diff --git a/src/summarize_deaths.py b/src/summarize_deaths.py new file mode 100644 index 0000000..920b7f6 --- /dev/null +++ b/src/summarize_deaths.py @@ -0,0 +1,93 @@ +import pandas as pd + + +def summarize_deaths(*args, by: list[str] | str, **kwargs) -> pd.DataFrame: + """ + Summarize deaths by one or more grouping columns. + + Groups a CDC mortality dataset by the specified column(s) and returns a + DataFrame with death counts for each group, sorted in descending order. + Optionally accepts multiple named datasets to compare across years. + + Parameters + ---------- + *args : pd.DataFrame + A single unnamed DataFrame for single-year usage. + by : str or list of str + Column name(s) to group by, e.g. "sex" or ["sex", "race_recode3"]. + **kwargs : pd.DataFrame + Named DataFrames for multi-year comparison, + e.g. mort1969=mort1969, mort1970=mort1970. + + Returns + ------- + pd.DataFrame + A DataFrame with the grouping columns, a `year` column (when multiple + datasets are provided), and an `n` column containing death counts, + sorted descending by `n`. + + Examples + -------- + # Single year + summarize_deaths(mort1969, by="sex") + + # Multiple years + summarize_deaths(mort1969=mort1969, mort1970=mort1970, by="sex") + """ + if isinstance(by, str): + by = [by] + + if len(args) == 0 and len(kwargs) == 0: + raise ValueError("At least one DataFrame must be provided.") + + if len(args) > 0 and len(kwargs) > 0: + raise ValueError( + "Provide either a single positional DataFrame or named DataFrames, not both." + ) + + # Single dataset — original behavior + if len(args) == 1: + df = args[0] + missing_cols = [col for col in by if col not in df.columns] + if missing_cols: + raise ValueError( + f"The following columns were not found in the data: {', '.join(missing_cols)}" + ) + + return ( + df.groupby(by) + .size() + .reset_index(name="n") + .sort_values("n", ascending=False) + .reset_index(drop=True) + ) + + # Multiple datasets — compare across years + if len(args) > 1: + raise ValueError( + "When providing multiple datasets, all must be named, " + "e.g. summarize_deaths(mort1969=mort1969, mort1970=mort1970, by='sex')." + ) + + frames = [] + for name, df in kwargs.items(): + missing_cols = [col for col in by if col not in df.columns] + if missing_cols: + raise ValueError( + f"The following columns were not found in '{name}': {', '.join(missing_cols)}" + ) + + summary = ( + df.groupby(by) + .size() + .reset_index(name="n") + ) + summary.insert(0, "year", name) + frames.append(summary) + + combined = pd.concat(frames, ignore_index=True) + return ( + combined + .sort_values(["year", "n"], ascending=[True, False]) + .reset_index(drop=True) + ) \ No newline at end of file diff --git a/tests/test_summarize_deaths.py b/tests/test_summarize_deaths.py new file mode 100644 index 0000000..bf00bee --- /dev/null +++ b/tests/test_summarize_deaths.py @@ -0,0 +1,138 @@ +import pytest +import pandas as pd +from summarize_deaths import summarize_deaths + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +@pytest.fixture +def mort1969(): + return pd.DataFrame({ + "sex": ["M", "M", "F", "F", "F", "M"], + "race_recode3": ["White", "Black", "White", "White", "Black", "White"], + "age": [45, 60, 32, 78, 55, 90], + }) + + +@pytest.fixture +def mort1970(): + return pd.DataFrame({ + "sex": ["M", "F", "F", "M", "M"], + "race_recode3": ["White", "Black", "White", "Black", "White"], + "age": [50, 40, 88, 70, 33], + }) + + +# --------------------------------------------------------------------------- +# Single dataset +# --------------------------------------------------------------------------- + +class TestSingleDataset: + + def test_group_by_single_column(self, mort1969): + result = summarize_deaths(mort1969, by="sex") + assert list(result.columns) == ["sex", "n"] + assert result["n"].iloc[0] >= result["n"].iloc[1], "Should be sorted descending" + assert set(result["sex"]) == {"M", "F"} + assert result["n"].sum() == len(mort1969) + + def test_group_by_multiple_columns(self, mort1969): + result = summarize_deaths(mort1969, by=["sex", "race_recode3"]) + assert "sex" in result.columns + assert "race_recode3" in result.columns + assert "n" in result.columns + assert result["n"].sum() == len(mort1969) + + def test_by_as_string_and_list_equivalent(self, mort1969): + result_str = summarize_deaths(mort1969, by="sex") + result_list = summarize_deaths(mort1969, by=["sex"]) + pd.testing.assert_frame_equal(result_str, result_list) + + def test_sorted_descending(self, mort1969): + result = summarize_deaths(mort1969, by="sex") + assert result["n"].is_monotonic_decreasing + + def test_counts_are_correct(self, mort1969): + result = summarize_deaths(mort1969, by="sex") + counts = dict(zip(result["sex"], result["n"])) + assert counts["M"] == 3 + assert counts["F"] == 3 + + def test_single_group_value(self): + df = pd.DataFrame({"sex": ["M", "M", "M"]}) + result = summarize_deaths(df, by="sex") + assert len(result) == 1 + assert result["n"].iloc[0] == 3 + + +# --------------------------------------------------------------------------- +# Multiple datasets +# --------------------------------------------------------------------------- + +class TestMultipleDatasets: + + def test_returns_year_column(self, mort1969, mort1970): + result = summarize_deaths(mort1969=mort1969, mort1970=mort1970, by="sex") + assert "year" in result.columns + assert set(result["year"]) == {"mort1969", "mort1970"} + + def test_year_is_first_column(self, mort1969, mort1970): + result = summarize_deaths(mort1969=mort1969, mort1970=mort1970, by="sex") + assert result.columns[0] == "year" + + def test_total_counts_correct(self, mort1969, mort1970): + result = summarize_deaths(mort1969=mort1969, mort1970=mort1970, by="sex") + assert result["n"].sum() == len(mort1969) + len(mort1970) + + def test_sorted_by_year_then_desc_n(self, mort1969, mort1970): + result = summarize_deaths(mort1969=mort1969, mort1970=mort1970, by="sex") + for year, group in result.groupby("year", sort=False): + assert group["n"].is_monotonic_decreasing, f"Year '{year}' not sorted descending by n" + + def test_multi_column_grouping(self, mort1969, mort1970): + result = summarize_deaths( + mort1969=mort1969, mort1970=mort1970, by=["sex", "race_recode3"] + ) + assert "year" in result.columns + assert "sex" in result.columns + assert "race_recode3" in result.columns + assert "n" in result.columns + + +# --------------------------------------------------------------------------- +# Error handling +# --------------------------------------------------------------------------- + +class TestErrors: + + def test_no_datasets_raises(self): + with pytest.raises(ValueError, match="At least one DataFrame"): + summarize_deaths(by="sex") + + def test_missing_column_single_dataset(self, mort1969): + with pytest.raises(ValueError, match="not found in the data"): + summarize_deaths(mort1969, by="nonexistent_col") + + def test_missing_column_multi_dataset(self, mort1969, mort1970): + with pytest.raises(ValueError, match="not found in 'mort1970'"): + summarize_deaths(mort1969=mort1969, mort1970=mort1970, by="nonexistent_col") + + def test_mixed_positional_and_named_raises(self, mort1969, mort1970): + with pytest.raises(ValueError, match="not both"): + summarize_deaths(mort1969, mort1970=mort1970, by="sex") + + def test_multiple_positional_raises(self, mort1969, mort1970): + with pytest.raises(ValueError, match="must be named"): + summarize_deaths(mort1969, mort1970, by="sex") + + def test_missing_one_of_multiple_by_columns(self, mort1969): + with pytest.raises(ValueError, match="not found in the data"): + summarize_deaths(mort1969, by=["sex", "does_not_exist"]) + + def test_empty_dataframe(self): + df = pd.DataFrame({"sex": pd.Series([], dtype=str)}) + result = summarize_deaths(df, by="sex") + assert result.empty + assert 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