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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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# Imports |
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import pandas as pd |
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from .utils import _diff_report |
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from .utils import _drop_duplicates |
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from .utils import _missing_vals |
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from .utils import _validate_input_range |
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from .utils import _validate_input_bool |
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def convert_datatypes(data, category=True, cat_threshold=0.05, cat_exclude=None): |
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''' |
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Converts columns to best possible dtypes using dtypes supporting pd.NA. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. |
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category: bool, default True |
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Change dtypes of columns with dtype "object" to "category". Set threshold using cat_threshold or exclude \ |
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columns using cat_exclude. |
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cat_threshold: float, default 0.05 |
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Ratio of unique values below which categories are inferred and column dtype is changed to categorical. |
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cat_exclude: list, default None |
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List of columns to exclude from categorical conversion. |
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Returns |
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------- |
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data: Pandas DataFrame |
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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 (category and |
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unique_vals_ratio < cat_threshold and |
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col not in cat_exclude and |
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data[col].dtype == 'object'): |
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data[col] = data[col].astype('category') |
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data[col] = data[col].convert_dtypes() |
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return data |
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def drop_missing(data, drop_threshold_cols=1, drop_threshold_rows=1): |
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''' |
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Drops completely empty columns and rows by default and optionally provides flexibility to loosen restrictions to \ |
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drop additional columns and rows based on the fraction of remaining NA-values. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. |
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drop_threshold_cols: float, default 1 |
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Drop columns with NA-ratio above the specified threshold. |
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drop_threshold_rows: float, default 1 |
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Drop rows with NA-ratio above the specified threshold. |
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Returns |
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------- |
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data_cleaned: Pandas DataFrame |
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Notes |
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----- |
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Columns are dropped first. Rows are dropped based on the remaining data. |
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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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data = pd.DataFrame(data).copy() |
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data = data.dropna(axis=0, how='all').dropna(axis=1, how='all') |
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data = data.drop(columns=data.loc[:, _missing_vals(data)['mv_cols_ratio'] > drop_threshold_cols].columns) |
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data_cleaned = data.drop(index=data.loc[_missing_vals(data)['mv_rows_ratio'] > drop_threshold_rows, :].index) |
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return data_cleaned |
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def data_cleaning(data, drop_threshold_cols=0.95, drop_threshold_rows=0.95, drop_duplicates=True, |
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convert_dtypes=True, category=True, cat_threshold=0.03, cat_exclude=None, show='changes'): |
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''' |
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Perform initial data cleaning tasks on a dataset, such as dropping single valued and empty rows, empty \ |
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columns as well as optimizing the datatypes. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. |
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drop_threshold_cols: float, default 0.95 |
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Drop columns with NA-ratio above the specified threshold. |
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drop_threshold_rows: float, default 0.95 |
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Drop rows with NA-ratio above the specified threshold. |
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drop_duplicates: bool, default True |
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Drops duplicate rows, keeping the first occurence. This step comes after the dropping of missing values. |
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convert_dtypes: bool, default True |
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Convert dtypes using pd.convert_dtypes(). |
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category: bool, default True |
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Change dtypes of columns to "category". Set threshold using cat_threshold. Requires convert_dtypes=True |
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cat_threshold: float, default 0.03 |
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Ratio of unique values below which categories are inferred and column dtype is changed to categorical. |
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cat_exclude: list, default None |
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List of columns to exclude from categorical conversion. |
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show: {'all', 'changes', None} default 'all' |
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Specify verbosity of the output. |
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* 'all': Print information about the data before and after cleaning as well as information about changes. |
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* 'changes': Print out differences in the data before and after cleaning. |
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* None: No information about the data and the data cleaning is printed. |
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Returns |
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------- |
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data_cleaned: Pandas DataFrame |
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See Also |
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-------- |
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convert_datatypes: Converts columns to best possible dtypes. |
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drop_missing : Flexibly drops columns and rows. |
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_memory_usage: Gives the total memory usage in kilobytes. |
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_missing_vals: Metrics about missing values in the dataset. |
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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 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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_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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_validate_input_range(cat_threshold, 'cat_threshold', 0, 1) |
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data = pd.DataFrame(data).copy() |
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data_cleaned = drop_missing(data, drop_threshold_cols, drop_threshold_rows) |
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single_val_cols = data_cleaned.columns[data_cleaned.nunique(dropna=False) == 1].tolist() |
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data_cleaned = data_cleaned.drop(columns=single_val_cols) |
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if drop_duplicates: |
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data_cleaned, dupl_rows = _drop_duplicates(data_cleaned) |
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if convert_dtypes: |
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data_cleaned = convert_datatypes(data_cleaned, category=category, cat_threshold=cat_threshold, |
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cat_exclude=cat_exclude) |
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_diff_report(data, data_cleaned, dupl_rows=dupl_rows, single_val_cols=single_val_cols, show=show) |
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return data_cleaned |
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akanz1 /
klib
