Merge pull request #185 from mnfienen/main

formatting geopandas for the html layout version

Author: mnfienen

notebooks/part0_python_intro/09_a_Geopandas.ipynb

@@ -678,7 +678,8 @@
    "id": "50525a00",
    "metadata": {},
    "source": [
-    "### How about some spatial joins?"
+    "## How about some spatial joins?\n",
+    "***"
    ]
   },
   {
@@ -824,6 +825,7 @@
    "metadata": {},
    "source": [
     "## TEST YOUR SKILLS #1\n",
+    "***\n",
     "Using the `bounds` geodataframe you just made, write a function to visualize predicate behaviors.\n",
     "- your function should accept a left geodataframe, a right geodataframe, and a string for the predicate\n",
     "- your function should plot:\n",
@@ -994,7 +996,11 @@
    "id": "e63a1ab3",
    "metadata": {},
    "source": [
-    "### As we've seen, spatial joins are powerful, but they really only gather data from multiple collections. What if we want to actually calculate the amount of overlap among shapes? Or create new shapes based on intersection or not intersection of shapes? [`overlay`](https://geopandas.org/en/stable/docs/user_guide/set_operations.html?highlight=overlay) does these things."
+    "## Overlays\n",
+    "***\n",
+    "### As we've seen, spatial joins are powerful, but they really only gather data from multiple collections. What if we want to actually calculate the amount of overlap among shapes? Or create new shapes based on intersection or not intersection of shapes?\n",
+    "\n",
+    "### [overlay](https://geopandas.org/en/stable/docs/user_guide/set_operations.html?highlight=overlay) does these things."
    ]
   },
   {
@@ -1016,6 +1022,17 @@
     "inters.plot()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b39f0f7-2e68-4840-b8fc-d53e3a3d1b3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inters = bounds.overlay(isthmus, how='intersection')\n",
+    "inters.plot()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1105,6 +1122,7 @@
    "metadata": {},
    "source": [
     "## TEST YOUR SKILLS _OPTIONAL_\n",
+    "***\n",
     "We have an Excel file that contains a crosswalk between SPECIES number as provided and species name. Can we bring that into our dataset and evaluate some conclusions about tree species by neighborhood?\n",
     "- start with the `trees_with_hoods` GeoDataFrame\n",
     "- load up and join the data from datapath / 'Madison_Tree_Species_Lookup.xlsx'\n",

notebooks/part0_python_intro/10a_Rasterio_intro.ipynb

@@ -815,18 +815,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
   }
  },
  "nbformat": 4,