chore(actions): add local execution of pre-commit hook (#1146)

ydataai · Nov 22, 2022 · 09b71ab · 09b71ab
1 parent a91abd1
commit 09b71ab
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,5 +1,11 @@
-files: ^src/|^tests/
+default_stages: [commit, push, manual]
+
 repos:
+-   repo: https://github.com/commitizen-tools/commitizen
+    rev: v2.37.0
+    hooks:
+    - id: commitizen
+      stages: [commit-msg]
 -   repo: https://github.com/psf/black
     rev: 22.3.0
     hooks:
@@ -29,6 +35,7 @@ repos:
     hooks:
     -   id: check-manifest
         args: [ "--ignore=src/pandas_profiling/version.py" ]
+        stages: [manual]
 -   repo: https://github.com/PyCQA/flake8
     rev: "4.0.1"
     hooks:

diff --git a/docsrc/source/conf.py b/docsrc/source/conf.py
@@ -86,6 +86,6 @@ def _GetApiWrapperVersion():
 autodoc_pydantic_model_show_json = False
 
 source_suffix = {
-    '.rst': 'restructuredtext',
-    '.md': 'markdown',
+    ".rst": "restructuredtext",
+    ".md": "markdown",
 }
diff --git a/docsrc/source/pages/support_contrib/contribution_guidelines.rst b/docsrc/source/pages/support_contrib/contribution_guidelines.rst
@@ -27,6 +27,20 @@ The following commands are supported:
     make clean
 
 
+Contribution quality standards
+------------------------------
+
+To guarantee a high quality of the contributed code, the project workflow validates the added modification as well as the introduced commit messages.
+The same mechanisms are used locally to find and solve existing issues before submitting a pull request.
+
+To active the local mechanisms (created using pre-commit hooks), run the following commands:
+
+.. code-block:: console
+
+    pip install -r requirements-dev.txt
+    pre-commit install --hook-type commit-msg --hook-type pre-commit
+
+
 Git workflow
 ------------
 

diff --git a/examples/features/correlation_auto_example.py b/examples/features/correlation_auto_example.py
@@ -1,4 +1,5 @@
 from pathlib import Path
+
 import pandas as pd
 
 from pandas_profiling import ProfileReport

diff --git a/examples/usaairquality/usaairquality.ipynb b/examples/usaairquality/usaairquality.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "b514dd38-2ebd-4c96-aed5-4e3695e20fa2",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "6bcf2de2-58bf-4995-8cf7-3145322b45f7",
    "metadata": {},
    "outputs": [],
@@ -68,15 +68,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "b33a26ed-4e1e-4689-93ce-fa0f98f48e89",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from pandas_profiling.utils.cache import cache_file\n",
-    "from pandas_profiling import ProfileReport"
+    "from pandas_profiling import ProfileReport\n",
+    "from pandas_profiling.utils.cache import cache_file"
    ]
   },
   {
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "7dab0b47-537d-4402-af71-1bdfd0cf6cdd",
    "metadata": {},
    "outputs": [],
@@ -101,9 +101,9 @@
     "\n",
     "df = pd.read_csv(file_name, index_col=[0])\n",
     "\n",
-    "#We will only consider the data from Arizone state for this example\n",
-    "df = df[df['State']=='Arizona']\n",
-    "df['Date Local']=pd.to_datetime(df['Date Local'])"
+    "# We will only consider the data from Arizone state for this example\n",
+    "df = df[df[\"State\"] == \"Arizona\"]\n",
+    "df[\"Date Local\"] = pd.to_datetime(df[\"Date Local\"])"
    ]
   },
   {
@@ -139,140 +139,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "15e613a6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='Time'>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pandas_profiling.visualisation.plot import timeseries_heatmap\n",
     "\n",
-    "timeseries_heatmap(dataframe=df, entity_column='Site Num', sortby='Date Local')"
+    "timeseries_heatmap(dataframe=df, entity_column=\"Site Num\", sortby=\"Date Local\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "b29a7e78-d52d-458d-ac9a-e509ffd373d1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating the profile for the Site num: 1011\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f139498017074ec6828e65ca98f293c4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a2d4124d4cbb4e548e727a3e8deab5c6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0828c63867ff43de8654bbebc6b8f283",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d3b758632d22449392be04fb55cc5b9a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating the profile for the Site num: 1028\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1a60fe25562d45e0acc08a5024f299d9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Return the profile per station\n",
-    "for group in df.groupby('Site Num'):\n",
-    "    #Running 1 profile per station\n",
+    "for group in df.groupby(\"Site Num\"):\n",
+    "    # Running 1 profile per station\n",
     "    profile = ProfileReport(\n",
     "        group[1],\n",
     "        tsmode=True,\n",
     "        sortby=\"Date Local\",\n",
-    "        #title=f\"Air Quality profiling - Site Num: {group[0]}\"\n",
+    "        # title=f\"Air Quality profiling - Site Num: {group[0]}\"\n",
     "    )\n",
-    "    \n",
-    "    profile.to_file(f'Ts_Profile_{group[0]}.html')"
+    "\n",
+    "    profile.to_file(f\"Ts_Profile_{group[0]}.html\")"
    ]
   }
  ],

diff --git a/examples/usaairquality/usaairquality.py b/examples/usaairquality/usaairquality.py
@@ -3,34 +3,30 @@
 """
 import pandas as pd
 
+from pandas_profiling import ProfileReport
 from pandas_profiling.utils.cache import cache_file
 from pandas_profiling.visualisation.plot import timeseries_heatmap
-from pandas_profiling import ProfileReport
 
-if __name__ == '__main__':
-    
+if __name__ == "__main__":
+
     file_name = cache_file(
         "pollution_us_2000_2016.csv",
         "https://query.data.world/s/mz5ot3l4zrgvldncfgxu34nda45kvb",
     )
 
     df = pd.read_csv(file_name, index_col=[0])
-    
-    #Prepare the dataset
-    #We will only consider the data from Arizone state for this example
-    df = df[df['State']=='Arizona']
-    df['Date Local']=pd.to_datetime(df['Date Local'])
-    
-    #Plot the time heatmap distribution for the per entity time-series
-    timeseries_heatmap(dataframe=df, entity_column='Site Num', sortby='Date Local')
-    
+
+    # Prepare the dataset
+    # We will only consider the data from Arizone state for this example
+    df = df[df["State"] == "Arizona"]
+    df["Date Local"] = pd.to_datetime(df["Date Local"])
+
+    # Plot the time heatmap distribution for the per entity time-series
+    timeseries_heatmap(dataframe=df, entity_column="Site Num", sortby="Date Local")
+
     # Return the profile per station
-    for group in df.groupby('Site Num'):
-        #Running 1 profile per station
-        profile = ProfileReport(
-            group[1],
-            tsmode=True,
-            sortby="Date Local"
-        )
-
-        profile.to_file(f'Ts_Profile_{group[0]}.html')
+    for group in df.groupby("Site Num"):
+        # Running 1 profile per station
+        profile = ProfileReport(group[1], tsmode=True, sortby="Date Local")
+
+        profile.to_file(f"Ts_Profile_{group[0]}.html")
diff --git a/setup.py b/setup.py
@@ -17,7 +17,7 @@
     version = "dev"
 
 with open(source_root / "src/pandas_profiling/version.py", "w") as version_file:
-  version_file.write(f'__version__ = \'{version}\'')
+    version_file.write(f"__version__ = '{version}'")
 
 setup(
     name="pandas-profiling",