def test_statistics(plot_df):
plot_df.set_meta(meta=["a", "b", "b", "a"], name="category")
stats = Statistics(
df=plot_df,
groupby={"category": ["b", "a"]},
filters=[(("scen", "test"), {
"scenario": "test_scenario"
})],
)
obs = stats_add(stats, plot_df).summarize(custom_format="{:.0f}")
idx = pd.MultiIndex(
levels=[["category", "scen"], ["b", "a", "test"]],
codes=[[0, 0, 1], [0, 1, 2]],
names=["", ""],
)
cols = pd.MultiIndex(
levels=[["count", "primary", "coal"], ["", 2005]],
codes=[[0, 1, 2], [0, 1, 1]],
names=[None, "mean (max, min)"],
)
exp = pd.DataFrame(
data=[
["2", "1 (2, 1)", "0 (0, 0)"],
["2", "1 (1, 1)", "0 (0, 0)"],
["2", "1 (1, 1)", "0 (0, 0)"],
],
index=idx,
columns=cols,
)
pd.testing.assert_frame_equal(obs, exp)
def test_statistics_with_percentiles(plot_df):
stats = Statistics(df=plot_df,
filters=[('test', {
'scenario': 'test_scenario'
})],
percentiles=[0.05, 0.95])
stats = stats_add(stats, plot_df)
obs = set(stats.stats.columns.get_level_values(2))
assert obs == set(
['count', 'mean', 'std', 'min', '5%', '50%', '95%', 'max'])
def test_statistics_with_percentiles(plot_df):
stats = Statistics(
df=plot_df,
filters=[("test", {
"scenario": "test_scenario"
})],
percentiles=[0.05, 0.95],
)
stats = stats_add(stats, plot_df)
obs = set(stats.stats.columns.get_level_values(2))
assert obs == set(
["count", "mean", "std", "min", "5%", "50%", "95%", "max"])
def test_statistics_by_filter(plot_df):
stats = Statistics(df=plot_df,
filters=[('test', {
'scenario': 'test_scenario'
})])
obs = stats_add(stats, plot_df).summarize(interquartile=True)
idx = pd.MultiIndex(levels=[['test']], codes=[[0]])
cols = pd.MultiIndex(levels=[['count', 'primary', 'coal'], ['', 2005]],
codes=[[0, 1, 2], [0, 1, 1]],
names=[None, 'mean (interquartile range)'])
exp = pd.DataFrame(data=['2', '0.85 (0.93, 0.77)', '0.42 (0.46, 0.39)'],
index=cols,
columns=idx).T
pd.testing.assert_frame_equal(obs, exp)
def test_statistics_by_filter(plot_df):
stats = Statistics(df=plot_df,
filters=[("test", {
"scenario": "test_scenario"
})])
obs = stats_add(stats, plot_df).summarize(interquartile=True)
idx = pd.MultiIndex(levels=[["test"]], codes=[[0]])
cols = pd.MultiIndex(
levels=[["count", "primary", "coal"], ["", 2005]],
codes=[[0, 1, 2], [0, 1, 1]],
names=[None, "mean (interquartile range)"],
)
exp = pd.DataFrame(data=["2", "0.85 (0.93, 0.77)", "0.42 (0.46, 0.39)"],
index=cols,
columns=idx).T
pd.testing.assert_frame_equal(obs, exp)
def test_statistics_with_rows(plot_df):
stats = Statistics(df=plot_df,
filters=[('test', {
'scenario': 'test_scenario'
})],
rows=True)
obs = stats_add_with_rows(stats, plot_df).summarize(center='50%')
idx = pd.MultiIndex(levels=[['test'], ['first', 'another']],
codes=[[0, 0], [0, 1]])
cols = pd.MultiIndex(levels=[['count', 'primary', 'coal'], ['', 2005]],
codes=[[0, 1, 2], [0, 1, 1]],
names=[None, 'median (max, min)'])
exp = pd.DataFrame(data=[['2', '0.85 (1.00, 0.70)', ''],
['2', '', '0.42 (0.50, 0.35)']],
index=idx,
columns=cols)
pd.testing.assert_frame_equal(obs, exp)
def test_statistics(plot_df):
plot_df.set_meta(meta=['a', 'b', 'b', 'a'], name='category')
stats = Statistics(df=plot_df,
groupby={'category': ['b', 'a']},
filters=[(('scen', 'test'), {
'scenario': 'test_scenario'
})])
obs = stats_add(stats, plot_df).summarize(custom_format='{:.0f}')
idx = pd.MultiIndex(levels=[['category', 'scen'], ['b', 'a', 'test']],
codes=[[0, 0, 1], [0, 1, 2]],
names=['', ''])
cols = pd.MultiIndex(levels=[['count', 'primary', 'coal'], ['', 2005]],
codes=[[0, 1, 2], [0, 1, 1]],
names=[None, 'mean (max, min)'])
exp = pd.DataFrame(data=[['2', '1 (2, 1)', '0 (0, 0)'],
['2', '1 (1, 1)', '0 (0, 0)'],
['2', '1 (1, 1)', '0 (0, 0)']],
index=idx,
columns=cols)
pd.testing.assert_frame_equal(obs, exp)
def test_statistics_with_rows(plot_df):
stats = Statistics(df=plot_df,
filters=[("test", {
"scenario": "test_scenario"
})],
rows=True)
obs = stats_add_with_rows(stats, plot_df).summarize(center="50%")
idx = pd.MultiIndex(levels=[["test"], ["first", "another"]],
codes=[[0, 0], [0, 1]])
cols = pd.MultiIndex(
levels=[["count", "primary", "coal"], ["", 2005]],
codes=[[0, 1, 2], [0, 1, 1]],
names=[None, "median (max, min)"],
)
exp = pd.DataFrame(
data=[["2", "0.85 (1.00, 0.70)", ""], ["2", "", "0.42 (0.50, 0.35)"]],
index=idx,
columns=cols,
)
pd.testing.assert_frame_equal(obs, exp)
def test_statistics_with_row_required(plot_df):
stats = Statistics(df=plot_df)
primary = plot_df.filter(variable='Primary Energy', year=2005).timeseries()
# test that kwarg `row` is required for `add`
pytest.raises(ValueError, stats.add, data=primary, header='primary')