def test_pivot_eliminate_cartesian_product_columns(): # single metric mock_df = DataFrame({ "dttm": to_datetime(["2019-01-01", "2019-01-01"]), "a": [0, 1], "b": [0, 1], "metric": [9, np.NAN], }) df = pivot( df=mock_df, index=["dttm"], columns=["a", "b"], aggregates={"metric": { "operator": "mean" }}, drop_missing_columns=False, ) df = flatten(df) assert list(df.columns) == ["dttm", "metric, 0, 0", "metric, 1, 1"] assert np.isnan(df["metric, 1, 1"][0]) # multiple metrics mock_df = DataFrame({ "dttm": to_datetime(["2019-01-01", "2019-01-01"]), "a": [0, 1], "b": [0, 1], "metric": [9, np.NAN], "metric2": [10, 11], }) df = pivot( df=mock_df, index=["dttm"], columns=["a", "b"], aggregates={ "metric": { "operator": "mean" }, "metric2": { "operator": "mean" }, }, drop_missing_columns=False, ) df = flatten(df) assert list(df.columns) == [ "dttm", "metric, 0, 0", "metric, 1, 1", "metric2, 0, 0", "metric2, 1, 1", ] assert np.isnan(df["metric, 1, 1"][0])
def test_flat_should_not_change(): df = pd.DataFrame(data={ "foo": [1, 2, 3], "bar": [4, 5, 6], }) assert pp.flatten(df).equals(df)
def test_flat_should_not_droplevel(): assert pp.flatten(timeseries_df, drop_levels=(0, )).equals( pd.DataFrame({ "index": pd.to_datetime( ["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]), "label": ["x", "y", "z", "q"], "y": [1.0, 2.0, 3.0, 4.0], }))
def test_rolling_after_pivot_with_multiple_metrics(): pivot_df = pp.pivot( df=multiple_metrics_df, index=["dttm"], columns=["country"], aggregates={ "sum_metric": { "operator": "sum" }, "count_metric": { "operator": "sum" }, }, flatten_columns=False, reset_index=False, ) """ count_metric sum_metric country UK US UK US dttm 2019-01-01 1 2 5 6 2019-01-02 3 4 7 8 """ rolling_df = pp.rolling( df=pivot_df, columns={ "count_metric": "count_metric", "sum_metric": "sum_metric", }, rolling_type="sum", window=2, min_periods=0, ) """ count_metric sum_metric country UK US UK US dttm 2019-01-01 1.0 2.0 5.0 6.0 2019-01-02 4.0 6.0 12.0 14.0 """ flat_df = pp.flatten(rolling_df) """ dttm count_metric, UK count_metric, US sum_metric, UK sum_metric, US 0 2019-01-01 1.0 2.0 5.0 6.0 1 2019-01-02 4.0 6.0 12.0 14.0 """ assert flat_df.equals( pd.DataFrame( data={ "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]), FLAT_COLUMN_SEPARATOR.join(["count_metric", "UK"]): [1.0, 4.0], FLAT_COLUMN_SEPARATOR.join(["count_metric", "US"]): [2.0, 6.0], FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5.0, 12.0], FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6.0, 14.0], }))
def test_compare_after_pivot(): pivot_df = pp.pivot( df=multiple_metrics_df, index=["dttm"], columns=["country"], aggregates={ "sum_metric": { "operator": "sum" }, "count_metric": { "operator": "sum" }, }, flatten_columns=False, reset_index=False, ) """ count_metric sum_metric country UK US UK US dttm 2019-01-01 1 2 5 6 2019-01-02 3 4 7 8 """ compared_df = pp.compare( pivot_df, source_columns=["count_metric"], compare_columns=["sum_metric"], compare_type=PPC.DIFF, drop_original_columns=True, ) """ difference__count_metric__sum_metric country UK US dttm 2019-01-01 -4 -4 2019-01-02 -4 -4 """ flat_df = pp.flatten(compared_df) """ dttm difference__count_metric__sum_metric, UK difference__count_metric__sum_metric, US 0 2019-01-01 -4 -4 1 2019-01-02 -4 -4 """ assert flat_df.equals( pd.DataFrame( data={ "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]), FLAT_COLUMN_SEPARATOR.join([ "difference__count_metric__sum_metric", "UK" ]): [-4, -4], FLAT_COLUMN_SEPARATOR.join([ "difference__count_metric__sum_metric", "US" ]): [-4, -4], }))
def test_flat_should_flat_datetime_index(): index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]) index.name = "__timestamp" df = pd.DataFrame(index=index, data={"foo": [1, 2, 3], "bar": [4, 5, 6]}) assert pp.flatten(df).equals( pd.DataFrame({ "__timestamp": index, "foo": [1, 2, 3], "bar": [4, 5, 6], }))
def test_pivot_with_multiple_columns(): """ Make sure pivot with multiple columns returns correct DataFrame """ df = pivot( df=categories_df, index=["name"], columns=["category", "dept"], aggregates=AGGREGATES_SINGLE, ) df = flatten(df) assert len(df.columns) == 1 + 3 * 5 # index + possible permutations
def test_cum_after_pivot_with_multiple_metrics(): pivot_df = pp.pivot( df=multiple_metrics_df, index=["dttm"], columns=["country"], aggregates={ "sum_metric": { "operator": "sum" }, "count_metric": { "operator": "sum" }, }, flatten_columns=False, reset_index=False, ) """ count_metric sum_metric country UK US UK US dttm 2019-01-01 1 2 5 6 2019-01-02 3 4 7 8 """ cum_df = pp.cum( df=pivot_df, operator="sum", columns={ "sum_metric": "sum_metric", "count_metric": "count_metric" }, ) """ count_metric sum_metric country UK US UK US dttm 2019-01-01 1 2 5 6 2019-01-02 4 6 12 14 """ flat_df = pp.flatten(cum_df) """ dttm count_metric, UK count_metric, US sum_metric, UK sum_metric, US 0 2019-01-01 1 2 5 6 1 2019-01-02 4 6 12 14 """ assert flat_df.equals( pd.DataFrame({ "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]), FLAT_COLUMN_SEPARATOR.join(["count_metric", "UK"]): [1, 4], FLAT_COLUMN_SEPARATOR.join(["count_metric", "US"]): [2, 6], FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5, 12], FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6, 14], }))
def test_flat_should_drop_index_level(): index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]) index.name = "__timestamp" columns = pd.MultiIndex.from_arrays( [["a"] * 3, ["b"] * 3, ["c", "d", "e"], ["ff", "ii", "gg"]], names=["level1", "level2", "level3", "level4"], ) df = pd.DataFrame(index=index, columns=columns, data=1) # drop level by index assert pp.flatten(df.copy(), drop_levels=( 0, 1, )).equals( pd.DataFrame({ "__timestamp": index, FLAT_COLUMN_SEPARATOR.join(["c", "ff"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["d", "ii"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["e", "gg"]): [1, 1, 1], })) # drop level by name assert pp.flatten(df.copy(), drop_levels=("level1", "level2")).equals( pd.DataFrame({ "__timestamp": index, FLAT_COLUMN_SEPARATOR.join(["c", "ff"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["d", "ii"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["e", "gg"]): [1, 1, 1], })) # only leave 1 level assert pp.flatten(df.copy(), drop_levels=(0, 1, 2)).equals( pd.DataFrame({ "__timestamp": index, FLAT_COLUMN_SEPARATOR.join(["ff"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["ii"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["gg"]): [1, 1, 1], }))
def test_flat_should_flat_multiple_index(): index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]) index.name = "__timestamp" iterables = [["foo", "bar"], [1, "two"]] columns = pd.MultiIndex.from_product(iterables, names=["level1", "level2"]) df = pd.DataFrame(index=index, columns=columns, data=1) assert pp.flatten(df).equals( pd.DataFrame({ "__timestamp": index, FLAT_COLUMN_SEPARATOR.join(["foo", "1"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["foo", "two"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["bar", "1"]): [1, 1, 1], FLAT_COLUMN_SEPARATOR.join(["bar", "two"]): [1, 1, 1], }))
def test_flat_integer_column_name(): index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]) index.name = "__timestamp" columns = pd.MultiIndex.from_arrays( [["a"] * 3, [100, 200, 300]], names=["level1", "level2"], ) df = pd.DataFrame(index=index, columns=columns, data=1) assert pp.flatten(df, drop_levels=(0, )).equals( pd.DataFrame({ "__timestamp": pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]), "100": [1, 1, 1], "200": [1, 1, 1], "300": [1, 1, 1], }))
def test_escape_column_name(): index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]) index.name = "__timestamp" columns = pd.MultiIndex.from_arrays( [ ["level1,value1", "level1,value2", "level1,value3"], ["level2, value1", "level2, value2", "level2, value3"], ], names=["level1", "level2"], ) df = pd.DataFrame(index=index, columns=columns, data=1) assert list(pp.flatten(df).columns.values) == [ "__timestamp", "level1\\,value1" + FLAT_COLUMN_SEPARATOR + "level2\\, value1", "level1\\,value2" + FLAT_COLUMN_SEPARATOR + "level2\\, value2", "level1\\,value3" + FLAT_COLUMN_SEPARATOR + "level2\\, value3", ]
def test_rolling_after_pivot_with_single_metric(): pivot_df = pp.pivot( df=single_metric_df, index=["dttm"], columns=["country"], aggregates={"sum_metric": { "operator": "sum" }}, ) """ sum_metric country UK US dttm 2019-01-01 5 6 2019-01-02 7 8 """ rolling_df = pp.rolling( df=pivot_df, columns={"sum_metric": "sum_metric"}, rolling_type="sum", window=2, min_periods=0, ) """ sum_metric country UK US dttm 2019-01-01 5.0 6.0 2019-01-02 12.0 14.0 """ flat_df = pp.flatten(rolling_df) """ dttm sum_metric, UK sum_metric, US 0 2019-01-01 5.0 6.0 1 2019-01-02 12.0 14.0 """ assert flat_df.equals( pd.DataFrame( data={ "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]), FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5.0, 12.0], FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6.0, 14.0], }))
def test_cum_after_pivot_with_single_metric(): pivot_df = pp.pivot( df=single_metric_df, index=["dttm"], columns=["country"], aggregates={"sum_metric": { "operator": "sum" }}, flatten_columns=False, reset_index=False, ) """ sum_metric country UK US dttm 2019-01-01 5 6 2019-01-02 7 8 """ cum_df = pp.cum(df=pivot_df, operator="sum", columns={"sum_metric": "sum_metric"}) """ sum_metric country UK US dttm 2019-01-01 5 6 2019-01-02 12 14 """ cum_and_flat_df = pp.flatten(cum_df) """ dttm sum_metric, UK sum_metric, US 0 2019-01-01 5 6 1 2019-01-02 12 14 """ assert cum_and_flat_df.equals( pd.DataFrame({ "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]), FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5, 12], FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6, 14], }))
def test_compare_multi_index_column(): index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]) index.name = "__timestamp" iterables = [["m1", "m2"], ["a", "b"], ["x", "y"]] columns = pd.MultiIndex.from_product(iterables, names=[None, "level1", "level2"]) df = pd.DataFrame(index=index, columns=columns, data=1) """ m1 m2 level1 a b a b level2 x y x y x y x y __timestamp 2021-01-01 1 1 1 1 1 1 1 1 2021-01-02 1 1 1 1 1 1 1 1 2021-01-03 1 1 1 1 1 1 1 1 """ post_df = pp.compare( df, source_columns=["m1"], compare_columns=["m2"], compare_type=PPC.DIFF, drop_original_columns=True, ) flat_df = pp.flatten(post_df) """ __timestamp difference__m1__m2, a, x difference__m1__m2, a, y difference__m1__m2, b, x difference__m1__m2, b, y 0 2021-01-01 0 0 0 0 1 2021-01-02 0 0 0 0 2 2021-01-03 0 0 0 0 """ assert flat_df.equals( pd.DataFrame( data={ "__timestamp": pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]), "difference__m1__m2, a, x": [0, 0, 0], "difference__m1__m2, a, y": [0, 0, 0], "difference__m1__m2, b, x": [0, 0, 0], "difference__m1__m2, b, y": [0, 0, 0], }))
def test_resample_after_pivot(): df = pd.DataFrame( data={ "__timestamp": pd.to_datetime( [ "2022-01-13", "2022-01-13", "2022-01-13", "2022-01-11", "2022-01-11", "2022-01-11", ] ), "city": ["Chicago", "LA", "NY", "Chicago", "LA", "NY"], "val": [6.0, 5.0, 4.0, 3.0, 2.0, 1.0], } ) pivot_df = pp.pivot( df=df, index=["__timestamp"], columns=["city"], aggregates={ "val": {"operator": "sum"}, }, flatten_columns=False, reset_index=False, ) """ val city Chicago LA NY __timestamp 2022-01-11 3.0 2.0 1.0 2022-01-13 6.0 5.0 4.0 """ resample_df = pp.resample( df=pivot_df, rule="1D", method="asfreq", fill_value=0, ) """ val city Chicago LA NY __timestamp 2022-01-11 3.0 2.0 1.0 2022-01-12 0.0 0.0 0.0 2022-01-13 6.0 5.0 4.0 """ flat_df = pp.flatten(resample_df) """ __timestamp val, Chicago val, LA val, NY 0 2022-01-11 3.0 2.0 1.0 1 2022-01-12 0.0 0.0 0.0 2 2022-01-13 6.0 5.0 4.0 """ assert flat_df.equals( pd.DataFrame( data={ "__timestamp": pd.to_datetime( ["2022-01-11", "2022-01-12", "2022-01-13"] ), "val, Chicago": [3.0, 0, 6.0], "val, LA": [2.0, 0, 5.0], "val, NY": [1.0, 0, 4.0], } ) )