Merge pull request #47 from cparmet/modernize

Modernize package

Author: cparmet

.pre-commit-config.yaml

@@ -20,7 +20,7 @@ repos:
     rev: 22.12.0
     hooks:
     - id: black
-      language_version: python3.8
+      language_version: python3.9
 
 - repo: https://github.com/pre-commit/mirrors-mypy
   rev: v1.5.0

.python-version

@@ -0,0 +1 @@
+3.11

README.md

@@ -1,7 +1,7 @@
 # Pandas Checks
 ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pandas-checks)
 
-<img src="https://raw.githubusercontent.com/cparmet/pandas-checks/main/static/pandas-check-gh-social.jpg" alt="Banner image for Pandas Checks">  
+<img src="https://raw.githubusercontent.com/cparmet/pandas-checks/main/static/pandas-check-gh-social.jpg" alt="Banner image for Pandas Checks" style="max-height: 125px; width: auto;">  
 
 ## What is it?
 **Pandas Checks** is a Python package for data science and data engineering. It adds non-invasive health checks for Pandas method chains.
@@ -121,7 +121,7 @@ New methods in Pandas Checks:
 - `.check.ncols()`: Count columns - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.ncols) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.ncols)
 - `.check.ndups()`: Count rows with duplicate values - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.ndups) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.ndups)
 - `.check.nnulls()`: Count rows with null values - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.nnulls) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.nnulls)
-- `.check.nrows()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.nrows) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.nrows)
+- `.check.nrows()`: Count rows - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.nrows) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.nrows)
 - `.check.print()`: Print a string, a variable, or the current dataframe - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.print) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.print)
 
 ### Export interim files

docs/index.md

@@ -1,7 +1,11 @@
-# About
-  
-<img src="https://raw.githubusercontent.com/cparmet/pandas-checks/main/static/pandas-check-gh-social.jpg" alt="Banner image for Pandas Checks">  
+---
+title: About
+---
+
+<img src="https://raw.githubusercontent.com/cparmet/pandas-checks/main/static/pandas-check-gh-social.jpg" alt="Banner image for Pandas Checks" style="max-height: 200px; width: auto;">  
 
+[TOC]
+
 ## What is it?
 
 **Pandas Checks** is a Python package for data science and data engineering. It adds non-invasive health checks for [Pandas](https://github.com/pandas-dev/pandas/) method chains.  
@@ -24,8 +28,17 @@ As Fleetwood Mac says, you would never break the chain.
 
 If you run into trouble or have questions, I'd love to know. Please [open an issue](https://github.com/cparmet/pandas-checks/issues).
 
-Contributions are appreciated! Please open an [issue](https://github.com/cparmet/pandas-checks/issues) or submit a [pull request](https://github.com/cparmet/pandas-checks/pulls). Pandas Checks uses the wonderful libraries [poetry](https://python-poetry.org) for package and dependency management, [nox](https://nox.thea.codes/en/stable/) for test automation, and [mkdocs](https://www.mkdocs.org/) for docs.
-
+Contributions are appreciated! Please open an [issue](https://github.com/cparmet/pandas-checks/issues) or submit a [pull request](https://github.com/cparmet/pandas-checks/pulls). To run the tests, run `uv run --group dev nox`
+  
+## Acknowledgments
+ 
+Pandas Checks uses the following wonderful libraries:
+
+- [uv](https://github.com/astral-sh/uv) for package and dependency management
+- [nox](https://nox.thea.codes/en/stable/) for test automation
+- [mkdocs](https://www.mkdocs.org/) for...making docs!
+- [pre-commit hooks](https://pre-commit.com/)
+- [black](https://black.readthedocs.io/en/stable/) for code formatting
 
 ## License
 

docs/usage.md

@@ -1,4 +1,8 @@
-# Usage
+---
+title: Usage
+---
+
+[TOC]
 
 ## Installation
 First make Pandas Check available in your environment.
@@ -93,7 +97,7 @@ New methods in Pandas Checks:
 - `.check.ncols()`: Count columns - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.ncols) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.ncols)
 - `.check.ndups()`: Count rows with duplicate values - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.ndups) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.ndups)
 - `.check.nnulls()`: Count rows with null values - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.nnulls) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.nnulls)
-- `.check.nrows()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.nrows) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.nrows)
+- `.check.nrows()`: Count rows - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.nrows) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.nrows)
 - `.check.print()`: Print a string, a variable, or the current dataframe - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.print) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.print)
 
 ### Export interim files
@@ -120,17 +124,20 @@ These methods can be used to disable subsequent Pandas Checks methods, either te
 
 ### Validate data
 Custom:
+
 - `.check.assert_data()`: Check that data passes an arbitrary condition - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_data) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_data)
 
 Types:
+
 - `.check.assert_datetime()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_datetime) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_datetime)
 - `.check.assert_float()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_float) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_float)
 - `.check.assert_int()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_int) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_int)
 - `.check.assert_str()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_str) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_str)
 - `.check.assert_timedelta()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_timedelta) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_timedelta)
 - `.check.assert_type()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_type) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_type)
-  
+
 Values:
+
 - `.check.assert_all_nulls()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_all_nulls) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_all_nulls)
 - `.check.assert_less_than()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_less_than) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_less_than)
 - `.check.assert_greater_than()` - [DataFrame](https://cparmet.github.io/pandas-checks/API%20reference/DataFrameChecks/#pandas_checks.DataFrameChecks.DataFrameChecks.assert_greater_than) | [Series](https://cparmet.github.io/pandas-checks/API%20reference/SeriesChecks/#pandas_checks.SeriesChecks.SeriesChecks.assert_greater_than)
@@ -210,14 +217,3 @@ You can also adjust settings within a method chain by bookending the chain, like
     .check.enable_checks() # Turn it back on for the next code
 )
 ```
-  
-
-### Hybrid EDA-Production data processing
-
-Exploratory Data Analysis is often taught as a one-time step we do to plan our production data processing. But sometimes EDA is a cyclical process we go back to for deeper inspection during debugging, code edits, or changes in the input data. If explorations were useful in EDA, they may be useful again.
-  
-Unfortunately, it's hard to go back to the original EDA code. It's too out of sync. The prod data processing pipeline has usually evolved too much, making the EDA code a historical artifact full of cobwebs that we can't easily fire up again. 
-  
-But if you use Pandas Checks during EDA, you could roll your `.check` methods into your first production code. Then in prod mode, disable Pandas Checks when you don't need it, to save compute and streamline output. When you ever need to pull out those EDA tools, enable Pandas Checks globally or locally.  
-  
-This can make your prod pipline more transparent and easier to inspect.  

mkdocs.yml

@@ -9,3 +9,5 @@ plugins:
 theme:
   name: material
 repo_url: https://github.com/cparmet/pandas-checks/
+markdown_extensions:
+  - toc

noxfile.py

@@ -1,13 +1,23 @@
-from nox_poetry import Session, session
+from nox import Session, options
+from nox_uv import session
 
+options.default_venv_backend = "uv"
 
-@session(python=["3.8", "3.9", "3.10", "3.11"])
-def tests(session: Session) -> None:
+
+@session(
+    python=["3.9", "3.10", "3.11", "3.12", "3.13"],
+    uv_groups=["test"],
+)
+def test(s: Session) -> None:
     """Run the test suite."""
+    s.install(".")  # Install pandas-checks
+    s.run("pytest")
+
+
+# def tests(session: Session) -> None:
 
-    session.install(".")  # Install pandas-checks
-    session.install(
-        "pytest", "pytest-cases", "pyarrow", "openpyxl"
-    )  # Install test packages
+#     # session.install(
+#     #     "pytest", "pytest-cases", "pyarrow", "openpyxl"
+#     # )  # Install test packages
 
-    session.run("pytest")
+#     session.run("pytest")

pandas_checks/DataFrameChecks.py

@@ -120,7 +120,7 @@ def assert_data(
                     iris
                     .check.assert_data(lambda df: df.shape[0]>0)
 
-                    # Or customize the message displayed when alert fails
+                    # Or customize the message displayed when assert fails
                     .check.assert_data(lambda df: df.shape[0]>0, "Assertion failed, DataFrame has no rows!")
 
                     # Or show a warning instead of raising an exception
@@ -1788,7 +1788,7 @@ def unique(
     ) -> pd.DataFrame:
         """Displays the unique values in a column, without modifying the DataFrame itself.
 
-        See Pandas docs for [unique()]((https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.unique.html)) for additional usage information, including more configuration options you can pass to this Pandas Checks method.
+        See Pandas docs for [unique()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.unique.html) for additional usage information, including more configuration options you can pass to this Pandas Checks method.
 
         Example:
             ```python
@@ -1832,7 +1832,7 @@ def value_counts(
     ) -> pd.DataFrame:
         """Displays the value counts for a column, without modifying the DataFrame itself.
 
-        See Pandas docs for [value_counts()]((https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.value_counts.html)) for additional usage information, including more configuration options you can pass to this Pandas Checks method.
+        See Pandas docs for [value_counts()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.value_counts.html) for additional usage information, including more configuration options you can pass to this Pandas Checks method.
 
         Example:
             ```python
@@ -1887,7 +1887,7 @@ def write(
             - .tsv # Tab-separated data file
             - .xlsx
 
-        This functions uses the corresponding Pandas export function, such as `to_csv()` and `to_feather()`. See [Pandas docs for those corresponding export functions][Pandas docs for those export functions](https://pandas.pydata.org/docs/reference/io.html) for additional usage information, including more configuration options you can pass to this Pandas Checks method.
+        This functions uses the corresponding Pandas export function, such as `to_csv()` and `to_feather()`. See [Pandas docs for those corresponding export functions](https://pandas.pydata.org/docs/reference/io.html) for additional usage information, including more configuration options you can pass to this Pandas Checks method.
 
         Example:
             ```python

pandas_checks/SeriesChecks.py

@@ -113,7 +113,7 @@ def assert_data(
                 # Validate that a Series has at least 1 row:
                 .check.assert_data(lambda s: s.shape[0]>0)
 
-                # Or customize the message displayed when alert fails
+                # Or customize the message displayed when assert fails
                 .check.assert_data(lambda df: s.shape[0]>0, "Assertion failed, Series has no rows!")
 
                 # Or show a warning instead of raising an exception

pandas_checks/utils.py

@@ -70,9 +70,9 @@ def _series_is_type(s: pd.Series, dtype: Type[Any]) -> bool:
     if dtype in [str, "str"]:
         return pd.api.types.is_string_dtype(s)
     elif dtype in [datetime, "datetime", "date"]:
-        return pd.api.types.is_datetime64_any_dtype(
-            s
-        ) or pd.api.types.is_datetime64tz_dtype(s)
+        return pd.api.types.is_datetime64_any_dtype(s) or isinstance(
+            s, pd.DatetimeTZDtype
+        )
     elif dtype in [timedelta, "timedelta"]:
         return pd.api.types.is_timedelta64_dtype(s)
     else: