def test_int_bins(self, long_df):
n = 24
h = Hist(bins=n)
bin_kws = h._define_bin_params(long_df, "x", "continuous")
assert bin_kws["range"] == (long_df["x"].min(), long_df["x"].max())
assert bin_kws["bins"] == n
def test_discrete_bins(self, long_df):
h = Hist(discrete=True)
x = long_df["x"].astype(int)
bin_kws = h._define_bin_params(long_df.assign(x=x), "x", "continuous")
assert bin_kws["range"] == (x.min() - .5, x.max() + .5)
assert bin_kws["bins"] == (x.max() - x.min() + 1)
def test_binwidth(self, long_df):
binwidth = .5
h = Hist(binwidth=binwidth)
bin_kws = h._define_bin_params(long_df, "x", "continuous")
n_bins = bin_kws["bins"]
left, right = bin_kws["range"]
assert (right - left) / n_bins == pytest.approx(binwidth)
def test_discrete_bins_from_nominal_scale(self, rng):
h = Hist()
x = rng.randint(0, 5, 10)
df = pd.DataFrame({"x": x})
bin_kws = h._define_bin_params(df, "x", "nominal")
assert bin_kws["range"] == (x.min() - .5, x.max() + .5)
assert bin_kws["bins"] == (x.max() - x.min() + 1)
def test_histogram_single(self, long_df, single_args):
h = Hist()
out = h(long_df, *single_args)
hist, edges = np.histogram(long_df["x"], bins="auto")
assert_array_equal(out["y"], hist)
assert_array_equal(out["space"], np.diff(edges))
def test_common_bins_subset(self, long_df, triple_args):
h = Hist(common_bins=False)
out = h(long_df, *triple_args)
bins = []
for _, out_part in out.groupby(["a"]):
bins.append(tuple(out_part["x"]))
assert len(set(bins)) == out["a"].nunique()
def test_common_bins_default(self, long_df, triple_args):
h = Hist()
out = h(long_df, *triple_args)
bins = []
for _, out_part in out.groupby(["a", "s"]):
bins.append(tuple(out_part["x"]))
assert len(set(bins)) == 1
def test_histogram_multiple(self, long_df, triple_args):
h = Hist()
out = h(long_df, *triple_args)
bins = np.histogram_bin_edges(long_df["x"], "auto")
for (a, s), out_part in out.groupby(["a", "s"]):
x = long_df.loc[(long_df["a"] == a) & (long_df["s"] == s), "x"]
hist, edges = np.histogram(x, bins=bins)
assert_array_equal(out_part["y"], hist)
assert_array_equal(out_part["space"], np.diff(edges))
def test_count_stat(self, long_df, single_args):
h = Hist(stat="count")
out = h(long_df, *single_args)
assert out["y"].sum() == len(long_df)
def test_cumulative_count(self, long_df, single_args):
h = Hist(stat="count", cumulative=True)
out = h(long_df, *single_args)
assert out["y"].max() == len(long_df)
def test_frequency_stat(self, long_df, single_args):
h = Hist(stat="frequency")
out = h(long_df, *single_args)
assert (out["y"] * out["space"]).sum() == len(long_df)
def test_cumulative_proportion(self, long_df, single_args):
h = Hist(stat="proportion", cumulative=True)
out = h(long_df, *single_args)
assert out["y"].max() == 1
def test_binrange(self, long_df):
binrange = (-4, 4)
h = Hist(binrange=binrange)
bin_kws = h._define_bin_params(long_df, "x", "continuous")
assert bin_kws["range"] == binrange
def test_cumulative_density(self, long_df, single_args):
h = Hist(stat="density", cumulative=True)
out = h(long_df, *single_args)
assert out["y"].max() == 1
def test_array_bins(self, long_df):
bins = [-3, -2, 1, 2, 3]
h = Hist(bins=bins)
bin_kws = h._define_bin_params(long_df, "x", "continuous")
assert_array_equal(bin_kws["bins"], bins)
def test_string_bins(self, long_df):
h = Hist(bins="sqrt")
bin_kws = h._define_bin_params(long_df, "x", "continuous")
assert bin_kws["range"] == (long_df["x"].min(), long_df["x"].max())
assert bin_kws["bins"] == int(np.sqrt(len(long_df)))
def test_density_stat(self, long_df, single_args):
h = Hist(stat="density")
out = h(long_df, *single_args)
assert (out["y"] * out["space"]).sum() == 1
def test_probability_stat(self, long_df, single_args):
h = Hist(stat="probability")
out = h(long_df, *single_args)
assert out["y"].sum() == 1
def test_common_norm_default(self, long_df, triple_args):
h = Hist(stat="percent")
out = h(long_df, *triple_args)
assert out["y"].sum() == pytest.approx(100)
def test_common_norm_subset(self, long_df, triple_args):
h = Hist(stat="percent", common_norm=["a"])
out = h(long_df, *triple_args)
for _, out_part in out.groupby(["a"]):
assert out_part["y"].sum() == pytest.approx(100)
def test_proportion_stat(self, long_df, single_args):
h = Hist(stat="proportion")
out = h(long_df, *single_args)
assert out["y"].sum() == 1
def test_percent_stat(self, long_df, single_args):
h = Hist(stat="percent")
out = h(long_df, *single_args)
assert out["y"].sum() == 100
