Skip to content

Commit 3a34a14

Browse files
fatsefatse
committed
Autoformat tests
1 parent 674caf5 commit 3a34a14

File tree

1 file changed

+24
-21
lines changed

1 file changed

+24
-21
lines changed

‎tests/test_eda_utils_py.py‎

Lines changed: 24 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,6 @@
66
import numpy as np
77

88

9-
109
def test_version():
1110
assert __version__ == "0.1.0"
1211

@@ -188,8 +187,10 @@ def test_scaler():
188187
)
189188

190189
mock_df_1_standard = pd.DataFrame(
191-
{"col1": [-0.3302891295379082, -0.8807710121010884, -0.8807710121010884, 0.7706746355884523, 1.3211565181516325],
192-
"col2": [1.714389230829046, -0.26375218935831474, -0.26375218935831474, -0.9231326627541017, -0.26375218935831474],
190+
{"col1": [-0.3302891295379082, -0.8807710121010884, -0.8807710121010884, 0.7706746355884523,
191+
1.3211565181516325],
192+
"col2": [1.714389230829046, -0.26375218935831474, -0.26375218935831474, -0.9231326627541017,
193+
-0.26375218935831474],
193194
"col3": [1.0, -1.0, -1.0, 1.0, 0.0]}
194195
)
195196

@@ -233,7 +234,6 @@ def test_scaler():
233234
minmax_scaled_mock_df_1, mock_df_1_minmax
234235
), "The returned dataframe using standard scaler method is not correct"
235236

236-
237237
assert pd.DataFrame.equals(
238238
standard_scaled_mock_df_2, mock_df_2_standard
239239
), "The returned dataframe using most_frequent inputer is not correct"
@@ -242,43 +242,47 @@ def test_scaler():
242242
), "The returned dataframe using constant imputer is not correct"
243243

244244

245-
246245
def test_outlier_identifier():
247246
test_df = pd.DataFrame({
248247
'SepalLengthCm': [5.1, 4.9, 4.7, 5.5, 5.1, 50, 5.4, 5.0, 5.2, 5.3, 5.1],
249248
'SepalWidthCm': [1.4, 1.4, 20, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
250-
'PetalWidthCm' :[0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5],
251-
'Species':['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
249+
'PetalWidthCm': [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5],
250+
'Species': ['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
251+
'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
252252
})
253253

254254
test_column = ['SepalLengthCm', 'SepalWidthCm', 'PetalWidthCm']
255255

256256
median_output = pd.DataFrame({
257257
'SepalLengthCm': [5.1, 4.9, 4.7, 5.5, 5.1, 5.1, 5.4, 5.0, 5.2, 5.3, 5.1],
258258
'SepalWidthCm': [1.4, 1.4, 1.5, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
259-
'PetalWidthCm' :[0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 0.4],
260-
'Species':['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
259+
'PetalWidthCm': [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 0.4],
260+
'Species': ['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
261+
'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
261262
})
262263

263264
trim_output = pd.DataFrame({
264265
'SepalLengthCm': [5.1, 4.9, 5.5, 5.1, 5.4, 5.0, 5.2, 5.3],
265266
'SepalWidthCm': [1.4, 1.4, 2.0, 0.7, 1.2, 1.4, 1.8, 1.5],
266-
'PetalWidthCm' :[0.2, 0.2, 0.3, 0.4, 0.5, 0.6, 0.4, 0.2],
267-
'Species':['Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
267+
'PetalWidthCm': [0.2, 0.2, 0.3, 0.4, 0.5, 0.6, 0.4, 0.2],
268+
'Species': ['Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
269+
'Iris-setosa', 'Iris-setosa']
268270
})
269271

270272
mean_output = pd.DataFrame({
271273
'SepalLengthCm': [5.1, 4.9, 4.7, 5.5, 5.1, 9.21, 5.4, 5.0, 5.2, 5.3, 5.1],
272274
'SepalWidthCm': [1.4, 1.4, 3.19, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
273-
'PetalWidthCm' :[0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 0.77],
274-
'Species':['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
275+
'PetalWidthCm': [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 0.77],
276+
'Species': ['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
277+
'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
275278
})
276279

277-
column_output= pd.DataFrame({
280+
column_output = pd.DataFrame({
278281
'SepalLengthCm': [5.1, 4.9, 4.7, 5.5, 5.1, 9.21, 5.4, 5.0, 5.2, 5.3, 5.1],
279282
'SepalWidthCm': [1.4, 1.4, 20, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
280-
'PetalWidthCm' :[0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5],
281-
'Species':['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
283+
'PetalWidthCm': [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5],
284+
'Species': ['Iris-setosa', 'Iris-virginica', 'Iris-germanica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
285+
'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa']
282286
})
283287

284288
# Test if the imput is not dataFrame
@@ -295,7 +299,7 @@ def test_outlier_identifier():
295299

296300
# Test if method input is not one of three methods provided
297301
with raises(Exception):
298-
eda_utils_py.outlier_identifier(test_df, columns=["SepalLengthCm"], method = "no")
302+
eda_utils_py.outlier_identifier(test_df, columns=["SepalLengthCm"], method="no")
299303

300304
# Test if column selected included non-numeric columns
301305
with raises(Exception):
@@ -305,12 +309,11 @@ def test_outlier_identifier():
305309
eda_utils_py.outlier_identifier(test_df, test_column), trim_output
306310
), "Default test not pass"
307311
assert pd.DataFrame.equals(
308-
eda_utils_py.outlier_identifier(test_df, test_column,method = "median"), median_output
312+
eda_utils_py.outlier_identifier(test_df, test_column, method="median"), median_output
309313
), "The median method is not correct"
310314
assert pd.DataFrame.equals(
311-
eda_utils_py.outlier_identifier(test_df, test_column, method = "mean"), mean_output
315+
eda_utils_py.outlier_identifier(test_df, test_column, method="mean"), mean_output
312316
), "The mean method is not correct"
313317
assert pd.DataFrame.equals(
314-
eda_utils_py.outlier_identifier(test_df, columns = ["SepalLengthCm"], method = "mean"), column_output
318+
eda_utils_py.outlier_identifier(test_df, columns=["SepalLengthCm"], method="mean"), column_output
315319
), "The selected column method is not correct"
316-

0 commit comments

Comments
 (0)