convert HW1 to Markdown

No need for it to be a notebook.
afeld · Jan 18, 2025 · 75f11cc · 75f11cc
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diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
@@ -10,7 +10,6 @@ jobs:
       matrix:
         notebook:
           - hw_0.ipynb
-          - hw_1.ipynb
           - hw_2.ipynb
           - hw_3.ipynb
           - hw_4.ipynb

diff --git a/hw_1.ipynb b/hw_1.ipynb
diff --git a/hw_1.md b/hw_1.md
@@ -0,0 +1,51 @@
+# Homework 1
+
+_[General assignment information](assignments.md)_
+
+## Coding
+
+1. [Find a dataset.](final_project/resources.md#open-data-portals)
+   - It must have:
+     - At least one numeric column
+     - Between one thousand and one million rows
+       - If it's larger than that, you can filter it down.
+   - Don't spend too long on this step.
+1. If there's more than one numeric column, pick one.
+1. Create a new notebook.
+1. Using pandas:
+   1. Read in the data.
+   1. Compute:
+      - The mean
+      - The median
+      - The mode
+   1. Do a `groupby()` with an [aggregation](https://pandas.pydata.org/docs/user_guide/groupby.html#aggregation).
+
+Now [turn in the assignment](assignments.md).
+
+## Tutorials
+
+1. Read [The Joys (and Woes) of the Craft of Software Engineering](https://cs.calvin.edu/courses/cs/262/kvlinden/references/brooksJoysAndWoes.html)
+   - Note not _everything_ in there is applicable to data analysis
+1. Filtering/indexing `DataFrame`s
+   - [Filter specific rows from a `DataFrame`](https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/03_subset_data.html#how-do-i-filter-specific-rows-from-a-dataframe)
+   - [Boolean indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing)
+1. Learn about functions
+   - [Video](https://www.youtube.com/watch?v=9Os0o3wzS_I&list=PL-osiE80TeTskrapNbzXhwoFUiLCjGgY7&index=8)
+   - [Blog post](https://python.land/introduction-to-python/functions)
+1. Coding Style Guides - Please skim these; I don't expect you to understand and follow everything in them. The most important guidelines to pay attention to are indentation and keeping each statement on its own line.
+   - [The Hitchhiker’s Guide to Python](https://docs.python-guide.org/writing/style/)
+   - [PEP 8](https://www.python.org/dev/peps/pep-0008/)
+1. [Guide to commenting your code](https://realpython.com/python-comments-guide/)
+1. [Quartz Guide to Bad Data](https://github.com/Quartz/bad-data-guide#readme)
+
+### Optional
+
+- [Learn about data dictionaries](https://analystanswers.com/what-is-a-data-dictionary-a-simple-thorough-overview/)
+- Glance through pandas' [comparison with other tools](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/index.html) for any you are familiar with
+- More on indexing:
+  - [How to Select Rows from Pandas DataFrame](https://datatofish.com/select-rows-pandas-dataframe/)
+  - Selecting Subsets of Data in Pandas: [Part 1](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c) and [Part 2](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-39e811c81a0c)
+
+## Participation
+
+Reminder about the [between-class participation requirement](syllabus.md#participation).
diff --git a/lecture_1_exercise.ipynb b/lecture_1_exercise.ipynb
@@ -16,20 +16,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-01-18T15:52:06.256870Z",
-     "iopub.status.busy": "2025-01-18T15:52:06.256737Z",
-     "iopub.status.idle": "2025-01-18T15:52:07.020501Z",
-     "shell.execute_reply": "2025-01-18T15:52:07.020017Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2025-01-18 10:52:06 URL:https://data.cityofnewyork.us/resource/m6nq-qud6.csv [136065] -> \"2021_yellow_taxi_trips.csv\" [1]\r\n"
+      "2025-01-18 12:39:39 URL:https://data.cityofnewyork.us/resource/m6nq-qud6.csv [136065] -> \"2021_yellow_taxi_trips.csv\" [1]\n"
      ]
     }
    ],
@@ -47,46 +40,39 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-01-18T15:52:07.025080Z",
-     "iopub.status.busy": "2025-01-18T15:52:07.023943Z",
-     "iopub.status.idle": "2025-01-18T15:52:07.329638Z",
-     "shell.execute_reply": "2025-01-18T15:52:07.327840Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2021_yellow_taxi_trips.csv        lecture_1_exercise.ipynb\r\n",
-      "LICENSE.md                        lecture_1_exercise_solution.ipynb\r\n",
-      "Makefile                          lecture_2.ipynb\r\n",
-      "\u001b[1m\u001b[36m__pycache__\u001b[m\u001b[m                       lecture_2.slides.html\r\n",
-      "\u001b[1m\u001b[36m_build\u001b[m\u001b[m                            lecture_2_exercise.ipynb\r\n",
-      "_config.yml                       lecture_3.ipynb\r\n",
-      "\u001b[1m\u001b[36m_static\u001b[m\u001b[m                           lecture_3.slides.html\r\n",
-      "_toc.yml                          lecture_3_exercise.ipynb\r\n",
-      "assignments.md                    lecture_3_exercise_solution.ipynb\r\n",
-      "conf.py                           lecture_4.ipynb\r\n",
-      "curve.ipynb                       lecture_4.slides.html\r\n",
-      "\u001b[35mdata\u001b[m\u001b[m                              lecture_5.ipynb\r\n",
-      "\u001b[1m\u001b[36mextras\u001b[m\u001b[m                            lecture_5.slides.html\r\n",
-      "\u001b[1m\u001b[36mfinal_project\u001b[m\u001b[m                     lecture_5_exercise_solution.ipynb\r\n",
-      "final_project.md                  lecture_6.ipynb\r\n",
-      "home.md                           lecture_6.slides.html\r\n",
-      "hw_0.ipynb                        lectures.md\r\n",
-      "hw_1.ipynb                        \u001b[1m\u001b[36mmeta\u001b[m\u001b[m\r\n",
-      "hw_2.ipynb                        meta.md\r\n",
-      "hw_3.ipynb                        nbdime_config.json\r\n",
-      "hw_4.ipynb                        pyproject.toml\r\n",
-      "index.md                          registration.md\r\n",
-      "joining_late.md                   resources.md\r\n",
-      "lecture_0.ipynb                   \u001b[35msolutions\u001b[m\u001b[m\r\n",
-      "lecture_0.slides.html             syllabus.md\r\n",
-      "lecture_1.ipynb                   \u001b[1m\u001b[36mtmp\u001b[m\u001b[m\r\n",
-      "lecture_1.slides.html\r\n"
+      "2021_yellow_taxi_trips.csv        lecture_1_exercise.ipynb\n",
+      "LICENSE.md                        lecture_1_exercise_solution.ipynb\n",
+      "Makefile                          lecture_2.ipynb\n",
+      "\u001b[1m\u001b[36m__pycache__\u001b[m\u001b[m                       lecture_2.slides.html\n",
+      "\u001b[1m\u001b[36m_build\u001b[m\u001b[m                            lecture_2_exercise.ipynb\n",
+      "_config.yml                       lecture_3.ipynb\n",
+      "\u001b[1m\u001b[36m_static\u001b[m\u001b[m                           lecture_3.slides.html\n",
+      "_toc.yml                          lecture_3_exercise.ipynb\n",
+      "assignments.md                    lecture_3_exercise_solution.ipynb\n",
+      "conf.py                           lecture_4.ipynb\n",
+      "curve.ipynb                       lecture_4.slides.html\n",
+      "\u001b[35mdata\u001b[m\u001b[m                              lecture_5.ipynb\n",
+      "\u001b[1m\u001b[36mextras\u001b[m\u001b[m                            lecture_5.slides.html\n",
+      "\u001b[1m\u001b[36mfinal_project\u001b[m\u001b[m                     lecture_5_exercise_solution.ipynb\n",
+      "final_project.md                  lecture_6.ipynb\n",
+      "home.md                           lecture_6.slides.html\n",
+      "hw_0.ipynb                        lectures.md\n",
+      "hw_1.md                           \u001b[1m\u001b[36mmeta\u001b[m\u001b[m\n",
+      "hw_2.ipynb                        meta.md\n",
+      "hw_3.ipynb                        nbdime_config.json\n",
+      "hw_4.ipynb                        pyproject.toml\n",
+      "index.md                          registration.md\n",
+      "joining_late.md                   resources.md\n",
+      "lecture_0.ipynb                   \u001b[35msolutions\u001b[m\u001b[m\n",
+      "lecture_0.slides.html             syllabus.md\n",
+      "lecture_1.ipynb                   \u001b[1m\u001b[36mtmp\u001b[m\u001b[m\n",
+      "lecture_1.slides.html\n"
      ]
     }
    ],
@@ -113,14 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-01-18T15:52:07.343336Z",
-     "iopub.status.busy": "2025-01-18T15:52:07.341837Z",
-     "iopub.status.idle": "2025-01-18T15:52:07.361088Z",
-     "shell.execute_reply": "2025-01-18T15:52:07.354933Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# your code here"
@@ -138,14 +117,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-01-18T15:52:07.377337Z",
-     "iopub.status.busy": "2025-01-18T15:52:07.376933Z",
-     "iopub.status.idle": "2025-01-18T15:52:07.395369Z",
-     "shell.execute_reply": "2025-01-18T15:52:07.387834Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# your code here"
@@ -163,14 +135,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-01-18T15:52:07.402215Z",
-     "iopub.status.busy": "2025-01-18T15:52:07.401931Z",
-     "iopub.status.idle": "2025-01-18T15:52:07.414170Z",
-     "shell.execute_reply": "2025-01-18T15:52:07.410143Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# your code here"