add clean_language function

dataprep/clean/__init__.py

@@ -29,6 +29,8 @@
 
 from .clean_text import clean_text, default_text_pipeline
 
+from .clean_language import clean_language, validate_language
+
 
 __all__ = [
     "clean_lat_long",
@@ -54,4 +56,6 @@
     "clean_df",
     "clean_text",
     "default_text_pipeline",
+    "clean_language",
+    "validate_language",
 ]

dataprep/clean/clean_language.py

@@ -0,0 +1,336 @@
+"""
+Clean and validate a DataFrame column containing language.
+"""
+
+# pylint: disable=too-many-arguments, global-statement
+
+from os import path
+from typing import Any, Union, Tuple, List, Optional, Dict
+from operator import itemgetter
+
+import dask.dataframe as dd
+import numpy as np
+import pandas as pd
+
+from ..progress_bar import ProgressBar
+from .utils import NULL_VALUES, to_dask
+from .clean_headers import clean_headers
+
+DEFAULT_LANGUAGE_DATA_FILE = path.join(path.split(path.abspath(__file__))[0], "language_data.csv")
+
+DATA = pd.read_csv(DEFAULT_LANGUAGE_DATA_FILE, encoding="utf-8", dtype=str)
+ALPHA2: Dict[str, List[int]] = {}
+ALPHA3: Dict[str, List[int]] = {}
+NAME: Dict[str, List[int]] = {}
+
+
+def clean_language(
+    df: Union[pd.DataFrame, dd.DataFrame],
+    column: str,
+    input_format: Union[str, Tuple[str, ...]] = "auto",
+    output_format: str = "name",
+    kb_path: str = "default",
+    encode: Optional[str] = None,
+    inplace: bool = False,
+    errors: str = "coerce",
+    progress: bool = True,
+) -> pd.DataFrame:
+    """
+    Clean language type data in a DataFrame column.
+
+    Parameters
+    ----------
+        df
+            A pandas or Dask DataFrame containing the data to be cleaned.
+        column
+            The name of the column containing data of language type.
+        input_format
+            The ISO 639 input format of the language.
+                - 'auto': infer the input format
+                - 'name': language name ('English')
+                - 'alpha-2': alpha-2 code ('en')
+                - 'alpha-3': alpha-3 code ('eng')
+
+            Can also be a tuple containing any combination of input formats,
+            for example to clean a column containing name and alpha-2
+            codes set input_format to ('name', 'alpha-2').
+
+            (default: 'auto')
+        output_format
+            The desired ISO 639 format of the language.
+                - 'name': language name ('English')
+                - 'alpha-2': alpha-2 code ('en')
+                - 'alpha-3': alpha-3 code ('eng')
+
+            (default: 'name')
+        kb_path
+            The path of user specified knowledge base.
+            In current stage, it should be in the user's local directory
+            following by the format we proposing.
+
+            (default: 'default')
+        encode
+            The encoding of the knowledge base. It will be passed to `pd.read_csv`.
+
+            (default: None)
+        inplace
+           If True, delete the column containing the data that was cleaned.
+           Otherwise, keep the original column.
+
+           (default: False)
+        errors
+            How to handle parsing errors.
+            - 'coerce': invalid parsing will be set to NaN.
+            - 'ignore': invalid parsing will return the input.
+            - 'raise': invalid parsing will raise an exception.
+
+            (default: 'coerce')
+        progress
+            If True, display a progress bar.
+
+            (default: True)
+
+    Examples
+    --------
+    Clean a column of language data.
+
+    >>> df = pd.DataFrame({'language': ['eng', 'zh', 'japanese']})
+    >>> clean_language(df, 'language')
+        language    language_clean
+    0       eng     English
+    1        zh     Chinese
+    2  japanese     Japanese
+    """
+    # load knowledge base
+    _load_kb(kb_path, encode)
+
+    valid_output_formats = {"name", "alpha-2", "alpha-3"}
+    if output_format not in valid_output_formats:
+        raise ValueError(
+            f'output_format {output_format} is invalid, it needs to be "name", '
+            '"alpha-2" or "alpha-3"'
+        )
+
+    valid_errors = {"coerce", "ignore", "raise"}
+    if errors not in valid_errors:
+        raise ValueError(
+            f'errors {errors} is invalid, it needs to be "coerce", ' '"ignore" or "raise"'
+        )
+
+    input_formats = _convert_format_to_tuple(input_format)
+
+    # convert to dask
+    df = to_dask(df)
+
+    df["clean_code_tup"] = df[column].map_partitions(
+        lambda srs: [_format(x, input_formats, output_format, errors) for x in srs],
+        meta=object,
+    )
+
+    df = df.assign(
+        _temp_=df["clean_code_tup"].map(itemgetter(0), meta=("_temp_", object)),
+    )
+
+    df = df.rename(columns={"_temp_": f"{column}_clean"})
+
+    df = df.drop(columns=["clean_code_tup"])
+
+    if inplace:
+        df[column] = df[f"{column}_clean"]
+        df = df.drop(columns=f"{column}_clean")
+        df = df.rename(columns={column: f"{column}_clean"})
+
+    with ProgressBar(minimum=1, disable=not progress):
+        df = df.compute()
+
+    return df
+
+
+def validate_language(
+    x: Union[str, pd.Series, dd.Series, pd.DataFrame, dd.DataFrame],
+    column: str = "",
+    input_format: Union[str, Tuple[str, ...]] = "auto",
+    kb_path: str = "default",
+    encode: Optional[str] = None,
+) -> Union[bool, pd.Series, pd.DataFrame]:
+    """
+    Validate language type data in a DataFrame column. For each cell, return True or False.
+
+    Parameters
+    ----------
+    x
+        Language data to be validated. It could be a single string, or
+        a pandas or Dask DataFrame, or a pandas or Dask Series.
+    column
+        The name of the column to be validated.
+        If x is not a pandas or Dask DataFrame, it would be ignored.
+        If x is a pandas or Dask DataFrame but `column` is not specified,
+        then the whole dataframe will be validated.
+
+        (default: None)
+    input_format
+        The ISO 639 input format of the language.
+            - 'auto': infer the input format
+            - 'name': language name ('English')
+            - 'alpha-2': alpha-2 code ('en')
+            - 'alpha-3': alpha-3 code ('eng')
+
+        Can also be a tuple containing any combination of input formats,
+        for example to clean a column containing name and alpha-2
+        codes set input_format to ('name', 'alpha-2').
+
+        (default: 'auto')
+    kb_path
+        The path of user specified knowledge base.
+        In current stage, it should be in the user's local directory
+        following by the format we proposing.
+
+        (default: "default")
+    encode
+        The encoding of the knowledge base. It will be passed to `pd.read_csv`.
+
+        (default: None)
+    """
+    # load knowledge base
+    _load_kb(kb_path, encode)
+
+    input_formats = _convert_format_to_tuple(input_format)
+
+    if isinstance(x, (pd.Series, dd.Series)):
+        return x.apply(_check_language, args=(input_formats, False))
+    elif isinstance(x, (pd.DataFrame, dd.DataFrame)):
+        if column != "":
+            return x[column].apply(_check_language, args=(input_formats, False))
+        else:
+            return x.applymap(lambda val: _check_language(val, input_formats, False))
+    return _check_language(x, input_formats, False)
+
+
+def _format(val: Any, input_formats: Tuple[str, ...], output_format: str, errors: str) -> Any:
+    """
+    Reformat a language string with proper output format.
+    """
+    result_index, status = _check_language(val, input_formats, True)
+
+    if status == "null":
+        return [np.nan]
+    if status == "unknown":
+        if errors == "raise":
+            raise ValueError(f"unable to parse value {val}")
+        return [val] if errors == "ignore" else [np.nan]
+
+    formated_val = DATA.loc[result_index, output_format]
+    if pd.isna(formated_val):
+        # country doesn't have the required output format
+        if errors == "raise":
+            raise ValueError(f"unable to parse value {val}")
+        return [val] if errors == "ignore" else [np.nan]
+
+    return [formated_val.title()] if output_format == "name" else [formated_val]
+
+
+def _check_language(val: Any, input_formats: Tuple[str, ...], clean: bool) -> Any:
+    """
+    Find the index of the given language string in the DATA dataframe.
+
+    Parameters
+    ----------
+    val
+        String containing the language value to be cleaned.
+    input_formats
+        Tuple containing potential ISO 639 input formats of the language.
+    clean
+        If True, a tuple (index, status) is returned. There are 3 status:
+             - "null": val is a null value.
+             - "unknown": val could not be parsed.
+             - "success": a successful parse of the value.
+        If False, the function returns True/False to be used by the validate function.
+    """
+    if val in NULL_VALUES:
+        return (None, "null") if clean else False
+
+    val = str(val).lower().strip()
+    first_letter = val[0]
+
+    # select possible formats from input_formats;
+    possible_formats: Tuple[str, ...] = ()
+    if len(val) > 1 and "name" in input_formats:
+        # it is a potential valid language
+        possible_formats = ("name",) + possible_formats
+
+    if len(val) == 3 and "alpha-3" in input_formats:
+        # alpha-3 or name, and alpha-3 is preferred
+        possible_formats = ("alpha-3",) + possible_formats
+    elif len(val) == 2 and "alpha-2" in input_formats:
+        # alpha-2 or name, and alpha-2 is preferred
+        possible_formats = ("alpha-2",) + possible_formats
+
+    # search the value
+    format_dicts = {"name": NAME, "alpha-2": ALPHA2, "alpha-3": ALPHA3}
+    for fmt in possible_formats:
+        format_dict = format_dicts[fmt]
+        inds = format_dict.get(
+            first_letter
+        )  # get the indices of value that starts with the same letter
+        if inds is None:  # no value starts with this letter
+            continue
+        df_temp = DATA.iloc[inds][fmt]  # extract these values
+        res = df_temp[df_temp.str.lower() == val]  # search the input value within them
+        if len(res) != 0:
+            return (res.index[0], "success") if clean else True
+
+    return (None, "unknown") if clean else False
+
+
+def _load_kb(kb_path: str, encode: Optional[str] = None) -> Any:
+    """
+    Load knowledge base from a specified path.
+    """
+    global DATA, NAME, ALPHA2, ALPHA3
+
+    if kb_path == "default":
+        DATA = pd.read_csv(DEFAULT_LANGUAGE_DATA_FILE, encoding="utf-8", dtype=str)
+    else:
+        DATA = pd.read_csv(kb_path, encoding=encode, dtype=str)
+        DATA = clean_headers(DATA, case="kebab", report=False)  # to lowercase
+        # check whether the format of the knowledge base is valid
+        valid_formats = {"name", "alpha-2", "alpha-3"}
+        for fmt in valid_formats:
+            if fmt not in DATA.columns:
+                raise KeyError(
+                    "knowledge base does not follow the format, "
+                    'it needs to contain "name", "alpha-2", and "alpha-3"'
+                )
+
+    # divide the dataset according to the first letter of each value, store the indices
+    # e.g. {'a': [12, 36, 39], 'b': [15, 89], ...}
+    NAME, ALPHA2, ALPHA3 = {}, {}, {}
+    format_dicts = {"name": NAME, "alpha-2": ALPHA2, "alpha-3": ALPHA3}
+    for fmt, fmt_dict in format_dicts.items():
+        first_letters = DATA[fmt].str.lower().dropna().apply(lambda x: x[0])
+        grps = DATA.groupby(first_letters).groups
+        fmt_dict.update({k: list(v) for k, v in grps.items()})
+
+
+def _convert_format_to_tuple(input_format: Union[str, Tuple[str, ...]]) -> Tuple[str, ...]:
+    """
+    Converts a string input format to a tuple of allowed input formats and
+    raises an error if an input format is not valid.
+    """
+    if isinstance(input_format, str):
+        if input_format == "auto":
+            return ("name", "alpha-2", "alpha-3")
+        else:
+            input_format = (input_format,)
+
+    valid_input_formats = {"auto", "name", "alpha-2", "alpha-3"}
+    for fmt in input_format:
+        if fmt not in valid_input_formats:
+            raise ValueError(
+                f'input_format {fmt} is invalid, it needs to be one of "auto", '
+                '"name", "alpha-2" or "alpha-3"'
+            )
+    if "auto" in input_format:
+        return ("name", "alpha-2", "alpha-3")
+
+    return input_format