def test_dataframe_index_name(dataframe):
# Setting a custom index name
dataframe.index.name = "custom_index"
grid = DataGrid(dataframe)
# Making sure no value is set for index_name
assert grid._index_name is None
# Checking index name matches DataFrame retrieved from grid
data = grid.get_visible_data()
assert data.index.name == "custom_index"
def test_get_row_index_of_primary_key(
invalid_prim_key: bool, datagrid: DataGrid
) -> None:
if invalid_prim_key:
assert datagrid._get_row_index_of_primary_key("Nay") == []
else:
assert datagrid._get_row_index_of_primary_key("Two") == [1]
def test_get_cell_value_by_numerical_index(
invalid_coords: bool, data_object: dict
) -> None:
if invalid_coords:
assert (
DataGrid._get_cell_value_by_numerical_index(data_object, 2, 2)
is None
)
else:
assert (
DataGrid._get_cell_value_by_numerical_index(data_object, 1, 0) == 4
)
def test_default_dataframe_index(dataframe):
# Setting a custom index name
grid = DataGrid(dataframe)
# Making sure index_name is set
assert grid._index_name is None
# Generate primary key schema object
index_key = grid.get_dataframe_index(dataframe)
data_obj = grid.generate_data_object(dataframe, "ipydguuid", index_key)
# Default and unused keys should not be in schema
assert "key" in data_obj["schema"]["primaryKey"]
def test_data_object_generation(dataframe: pd.DataFrame) -> None:
data_object = DataGrid.generate_data_object(dataframe, "ipydguuid", "key")
expected_output = {
"data": [
{"key": "One", "A": 1, "B": 4, "ipydguuid": 0},
{"key": "Two", "A": 2, "B": 5, "ipydguuid": 1},
{"key": "Three", "A": 3, "B": 6, "ipydguuid": 2},
],
"schema": {
"fields": [
{"name": "key", "type": "string"},
{"name": "A", "type": "integer"},
{"name": "B", "type": "integer"},
{"name": "ipydguuid", "type": "integer"},
],
"primaryKey": ["key", "ipydguuid"],
"pandas_version": "0.20.0",
"primaryKeyUuid": "ipydguuid",
},
"fields": [
{"key": None},
{"A": None},
{"B": None},
{"ipydguuid": None},
],
}
assert data_object == expected_output
def test_user_defined_index_name(dataframe):
# Setting a custom index name
dataframe.index.name = "unused_index"
grid = DataGrid(dataframe, index_name="custom_index")
# Making sure index_name is set
assert grid._index_name is not None
# Checking index name matches DataFrame retrieved from grid
index_key = grid.get_dataframe_index(dataframe)
data_obj = grid.generate_data_object(dataframe, "ipydguuid", index_key)
# Default and unused keys should not be in schema
assert "key" not in data_obj["schema"]["primaryKey"]
assert "unused_index" not in data_obj["schema"]["primaryKey"]
# User defined key should be in primary key schema
assert "custom_index" in data_obj["schema"]["primaryKey"]
def test_selections(clear: bool, dataframe: pd.DataFrame) -> None:
grid = DataGrid(dataframe, selection_mode="cell", editable=True)
grid.select(1, 0, 2, 1) # Select 1A to 2B
if clear:
grid.clear_selection()
assert grid.selected_cells == []
else:
assert grid.selected_cells == [
{
"c": 0,
"r": 1
},
{
"c": 1,
"r": 1
},
{
"c": 0,
"r": 2
},
{
"c": 1,
"r": 2
},
]
def plot_tabular(df: pd.DataFrame, opts: ComputeOpts) -> DataGrid:
renderer = TextRenderer(
format=',') if not opts.normalize else TextRenderer(format=',.4f')
grid = DataGrid(
df,
base_row_size=30,
selection_mode="cell",
editable=False,
base_column_size=80,
# layout={'height': '200px'}
renderers={
c: renderer
for c in df.columns if c not in TEMPORAL_GROUP_BY
},
)
grid.transform([{"type": "sort", "columnIndex": 0, "desc": False}])
grid.auto_fit_params = {"area": "all", "padding": 40, "numCols": 1}
grid.auto_fit_columns = True
return grid
def display_grid(data: Union[dict, pd.DataFrame], **opts) -> DataGrid:
column_formats: dict = opts.get('column_formats', {})
renderers = {
c: TextRenderer(format=column_formats[c])
if c in column_formats else TextRenderer()
for c in data.columns
}
grid = DataGrid(
data,
base_row_size=30,
selection_mode="cell",
editable=False,
base_column_size=80,
# layout={'height': '200px'}
renderers=renderers,
)
grid.transform([{"type": "sort", "columnIndex": 0, "desc": False}])
grid.auto_fit_params = {"area": "all", "padding": 40, "numCols": 1}
grid.auto_fit_columns = True
return grid
def test_column_index_to_name(invalid_index: bool, datagrid: DataGrid) -> None:
if invalid_index:
assert datagrid._column_index_to_name(4) is None
else:
assert datagrid._column_index_to_name(1) == "B"
def test_column_name_to_index(invalid_index: bool, datagrid: DataGrid) -> None:
if invalid_index:
assert datagrid._column_name_to_index("Z") is None
else:
assert datagrid._column_name_to_index("A") == 0
def get_cell_value_by_index(datagrid: DataGrid) -> None:
cell = datagrid.get_cell_value_by_index("B", 1)
assert cell == [5]
def test_get_cell_value(datagrid: DataGrid) -> None:
cell = datagrid.get_cell_value("B", "Three")
assert cell == [6]
def test_data_setter(dataframe) -> None:
grid = DataGrid(dataframe)
new_df = pd.DataFrame(data={"A": [0, 0, 0], "B": [4, 5, 6]})
grid.data = new_df
assert grid.data.equals(new_df)
def test_data_getter(dataframe) -> None:
grid = DataGrid(dataframe)
assert grid.data.equals(dataframe)
def data_object(dataframe) -> None:
return DataGrid.generate_data_object(dataframe, "ipydguuid", "key")
def datagrid(dataframe) -> None:
return DataGrid(dataframe)
def test_get_cell_value_by_numerical_index(invalid_coords: bool,
datagrid: DataGrid) -> None:
if invalid_coords:
assert datagrid._get_cell_value_by_numerical_index(2, 2) is None
else:
assert datagrid._get_cell_value_by_numerical_index(1, 0) == 4
def test_get_col_headers(data_object) -> None:
assert DataGrid._get_col_headers(data_object) == ["A", "B"]
def test_column_index_to_name(invalid_index: bool, data_object: dict) -> None:
if invalid_index:
assert DataGrid._column_index_to_name(data_object, 4) is None
else:
assert DataGrid._column_index_to_name(data_object, 1) == "B"
