ex 04

Author: kiddopro

.learn/exercises/03-Dataset/README.md

@@ -1,4 +1,4 @@
-# Download the Dataset
+# `03` Download the Dataset
 
 In machine learning "datasets" are the data we use for our experiments, we usually feed these datasets to "models" and experiment in different ways, the goal is always to predict something.
 

.learn/exercises/04-Series/README.md

@@ -1,4 +1,4 @@
-# Series
+# `04` Series
 
 A series is similar to an array or list, it's a one dimentional data structure.
 

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

@@ -0,0 +1,31 @@
+import pytest, re, os, io, sys
+
+@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('The variable ages must exist')
+def test_vatiable_existence():
+    from app import ages
+
+@pytest.mark.it('The output should be the expected')
+def test_expected_output(capsys):
+    import app
+    captured = capsys.readouterr()
+    assert captured.out == """0    23
+1    45
+2     7
+3    34
+4     6
+5    63
+6    36
+7    78
+8    54
+9    34
+dtype: int64
+
+"""

app.py

@@ -1,5 +1,6 @@
-import pandas as pd 
+import pandas as pd
+# two dimensional array of name,age values.
+ages = pd.Series([23,45,7,34,6,63,36,78,54,34])
 
-data_frame = pd.read_csv('.learn/assets/pokemon_data.csv')
-print(data_frame)
+print(ages)
 

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":2664118464753879600,"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":7340087717416326000,"translations":[]}')])