Repository: moj-analytical-services/data_linter (language: python)

Issue:
Pandas Validator
**TODO:**

Should write a Pandas validator that can do the following:

- [ ] **Pandas data reader** (potentially using [pd_arrow_parser](https://github.com/moj-analytical-services/mojap-arrow-pd-parser/))
- [ ] **Col type conversion** (completed if using `pd_arrow_parser`, would need to let pd_arrow_parser read in the data as is. Then attempt to cast a single column (again using pd_arrow_parser/pyarrow) this will allow you to iterate through columns skipping tests on those that fail type conversion therefore allowing you to move onto the other columns)
- [ ] **minimum/maximum**
- [ ] **minLength/maxLength**
- [ ] **nullable**
- [ ] **enum**
- [ ] **pattern**

The pandas validator would be the default validator engine and would just install pandas by default. This would allow us to ensure the package is lightweight as possible (frictionless and GE install a lot of extras that we don't need). 

Note should be using a pandas version >= 1.2.0.


Discussion / hints:

Fix patch:
diff --git a/data_linter/validation.py b/data_linter/validation.py
index 599429e..bfc417d 100644
--- a/data_linter/validation.py
+++ b/data_linter/validation.py
@@ -513,7 +513,6 @@ def collect_all_status(config: dict):
             if compress:
                 log.info(f"Compressing file from {matched_file} to {final_outpath}")
                 compress_data(matched_file, final_outpath)
-                log.info("KARIK YOLO")
             else:
                 log.info(f"Copying file from {matched_file} to {final_outpath}")
                 copy_data(matched_file, final_outpath)
diff --git a/data_linter/validators/base.py b/data_linter/validators/base.py
index 60f51d4..105b33b 100644
--- a/data_linter/validators/base.py
+++ b/data_linter/validators/base.py
@@ -1,8 +1,84 @@
 import logging
 from copy import deepcopy
+from typing import List
 
 
-class BaseTableValidator(object):
+class ValidatorResult:
+    """
+    Little class to manage adding to validator dict
+    """
+
+    def __init__(self, result_dict=None, validator_valid_key_name=None):
+        if result_dict:
+            if not isinstance(result_dict, dict):
+                raise TypeError("result_dict must be a dict type")
+            self.result = result_dict
+        else:
+            self.result = {"valid": True}
+
+        if validator_valid_key_name:
+            if not isinstance(validator_valid_key_name, str):
+                raise TypeError("validator_valid_key_name must be a str type")
+            self.vvkn = validator_valid_key_name
+        else:
+            self.vvkn = "valid"
+
+    def get_result(self, copy=True):
+        if copy:
+            return deepcopy(self.result)
+        else:
+            return self.result
+
+    def init_col(self, colname):
+        if colname not in self.result:
+            self.result[colname] = {"valid": True}
+
+    def get_names_of_column_failures(self, test_names: List[str] = []):
+        """
+
+        Return col names which have an overall fail. If test_names is given
+        only returns cols that failed those particular tests is given.
+        Args:
+            test_name (List[str], optional): [description]. List of tests to
+            check against Defaults to [].
+        """
+        failed_cols = []
+        for colname in self.result:
+            if colname == "valid" or colname.startswith("validator-table-test-"):
+                continue
+
+            if test_names:
+                overall_success = True
+                for k, v in self.result[colname].items():
+                    if k in test_names:
+                        overall_success = overall_success and v.get(self.vvkn, True)
+            else:
+                overall_success = self.result[colname].get("valid", True)
+
+            if not overall_success:
+                failed_cols.append(colname)
+
+        return failed_cols
+
+    def add_table_test(self, testname, test_result):
+        # Same setup - treats overall test as a colname
+        self.init_col(testname)
+        self.result[testname] = test_result
+        if self.vvkn in test_result:
+            self.result["valid"] = self.result["valid"] and test_result[self.vvkn]
+
+    def add_test_to_col(self, colname, testname, test_result):
+        self.init_col(colname)
+
+        self.result[colname][testname] = test_result
+        if self.vvkn in test_result:
+            self.result["valid"] = self.result["valid"] and test_result[self.vvkn]
+            self.result[colname]["valid"] = (
+                self.result[colname]["valid"] and test_result[self.vvkn]
+            )
+
+
+class BaseTableValidator:
     def __init__(self, filepath: str, table_params: dict, metadata: dict, **kwargs):
         """Base class for validators. Not a useable,
         but used to be inherited for other validators.
@@ -16,8 +92,12 @@ def __init__(self, filepath: str, table_params: dict, metadata: dict, **kwargs):
         self.filepath = filepath
         self.table_params = table_params
         self.metadata = metadata
-        self.valid = None
-        self.response = None
+        # self.valid = None
+
+        self.response = ValidatorResult(
+            result_dict=kwargs.get("result_dict"),
+            validator_valid_key_name=kwargs.get("validator_valid_key_name"),
+        )
 
     def write_validation_result_to_log(self, log: logging.Logger):
         """Writes a the validators response to log provided.
diff --git a/data_linter/validators/frictionless_validator.py b/data_linter/validators/frictionless_validator.py
index f7bd8dc..62ed44d 100644
--- a/data_linter/validators/frictionless_validator.py
+++ b/data_linter/validators/frictionless_validator.py
@@ -23,6 +23,7 @@ class FrictionlessValidator(BaseTableValidator):
 
     def __init__(self, filepath: str, table_params: dict, metadata: dict):
         super().__init__(filepath, table_params, metadata)
+        self.response = None  # Not yet integrated with ValidatorResult class
         self.schema = convert_meta_to_goodtables_schema(metadata)
         if optional_import_errors:
             imp_err = (
diff --git a/data_linter/validators/great_expectations_validator.py b/data_linter/validators/great_expectations_validator.py
index cb7952c..bc71dfe 100644
--- a/data_linter/validators/great_expectations_validator.py
+++ b/data_linter/validators/great_expectations_validator.py
@@ -1,7 +1,10 @@
 import logging
-from data_linter.validators.base import BaseTableValidator
+from data_linter.validators.base import (
+    BaseTableValidator,
+    ValidatorResult,
+)
+
 from copy import deepcopy
-from typing import List
 
 optional_import_errors = ""
 try:
@@ -40,73 +43,6 @@
 log = logging.getLogger("root")
 
 
-class ValidatorResult(object):
-    """
-    Little class to manage adding to validator dict
-    """
-
-    def __init__(self, result_dict={}):
-        if result_dict:
-            self.result = result_dict
-        else:
-            self.result = {"valid": True}
-
-    def get_result(self, copy=True):
-        if copy:
-            return deepcopy(self.result)
-        else:
-            return self.result
-
-    def init_col(self, colname):
-        if colname not in self.result:
-            self.result[colname] = {"valid": True}
-
-    def get_names_of_column_failures(self, test_names: List[str] = []):
-        """
-
-        Return col names which have an overall fail. If test_names is given
-        only returns cols that failed those particular tests is given.
-        Args:
-            test_name (List[str], optional): [description]. List of tests to
-            check against Defaults to [].
-        """
-        non_column_names = ["valid", "validator-table-test-"]
-        failed_cols = []
-        for colname in self.result:
-            if colname in non_column_names:
-                continue
-
-            if test_names:
-                overall_success = True
-                for k, v in self.result[colname].items():
-                    if k in test_names:
-                        overall_success = overall_success and v.get("success", True)
-            else:
-                overall_success = self.result[colname].get("valid", True)
-
-            if not overall_success:
-                failed_cols.append(colname)
-
-        return failed_cols
-
-    def add_table_test(self, testname, test_result):
-        # Same setup - treats overall test as a colname
-        self.init_col(testname)
-        self.result[testname] = test_result
-        if "success" in test_result:
-            self.result["valid"] = self.result["valid"] and test_result["success"]
-
-    def add_test_to_col(self, colname, testname, test_result):
-        self.init_col(colname)
-
-        self.result[colname][testname] = test_result
-        if "success" in test_result:
-            self.result["valid"] = self.result["valid"] and test_result["success"]
-            self.result[colname]["valid"] = (
-                self.result[colname]["valid"] and test_result["success"]
-            )
-
-
 class GreatExpectationsValidator(BaseTableValidator):
     """
     Great expectations data validator
@@ -120,7 +56,9 @@ def __init__(
         default_result_fmt="COMPLETE",
         ignore_missing_cols=False,
     ):
-        super().__init__(filepath, table_params, metadata)
+        super().__init__(
+            filepath, table_params, metadata, validator_valid_key_name="success"
+        )
 
         if optional_import_errors:
             imp_err = (
@@ -132,7 +70,6 @@ def __init__(
         self.default_result_fmt = default_result_fmt
         self.ignore_missing_cols = ignore_missing_cols
 
-        self.response = ValidatorResult()
         self.valid = self.response.result["valid"]
 
     def write_validation_errors_to_log(self):
diff --git a/data_linter/validators/pandas_validator.py b/data_linter/validators/pandas_validator.py
new file mode 100644
index 0000000..de5bc04
--- /dev/null
+++ b/data_linter/validators/pandas_validator.py
@@ -0,0 +1,412 @@
+import logging
+import pandas as pd
+import inspect
+
+from functools import wraps
+
+from datetime import datetime
+
+from arrow_pd_parser.parse import (
+    pa_read_csv_to_pandas,
+    pa_read_json_to_pandas,
+)
+
+from typing import Union
+
+from arrow_pd_parser.pa_pd import arrow_to_pandas
+
+from pyarrow import parquet as pq, fs, csv
+
+from data_linter.validators.base import (
+    BaseTableValidator,
+)
+
+default_datetime_format = "%Y-%m-%d %H:%M:%S"
+
+
+class PandasValidator(BaseTableValidator):
+    """
+    Validator using Pandas
+    """
+
+    def __init__(
+        self,
+        filepath: str,
+        table_params: dict,
+        metadata: dict,
+        ignore_missing_cols=False,
+    ):
+        super().__init__(filepath, table_params, metadata)
+
+        self.ignore_missing_cols = ignore_missing_cols
+
+    @property
+    def valid(self):
+        return self.response.result["valid"]
+
+    def write_validation_errors_to_log(self, log: logging.Logger):
+        table_result = self.response.get_result()
+        if not table_result["valid"]:
+            failed_cols = self.response.get_names_of_column_failures()
+            err_msg = (
+                "Table failed validation. "
+                f"Col failures: {failed_cols}. "
+                "See response error log for more details."
+            )
+            log.error(err_msg, extra={"context": "VALIDATION"})
+            log.debug(str(table_result), extra={"context": "VALIDATION"})
+
+    def read_data_and_validate(self):
+        """Reads data from filepath and validates it.
+
+        Data is read using pd_arrow_parser.
+        """
+
+        df = _parse_data_to_pandas(self.filepath, self.table_params, self.metadata)
+        self.validate_df(df)
+
+    def get_response_dict(self):
+        return self.response.get_result()
+
+    def validate_df(self, df):  # STEPHEN TODO
+
+        meta_cols = [col for col in self.metadata["columns"] if col["name"] in df]
+
+        for m in meta_cols:
+            self.validate_col(df[m["name"]], m)
+
+    def validate_col(self, col, meta_col):
+
+        self.min_max_test(col, meta_col)
+        self.min_max_length_test(col, meta_col)
+        self.pattern_test(col, meta_col)
+        self.enum_test(col, meta_col)
+        self.nullable_test(col, meta_col)
+        self.datetime_format_test(col, meta_col)
+        self.date_format_test(col, meta_col)
+
+    def min_max_test(self, col, meta_col):
+        res_dict = _min_max_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "min max numerical", res_dict)
+
+    def min_max_length_test(self, col, meta_col):
+        res_dict = _min_max_length_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "min max length", res_dict)
+
+    def pattern_test(self, col, meta_col):
+        res_dict = _pattern_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "regex pattern", res_dict)
+
+    def enum_test(self, col, meta_col):
+        res_dict = _enum_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "enum", res_dict)
+
+    def nullable_test(self, col, meta_col):
+        res_dict = _nullable_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "nullable", res_dict)
+
+    def datetime_format_test(self, col, meta_col):
+        res_dict = _datetime_format_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "datetime", res_dict)
+
+    def date_format_test(self, col, meta_col):
+        res_dict = _date_format_test(col, meta_col)
+        col_name = meta_col["name"]
+        if res_dict is not None:
+            self.response.add_test_to_col(col_name, "date", res_dict)
+
+
+def check_run_validation_for_meta(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        sig = inspect.signature(func)
+        argmap = sig.bind_partial(*args, **kwargs).arguments
+        mc = argmap.get("meta_col")
+        if func.__name__ == "_min_max_test" and _check_meta_has_params(
+            ["minimum", "maximum"], mc
+        ):
+            return func(*args, **kwargs)
+        elif func.__name__ == "_min_max_length_test" and _check_meta_has_params(
+            ["minLength", "maxLength"], mc
+        ):
+            return func(*args, **kwargs)
+        elif func.__name__ == "_pattern_test" and _check_meta_has_params(
+            ["pattern"], mc
+        ):
+            return func(*args, **kwargs)
+        elif func.__name__ == "_enum_test" and _check_meta_has_params(["enum"], mc):
+            return func(*args, **kwargs)
+        elif func.__name__ == "_nullable_test" and not _check_meta_has_params(
+            [None, True], [mc.get("nullable")]
+        ):
+            return func(*args, **kwargs)
+        elif func.__name__ == "_date_format_test" and _check_meta_has_params(
+            ["date"], [mc.get("type")]
+        ):
+            return func(*args, **kwargs)
+        elif func.__name__ == "_datetime_format_test" and _check_meta_has_params(
+            ["datetime"], [mc.get("type")]
+        ):
+            return func(*args, **kwargs)
+        else:
+            pass
+
+    return wrapper
+
+
+@check_run_validation_for_meta
+def _min_max_test(col: pd.Series, meta_col: dict) -> dict:
+
+    col_name = meta_col["name"]
+    mi = meta_col.get("minimum")
+    ma = meta_col.get("maximum")
+
+    test_inputs = {"column": col_name, "minimum_value": mi, "maximum_value": ma}
+    res_dict = _result_dict("min max numerical", test_inputs)
+
+    col_oob = _get_min_max_series_out_of_bounds_col(col, col_name, mi, ma)
+
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+@check_run_validation_for_meta
+def _min_max_length_test(col: pd.Series, meta_col: dict) -> dict:
+
+    col_name = meta_col["name"]
+    mil = meta_col.get("minLength")
+    mal = meta_col.get("maxLength")
+
+    test_inputs = {"column": col_name, "minimum_length": mil, "maximum_length": mal}
+    res_dict = _result_dict("min max length", test_inputs)
+
+    col_oob = _get_min_max_series_out_of_bounds_col(col.str.len(), col_name, mil, mal)
+
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+@check_run_validation_for_meta
+def _pattern_test(col: pd.Series, meta_col: dict) -> dict:
+
+    col_name = meta_col["name"]
+    pattern = meta_col.get("pattern")
+
+    test_inputs = {"column": col_name, "regex": pattern}
+
+    res_dict = _result_dict("regex", test_inputs)
+
+    col_oob = ~col.str.match(pattern)
+
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+@check_run_validation_for_meta
+def _enum_test(col: pd.Series, meta_col: dict) -> dict:
+
+    col_name = meta_col["name"]
+    enum = meta_col.get("enum")
+
+    test_inputs = {"column": col_name, "enum_value_set": enum}
+
+    res_dict = _result_dict("enum", test_inputs)
+
+    if meta_col.get("nullable", True):
+        col_oob = ~col.fillna(enum[0]).isin(enum)
+    else:
+        col_oob = ~col.isin(enum)
+
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+@check_run_validation_for_meta
+def _nullable_test(col: pd.Series, meta_col: dict) -> dict:
+
+    col_name = meta_col.get("name")
+
+    test_inputs = {
+        "column": col_name,
+    }
+
+    res_dict = _result_dict("nullable", test_inputs)
+
+    col_oob = col.isnull()
+
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+@check_run_validation_for_meta
+def _date_format_test(col: pd.Series, meta_col) -> dict:
+
+    col_name = meta_col["name"]
+    datetime_format = meta_col.get("datetime_format", default_datetime_format)
+    test_inputs = {"column": col_name, "datetime format": datetime_format}
+
+    res_dict = _result_dict("datetime format", test_inputs)
+
+    col_oob = ~col.apply(
+        lambda x: _valid_date_or_datetime_conversion(x, datetime_format, True)
+    )
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+@check_run_validation_for_meta
+def _datetime_format_test(col: pd.Series, meta_col):
+
+    col_name = meta_col["name"]
+    datetime_format = meta_col.get("datetime_format", default_datetime_format)
+    test_inputs = {"column": col_name, "datetime format": datetime_format}
+
+    res_dict = _result_dict("datetime format", test_inputs)
+
+    col_oob = ~col.apply(
+        lambda x: _valid_date_or_datetime_conversion(x, datetime_format)
+    )
+    return _fill_res_dict(col, col_oob, res_dict)
+
+
+def _valid_date_or_datetime_conversion(
+    date_or_datetime_str: str, dt_format: str, check_for_no_time_component=False
+):
+    if pd.isna(date_or_datetime_str) or not bool(date_or_datetime_str):
+        return True
+    else:
+        try:
+            dt = datetime.strptime(date_or_datetime_str, dt_format)
+            if check_for_no_time_component:
+                return _check_no_time_component_in_expected_date(dt)
+            else:
+                return True
+        except ValueError:
+            return False
+
+
+def _check_no_time_component_in_expected_date(dt: datetime):
+    result = dt.hour == 0 and dt.minute == 0 and dt.second == 0 and dt.microsecond == 0
+    return result
+
+
+def _result_dict(test_name: str, test_inputs: dict) -> dict:
+
+    d = {
+        "valid": None,
+        "test_inputs": test_inputs,
+    }
+
+    return d
+
+
+def _fill_res_dict(col, col_oob, res_dict) -> dict:
+
+    valid = not col_oob.any()
+    res_dict["valid"] = valid
+
+    if not valid:
+        col_oob = col_oob.fillna(False)
+        unexpected_index_list = col_oob.index[col_oob].tolist()
+        unexpected_list = col[unexpected_index_list].tolist()
+
+        res_dict["unexpected_index_list"] = unexpected_index_list
+        res_dict["unexpected_list"] = unexpected_list
+
+    return res_dict
+
+
+def _get_min_max_series_out_of_bounds_col(
+    col: pd.Series, colname: str, mi: Union[int, None], ma: Union[int, None]
+) -> pd.Series:
+
+    # Test if values out of bounds
+    if mi is not None and ma is None:
+        return col < mi
+    elif ma is not None and mi is None:
+        return col > ma
+    elif mi is not None and ma is not None:
+        return (col < mi) | (col > ma)
+    else:
+        raise ValueError(f"invalid min/max values for column: {colname}")
+
+
+def _check_meta_has_params(any_of: list, meta_col: dict):
+    return any([a in meta_col for a in any_of])
+
+
+def _parse_data_to_pandas(filepath: str, table_params: dict, metadata: dict):
+    """
+    Reads in the data from the given filepath and returns
+    a dataframe
+    """
+
+    meta_col_names = [
+        c["name"]
+        for c in metadata["columns"]
+        if c["name"] not in metadata.get("partitions", [])
+    ]
+
+    # Set the reader type
+    if filepath.startswith("s3://"):
+        reader_fs = fs.S3FileSystem(region="eu-west-1")
+        fp_for_file_reader = filepath.replace("s3://", "", 1)
+
+    else:
+        reader_fs = fs.LocalFileSystem()
+        fp_for_file_reader = filepath
+
+    with reader_fs.open_input_stream(fp_for_file_reader) as f:
+        if "csv" in metadata["data_format"]:
+
+            # Safer CSV load for newlines_in_values set to True
+            if table_params.get("expect-header", True):
+                po = csv.ParseOptions(newlines_in_values=True)
+            else:
+                po = csv.ParseOptions(
+                    newlines_in_values=True, column_names=meta_col_names
+                )
+
+            df = pa_read_csv_to_pandas(
+                input_file=f,
+                schema=None,  # Needs actual schema
+                expect_full_schema=False,
+                parse_options=po,
+            )
+            # dates/datetimes == string
+
+        elif "json" in metadata["data_format"]:
+            df = pa_read_json_to_pandas(
+                input_file=f,
+                schema=None,  # Needs actual schema
+                expect_full_schema=False,
+            )
+            # dates/datetimes == string
+
+        elif "parquet" in metadata["data_format"]:
+            df = arrow_to_pandas(pq.read_table(f))
+            # dates/datetimes == datetime / date
+
+        else:
+            raise ValueError(
+                f"Unknown data_format in metadata: {metadata['data_format']}."
+            )
+
+    if table_params.get("row-limit"):
+        df = df.sample(table_params.get("row-limit"))
+
+    if table_params.get("headers-ignore-case"):
+        df_cols = [c.lower() for c in df.columns]
+        df.columns = df_cols
+
+    if table_params.get("only-test-cols-in-metadata", False):
+        keep_cols = [c for c in df.columns if c in meta_col_names]
+        df = df[keep_cols]
+
+    return df
diff --git a/poetry.lock b/poetry.lock
index 00e4974..7bacfda 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -50,6 +50,22 @@ dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "p
 docs = ["sphinx"]
 tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
 
+[[package]]
+name = "arrow-pd-parser"
+version = "0.1.0"
+description = "MoJ arrow-pd-parser"
+category = "main"
+optional = false
+python-versions = ">=3.7,<3.9"
+
+[package.dependencies]
+pandas = ">=1.1,<2.0"
+pyarrow = ">=2.0.0,<3.0.0"
+s3fs = ">=0.2.2,<0.5.0"
+
+[package.source]
+type = "url"
+url = "https://github.com/moj-analytical-services/mojap-arrow-pd-parser/archive/v0.1.0.zip"
 [[package]]
 name = "async-generator"
 version = "1.10"
@@ -200,20 +216,20 @@ python-versions = "*"
 
 [[package]]
 name = "boto3"
-version = "1.17.0"
+version = "1.17.19"
 description = "The AWS SDK for Python"
 category = "main"
 optional = false
 python-versions = ">= 2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
 [package.dependencies]
-botocore = ">=1.20.0,<1.21.0"
+botocore = ">=1.20.19,<1.21.0"
 jmespath = ">=0.7.1,<1.0.0"
 s3transfer = ">=0.3.0,<0.4.0"
 
 [[package]]
 name = "botocore"
-version = "1.20.0"
+version = "1.20.19"
 description = "Low-level, data-driven core of boto 3."
 category = "main"
 optional = false
@@ -234,7 +250,7 @@ python-versions = "*"
 
 [[package]]
 name = "cffi"
-version = "1.14.4"
+version = "1.14.5"
 description = "Foreign Function Interface for Python calling C code."
 category = "main"
 optional = false
@@ -245,14 +261,14 @@ pycparser = "*"
 
 [[package]]
 name = "cfn-lint"
-version = "0.44.6"
+version = "0.46.0"
 description = "Checks CloudFormation templates for practices and behaviour that could potentially be improved"
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [package.dependencies]
-aws-sam-translator = ">=1.25.0"
+aws-sam-translator = ">=1.34.0"
 jsonpatch = {version = "*", markers = "python_version != \"3.4\""}
 jsonschema = ">=3.0,<4.0"
 junit-xml = ">=1.9,<2.0"
@@ -286,33 +302,33 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "cryptography"
-version = "3.3.1"
+version = "3.4.6"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 category = "dev"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
 cffi = ">=1.12"
-six = ">=1.4.1"
 
 [package.extras]
 docs = ["sphinx (>=1.6.5,<1.8.0 || >1.8.0,<3.1.0 || >3.1.0,<3.1.1 || >3.1.1)", "sphinx-rtd-theme"]
 docstest = ["doc8", "pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"]
 pep8test = ["black", "flake8", "flake8-import-order", "pep8-naming"]
+sdist = ["setuptools-rust (>=0.11.4)"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["pytest (>=3.6.0,<3.9.0 || >3.9.0,<3.9.1 || >3.9.1,<3.9.2 || >3.9.2)", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,<3.79.2 || >3.79.2)"]
+test = ["pytest (>=6.0)", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,<3.79.2 || >3.79.2)"]
 
 [[package]]
 name = "dataengineeringutils3"
-version = "1.1.0"
+version = "1.1.3"
 description = "Data engineering utils Python 3 version"
 category = "main"
 optional = false
-python-versions = ">=3.6,<4.0"
+python-versions = ">=3.6,<4"
 
 [package.dependencies]
-boto3 = ">=1.10,<2.0"
+boto3 = ">=1.10,<2.0.0"
 
 [[package]]
 name = "decorator"
@@ -332,14 +348,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "docker"
-version = "4.2.2"
+version = "4.4.4"
 description = "A Python library for the Docker Engine API."
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [package.dependencies]
-pypiwin32 = {version = "223", markers = "sys_platform == \"win32\" and python_version >= \"3.6\""}
+pywin32 = {version = "227", markers = "sys_platform == \"win32\""}
 requests = ">=2.14.2,<2.18.0 || >2.18.0"
 six = ">=1.4.0"
 websocket-client = ">=0.32.0"
@@ -434,6 +450,33 @@ spss = ["savReaderWriter (>=3.0)"]
 sql = ["sqlalchemy (>=1.3)"]
 tsv = ["linear-tsv (>=1.0)"]
 
+[[package]]
+name = "fsspec"
+version = "0.8.7"
+description = "File-system specification"
+category = "main"
+optional = false
+python-versions = ">3.6"
+
+[package.dependencies]
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+dask = ["dask", "distributed"]
+dropbox = ["dropboxdrivefs", "requests", "dropbox"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+hdfs = ["pyarrow"]
+http = ["requests", "aiohttp"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+
 [[package]]
 name = "future"
 version = "0.18.2"
@@ -502,7 +545,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "ijson"
-version = "3.1.3"
+version = "3.1.4"
 description = "Iterative JSON parser with standard Python iterator interfaces"
 category = "main"
 optional = true
@@ -525,7 +568,7 @@ testing = ["packaging", "pep517", "importlib-resources (>=1.3)"]
 
 [[package]]
 name = "ipykernel"
-version = "5.4.3"
+version = "5.5.0"
 description = "IPython Kernel for Jupyter"
 category = "main"
 optional = false
@@ -543,7 +586,7 @@ test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose", "jedi (<=0.17.2)"]
 
 [[package]]
 name = "ipython"
-version = "7.20.0"
+version = "7.21.0"
 description = "IPython: Productive Interactive Computing"
 category = "main"
 optional = false
@@ -684,7 +727,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "jsonpatch"
-version = "1.28"
+version = "1.30"
 description = "Apply JSON-Patches (RFC 6902)"
 category = "main"
 optional = false
@@ -695,7 +738,7 @@ jsonpointer = ">=1.9"
 
 [[package]]
 name = "jsonpickle"
-version = "1.5.1"
+version = "2.0.0"
 description = "Python library for serializing any arbitrary object graph into JSON"
 category = "dev"
 optional = false
@@ -885,7 +928,7 @@ test = ["pytest (<5.4)", "pytest-cov"]
 
 [[package]]
 name = "more-itertools"
-version = "8.6.0"
+version = "8.7.0"
 description = "More routines for operating on iterables, beyond itertools"
 category = "dev"
 optional = false
@@ -949,11 +992,11 @@ python-versions = "*"
 
 [[package]]
 name = "nbclient"
-version = "0.5.1"
+version = "0.5.3"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.6.1"
 
 [package.dependencies]
 async-generator = "*"
@@ -1219,7 +1262,7 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.14"
+version = "3.0.16"
 description = "Library for building powerful interactive command lines in Python"
 category = "main"
 optional = false
@@ -1249,7 +1292,7 @@ name = "pyarrow"
 version = "2.0.0"
 description = "Python library for Apache Arrow"
 category = "main"
-optional = true
+optional = false
 python-versions = ">=3.5"
 
 [package.dependencies]
@@ -1289,7 +1332,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "pygments"
-version = "2.7.4"
+version = "2.8.0"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "main"
 optional = false
@@ -1315,17 +1358,6 @@ category = "main"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
-[[package]]
-name = "pypiwin32"
-version = "223"
-description = ""
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-pywin32 = ">=223"
-
 [[package]]
 name = "pyrsistent"
 version = "0.17.3"
@@ -1406,7 +1438,7 @@ python-versions = "*"
 
 [[package]]
 name = "pywin32"
-version = "300"
+version = "227"
 description = "Python for Window Extensions"
 category = "main"
 optional = false
@@ -1430,7 +1462,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
 [[package]]
 name = "pyzmq"
-version = "22.0.2"
+version = "22.0.3"
 description = "Python bindings for 0MQ"
 category = "main"
 optional = false
@@ -1442,7 +1474,7 @@ py = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "redshift-connector"
-version = "2.0.873"
+version = "2.0.874"
 description = "Redshift interface library"
 category = "main"
 optional = true
@@ -1515,7 +1547,7 @@ idna2008 = ["idna"]
 
 [[package]]
 name = "rsa"
-version = "4.7"
+version = "4.7.2"
 description = "Pure-Python RSA implementation"
 category = "dev"
 optional = false
@@ -1547,6 +1579,18 @@ category = "main"
 optional = true
 python-versions = "*"
 
+[[package]]
+name = "s3fs"
+version = "0.4.2"
+description = "Convenient Filesystem interface over S3"
+category = "main"
+optional = false
+python-versions = ">= 3.5"
+
+[package.dependencies]
+botocore = ">=1.12.91"
+fsspec = ">=0.6.0"
+
 [[package]]
 name = "s3transfer"
 version = "0.3.4"
@@ -1560,7 +1604,7 @@ botocore = ">=1.12.36,<2.0a.0"
 
 [[package]]
 name = "scipy"
-version = "1.6.0"
+version = "1.6.1"
 description = "SciPy: Scientific Library for Python"
 category = "main"
 optional = true
@@ -1611,11 +1655,11 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "soupsieve"
-version = "2.1"
+version = "2.2"
 description = "A modern CSS selector implementation for Beautiful Soup."
 category = "main"
 optional = true
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [[package]]
 name = "stringcase"
@@ -1627,7 +1671,7 @@ python-versions = "*"
 
 [[package]]
 name = "tabulate"
-version = "0.8.7"
+version = "0.8.9"
 description = "Pretty-print tabular data"
 category = "main"
 optional = true
@@ -1780,8 +1824,8 @@ python-versions = "*"
 
 [[package]]
 name = "websocket-client"
-version = "0.57.0"
-description = "WebSocket client for Python. hybi13 is supported."
+version = "0.58.0"
+description = "WebSocket client for Python with low level API options"
 category = "dev"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
@@ -1868,7 +1912,7 @@ ge = ["great-expectations", "awswrangler"]
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.7.1,<3.9"
-content-hash = "04d4f56153b706283a1720505aa66cfbd091cee6142dee6f0d523d1fd93032b8"
+content-hash = "3c2468dc09227922fdc7a7c2afe30cdfdf6b616e74207caec4d819d3265fbbb2"
 
 [metadata.files]
 altair = [
@@ -1901,6 +1945,7 @@ argon2-cffi = [
     {file = "argon2_cffi-20.1.0-cp38-cp38-win32.whl", hash = "sha256:77e909cc756ef81d6abb60524d259d959bab384832f0c651ed7dcb6e5ccdbb78"},
     {file = "argon2_cffi-20.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:9dfd5197852530294ecb5795c97a823839258dfd5eb9420233c7cfedec2058f2"},
 ]
+arrow-pd-parser = []
 async-generator = [
     {file = "async_generator-1.10-py3-none-any.whl", hash = "sha256:01c7bf666359b4967d2cda0000cc2e4af16a0ae098cbffcb8472fb9e8ad6585b"},
     {file = "async_generator-1.10.tar.gz", hash = "sha256:6ebb3d106c12920aaae42ccb6f787ef5eefdcdd166ea3d628fa8476abe712144"},
@@ -1948,58 +1993,59 @@ boto = [
     {file = "boto-2.49.0.tar.gz", hash = "sha256:ea0d3b40a2d852767be77ca343b58a9e3a4b00d9db440efb8da74b4e58025e5a"},
 ]
 boto3 = [
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-    {file = "boto3-1.17.0.tar.gz", hash = "sha256:2a39bd5e5f2d50ce9267d682cc92750f8771399665021f47e80f9c8d2fb812a6"},
+    {file = "boto3-1.17.19-py2.py3-none-any.whl", hash = "sha256:c9513a9ea00f8d17ecdc02c391ae956bf0f990aa07deec11c421607c09b294e1"},
+    {file = "boto3-1.17.19.tar.gz", hash = "sha256:f84ca60e9605af69022f039c035b33d519531eeaac52724b9223a5465f4a8b6b"},
 ]
 botocore = [
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-    {file = "botocore-1.20.0.tar.gz", hash = "sha256:a608d6d644b852f3c154fc433eaae52febbebc7c474fa8f4d666797d0931770a"},
+    {file = "botocore-1.20.19-py2.py3-none-any.whl", hash = "sha256:135b5f30e6662b46d804f993bf31d9c7769c6c0848321ed0aa0393f5b9c19a94"},
+    {file = "botocore-1.20.19.tar.gz", hash = "sha256:8e42c78d2eb888551635309158c04ef2648a96d8c2c70dbce7712c6ce8629759"},
 ]
 certifi = [
     {file = "certifi-2020.12.5-py2.py3-none-any.whl", hash = "sha256:719a74fb9e33b9bd44cc7f3a8d94bc35e4049deebe19ba7d8e108280cfd59830"},
     {file = "certifi-2020.12.5.tar.gz", hash = "sha256:1a4995114262bffbc2413b159f2a1a480c969de6e6eb13ee966d470af86af59c"},
 ]
 cffi = [
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+    {file = "scipy-1.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:5f331eeed0297232d2e6eea51b54e8278ed8bb10b099f69c44e2558c090d06bf"},
+    {file = "scipy-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0c8a51d33556bf70367452d4d601d1742c0e806cd0194785914daf19775f0e67"},
+    {file = "scipy-1.6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:83bf7c16245c15bc58ee76c5418e46ea1811edcc2e2b03041b804e46084ab627"},
+    {file = "scipy-1.6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:794e768cc5f779736593046c9714e0f3a5940bc6dcc1dba885ad64cbfb28e9f0"},
+    {file = "scipy-1.6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:5da5471aed911fe7e52b86bf9ea32fb55ae93e2f0fac66c32e58897cfb02fa07"},
+    {file = "scipy-1.6.1-cp39-cp39-win32.whl", hash = "sha256:8e403a337749ed40af60e537cc4d4c03febddcc56cd26e774c9b1b600a70d3e4"},
+    {file = "scipy-1.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:a5193a098ae9f29af283dcf0041f762601faf2e595c0db1da929875b7570353f"},
+    {file = "scipy-1.6.1.tar.gz", hash = "sha256:c4fceb864890b6168e79b0e714c585dbe2fd4222768ee90bc1aa0f8218691b11"},
 ]
 scramp = [
     {file = "scramp-1.2.0-py3-none-any.whl", hash = "sha256:74815c25aad1fe0b5fb994e96c3de63e8695164358a80138352aaadfa4760350"},
@@ -2821,15 +2871,15 @@ six = [
     {file = "six-1.15.0.tar.gz", hash = "sha256:30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259"},
 ]
 soupsieve = [
-    {file = "soupsieve-2.1-py3-none-any.whl", hash = "sha256:4bb21a6ee4707bf43b61230e80740e71bfe56e55d1f1f50924b087bb2975c851"},
-    {file = "soupsieve-2.1.tar.gz", hash = "sha256:6dc52924dc0bc710a5d16794e6b3480b2c7c08b07729505feab2b2c16661ff6e"},
+    {file = "soupsieve-2.2-py3-none-any.whl", hash = "sha256:d3a5ea5b350423f47d07639f74475afedad48cf41c0ad7a82ca13a3928af34f6"},
+    {file = "soupsieve-2.2.tar.gz", hash = "sha256:407fa1e8eb3458d1b5614df51d9651a1180ea5fedf07feb46e45d7e25e6d6cdd"},
 ]
 stringcase = [
     {file = "stringcase-1.2.0.tar.gz", hash = "sha256:48a06980661908efe8d9d34eab2b6c13aefa2163b3ced26972902e3bdfd87008"},
 ]
 tabulate = [
-    {file = "tabulate-0.8.7-py3-none-any.whl", hash = "sha256:ac64cb76d53b1231d364babcd72abbb16855adac7de6665122f97b593f1eb2ba"},
-    {file = "tabulate-0.8.7.tar.gz", hash = "sha256:db2723a20d04bcda8522165c73eea7c300eda74e0ce852d9022e0159d7895007"},
+    {file = "tabulate-0.8.9-py3-none-any.whl", hash = "sha256:d7c013fe7abbc5e491394e10fa845f8f32fe54f8dc60c6622c6cf482d25d47e4"},
+    {file = "tabulate-0.8.9.tar.gz", hash = "sha256:eb1d13f25760052e8931f2ef80aaf6045a6cceb47514db8beab24cded16f13a7"},
 ]
 termcolor = [
     {file = "termcolor-1.1.0.tar.gz", hash = "sha256:1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b"},
@@ -2958,8 +3008,8 @@ webencodings = [
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
 websocket-client = [
-    {file = "websocket_client-0.57.0-py2.py3-none-any.whl", hash = "sha256:0fc45c961324d79c781bab301359d5a1b00b13ad1b10415a4780229ef71a5549"},
-    {file = "websocket_client-0.57.0.tar.gz", hash = "sha256:d735b91d6d1692a6a181f2a8c9e0238e5f6373356f561bb9dc4c7af36f452010"},
+    {file = "websocket_client-0.58.0-py2.py3-none-any.whl", hash = "sha256:44b5df8f08c74c3d82d28100fdc81f4536809ce98a17f0757557813275fbb663"},
+    {file = "websocket_client-0.58.0.tar.gz", hash = "sha256:63509b41d158ae5b7f67eb4ad20fecbb4eee99434e73e140354dc3ff8e09716f"},
 ]
 werkzeug = [
     {file = "Werkzeug-1.0.1-py2.py3-none-any.whl", hash = "sha256:2de2a5db0baeae7b2d2664949077c2ac63fbd16d98da0ff71837f7d1dea3fd43"},
diff --git a/pyproject.toml b/pyproject.toml
index bf07be4..053a521 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,9 +17,9 @@ pyyaml = "^5.3.1"
 iam_builder = "^3.7.0"
 importlib-metadata = "^1.7"
 pandas = "^1.2"
+arrow-pd-parser = { url = "https://github.com/moj-analytical-services/mojap-arrow-pd-parser/archive/v0.1.0.zip"}
 great-expectations = { version = "0.12.9", optional = true }
 awswrangler = {version = "2.3.0", optional = true}
-
 frictionless = {version = "3.24.0", optional = true}
 
 [tool.poetry.dev-dependencies]
diff --git a/table1.csv b/table1.csv
deleted file mode 100644
index f31d5a8..0000000
--- a/table1.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-my_int,animal,my_email,my_datetime,my_date
-16,fish,omar65@waller.info,2013-01-14 15:54:20,1993-09-27
-13,cat,nataliechavez@hotmail.com,2006-12-16 01:44:21,1989-04-08
-13,dog,stephanie12@leblanc.com,1972-06-19 11:22:59,2006-10-01
-18,chicken,cameronscott@bradshaw.com,2009-07-11 00:45:28,1992-07-24
-10,fish,rossrebecca@gould.net,1970-09-11 22:26:13,2011-11-30
-13,cat,escobarryan@gmail.com,2018-09-18 16:18:37,1979-01-22
-16,dog,shelly51@mitchell.com,2002-08-26 10:25:03,1981-03-27
-11,dog,kwilliams@hotmail.com,1989-02-26 16:33:37,2012-06-19
-19,chicken,lisacharles@kennedy.com,1974-07-25 04:41:22,2003-11-06
-11,chicken,olsonchelsea@gray.org,1992-04-03 09:47:30,1972-07-06


Test patch:
diff --git a/tests/test_pandas_validator.py b/tests/test_pandas_validator.py
new file mode 100644
index 0000000..7ae1336
--- /dev/null
+++ b/tests/test_pandas_validator.py
@@ -0,0 +1,300 @@
+import pytest
+from data_linter.validators import pandas_validator as pv
+import pandas as pd
+
+int_not_null = pd.Series([1, 2, 3, 4, 5], dtype=pd.Int64Dtype())
+int_is_null = pd.Series([1, 2, None, 4, 5], dtype=pd.Int64Dtype())
+double_not_null = pd.Series([1.0, 2.23545, 3.532513, 4.35, 5.93567])
+double_is_null = pd.Series([1.0, 2.23545, 3.532513, None, 5.93567])
+str_not_null = pd.Series(
+    ["cat", "dog", "fish", "apple", "pineapple"], dtype=pd.StringDtype()
+)
+str_is_null = pd.Series(
+    ["cat", "dog", None, "apple", "pineapple"], dtype=pd.StringDtype()
+)
+date_str_not_null = pd.Series(
+    ["01/01/2020", "29/12/3000", "25/06/1903"], dtype=pd.StringDtype()
+)
+date_str_is_null = pd.Series([None, "29/12/3000", "25/06/1903"], dtype=pd.StringDtype())
+datetime_str_not_null = pd.Series(
+    ["2020-01-01 10:00:31", "3000-12-29 09:12:23", "1903-06-25 12:00:00"],
+    dtype=pd.StringDtype(),
+)
+datetime_str_is_null = pd.Series(
+    [None, "3000-12-29 09:12:23", "1903-06-25 12:00:00"], dtype=pd.StringDtype()
+)
+
+
+@pytest.mark.parametrize(
+    "col,expected_valid",
+    [
+        (int_is_null, False),
+        (int_not_null, True),
+        (double_is_null, False),
+        (double_not_null, True),
+        (str_is_null, False),
+        (str_not_null, True),
+    ],
+)
+def test_nullable_validation(col, expected_valid):
+    """
+    Check that nullable validation test gives expected result T/F
+    """
+    res = pv._nullable_test(col, {"name": "test_col", "nullable": False})
+    assert isinstance(res, dict)
+    assert expected_valid == res["valid"]
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        int_is_null,
+        int_not_null,
+        double_is_null,
+        double_not_null,
+    ],
+)
+@pytest.mark.parametrize(
+    "meta_col",
+    [
+        {"name": "test_col", "minimum": 0, "maximum": 6},
+        {"name": "test_col", "minimum": -6},
+        {"name": "test_col", "maximum": 6},
+        {"name": "test_col", "minimum": 0.0, "maximum": 6.0},
+        {"name": "test_col", "minimum": -6.1},
+        {"name": "test_col", "maximum": 6.2},
+    ],
+)
+def test_min_max_validation_pass(col, meta_col):
+    """
+    Check that every combination of pandas series and
+    metadata col passes min/max validation test
+    """
+    res = pv._min_max_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"]
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        int_is_null,
+        int_not_null,
+        double_is_null,
+        double_not_null,
+    ],
+)
+@pytest.mark.parametrize(
+    "meta_col",
+    [
+        {"name": "test_col", "minimum": 5, "maximum": 6},
+        {"name": "test_col", "minimum": 5},
+        {"name": "test_col", "maximum": 3},
+    ],
+)
+def test_min_max_validation_fail(col, meta_col):
+    """
+    Check that every combination of pandas series and
+    metadata col fails min/max validation test
+    """
+    res = pv._min_max_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"] is False
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        str_is_null,
+        str_not_null,
+    ],
+)
+@pytest.mark.parametrize(
+    "meta_col",
+    [
+        {"name": "test_col", "minLength": 0, "maxLength": 10},
+        {"name": "test_col", "minLength": 0},
+        {"name": "test_col", "maxLength": 10},
+    ],
+)
+def test_min_max_length_test_pass(col, meta_col):
+    """
+    Check that every combination of pandas series and
+    metadata col passes min/max length validation test
+    """
+    res = pv._min_max_length_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"]
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        str_is_null,
+        str_not_null,
+    ],
+)
+@pytest.mark.parametrize(
+    "meta_col",
+    [
+        {"name": "test_col", "minLength": 5, "maxLength": 6},
+        {"name": "test_col", "minLength": 5},
+        {"name": "test_col", "maxLength": 6},
+    ],
+)
+def test_min_max_length_test_fail(col, meta_col):
+    """
+    Check that every combination of pandas series and
+    metadata col passes min/max length validation test
+    """
+    res = pv._min_max_length_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"] is False
+
+
+@pytest.mark.parametrize(
+    "col_values,expected_valid",
+    [
+        (["abc-1235", "xyz-4468", "xyz-0284", "acx-8936"], True),
+        (["abc-1235", None, "xyz-0284", "acx-8936"], True),
+        (["abc-1235", "xyz-4468", "xyz-0284", "1acx-8936"], False),
+        (["abc-1235", "xyz-4468", None, "cx-8936"], False),
+    ],
+)
+def test_pattern_test_pass(col_values, expected_valid):
+    """
+    Check that pattern validation test gives expected result T/F
+    """
+    col = pd.Series(col_values, dtype=pd.StringDtype())
+    meta_col = {"name": "c", "pattern": "^\\D{3}-\\d{4}$"}
+    res = pv._pattern_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"] == expected_valid
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        str_is_null,
+        str_not_null,
+    ],
+)
+@pytest.mark.parametrize(
+    "meta_col,expected_valid",
+    [
+        (
+            {"name": "test_col", "enum": ["cat", "dog", "fish", "apple", "pineapple"]},
+            True,
+        ),
+        ({"name": "test_col", "enum": ["cat", "dog", "fish", "robot"]}, False),
+    ],
+)
+def test_enum_test(col, meta_col, expected_valid):
+    """
+    Check that pattern validation test gives expected meta and result T/F
+    For all nullable and non-nullable strings.
+    """
+    res = pv._enum_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"] == expected_valid
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        date_str_is_null,
+        date_str_not_null,
+    ],
+)
+def test_date_format_test_pass(col):
+    meta_col = {"name": "test_col", "type": "date", "datetime_format": "%d/%m/%Y"}
+    res = pv._date_format_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"]
+
+    # Test datetime format can be safely cast to dates
+    meta_col2 = {
+        "name": "test_col",
+        "type": "date",
+        "datetime_format": "%d/%m/%Y %H:%M:%S",
+    }
+    # Add zeros to date str
+    col2 = col.apply(lambda x: x + " 00:00:00" if not pd.isna(x) else x)
+    res = pv._date_format_test(col2, meta_col2)
+    assert isinstance(res, dict)
+    assert res["valid"]
+
+
+@pytest.mark.parametrize(
+    "col,meta_col",
+    [
+        (
+            # expects iso by default
+            date_str_is_null,
+            {"name": "test_col", "type": "date"},
+        ),
+        (
+            # expects iso by default
+            date_str_not_null,
+            {"name": "test_col", "type": "date"},
+        ),
+        (
+            # datetime strs with time component not date
+            datetime_str_is_null,
+            {
+                "name": "test_col",
+                "type": "date",
+                "datetime_format": "%Y/%m/%d %H:%M:%S",
+            },
+        ),
+        (
+            # datetime strs with time component not date
+            datetime_str_not_null,
+            {
+                "name": "test_col",
+                "type": "date",
+                "datetime_format": "%Y/%m/%d %H:%M:%S",
+            },
+        ),
+    ],
+)
+def test_date_format_test_fail(col, meta_col):
+    res = pv._date_format_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert not res["valid"]
+
+
+@pytest.mark.parametrize(
+    "col",
+    [
+        datetime_str_is_null,
+        datetime_str_not_null,
+    ],
+)
+@pytest.mark.parametrize(
+    "datetime_format,expected_valid",
+    [
+        (None, True),
+        ("%Y-%m-%d %H:%M:%S", True),
+        ("%d/%m/%Y %H:%M:%S", False),
+    ],
+)
+def test_datetime_format_test(col, datetime_format, expected_valid):
+    meta_col = {"name": "test_col", "type": "datetime"}
+    if datetime_format:
+        meta_col["datetime_format"] = datetime_format
+
+    res = pv._datetime_format_test(col, meta_col)
+    assert isinstance(res, dict)
+    assert res["valid"] == expected_valid
+
+
+def test_validation_function_skips():
+    assert pv._nullable_test(str_is_null, {"name": "c"}) is None
+    assert pv._nullable_test(str_is_null, {"name": "c", "nullable": True}) is None
+    assert pv._min_max_test(str_is_null, {"name": "c"}) is None
+    assert pv._min_max_length_test(str_is_null, {"name": "c"}) is None
+    assert pv._pattern_test(str_is_null, {"name": "c"}) is None
+    assert pv._enum_test(str_is_null, {"name": "c"}) is None
+    assert pv._datetime_format_test(str_is_null, {"name": "c"}) is None
+    assert pv._date_format_test(str_is_null, {"name": "c"}) is None