ex 04, 04.1, 04.2

Author: kiddopro

.learn/exercises/04-Series/test.py

@@ -1,4 +1,4 @@
-import pytest, re, os, io, sys
+import pytest, io, os, sys, re
 
 @pytest.mark.it('You must import pandas')
 def test_import_pandas():
@@ -12,6 +12,12 @@ def test_import_pandas():
 def test_vatiable_existence():
     from app import ages
 
+@pytest.mark.it("Use the print function")
+def test_output():
+    f = open(os.path.dirname(os.path.abspath('app.py')) + '/app.py')
+    content = f.read()
+    assert content.find("print(") > 0
+
 @pytest.mark.it('The output should be the expected')
 def test_expected_output(capsys):
     import app
@@ -27,5 +33,4 @@ def test_expected_output(capsys):
 8    54
 9    34
 dtype: int64
-
 """

.learn/exercises/04.1-Date_range/test.py

@@ -0,0 +1,26 @@
+import pytest, io, os, sys, re
+
+@pytest.mark.it('You must import pandas')
+def test_import_pandas():
+    path = os.path.dirname(os.path.abspath('app.py'))+'/app.py'
+    with open(path, 'r') as content_file:
+        content = content_file.read()
+        regex = re.compile(r"import\s*pandas")
+        assert bool(regex.search(content)) == True
+
+@pytest.mark.it("Use the print function")
+def test_output():
+    f = open(os.path.dirname(os.path.abspath('app.py')) + '/app.py')
+    content = f.read()
+    assert content.find("print(") > 0
+
+
+@pytest.mark.it('The output should be the expected')
+def test_expected_output(capsys):
+    import app
+    captured = capsys.readouterr()
+    assert captured.out == """DatetimeIndex(['2021-05-01', '2021-05-02', '2021-05-03', '2021-05-04',
+               '2021-05-05', '2021-05-06', '2021-05-07', '2021-05-08',
+               '2021-05-09', '2021-05-10', '2021-05-11', '2021-05-12'],
+              dtype='datetime64[ns]', freq='D')
+"""

.learn/exercises/04.2-Series_Apply/test.py

@@ -0,0 +1,30 @@
+import io, os, re, sys, pytest
+
+@pytest.mark.it('You must import pandas')
+def test_import_pandas():
+    path = os.path.dirname(os.path.abspath('app.py'))+'/app.py'
+    with open(path, 'r') as content_file:
+        content = content_file.read()
+        regex = re.compile(r"import\s*pandas")
+        assert bool(regex.search(content)) == True
+
+@pytest.mark.it("Use the print function")
+def test_output():
+    f = open(os.path.dirname(os.path.abspath('app.py')) + '/app.py')
+    content = f.read()
+    assert content.find("print(") > 0
+
+@pytest.mark.it('The variable my_series should exist')
+def test_variable_existence():
+    from app import my_series
+
+@pytest.mark.it('The output should be the expected')
+def test_expected_output(capsys):
+    import app
+    captured = capsys.readouterr()
+    assert captured.out == """0    1.0
+1    2.0
+2    3.0
+3    4.0
+4    5.0
+dtype: float64"""

app.py

@@ -1,6 +1,5 @@
 import pandas as pd
-# two dimensional array of name,age values.
-ages = pd.Series([23,45,7,34,6,63,36,78,54,34])
-
-print(ages)
 
+my_series = pd.Series([2, 4, 6, 8, 10])
+modified_series = my_series.apply(lambda x:x/2)
+print(modified_series)

conftest.py

@@ -24,4 +24,4 @@ def pytest_generate_tests(metafunc):
         except AttributeError:
           metafunc.parametrize("app",[cached_app])
     if 'configuration' in metafunc.fixturenames:
-        metafunc.parametrize("configuration", [json.loads('{"port":3000,"address":"https://kiddopro-pythonpandastut-zzjbcc6h0h1.ws-us34.gitpod.io","editor":{"mode":"preview","agent":"gitpod","version":"1.0.72"},"dirPath":"./.learn","configPath":"learn.json","outputPath":".learn/dist","publicPath":"/preview","publicUrl":"https://3000-kiddopro-pythonpandastut-zzjbcc6h0h1.ws-us34.gitpod.io","language":"auto","grading":"incremental","exercisesPath":".learn/exercises","webpackTemplate":null,"disableGrading":false,"disabledActions":["build"],"actions":[],"entries":{"html":"index.html","vanillajs":"index.js","react":"app.jsx","node":"app.js","python3":"app.py","java":"app.java"},"preview":"https://github.com/4GeeksAcademy/python-pandas-tutorial/blob/main/.learn/assets/pandas-preview.jpeg?raw=true","difficulty":"beginner","duration":3,"description":"Learn the basics and become comfortable using pandas for manipulating machine learning datasets .","title":"Pandas for Machine Learning","slug":"pandas-for-machine-learning","session":7340087717416326000,"translations":[]}')])
+        metafunc.parametrize("configuration", [json.loads('{"port":3000,"address":"https://kiddopro-pythonpandastut-zzjbcc6h0h1.ws-us34.gitpod.io","editor":{"mode":"preview","agent":"gitpod","version":"1.0.72"},"dirPath":"./.learn","configPath":"learn.json","outputPath":".learn/dist","publicPath":"/preview","publicUrl":"https://3000-kiddopro-pythonpandastut-zzjbcc6h0h1.ws-us34.gitpod.io","language":"auto","grading":"incremental","exercisesPath":".learn/exercises","webpackTemplate":null,"disableGrading":false,"disabledActions":["build"],"actions":[],"entries":{"html":"index.html","vanillajs":"index.js","react":"app.jsx","node":"app.js","python3":"app.py","java":"app.java"},"preview":"https://github.com/4GeeksAcademy/python-pandas-tutorial/blob/main/.learn/assets/pandas-preview.jpeg?raw=true","difficulty":"beginner","duration":3,"description":"Learn the basics and become comfortable using pandas for manipulating machine learning datasets .","title":"Pandas for Machine Learning","slug":"pandas-for-machine-learning","session":8607061631283586000,"translations":[]}')])