Merge pull request #13 from DahnJ/feature/dj-general-upkeep

Feature/dj general upkeep

.codespellrc

@@ -0,0 +1,2 @@
+[codespell]
+skip = *.ipynb

.pre-commit-config.yaml

@@ -10,3 +10,9 @@ repos:
     hooks:
       - id: flake8
         language_version: python3
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v1.16.0
+    hooks:
+      - id: codespell
+        language_version: python3

README.md

@@ -29,7 +29,6 @@ and [Pandas](https://github.com/pandas-dev/pandas).
 ## Installation
 ### pip
 [![image](https://img.shields.io/pypi/v/h3pandas.svg)](https://pypi.python.org/pypi/h3pandas)
-[![image](https://pepy.tech/badge/h3pandas)](https://pepy.tech/project/h3pandas)
 ```bash
 pip install h3pandas
 ```
@@ -119,13 +118,16 @@ For a full API documentation and more usage examples, see the
 [documentation](https://h3-pandas.readthedocs.io/en/latest/).
 
 ## Development
-This package is under active development, **suggestions and contributions are very welcome**!
+H3-Pandas cover the basics of the H3 API, but there are still many possible improvements.
+
+**Any suggestions and contributions are very welcome**!
 
 In particular, the next steps are:
-- [ ] Improve documentation, examples
-- [ ] Greater coverage of the H3 API
+- [ ] Improvements & stability of the "Extended API", e.g. `k_ring_smoothing`. 
 
 Additional possible directions
 - [ ] Allow for alternate h3-py APIs such as [memview_int](https://github.com/uber/h3-py#h3apimemview_int)
 - [ ] Performance improvements through [Cythonized h3-py](https://github.com/uber/h3-py/pull/147)
-- [ ] [Dask](https://github.com/dask/dask) integration trough [dask-geopandas](https://github.com/geopandas/dask-geopandas) (experimental as of now)
+- [ ] [Dask](https://github.com/dask/dask) integration through [dask-geopandas](https://github.com/geopandas/dask-geopandas) (experimental as of now)
+
+See [issues](https://github.com/DahnJ/H3-Pandas/issues) for more.

docs/source/notebook/00-intro.ipynb

@@ -98,6 +98,13 @@
     "We can use `geo_to_h3` to add an index with H3 addresses resolution 10"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`geo_to_h3` assumes coordinates are in `epsg=4326`"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -247,7 +254,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For now, we will pretend H3-Pandas do not have [aggregation functions](#Aggregation-functions) and perform the aggregations manually"
+    "For now, we will pretend H3-Pandas does not have [aggregation functions](#Aggregation-functions) and perform the aggregations manually"
    ]
   },
   {
@@ -649,7 +656,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Aggregation functions: The same workflow, streamlined"
+    "## H3-Pandas Extended API: The same workflow, streamlined"
    ]
   },
   {
@@ -705,7 +712,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

environment.yml

@@ -6,7 +6,7 @@ dependencies:
   - python=3.9
   - shapely
   - h3-py=3.7.*
-  - geopandas=0.9.*
+  - geopandas>=0.9.*
   - pandas
   # For Python <3.8
   # - typing_extensions

h3pandas/h3pandas.py

@@ -44,6 +44,8 @@ def geo_to_h3(
         pd.DataFrame: uses `lat_col` and `lng_col` (default `lat` and `lng`)
         gpd.GeoDataFrame: uses `geometry`
 
+        Assumes coordinates in epsg=4326.
+
         Parameters
         ----------
         resolution : int
@@ -255,7 +257,7 @@ def k_ring(self, k: int = 1, explode: bool = False) -> AnyDataFrame:
         func = wrapped_partial(h3.k_ring, k=k)
         column_name = "h3_k_ring"
         if explode:
-            return self.__apply_index_explode(func, column_name, list)
+            return self._apply_index_explode(func, column_name, list)
         return self._apply_index_assign(func, column_name, list)
 
     @doc_standard(
@@ -300,7 +302,7 @@ def hex_ring(self, k: int = 1, explode: bool = False) -> AnyDataFrame:
         func = wrapped_partial(h3.hex_ring, k=k)
         column_name = "h3_hex_ring"
         if explode:
-            return self.__apply_index_explode(func, column_name, list)
+            return self._apply_index_explode(func, column_name, list)
         return self._apply_index_assign(func, column_name, list)
 
     @doc_standard("h3_{resolution}", "containing the parent of each H3 address")
@@ -668,7 +670,7 @@ def k_ring_smoothing(
 
         # Unweighted case
         if weights is None:
-            result = (
+            result = pd.DataFrame(
                 self._df.h3.k_ring(k, explode=True)
                 .groupby("h3_k_ring")
                 .sum()
@@ -788,7 +790,7 @@ def _apply_index_assign(
         assign_args = {column_name: result}
         return finalizer(self._df.assign(**assign_args))
 
-    def __apply_index_explode(
+    def _apply_index_explode(
         self,
         func: Callable,
         column_name: str,

notebook/00-intro.ipynb

@@ -98,6 +98,13 @@
     "We can use `geo_to_h3` to add an index with H3 addresses resolution 10"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`geo_to_h3` assumes coordinates are in `epsg=4326`"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -705,7 +712,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

notebook/01-unified-data-layers.ipynb

@@ -714,7 +714,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },