Feature; Make the use of Optional fields possible when parsing df (Optional columns) (#16)

* use the right pyproject.toml to get rid of problems between black and isort

* create tests for optional types, adjust code accordanly and create new optional type with helper function to parse nan's

* create tests for optional types, adjust code accordanly and create new optional type with helper function to parse nan's

* update lock file, update pydantic version and specify deps constraint for future updates

* add pandas stubs

* add multiprocess to the pyproject.toml

* run isort on all files

---------

Co-authored-by: Wessel Huising <wesselhuising@gmail.com>

Author: wesselhuising

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ repos:
         language: system
         types: [python]
         require_serial: true
-        args: [--check, --diff , --config, pyproject.toml, src, tests]
+        args: [--config, pyproject.toml, src, tests]
       - id: check-added-large-files
         name: check for added large files
         entry: check-added-large-files

Makefile

@@ -1,4 +1,4 @@
 PHONY: black
 
 black:
-	black --config pyproject.toml src/
+	black --config pyproject.toml .

README.md

@@ -85,5 +85,24 @@ df_raised_error = DataFrameSchema.parse_df(
     errors="raise",
 )
 ```
+
+## Special fields and types
+### Optional
+As the DataFrame is being parsed into a dict, a `None` value is considered as a `nan` value in cases there are different values in the dict. Therefore, specifying `Optional` columns (where the value can be empty) can be speciyfied by using the custom `pandantic.Optional` type. This type is a replacement for `typing.Optional`.
+
+```
+from pandantic import BaseModel, Optional
+
+# GIVEN
+class Model(BaseModel):
+    a: Optional[int] = None
+    b: int
+
+df_example = pd.DataFrame({"a": [1, None, 2], "b": ["str", 2, 3]})
+
+# WHEN
+df_filtered = Model.parse_df(df_example, errors="filter", verbose=True)
+```
+
 ## Docs
 Documentation can be found [here](https://pandantic-rtd.readthedocs.io/en/latest/)