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Update tests
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@Tim-Abwao
Tim-Abwao committed Aug 17, 2024
1 parent 2cace95 commit 71e2e4f
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Showing 3 changed files with 39 additions and 35 deletions.
8 changes: 5 additions & 3 deletions eda_report/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@
@mpl.rc_context(GENERAL_RC_PARAMS)
def _savefig(figure: Figure) -> BytesIO:
"""Saves the contents of a :class:`~matplotlib.figure.Figure` in PNG
format, as bytes in a file-like object. This allows rapid in-memory
format, as bytes in a file-like object. This allows rapid in-memory
access when compiling the report.
Args:
Expand Down Expand Up @@ -116,15 +116,17 @@ def box_plot(
if hue is None:
bxplot = ax.boxplot(
data,
labels=[label],
tick_labels=[label],
sym=".",
boxprops=dict(facecolor=color, alpha=0.75),
)
ax.set_yticklabels("")
else:
hue = _validate_univariate_input(hue)[original_data.notna()]
groups = {key: sub_series for key, sub_series in data.groupby(hue)}
bxplot = ax.boxplot(groups.values(), labels=groups.keys(), sym=".")
bxplot = ax.boxplot(
groups.values(), tick_labels=groups.keys(), sym="."
)

if color is None:
colors = [f"C{idx}" for idx in range(hue.nunique())]
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3 changes: 1 addition & 2 deletions eda_report/univariate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@


class Variable:

"""Obtain summary statistics and properties such as data type, missing
value info & cardinality from one-dimensional datasets.
Expand All @@ -37,7 +36,7 @@ def __init__(self, data: Iterable, *, name: str = None) -> None:
data = _validate_univariate_input(data, name=name)

#: str: The variable's *name*. If no name is specified, the name will
#: be set the value of the ``name`` attribute of the input data, or
#: be set the value of the ``name`` attribute of the input data, or
#: ``None``.
self.name = data.name

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63 changes: 33 additions & 30 deletions tests/test_univariate_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ def test_unique_values(self):
assert self.variable.unique_values == pytest.approx(list(range(20)))

assert self.unnamed_variable.num_unique == 4
assert self.unnamed_variable.unique_values == list("abde")
assert all(self.unnamed_variable.unique_values == list("abde"))


class TestCategoricalVariables:
Expand Down Expand Up @@ -79,12 +79,13 @@ def test_normality_results(self):

def test_repr(self):
assert str(self.categorical_variable) == (
"\nName: None\nType: categorical\nNon-null Observations: 5\nUniqu"
"e Values: 4 -> ['a', 'b', 'c', 'd']\nMissing Values: 1 (16.67%)"
"\nMode (Most frequent): a\nMaximum frequency: 2\n\n\t\tMost Comm"
"on Items\n\t\t-----------------\n a: 2 (40"
".00%)\n b: 1 (20.00%)\n "
" c: 1 (20.00%)\n d: 1 (20.00%)"
"\nName: None\nType: categorical\nNon-null Observations: 5"
"\nUnique Values: 4 -> ['a' 'b' 'c' 'd']\nMissing Values: "
"1 (16.67%)\nMode (Most frequent): a\nMaximum frequency: 2"
"\n\n\t\tMost Common Items\n\t\t-----------------\n "
" a: 2 (40.00%)\n b: "
"1 (20.00%)\n c: 1 (20.00%)\n "
" d: 1 (20.00%)"
)


Expand Down Expand Up @@ -126,14 +127,14 @@ def test_normality_results(self):

def test_repr(self):
assert str(self.datetime_variable) == (
"\nName: dates\nType: datetime\nNon-null Observations: 10\nUnique"
" Values: 10 -> [Timestamp('2022-01-01 00:00:00'), [...]\nMissing"
" Values: None\n\n\t\t Summary Statistics\n\t\t ---------------"
"---\n\tAverage: 2022-01-05 12:00:00\n\tMinimum: "
" 2022-01-01 00:00:00\n\tLower Quartile: 2022-01-0"
"3 06:00:00\n\tMedian: 2022-01-05 12:00:00\n\tUpper"
" Quartile: 2022-01-07 18:00:00\n\tMaximum: 20"
"22-01-10 00:00:00"
"\nName: dates\nType: datetime\nNon-null Observations: 10\n"
"Unique Values: 10 -> ['2022-01-01T00:00:00.000000000' ... "
"]\nMissing Values: None\n\n\t\t Summary Statistics\n\t\t "
" ------------------\n\tAverage: 2022-01-05 12"
":00:00\n\tMinimum: 2022-01-01 00:00:00\n\t"
"Lower Quartile: 2022-01-03 06:00:00\n\tMedian: "
" 2022-01-05 12:00:00\n\tUpper Quartile: 2022"
"-01-07 18:00:00\n\tMaximum: 2022-01-10 00:00:00"
)


Expand Down Expand Up @@ -167,7 +168,7 @@ def test_normality_results(self):
"p-value": {
"D'Agostino's K-squared test": "0.0015981",
"Kolmogorov-Smirnov test": "0.0000000",
"Shapiro-Wilk test": "0.0580895",
"Shapiro-Wilk test": "0.0580919",
},
"Conclusion at α = 0.05": {
"D'Agostino's K-squared test": "Unlikely to be normal",
Expand All @@ -178,20 +179,22 @@ def test_normality_results(self):

def test_repr(self):
assert str(self.numeric_variable) == (
"\nName: 1 to 50\nType: numeric\nNon-null Observations: 50\nUniqu"
"e Values: 50 -> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ["
"...]\nMissing Values: None\n\n\t\t Summary Statistics\n\t\t --"
"----------------\n\tAverage: 24.5000\n\tSta"
"ndard Deviation: 14.5774\n\tMinimum: "
" 0.0000\n\tLower Quartile: 12.2500\n\tMedian: "
" 24.5000\n\tUpper Quartile: 3"
"6.7500\n\tMaximum: 49.0000\n\tSkewness: "
" 0.0000\n\tKurtosis: -1.200"
"0\n\n\t\t Tests for Normality\n\t\t -------------------\n "
" p-value Conclusion at α = 0.05\nD'Agos"
"tino's K-squared test 0.0015981 Unlikely to be normal\nKolmogo"
"rov-Smirnov test 0.0000000 Unlikely to be normal\nShapiro-"
"Wilk test 0.0580895 Possibly normal"
"\nName: 1 to 50\nType: numeric\nNon-null Observations: 50"
"\nUnique Values: 50 -> [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 1"
"4 15 16 17 18 ... ]\nMissing Values: None\n\n\t\t Summary"
" Statistics\n\t\t ------------------\n\tAverage: "
" 24.5000\n\tStandard Deviation: 14.5"
"774\n\tMinimum: 0.0000\n\tLower Quar"
"tile: 12.2500\n\tMedian: "
" 24.5000\n\tUpper Quartile: 36.7500\n\tMax"
"imum: 49.0000\n\tSkewness: "
" 0.0000\n\tKurtosis: -1.2000"
"\n\n\t\t Tests for Normality\n\t\t -------------------\n"
" p-value Conclusion at α = 0"
".05\nD'Agostino's K-squared test 0.0015981 Unlikely to "
"be normal\nKolmogorov-Smirnov test 0.0000000 Unlikel"
"y to be normal\nShapiro-Wilk test 0.0580919 "
" Possibly normal"
)


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