Source code for dataprep.clean.clean_headers

Clean and standardize column headers for a DataFrame.
import re
from typing import Any, Dict, List, Optional, Union
from unicodedata import normalize

import dask.dataframe as dd
import numpy as np
import pandas as pd

NULL_VALUES = {np.nan, "", None}


[docs]def clean_headers( df: Union[pd.DataFrame, dd.DataFrame], case: str = "snake", replace: Optional[Dict[str, str]] = None, remove_accents: bool = True, report: bool = True, ) -> pd.DataFrame: """ Function to clean column headers (column names). Read more in the :ref:`User Guide <clean_headers_user_guide>`. Parameters ---------- df Dataframe from which column names are to be cleaned. case The desired case style of the column name. - 'snake': 'column_name' - 'kebab': 'column-name' - 'camel': 'columnName' - 'pascal': 'ColumnName' - 'const': 'COLUMN_NAME' - 'sentence': 'Column name' - 'title': 'Column Name' - 'lower': 'column name' - 'upper': 'COLUMN NAME' (default: 'snake') replace Values to replace in the column names. - {'old_value': 'new_value'} (default: None) remove_accents If True, strip accents from the column names. (default: True) report If True, output the summary report. Otherwise, no report is outputted. (default: True) Examples -------- Clean column names by converting the names to camel case style, removing accents, and correcting a mispelling. >>> df = pd.DataFrame({'FirstNom': ['Philip', 'Turanga'], 'lastName': ['Fry', 'Leela'], \ 'Téléphone': ['555-234-5678', '(604) 111-2335']}) >>> clean_headers(df, case='camel', replace={'Nom': 'Name'}) Column Headers Cleaning Report: 2 values cleaned (66.67%) firstName lastName telephone 0 Philip Fry 555-234-5678 1 Turanga Leela (604) 111-2335 """ if case not in CASE_STYLES: raise ValueError( f"case {case} is invalid, it needs to be one of {', '.join(c for c in CASE_STYLES)}" ) # Store original column names for creating cleaning report orig_columns = df.columns.astype(str).tolist() if replace: df = df.rename(columns=lambda col: _replace_values(col, replace)) if remove_accents: df = df.rename(columns=_remove_accents) df = df.rename(columns=lambda col: _convert_case(col, case)) df.columns = _rename_duplicates(df.columns, case) # Count the number of changed column names new_columns = df.columns.astype(str).tolist() cleaned = [1 if new_columns[i] != orig_columns[i] else 0 for i in range(len(orig_columns))] stats = {"cleaned": sum(cleaned)} # Output a report describing the result of clean_headers if report: _create_report(stats, len(df.columns)) return df
def _convert_case(name: Any, case: str) -> Any: """ Convert case style of a column name. Parameters ---------- name Column name. case The desired case style of the column name. """ if name in NULL_VALUES: name = "header" if case in {"snake", "kebab", "camel", "pascal", "const"}: words = _split_strip_string(str(name)) else: words = _split_string(str(name)) if case == "snake": name = "_".join(words).lower() elif case == "kebab": name = "-".join(words).lower() elif case == "camel": name = words[0].lower() + "".join(w.capitalize() for w in words[1:]) elif case == "pascal": name = "".join(w.capitalize() for w in words) elif case == "const": name = "_".join(words).upper() elif case == "sentence": name = " ".join(words).capitalize() elif case == "title": name = " ".join(w.capitalize() for w in words) elif case == "lower": name = " ".join(words).lower() elif case == "upper": name = " ".join(words).upper() return name def _split_strip_string(string: str) -> List[str]: """ Split the string into separate words and strip punctuation and special characters. """ string = re.sub(r"[!()*+\,\-./:;<=>?[\]^_{|}~]", " ", string) string = re.sub(r"[\'\"\`]", "", string) return re.sub(r"([A-Z][a-z]+)", r" \1", re.sub(r"([A-Z]+|[0-9]+|\W+)", r" \1", string)).split() def _split_string(string: str) -> List[str]: """ Split the string into separate words. """ string = re.sub(r"[\-_]", " ", string) return re.sub(r"([A-Z][a-z]+)", r" \1", re.sub(r"([A-Z]+)", r"\1", string)).split() def _replace_values(name: Any, mapping: Dict[str, str]) -> Any: """ Replace string values in the column name. Parameters ---------- name Column name. mapping Maps old values in the column name to the new values. """ if name in NULL_VALUES: return name name = str(name) for old_value, new_value in mapping.items(): # If the old value or the new value is not alphanumeric, add underscores to the # beginning and end so the new value will be parsed correctly for _convert_case() new_val = ( fr"{new_value}" if old_value.isalnum() and new_value.isalnum() else fr"_{new_value}_" ) name = re.sub(fr"{old_value}", new_val, name, flags=re.IGNORECASE) return name def _remove_accents(name: Any) -> Any: """ Return the normal form for a Unicode string name using canonical decomposition. """ if not isinstance(name, str): return name return normalize("NFD", name).encode("ascii", "ignore").decode("ascii") def _rename_duplicates(names: pd.Index, case: str) -> Any: """ Rename duplicated column names to append a number at the end. """ if case in {"snake", "const"}: sep = "_" elif case in {"camel", "pascal"}: sep = "" elif case == "kebab": sep = "-" else: sep = " " names = list(names) counts: Dict[str, int] = {} for i, col in enumerate(names): cur_count = counts.get(col, 0) if cur_count > 0: names[i] = f"{col}{sep}{cur_count}" counts[col] = cur_count + 1 return names def _create_report(stats: Dict[str, int], ncols: int) -> None: """ Describe what was done in the cleaning process. """ print("Column Headers Cleaning Report:") if stats["cleaned"] > 0: nclnd = stats["cleaned"] pclnd = round(nclnd / ncols * 100, 2) print(f"\t{nclnd} values cleaned ({pclnd}%)")