def test_group_date_prompt_upgrade_timestamp_to_date():
assert_result_equals(
render(
make_table(make_column("A", [datetime.datetime(2021, 5, 5)])),
P(
groups=dict(
colnames=["A"], group_dates=True, date_granularities={"A": "Y"}
),
aggregations=[dict(operation="size", colname="", outname="size")],
),
),
ArrowRenderResult(
make_table(
make_column("A", [datetime.datetime(2021, 1, 1)]),
make_column("size", [1], format="{:,d}"),
),
[
RenderError(
i18n_message("group_dates.granularity_deprecated.need_dates"),
[
QuickFix(
i18n_message(
"group_dates.granularity_deprecated.quick_fix.convert_to_date"
),
QuickFixAction.PrependStep(
"converttimestamptodate",
dict(colnames=["A"], unit="year"),
),
)
],
)
],
),
)
def test_default_outnames():
assert_result_equals(
render(
make_table(
make_column("A", ["x", "x"]), make_column("B", [1, 2], format="{:d}")
),
P(
groups=dict(colnames=["A"], group_dates=False, date_granularities={}),
aggregations=[
dict(operation="size", colname="", outname=""),
dict(operation="nunique", colname="B", outname=""),
dict(operation="sum", colname="B", outname=""),
dict(operation="mean", colname="B", outname=""),
dict(operation="median", colname="B", outname=""),
dict(operation="min", colname="B", outname=""),
dict(operation="max", colname="B", outname=""),
dict(operation="first", colname="B", outname=""),
],
),
),
ArrowRenderResult(
make_table(
make_column("A", ["x"]),
make_column("Group Size", [2], format="{:,d}"),
make_column("Unique count of B", [2], format="{:,d}"),
make_column("Sum of B", [3], format="{:d}"),
make_column("Average of B", [1.5], format="{:,}"),
make_column("Median of B", [1.5], format="{:,}"),
make_column("Minimum of B", [1], format="{:d}"),
make_column("Maximum of B", [2], format="{:d}"),
make_column("First of B", [1], format="{:d}"),
)
),
)
def test_group_date_prompt_all_is_well_when_date_column_present():
assert_result_equals(
render(
make_table(
make_column("A", [datetime.date(2021, 5, 10)], unit="week"),
make_column("B", [1]),
),
P(
groups=dict(
colnames=["A", "B"], group_dates=True, date_granularities={}
),
aggregations=[dict(operation="size", colname="", outname="size")],
),
),
ArrowRenderResult(
make_table(
make_column("A", [datetime.date(2021, 5, 10)], unit="week"),
make_column("B", [1]),
make_column("size", [1], format="{:,d}"),
),
[
RenderError(
i18n_message(
"group_dates.date_selected",
dict(columns=1, column0="A", unit0="week"),
)
)
],
),
)
def test_ignore_non_date_timestamps():
# Steps for the user to get here:
# 1. Make a date column, 'A'
# 2. Check "Group Dates". The column appears.
# 3. Select column 'A', and select a date granularity for it
# 4. Alter the input DataFrame such that 'A' is no longer datetime
#
# Expected results: you can't group it by date any more.
assert_result_equals(
render(
make_table(
make_column("A", [1]), # "used to be a datetime"
make_column(
"B", [datetime.datetime(2019, 1, 4)]
), # so we don't need quickfix
),
P(
groups=dict(
colnames=["A"], group_dates=True, date_granularities={"A": "T"}
),
aggregations=[dict(operation="size", colname="", outname="size")],
),
),
ArrowRenderResult(
make_table(make_column("A", [1]), make_column("size", [1], format="{:,d}")),
[RenderError(i18n_message("group_dates.select_date_columns"))],
),
)
def test_count_no_colnames_is_no_op_TEMPORARY():
# Added for https://www.pivotaltracker.com/story/show/164375369
# Change behavior for https://www.pivotaltracker.com/story/show/164375318
assert_result_equals(
render(
make_table(make_column("A", [1, 2])),
P(
groups=dict(colnames=[], group_dates=False, date_granularities={}),
aggregations=[],
),
),
ArrowRenderResult(make_table(make_column("A", [1, 2]))),
)
def test_group_date_prompt_when_nothing_selected():
assert_result_equals(
render(
make_table(make_column("A", [1])),
P(
groups=dict(colnames=[], group_dates=True, date_granularities={}),
aggregations=[dict(operation="sum", colname="A", outname="sum")],
),
),
ArrowRenderResult(
make_table(make_column("sum", [1])),
[RenderError(i18n_message("group_dates.select_date_columns"))],
),
)
def test_group_dates_prompt_select_date_column():
assert_result_equals(
render(
make_table(make_column("A", [1])),
P(
groups=dict(colnames=["A"], group_dates=True, date_granularities={}),
aggregations=[dict(operation="size", colname="", outname="size")],
),
),
ArrowRenderResult(
make_table(make_column("A", [1]), make_column("size", [1], format="{:,d}")),
errors=[RenderError(i18n_message("group_dates.select_date_columns"))],
),
)
def test_group_date_prompt_convert_text_to_date():
assert_result_equals(
render(
make_table(
make_column("A", ["2021-05-05"]),
make_column("B", ["2021-05-05"]),
),
P(
groups=dict(
colnames=["A", "B"], group_dates=True, date_granularities={}
),
aggregations=[dict(operation="size", colname="", outname="size")],
),
),
ArrowRenderResult(
make_table(
make_column("A", ["2021-05-05"]),
make_column("B", ["2021-05-05"]),
make_column("size", [1], format="{:,d}"),
),
[
RenderError(
i18n_message(
"group_dates.text_selected", dict(columns=2, column0="A")
),
[
QuickFix(
i18n_message("group_dates.quick_fix.convert_text_to_date"),
QuickFixAction.PrependStep(
"converttexttodate", dict(colnames=["A", "B"])
),
),
QuickFix(
i18n_message(
"group_dates.quick_fix.convert_text_to_timestamp"
),
QuickFixAction.PrependStep(
"convert-date", dict(colnames=["A", "B"])
),
),
],
)
],
),
)
def test_count_with_colnames():
# Check for obvious bug when adding
# https://www.pivotaltracker.com/story/show/164375369
assert_result_equals(
render(
make_table(make_column("A", [1, 2])),
P(
groups=dict(colnames=["A"], group_dates=False, date_granularities={}),
aggregations=[],
),
),
ArrowRenderResult(
make_table(
make_column("A", [1, 2]),
make_column("Group Size", [1, 1], format="{:,d}"),
)
),
)
def test_ignore_aggregation_with_empty_colname():
# Workbench replaces non-existent column names with "". So we can end up
# running groupby with aggregations that have no column.
#
# These appear in the UI, so users can select new columns. But we won't
# render them.
assert_result_equals(
render(
make_table(make_column("A", [1])),
P(
groups=dict(colnames=[], group_dates=False, date_granularities={}),
aggregations=[
dict(operation="size", colname="", outname="size"),
dict(operation="sum", colname="", outname="sum"),
],
),
),
ArrowRenderResult(make_table(make_column("size", [1], format="{:,d}"))),
)
def test_group_date_prompt_upgrade_timestampmath():
assert_result_equals(
render(
make_table(make_column("A", [datetime.datetime(2021, 5, 5, 1, 2, 3, 4)])),
P(
groups=dict(
colnames=["A"], group_dates=True, date_granularities={"A": "S"}
),
aggregations=[dict(operation="size", colname="", outname="size")],
),
),
ArrowRenderResult(
make_table(
make_column("A", [datetime.datetime(2021, 5, 5, 1, 2, 3)]),
make_column("size", [1], format="{:,d}"),
),
[
RenderError(
i18n_message("group_dates.granularity_deprecated.need_rounding"),
[
QuickFix(
i18n_message(
"group_dates.granularity_deprecated.quick_fix.round_timestamps"
),
QuickFixAction.PrependStep(
"timestampmath",
dict(
colnames=["A"],
operation="startof",
roundunit="second",
),
),
)
],
)
],
),
)
def test_quickfix_convert_value_strings_to_numbers():
assert_result_equals(
render(
make_table(
make_column("A", [1, 1, 1]),
make_column("B", ["a", "b", "a"]),
make_column("C", ["a", "b", "a"]),
),
P(
groups=dict(colnames=["A"], group_dates=False, date_granularities={}),
aggregations=[
dict(operation="mean", colname="B", outname="mean"),
dict(operation="sum", colname="C", outname="sum"),
],
),
),
ArrowRenderResult(
make_table(),
[
RenderError(
i18n_message(
"non_numeric_colnames.error",
{"n_columns": 2, "first_colname": "B"},
),
quick_fixes=[
QuickFix(
i18n_message("non_numeric_colnames.quick_fix.text"),
QuickFixAction.PrependStep(
"converttexttonumber", {"colnames": ["B", "C"]}
),
)
],
)
],
),
)