Lab6 ci

.github/workflows/tests.yml

@@ -0,0 +1,31 @@
+name: Run tests
+
+on: push
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clone repository
+        uses: actions/checkout@v6
+      # https://github.com/actions/setup-python
+      - name: Install Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.14"
+          cache: pip
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+      - name: Run tests
+        # https://pytest-cov.readthedocs.io/en/latest/readme.html
+        run: pytest --cov
+
+      # https://github.com/astral-sh/ruff-action
+      - name: Set up ruff
+        uses: astral-sh/ruff-action@v3
+        with:
+          version: latest
+      - name: Lint
+        run: ruff check
+      - name: Check formatting
+        run: ruff format --check

mta_ridership_project .ipynb

@@ -11,16 +11,17 @@
     "**Dataset:** MTA Daily Ridership Data: Beginning 2020\n",
     "\n",
     "**Research Questions:**\n",
+    "\n",
     "1. What's the difference between weekday and weekend travel patterns?\n",
     "2. Do holidays and big events show up in the ridership numbers?\n",
-    "3. Which parts of the MTA system bounced back fastest after 2020?"
+    "3. Which parts of the MTA system bounced back fastest after 2020?\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Setup and Data Loading"
+    "## 1. Setup and Data Loading\n"
    ]
   },
   {
@@ -44,7 +45,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Data Cleaning"
+    "## 2. Data Cleaning\n"
    ]
   },
   {
@@ -65,7 +66,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. Visualization: MTA Ridership Recovery by Transit Mode"
+    "## 3. Visualization: MTA Ridership Recovery by Transit Mode\n"
    ]
   },
   {
@@ -76,21 +77,51 @@
    "source": [
     "plt.figure(figsize=(14, 7))\n",
     "\n",
-    "plt.plot(df['date'], df['subways_of_comparable_pre_pandemic_day'], \n",
-    "         label='Subway', alpha=0.8, linewidth=1.2)\n",
-    "plt.plot(df['date'], df['buses_of_comparable_pre_pandemic_day'], \n",
-    "         label='Bus', alpha=0.8, linewidth=1.2)\n",
-    "plt.plot(df['date'], df['lirr_of_comparable_pre_pandemic_day'], \n",
-    "         label='LIRR', alpha=0.8, linewidth=1.2)\n",
-    "plt.plot(df['date'], df['metro_north_of_comparable_pre_pandemic_day'], \n",
-    "         label='Metro-North', alpha=0.8, linewidth=1.2)\n",
+    "plt.plot(\n",
+    "    df[\"date\"],\n",
+    "    df[\"subways_of_comparable_pre_pandemic_day\"],\n",
+    "    label=\"Subway\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1.2,\n",
+    ")\n",
+    "plt.plot(\n",
+    "    df[\"date\"],\n",
+    "    df[\"buses_of_comparable_pre_pandemic_day\"],\n",
+    "    label=\"Bus\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1.2,\n",
+    ")\n",
+    "plt.plot(\n",
+    "    df[\"date\"],\n",
+    "    df[\"lirr_of_comparable_pre_pandemic_day\"],\n",
+    "    label=\"LIRR\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1.2,\n",
+    ")\n",
+    "plt.plot(\n",
+    "    df[\"date\"],\n",
+    "    df[\"metro_north_of_comparable_pre_pandemic_day\"],\n",
+    "    label=\"Metro-North\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1.2,\n",
+    ")\n",
     "\n",
-    "plt.axhline(y=1.0, color='gray', linestyle='--', linewidth=1.5, label='Pre-pandemic baseline (100%)')\n",
+    "plt.axhline(\n",
+    "    y=1.0,\n",
+    "    color=\"gray\",\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=1.5,\n",
+    "    label=\"Pre-pandemic baseline (100%)\",\n",
+    ")\n",
     "\n",
-    "plt.xlabel('Date', fontsize=12)\n",
-    "plt.ylabel('% of Pre-Pandemic Ridership', fontsize=12)\n",
-    "plt.title('MTA Ridership Recovery: Subway vs Bus vs Commuter Rail (2020-Present)', fontsize=14, fontweight='bold')\n",
-    "plt.legend(loc='lower right', fontsize=10)\n",
+    "plt.xlabel(\"Date\", fontsize=12)\n",
+    "plt.ylabel(\"% of Pre-Pandemic Ridership\", fontsize=12)\n",
+    "plt.title(\n",
+    "    \"MTA Ridership Recovery: Subway vs Bus vs Commuter Rail (2020-Present)\",\n",
+    "    fontsize=14,\n",
+    "    fontweight=\"bold\",\n",
+    ")\n",
+    "plt.legend(loc=\"lower right\", fontsize=10)\n",
     "plt.grid(True, alpha=0.3)\n",
     "plt.ylim(0, 1.5)\n",
     "plt.tight_layout()\n",

requirements.txt

@@ -1,4 +1,7 @@
 streamlit
 pandas
 plotly
-requests
+requests
+pytest
+pytest-cov
+matplotlib