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""" |
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Functions for data cleaning. |
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:author: Andreas Kanz |
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""" |
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from __future__ import annotations |
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import itertools |
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import re |
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from typing import Literal |
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from typing import Optional |
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import numpy as np |
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import pandas as pd |
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from klib.describe import corr_mat |
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from klib.utils import _diff_report |
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from klib.utils import _drop_duplicates |
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from klib.utils import _missing_vals |
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from klib.utils import _validate_input_bool |
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from klib.utils import _validate_input_range |
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__all__ = [ |
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"clean_column_names", |
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"convert_datatypes", |
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"data_cleaning", |
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"drop_missing", |
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"mv_col_handling", |
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] |
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def _optimize_ints(data: pd.Series | pd.DataFrame) -> pd.DataFrame: |
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df = pd.DataFrame(data).copy() |
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ints = df.select_dtypes(include=["int64"]).columns.tolist() |
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df[ints] = df[ints].apply(pd.to_numeric, downcast="integer") |
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return df |
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def _optimize_floats(data: pd.Series | pd.DataFrame) -> pd.DataFrame: |
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data = pd.DataFrame(data).copy() |
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floats = data.select_dtypes(include=["float64"]).columns.tolist() |
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data[floats] = data[floats].apply(pd.to_numeric, downcast="float") |
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return data |
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def clean_column_names(data: pd.DataFrame, hints: bool = True) -> pd.DataFrame: |
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"""Clean the column names of the provided Pandas Dataframe and optionally \ |
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provides hints on duplicate and long column names. |
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Parameters |
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---------- |
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data : pd.DataFrame |
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Original Dataframe with columns to be cleaned |
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hints : bool, optional |
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Print out hints on column name duplication and colum name length, by default \ |
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True |
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Returns |
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------- |
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pd.DataFrame |
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Pandas DataFrame with cleaned column names |
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""" |
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_validate_input_bool(hints, "hints") |
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# Handle CamelCase |
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for i, col in enumerate(data.columns): |
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matches = re.findall(re.compile("[a-z][A-Z]"), col) |
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column = col |
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for match in matches: |
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column = column.replace(match, f"{match[0]}_{match[1]}") |
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data.rename(columns={data.columns[i]: column}, inplace=True) |
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data.columns = ( |
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data.columns.str.replace("\n", "_", regex=False) |
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.str.replace("(", "_", regex=False) |
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.str.replace(")", "_", regex=False) |
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.str.replace("'", "_", regex=False) |
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.str.replace('"', "_", regex=False) |
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.str.replace(".", "_", regex=False) |
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.str.replace("-", "_", regex=False) |
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.str.replace(r"[!?:;/]", "_", regex=True) |
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.str.replace("+", "_plus_", regex=False) |
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.str.replace("*", "_times_", regex=False) |
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.str.replace("<", "_smaller", regex=False) |
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.str.replace(">", "_larger_", regex=False) |
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.str.replace("=", "_equal_", regex=False) |
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.str.replace("ä", "ae", regex=False) |
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.str.replace("ö", "oe", regex=False) |
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.str.replace("ü", "ue", regex=False) |
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.str.replace("ß", "ss", regex=False) |
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.str.replace("%", "_percent_", regex=False) |
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.str.replace("$", "_dollar_", regex=False) |
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.str.replace("€", "_euro_", regex=False) |
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.str.replace("@", "_at_", regex=False) |
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.str.replace("#", "_hash_", regex=False) |
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.str.replace("&", "_and_", regex=False) |
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.str.replace(r"\s+", "_", regex=True) |
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.str.replace(r"_+", "_", regex=True) |
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.str.strip("_") |
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.str.lower() |
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) |
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if dupl_idx := [i for i, x in enumerate(data.columns.duplicated()) if x]: |
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dupl_before = data.columns[dupl_idx].tolist() |
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data.columns = [ |
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col if col not in data.columns[:i] else f"{col}_{str(i)}" |
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for i, col in enumerate(data.columns) |
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] |
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if hints: |
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print( |
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f"Duplicate column names detected! Columns with index {dupl_idx} and " |
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f"names {dupl_before}) have been renamed to " |
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f"{data.columns[dupl_idx].tolist()}." |
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) |
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long_col_names = [x for x in data.columns if len(x) > 25] |
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if long_col_names and hints: |
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print( |
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"Long column names detected (>25 characters). Consider renaming the " |
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f"following columns {long_col_names}." |
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) |
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return data |
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def convert_datatypes( |
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data: pd.DataFrame, |
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category: bool = True, |
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cat_threshold: float = 0.05, |
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cat_exclude: Optional[list[str | int]] = None, |
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) -> pd.DataFrame: |
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"""Convert columns to best possible dtypes using dtypes supporting pd.NA. |
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Temporarily not converting to integers due to an issue in pandas. This is expected \ |
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to be fixed in pandas 1.1. See https://github.com/pandas-dev/pandas/issues/33803 |
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Parameters |
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---------- |
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data : pd.DataFrame |
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2D dataset that can be coerced into Pandas DataFrame |
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category : bool, optional |
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Change dtypes of columns with dtype "object" to "category". Set threshold \ |
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using cat_threshold or exclude columns using cat_exclude, by default True |
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cat_threshold : float, optional |
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Ratio of unique values below which categories are inferred and column dtype is \ |
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changed to categorical, by default 0.05 |
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cat_exclude : Optional[list[str | int]], optional |
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List of columns to exclude from categorical conversion, by default None |
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Returns |
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------- |
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pd.DataFrame |
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Pandas DataFrame with converted Datatypes |
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""" |
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# Validate Inputs |
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_validate_input_bool(category, "Category") |
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_validate_input_range(cat_threshold, "cat_threshold", 0, 1) |
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cat_exclude = [] if cat_exclude is None else cat_exclude.copy() |
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data = pd.DataFrame(data).copy() |
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for col in data.columns: |
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unique_vals_ratio = data[col].nunique(dropna=False) / data.shape[0] |
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if ( |
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category |
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and unique_vals_ratio < cat_threshold |
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and col not in cat_exclude |
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and data[col].dtype == "object" |
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): |
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data[col] = data[col].astype("category") |
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data[col] = data[col].convert_dtypes( |
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infer_objects=True, |
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convert_string=True, |
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convert_integer=False, |
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convert_boolean=True, |
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) |
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data = _optimize_ints(data) |
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data = _optimize_floats(data) |
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return data |
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def drop_missing( |
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data: pd.DataFrame, |
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drop_threshold_cols: float = 1, |
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drop_threshold_rows: float = 1, |
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col_exclude: Optional[list[str]] = None, |
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) -> pd.DataFrame: |
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"""Drop completely empty columns and rows by default and optionally provides \ |
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flexibility to loosen restrictions to drop additional non-empty columns and \ |
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rows based on the fraction of NA-values. |
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Parameters |
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---------- |
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data : pd.DataFrame |
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2D dataset that can be coerced into Pandas DataFrame |
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drop_threshold_cols : float, optional |
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Drop columns with NA-ratio equal to or above the specified threshold, by \ |
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default 1 |
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drop_threshold_rows : float, optional |
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Drop rows with NA-ratio equal to or above the specified threshold, by default 1 |
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col_exclude : Optional[list[str]], optional |
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Specify a list of columns to exclude from dropping. The excluded columns do \ |
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not affect the drop thresholds, by default None |
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Returns |
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------- |
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pd.DataFrame |
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Pandas DataFrame without any empty columns or rows |
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Notes |
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----- |
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Columns are dropped first |
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""" |
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# Validate Inputs |
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_validate_input_range(drop_threshold_cols, "drop_threshold_cols", 0, 1) |
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_validate_input_range(drop_threshold_rows, "drop_threshold_rows", 0, 1) |
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col_exclude = [] if col_exclude is None else col_exclude.copy() |
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data_exclude = data[col_exclude] |
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data = pd.DataFrame(data).copy() |
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data_dropped = data.drop(columns=col_exclude, errors="ignore") |
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data_dropped = data_dropped.drop( |
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columns=data_dropped.loc[ |
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:, _missing_vals(data)["mv_cols_ratio"] > drop_threshold_cols |
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].columns |
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).dropna(axis=1, how="all") |
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data = pd.concat([data_dropped, data_exclude], axis=1) |
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return data.drop( |
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index=data.loc[ |
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_missing_vals(data)["mv_rows_ratio"] > drop_threshold_rows, : |
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].index |
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).dropna(axis=0, how="all") |
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def data_cleaning( |
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data: pd.DataFrame, |
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drop_threshold_cols: float = 0.9, |
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drop_threshold_rows: float = 0.9, |
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drop_duplicates: bool = True, |
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convert_dtypes: bool = True, |
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col_exclude: Optional[list[str]] = None, |
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category: bool = True, |
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cat_threshold: float = 0.03, |
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cat_exclude: Optional[list[str | int]] = None, |
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clean_col_names: bool = True, |
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show: Optional[Literal["all", "changes"]] = "changes", |
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) -> pd.DataFrame: |
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"""Perform initial data cleaning tasks on a dataset, such as dropping single \ |
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valued and empty rows, empty columns as well as optimizing the datatypes. |
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Parameters |
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---------- |
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data : pd.DataFrame |
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2D dataset that can be coerced into Pandas DataFrame |
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drop_threshold_cols : float, optional |
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Drop columns with NA-ratio equal to or above the specified threshold, by \ |
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default 0.9 |
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drop_threshold_rows : float, optional |
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Drop rows with NA-ratio equal to or above the specified threshold, by \ |
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default 0.9 |
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drop_duplicates : bool, optional |
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Drop duplicate rows, keeping the first occurence. This step comes after the \ |
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dropping of missing values, by default True |
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convert_dtypes : bool, optional |
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Convert dtypes using pd.convert_dtypes(), by default True |
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col_exclude : Optional[list[str]], optional |
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Specify a list of columns to exclude from dropping, by default None |
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category : bool, optional |
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Enable changing dtypes of "object" columns to "category". Set threshold using \ |
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cat_threshold. Requires convert_dtypes=True, by default True |
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cat_threshold : float, optional |
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Ratio of unique values below which categories are inferred and column dtype is \ |
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changed to categorical, by default 0.03 |
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cat_exclude : Optional[list[str]], optional |
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284
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List of columns to exclude from categorical conversion, by default None |
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285
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clean_column_names: bool, optional |
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286
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Cleans the column names and provides hints on duplicate and long names, by \ |
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default True |
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288
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show : Optional[Literal["all", "changes"]], optional |
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{"all", "changes", None}, by default "changes" |
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Specify verbosity of the output: |
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292
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* "all": Print information about the data before and after cleaning as \ |
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well as information about changes and memory usage (deep). Please be \ |
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aware, that this can slow down the function by quite a bit. |
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* "changes": Print out differences in the data before and after cleaning. |
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296
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* None: No information about the data and the data cleaning is printed. |
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297
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298
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Returns |
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299
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------- |
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300
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pd.DataFrame |
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301
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Cleaned Pandas DataFrame |
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303
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See Also |
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304
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-------- |
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305
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convert_datatypes: Convert columns to best possible dtypes. |
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306
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drop_missing : Flexibly drop columns and rows. |
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307
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_memory_usage: Gives the total memory usage in megabytes. |
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308
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_missing_vals: Metrics about missing values in the dataset. |
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310
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Notes |
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----- |
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The category dtype is not grouped in the summary, unless it contains exactly the \ |
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same categories. |
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""" |
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# Validate Inputs |
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_validate_input_range(drop_threshold_cols, "drop_threshold_cols", 0, 1) |
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_validate_input_range(drop_threshold_rows, "drop_threshold_rows", 0, 1) |
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318
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_validate_input_bool(drop_duplicates, "drop_duplicates") |
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_validate_input_bool(convert_dtypes, "convert_datatypes") |
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_validate_input_bool(category, "category") |
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321
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_validate_input_range(cat_threshold, "cat_threshold", 0, 1) |
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323
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data = pd.DataFrame(data).copy() |
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data_cleaned = drop_missing( |
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325
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data, drop_threshold_cols, drop_threshold_rows, col_exclude=col_exclude |
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326
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) |
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327
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328
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if clean_col_names: |
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329
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data_cleaned = clean_column_names(data_cleaned) |
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330
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331
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single_val_cols = data_cleaned.columns[ |
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332
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data_cleaned.nunique(dropna=False) == 1 |
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333
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].tolist() |
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334
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data_cleaned = data_cleaned.drop(columns=single_val_cols) |
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335
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336
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dupl_rows = None |
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337
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338
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if drop_duplicates: |
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data_cleaned, dupl_rows = _drop_duplicates(data_cleaned) |
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340
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if convert_dtypes: |
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341
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data_cleaned = convert_datatypes( |
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data_cleaned, |
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category=category, |
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344
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cat_threshold=cat_threshold, |
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345
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cat_exclude=cat_exclude, |
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346
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) |
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347
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348
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_diff_report( |
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349
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data, |
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350
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data_cleaned, |
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351
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dupl_rows=dupl_rows, |
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352
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single_val_cols=single_val_cols, |
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353
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show=show, |
|
354
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) |
|
355
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|
356
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return data_cleaned |
|
357
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358
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359
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def mv_col_handling( |
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360
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data: pd.DataFrame, |
|
361
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target: Optional[str | pd.Series | list[str]] = None, |
|
362
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mv_threshold: float = 0.1, |
|
363
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corr_thresh_features: float = 0.5, |
|
364
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corr_thresh_target: float = 0.3, |
|
365
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return_details: bool = False, |
|
366
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|
) -> pd.DataFrame | tuple[pd.DataFrame, list[str], list[str]]: |
|
367
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|
"""Convert columns with a high ratio of missing values into binary features and \ |
|
368
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|
eventually drops them based on their correlation with other features and the \ |
|
369
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|
|
target variable. |
|
370
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|
|
|
|
371
|
|
|
This function follows a three step process: |
|
372
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|
|
- 1) Identify features with a high ratio of missing values (above 'mv_threshold'). |
|
373
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|
|
- 2) Identify high correlations of these features among themselves and with \ |
|
374
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|
|
other features in the dataset (above 'corr_thresh_features'). |
|
375
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|
|
- 3) Features with high ratio of missing values and high correlation among each \ |
|
376
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|
|
other are dropped unless they correlate reasonably well with the target \ |
|
377
|
|
|
variable (above 'corr_thresh_target'). |
|
378
|
|
|
|
|
379
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|
|
Note: If no target is provided, the process exits after step two and drops columns \ |
|
380
|
|
|
identified up to this point. |
|
381
|
|
|
|
|
382
|
|
|
Parameters |
|
383
|
|
|
---------- |
|
384
|
|
|
data : pd.DataFrame |
|
385
|
|
|
2D dataset that can be coerced into Pandas DataFrame |
|
386
|
|
|
target : Optional[str | pd.Series | list]], optional |
|
387
|
|
|
Specify target for correlation. I.e. label column to generate only the \ |
|
388
|
|
|
correlations between each feature and the label, by default None |
|
389
|
|
|
mv_threshold : float, optional |
|
390
|
|
|
Value between 0 <= threshold <= 1. Features with a missing-value-ratio larger \ |
|
391
|
|
|
than mv_threshold are candidates for dropping and undergo further analysis, by \ |
|
392
|
|
|
default 0.1 |
|
393
|
|
|
corr_thresh_features : float, optional |
|
394
|
|
|
Value between 0 <= threshold <= 1. Maximum correlation a previously identified \ |
|
395
|
|
|
features (with a high mv-ratio) is allowed to have with another feature. If \ |
|
396
|
|
|
this threshold is overstepped, the feature undergoes further analysis, by \ |
|
397
|
|
|
default 0.5 |
|
398
|
|
|
corr_thresh_target : float, optional |
|
399
|
|
|
Value between 0 <= threshold <= 1. Minimum required correlation of a remaining \ |
|
400
|
|
|
feature (i.e. feature with a high mv-ratio and high correlation to another \ |
|
401
|
|
|
existing feature) with the target. If this threshold is not met the feature is \ |
|
402
|
|
|
ultimately dropped, by default 0.3 |
|
403
|
|
|
return_details : bool, optional |
|
404
|
|
|
Provdies flexibility to return intermediary results, by default False |
|
405
|
|
|
|
|
406
|
|
|
Returns |
|
407
|
|
|
------- |
|
408
|
|
|
pd.DataFrame |
|
409
|
|
|
Updated Pandas DataFrame |
|
410
|
|
|
|
|
411
|
|
|
optional: |
|
412
|
|
|
cols_mv: Columns with missing values included in the analysis |
|
413
|
|
|
drop_cols: List of dropped columns |
|
414
|
|
|
""" |
|
415
|
|
|
# Validate Inputs |
|
416
|
|
|
_validate_input_range(mv_threshold, "mv_threshold", 0, 1) |
|
417
|
|
|
_validate_input_range(corr_thresh_features, "corr_thresh_features", 0, 1) |
|
418
|
|
|
_validate_input_range(corr_thresh_target, "corr_thresh_target", 0, 1) |
|
419
|
|
|
|
|
420
|
|
|
data = pd.DataFrame(data).copy() |
|
421
|
|
|
data_local = data.copy() |
|
422
|
|
|
mv_ratios = _missing_vals(data_local)["mv_cols_ratio"] |
|
423
|
|
|
cols_mv = mv_ratios[mv_ratios > mv_threshold].index.tolist() |
|
424
|
|
|
data_local[cols_mv] = ( |
|
425
|
|
|
data_local[cols_mv].applymap(lambda x: x if pd.isnull(x) else 1).fillna(0) |
|
426
|
|
|
) |
|
427
|
|
|
|
|
428
|
|
|
high_corr_features = [] |
|
429
|
|
|
data_temp = data_local.copy() |
|
430
|
|
|
for col in cols_mv: |
|
431
|
|
|
corrmat = corr_mat(data_temp, colored=False) |
|
432
|
|
|
if abs(corrmat[col]).nlargest(2)[1] > corr_thresh_features: |
|
433
|
|
|
high_corr_features.append(col) |
|
434
|
|
|
data_temp = data_temp.drop(columns=[col]) |
|
435
|
|
|
|
|
436
|
|
|
drop_cols = [] |
|
437
|
|
|
if target is None: |
|
438
|
|
|
data = data.drop(columns=high_corr_features) |
|
439
|
|
|
else: |
|
440
|
|
|
corrs = corr_mat(data_local, target=target, colored=False).loc[ |
|
441
|
|
|
high_corr_features |
|
442
|
|
|
] |
|
443
|
|
|
drop_cols = corrs.loc[abs(corrs.iloc[:, 0]) < corr_thresh_target].index.tolist() |
|
444
|
|
|
data = data.drop(columns=drop_cols) |
|
445
|
|
|
|
|
446
|
|
|
return (data, cols_mv, drop_cols) if return_details else data |
|
447
|
|
|
|
|
448
|
|
|
|
|
449
|
|
|
def pool_duplicate_subsets( |
|
450
|
|
|
data: pd.DataFrame, |
|
451
|
|
|
col_dupl_thresh: float = 0.2, |
|
452
|
|
|
subset_thresh: float = 0.2, |
|
453
|
|
|
min_col_pool: int = 3, |
|
454
|
|
|
exclude: Optional[list[str]] = None, |
|
455
|
|
|
return_details=False, |
|
456
|
|
|
) -> pd.DataFrame | tuple[pd.DataFrame, list[str]]: |
|
457
|
|
|
"""Check for duplicates in subsets of columns and pools them. This can reduce \ |
|
458
|
|
|
the number of columns in the data without loosing much information. Suitable \ |
|
459
|
|
|
columns are combined to subsets and tested for duplicates. In case sufficient \ |
|
460
|
|
|
duplicates can be found, the respective columns are aggregated into a \ |
|
461
|
|
|
"pooled_var" column. Identical numbers in the "pooled_var" column indicate \ |
|
462
|
|
|
identical information in the respective rows. |
|
463
|
|
|
|
|
464
|
|
|
Note: It is advised to exclude features that provide sufficient informational \ |
|
465
|
|
|
content by themselves as well as the target column by using the "exclude" \ |
|
466
|
|
|
setting. |
|
467
|
|
|
|
|
468
|
|
|
Parameters |
|
469
|
|
|
---------- |
|
470
|
|
|
data : pd.DataFrame |
|
471
|
|
|
2D dataset that can be coerced into Pandas DataFrame |
|
472
|
|
|
col_dupl_thresh : float, optional |
|
473
|
|
|
Columns with a ratio of duplicates higher than "col_dupl_thresh" are \ |
|
474
|
|
|
considered in the further analysis. Columns with a lower ratio are not \ |
|
475
|
|
|
considered for pooling, by default 0.2 |
|
476
|
|
|
subset_thresh : float, optional |
|
477
|
|
|
The first subset with a duplicate threshold higher than "subset_thresh" is \ |
|
478
|
|
|
chosen and aggregated. If no subset reaches the threshold, the algorithm \ |
|
479
|
|
|
continues with continuously smaller subsets until "min_col_pool" is reached, \ |
|
480
|
|
|
by default 0.2 |
|
481
|
|
|
min_col_pool : int, optional |
|
482
|
|
|
Minimum number of columns to pool. The algorithm attempts to combine as many \ |
|
483
|
|
|
columns as possible to suitable subsets and stops when "min_col_pool" is \ |
|
484
|
|
|
reached, by default 3 |
|
485
|
|
|
exclude : Optional[list[str]], optional |
|
486
|
|
|
List of column names to be excluded from the analysis. These columns are \ |
|
487
|
|
|
passed through without modification, by default None |
|
488
|
|
|
return_details : bool, optional |
|
489
|
|
|
Provdies flexibility to return intermediary results, by default False |
|
490
|
|
|
|
|
491
|
|
|
Returns |
|
492
|
|
|
------- |
|
493
|
|
|
pd.DataFrame |
|
494
|
|
|
DataFrame with low cardinality columns pooled |
|
495
|
|
|
|
|
496
|
|
|
optional: |
|
497
|
|
|
subset_cols: List of columns used as subset |
|
498
|
|
|
""" |
|
499
|
|
|
# Input validation |
|
500
|
|
|
_validate_input_range(col_dupl_thresh, "col_dupl_thresh", 0, 1) |
|
501
|
|
|
_validate_input_range(subset_thresh, "subset_thresh", 0, 1) |
|
502
|
|
|
_validate_input_range(min_col_pool, "min_col_pool", 0, data.shape[1]) |
|
503
|
|
|
|
|
504
|
|
|
excluded_cols = [] |
|
505
|
|
|
if exclude is not None: |
|
506
|
|
|
excluded_cols = data[exclude] |
|
507
|
|
|
data = data.drop(columns=exclude) |
|
508
|
|
|
|
|
509
|
|
|
subset_cols = [] |
|
510
|
|
|
for i in range(data.shape[1] + 1 - min_col_pool): |
|
511
|
|
|
# Consider only columns with lots of duplicates |
|
512
|
|
|
check_list = [ |
|
513
|
|
|
col |
|
514
|
|
|
for col in data.columns |
|
515
|
|
|
if data.duplicated(subset=col).mean() > col_dupl_thresh |
|
516
|
|
|
] |
|
517
|
|
|
|
|
518
|
|
|
# Identify all possible combinations for the current interation |
|
519
|
|
|
if check_list: |
|
520
|
|
|
combinations = itertools.combinations(check_list, len(check_list) - i) |
|
521
|
|
|
else: |
|
522
|
|
|
continue |
|
523
|
|
|
|
|
524
|
|
|
# Check subsets for all possible combinations |
|
525
|
|
|
ratios = [ |
|
526
|
|
|
*map(lambda comb: data.duplicated(subset=list(comb)).mean(), combinations) |
|
527
|
|
|
] |
|
528
|
|
|
max_idx = np.argmax(ratios) |
|
529
|
|
|
|
|
530
|
|
|
if max(ratios) > subset_thresh: |
|
531
|
|
|
# Get the best possible iterator and process the data |
|
532
|
|
|
best_subset = itertools.islice( |
|
533
|
|
|
itertools.combinations(check_list, len(check_list) - i), |
|
534
|
|
|
max_idx, |
|
535
|
|
|
max_idx + 1, |
|
536
|
|
|
) |
|
537
|
|
|
|
|
538
|
|
|
best_subset = data[list(list(best_subset)[0])] |
|
539
|
|
|
subset_cols = best_subset.columns.tolist() |
|
540
|
|
|
|
|
541
|
|
|
unique_subset = ( |
|
542
|
|
|
best_subset.drop_duplicates() |
|
543
|
|
|
.reset_index() |
|
544
|
|
|
.rename(columns={"index": "pooled_vars"}) |
|
545
|
|
|
) |
|
546
|
|
|
data = data.merge(unique_subset, how="left", on=subset_cols).drop( |
|
547
|
|
|
columns=subset_cols |
|
548
|
|
|
) |
|
549
|
|
|
data.index = pd.RangeIndex(len(data)) |
|
550
|
|
|
break |
|
551
|
|
|
|
|
552
|
|
|
data = pd.concat([data, pd.DataFrame(excluded_cols)], axis=1) |
|
553
|
|
|
|
|
554
|
|
|
return (data, subset_cols) if return_details else data |
|
555
|
|
|
|
akanz1 /
klib
