNumericColumnError): table = Table(pd.DataFrame(data={"col1": ["col1_1", 2]})) column = table.get_column("col1") column.statistics.median() diff --git a/Runtime/safe-ds/tests/data/_column/test_min.py b/Runtime/safe-ds/tests/data/_column/test_min.py index 92fb2561c..cfc52ce1e 100644 --- a/Runtime/safe-ds/tests/data/_column/test_min.py +++ b/Runtime/safe-ds/tests/data/_column/test_min.py @@ -1,10 +1,11 @@ import pandas as pd import pytest from safe_ds.data import Table +from safe_ds.exceptions import NonNumericColumnError def test_min_invalid() -> None: - with pytest.raises(TypeError): + with pytest.raises(NonNumericColumnError): table = Table(pd.DataFrame(data={"col1": ["col1_1", 2]})) column = table.get_column("col1") column.statistics.min() diff --git a/Runtime/safe-ds/tests/data/_column/test_stability.py b/Runtime/safe-ds/tests/data/_column/test_stability.py index 703a1f631..9a7f20c53 100644 --- a/Runtime/safe-ds/tests/data/_column/test_stability.py +++ b/Runtime/safe-ds/tests/data/_column/test_stability.py @@ -15,14 +15,14 @@ (["b", "a", "abc", "abc", "abc"], 3 / 5), ], ) -def test_table_stability(values: list[typing.Any], expected: float) -> None: +def test_stability(values: list[typing.Any], expected: float) -> None: column = Column(pd.Series(values), "A") - assert column.stability() == expected + assert column.statistics.stability() == expected -def test_table_error() -> None: +def test_stability_error() -> None: column = Column( pd.Series([], dtype=np.dtype("float64")), "A" ) # Fix warning against unknown type with pytest.raises(ColumnSizeError): - column.stability() + column.statistics.stability() diff --git a/Runtime/safe-ds/tests/data/_row/test_get_column_names.py b/Runtime/safe-ds/tests/data/_row/test_get_column_names.py new file mode 100644 index 000000000..f89453eea --- /dev/null +++ b/Runtime/safe-ds/tests/data/_row/test_get_column_names.py @@ -0,0 +1,21 @@ +import numpy as np +import pandas as pd +from safe_ds.data import ColumnType, Row, TableSchema + + +def test_get_column_names() -> None: + row = Row( + pd.Series(data=[1, 2]), + TableSchema( + { + "col1": ColumnType.from_numpy_dtype(np.dtype(float)), + "col2": ColumnType.from_numpy_dtype(np.dtype(float)), + } + ), + ) + assert row.get_column_names() == ["col1", "col2"] + + +def test_get_column_names_empty() -> None: + row = Row(pd.Series(data=[]), TableSchema({})) + assert not row.get_column_names() diff --git a/Runtime/safe-ds/tests/data/_table/test_get_column_names.py b/Runtime/safe-ds/tests/data/_table/test_get_column_names.py new file mode 100644 index 000000000..65db87d5a --- /dev/null +++ b/Runtime/safe-ds/tests/data/_table/test_get_column_names.py @@ -0,0 +1,12 @@ +import pandas as pd +from safe_ds.data import Table, TableSchema + + +def test_get_column_names() -> None: + table = Table(pd.DataFrame(data={"col1": [1], "col2": [1]})) + assert table.get_column_names() == ["col1", "col2"] + + +def test_get_column_names_empty() -> None: + table = Table(pd.DataFrame(), TableSchema({})) + assert not table.get_column_names() diff --git a/Runtime/safe-ds/tests/data/_table/test_summary.py b/Runtime/safe-ds/tests/data/_table/test_summary.py new file mode 100644 index 000000000..6f8e8231c --- /dev/null +++ b/Runtime/safe-ds/tests/data/_table/test_summary.py @@ -0,0 +1,42 @@ +import pandas as pd +from safe_ds.data import Table + + +def test_summary() -> None: + table = Table(pd.DataFrame(data={"col1": [1, 2, 1], "col2": ["a", "b", "c"]})) + + truth = Table( + pd.DataFrame( + data={ + "": [ + "max", + "min", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + "row count", + ], + "col1": [ + 2.0, + 1.0, + 4.0 / 3, + 1.0, + 1.0, + 4.0, + 1.0 / 3, + table._data[0].std(), + 2.0 / 3, + 2.0 / 3, + 3.0, + ], + "col2": ["-", "-", "-", "a", "-", "-", "-", "-", 1.0, 1.0 / 3, 3], + } + ) + ) + + assert truth == table.summary() diff --git a/Runtime/safe-ds/tests/data/_table_schema/test__str__.py b/Runtime/safe-ds/tests/data/_table_schema/test__str__.py index 1f1263e15..ad8a8d286 100644 --- a/Runtime/safe-ds/tests/data/_table_schema/test__str__.py +++ b/Runtime/safe-ds/tests/data/_table_schema/test__str__.py @@ -6,5 +6,5 @@ def test__str__() -> None: table = Table(pd.DataFrame(data={"col1": ["col1_1"], "col2": [1]})) assert ( str(table.schema) - == "TableSchema:\nColumn Count: 2\nColumns:\n col1, string\n col2, int\n" + == "TableSchema:\nColumn Count: 2\nColumns:\n col1: string\n col2: int\n" )