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add mock
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‎Chapter5/testing.ipynb

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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "52c638a0",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "781ff6d9",
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"metadata": {},
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"source": [
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"### Test for Specific Exceptions in Unit Testing"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c087d03",
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"metadata": {},
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"source": [
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"To test for a specific exception in unit testing, use `pytest.raises`.\n",
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "24775262",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "e2de71c1",
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"metadata": {},
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"source": [
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"```bash\n",
@@ -1363,6 +1368,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "2898168f",
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"metadata": {
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"tags": [
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"remove-input"
@@ -2399,17 +2405,163 @@
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"\n",
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"A mock object can control the behavior of a real object in a testing environment by simulating responses from external services.\n",
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"\n",
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"The following code uses a mock object to test the `get_data` function's behavior when calling an API that may either succeed or fail."
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"Here are two common use cases with examples:"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "aa19ebb6",
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"id": "fa134f51",
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"metadata": {},
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"source": [
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"```python\n",
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"1. **Mocking Time-Dependent Functions**\n",
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"\n",
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"When testing functions that depend on the current time or date, you can mock the time to ensure consistent results.\n",
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"\n",
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"Example: Testing a function that returns data for the last week"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"id": "25d20b5a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting main.py\n"
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]
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}
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],
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"source": [
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"%%writefile main.py\n",
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"from datetime import datetime, timedelta\n",
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"\n",
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"\n",
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"def get_data_for_last_week():\n",
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" end_date = datetime.now().date()\n",
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" start_date = end_date - timedelta(days=7)\n",
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" return {\n",
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" \"start_date\": start_date.strftime(\"%Y-%m-%d\"),\n",
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" \"end_date\": end_date.strftime(\"%Y-%m-%d\"),\n",
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" }"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e66a0f82",
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"metadata": {},
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"source": [
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"Now, let's create a test for this function using mock:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"id": "7754b84b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting test_main.py\n"
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]
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}
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],
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"source": [
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"%%writefile test_main.py\n",
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"from datetime import datetime\n",
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"from unittest.mock import patch\n",
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"from main import get_data_for_last_week\n",
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"\n",
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"\n",
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"@patch(\"main.datetime\")\n",
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"def test_get_data_for_last_week(mock_datetime):\n",
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" # Set a fixed date for the test\n",
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" mock_datetime.now.return_value = datetime(2024, 8, 5)\n",
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"\n",
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" # Call the function\n",
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" result = get_data_for_last_week()\n",
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"\n",
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" # Assert the results\n",
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" assert result[\"start_date\"] == \"2024-07-29\"\n",
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" assert result[\"end_date\"] == \"2024-08-05\"\n",
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"\n",
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" # Verify that datetime.now() was called\n",
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" mock_datetime.now.assert_called_once()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8124b826",
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"metadata": {},
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"source": [
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"This test mocks the `datetime.now()` method to return a fixed date, allowing for predictable and consistent test results."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "a6c35293",
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"metadata": {
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"tags": [
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"hide-cell"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1m============================= test session starts ==============================\u001b[0m\n",
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"platform darwin -- Python 3.11.2, pytest-7.4.3, pluggy-1.3.0 -- /Users/khuyentran/.pyenv/versions/3.11.2/bin/python3\n",
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"cachedir: .pytest_cache\n",
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"hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase(PosixPath('/Users/khuyentran/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter5/.hypothesis/examples'))\n",
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"rootdir: /Users/khuyentran/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter5\n",
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"plugins: dvc-3.28.0, hydra-core-1.3.2, typeguard-4.1.5, anyio-4.2.0, hypothesis-6.88.4\n",
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"collected 1 item \u001b[0m\n",
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"\n",
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"test_main.py::test_get_data_for_last_week \u001b[32mPASSED\u001b[0m\n",
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"\n",
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"\u001b[32m============================== \u001b[32m\u001b[1m1 passed\u001b[0m\u001b[32m in 0.09s\u001b[0m\u001b[32m ===============================\u001b[0m\n",
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"\u001b[0m"
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]
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}
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],
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"source": [
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"!pytest -sv test_main.py"
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]
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},
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{
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"cell_type": "markdown",
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"id": "161d68ef-2f73-43ab-98cf-8268bab793a0",
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"metadata": {},
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"source": [
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"2. **Mocking API calls**\n",
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"\n",
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"When testing code that makes external API calls, mocking helps avoid actual network requests during testing.\n",
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"\n",
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"Example: Testing a function that makes an API call"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "c0a3fb32",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting main.py\n"
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]
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}
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],
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"source": [
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"%%writefile main.py\n",
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"import requests\n",
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"from requests.exceptions import ConnectionError\n",
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"\n",
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" response = requests.get(\"http://localhost:5432\")\n",
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" return response.json()\n",
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" except ConnectionError:\n",
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" return None\n",
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" return None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "2f5b1755",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting test_main.py\n"
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]
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}
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],
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"source": [
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"%%writefile test_main.py\n",
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"from unittest.mock import patch\n",
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"from requests.exceptions import ConnectionError\n",
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"from main import get_data\n",
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"\n",
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"\n",
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"def test_get_data_fails():\n",
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"@patch(\"main.requests.get\")\n",
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"def test_get_data_fails(mock_get):\n",
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" \"\"\"Test the get_data function when the API call fails\"\"\"\n",
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" # Mock the requests.get function\n",
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" with patch(\"requests.get\") as mock_get:\n",
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" # Define what happens when the function is called\n",
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" mock_get.side_effect = ConnectionError\n",
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" assert get_data() is None\n",
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" # Define what happens when the function is called\n",
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" mock_get.side_effect = ConnectionError\n",
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" assert get_data() is None\n",
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"\n",
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"\n",
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"def test_get_data_succeeds():\n",
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"@patch(\"main.requests.get\")\n",
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"def test_get_data_succeeds(mock_get):\n",
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" \"\"\"Test the get_data function when the API call succeeds\"\"\"\n",
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" # Mock the requests.get function\n",
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" with patch(\"requests.get\") as mock_get:\n",
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" # Define the return value of the function\n",
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" mock_get.return_value.json.return_value = {\"data\": \"test\"}\n",
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" assert get_data() == {\"data\": \"test\"}\n",
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"\n",
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"```"
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" # Define the return value of the function\n",
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" mock_get.return_value.json.return_value = {\"data\": \"test\"}\n",
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" assert get_data() == {\"data\": \"test\"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a0701b9",
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"metadata": {},
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"source": [
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"These tests mock the `requests.get()` function to simulate both successful and failed API calls, allowing us to test our function's behavior in different scenarios without making actual network requests."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "3af021c9",
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"metadata": {
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"tags": [
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"hide-cell"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1m============================= test session starts ==============================\u001b[0m\n",
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"platform darwin -- Python 3.11.2, pytest-7.4.3, pluggy-1.3.0\n",
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"rootdir: /Users/khuyentran/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter5\n",
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"plugins: dvc-3.28.0, hydra-core-1.3.2, typeguard-4.1.5, anyio-4.2.0, hypothesis-6.88.4\n",
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"collected 2 items \u001b[0m\n",
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"\n",
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"test_main.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m [100%]\u001b[0m\n",
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"\n",
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"\u001b[32m============================== \u001b[32m\u001b[1m2 passed\u001b[0m\u001b[32m in 0.12s\u001b[0m\u001b[32m ===============================\u001b[0m\n",
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"\u001b[0m"
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]
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}
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],
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"source": [
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"!pytest test_main.py"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"id": "ff3f0491",
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"id": "e2ffc227-fd00-40ef-aaa2-74e8997aa2cd",
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"metadata": {},
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"source": [
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"[Link to mock](https://docs.python.org/3/library/unittest.mock.html)."
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"By using mocks in these ways, we can create more reliable and controlled unit tests for our data projects, ensuring that our code behaves correctly under various conditions."
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]
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},
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{
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"cell_type": "code",
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"id": "e0ca0f88",
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{
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"celltoolbar": "Tags",
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"hide_input": false,
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"kernelspec": {
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"display_name": "venv",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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"version": "3.11.6"
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},
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"toc": {
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"base_numbering": 1,

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