ex 05.1, 05.2, 05.3, 05.4

Author: kiddopro

.learn/exercises/05.1-from_dict/test.py

@@ -0,0 +1,27 @@
+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 == """    brand     make   color
+0  Toyota  Corolla    Blue
+1    Ford        K  Yellow
+2  Porche  Cayenne   White
+3   Tesla  Model S     Red
+"""

.learn/exercises/05.2-iloc/solution.hiden.py

@@ -0,0 +1,22 @@
+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 == ""

.learn/exercises/05.2-iloc/test.py

@@ -0,0 +1,34 @@
+import pytest, io, os, sys, re, pandas
+data_frame = pandas.read_csv('.learn/assets/pokemon_data.csv').head(3)
+
+@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 == str(data_frame) + '\n'
+
+# @pytest.mark.it('The variable data_frame should exist')
+# def test_variable_existence():
+#     from app import data_frame
+
+"""
+   #       Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  Generation  Legendary
+0  1  Bulbasaur  Grass  Poison  45      49       49       65       65     45           1      False
+1  2    Ivysaur  Grass  Poison  60      62       63       80       80     60           1      False
+2  3   Venusaur  Grass  Poison  80      82       83      100      100     80           1      False
+"""

.learn/exercises/05.3-head/test.py

@@ -0,0 +1,33 @@
+import io, os, re, sys, pytest, pandas
+data_frame = pandas.read_csv('.learn/assets/pokemon_data.csv').tail(3)
+
+@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 == str(data_frame)+'\n'
+
+# @pytest.mark.it('The variable data_frame should exist')
+# def test_variable_existence():
+#     from app import data_frame
+
+"""       #                 Name   Type 1 Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  Generation  Legendary
+797  720  HoopaHoopa Confined  Psychic  Ghost  80     110       60      150      130     70           6       True
+798  720   HoopaHoopa Unbound  Psychic   Dark  80     160       60      170      130     80           6       True
+799  721            Volcanion     Fire  Water  80     110      120      130       90     70           6       True
+"""

.learn/exercises/05.4-tail/test.py

@@ -1,9 +0,0 @@
-import pandas as pd
-# two dimensional array of name,age values.
-data = [["Toyota", "Corolla", "Blue"], ["Ford", "K", "Yellow"], ["Porche", "Cayenne", "White"]]
-
-# create the dataframe and name the columns
-df = pd.DataFrame(data,columns=['Brand', 'Make', 'Color'])
-
-# print the dataframe
-print(df)

app.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":4383489070513313300,"translations":[]}')])
+        metafunc.parametrize("configuration", [json.loads('{"port":3000,"address":"https://kiddopro-pythonpandastu-mcial2n3lf7.ws-us41.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-pythonpandastu-mcial2n3lf7.ws-us41.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":6793656476394450000,"translations":[]}')])