def test_count_summary_category():
s = pd.Categorical(
["Poor", "Neutral"] + [np.nan] * 100,
categories=["Poor", "Neutral", "Excellent"],
)
sn, r = describe_counts(s, {})
assert len(r["value_counts_without_nan"].index) == 2
def test_summary_supported_empty_df(config, empty_data):
_, series, summary = describe_counts(config, empty_data["A"], {})
assert summary["n_missing"] == 0
assert "p_missing" not in summary
_, series, summary = describe_generic(config, series, summary)
assert summary["n_missing"] == 0
assert summary["p_missing"] == 0
assert summary["count"] == 0
_, _, summary = describe_supported(config, series, summary)
assert summary["n_distinct"] == 0
assert summary["p_distinct"] == 0
assert summary["n_unique"] == 0
assert not summary["is_unique"]
def test_count_summary_sorted():
s = pd.Series([1] + [2] * 1000)
sn, r = describe_counts(s, {})
assert r["value_counts_without_nan"].index[0] == 2
assert r["value_counts_without_nan"].index[1] == 1
def test_count_summary_nat():
s = pd.to_datetime(pd.Series([1, 2] + [np.nan, pd.NaT]))
sn, r = describe_counts(s, {})
assert len(r["value_counts_without_nan"].index) == 2