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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 _memory_usage |
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from .utils import _missing_vals |
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from .utils import _validate_input_0_1 |
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def convert_datatypes(data, category=True, cat_threshold=0.05, cat_exclude=[]): |
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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. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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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: default [] (empty list) |
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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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Pandas DataFrame. |
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''' |
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_validate_input_0_1(cat_threshold, 'cat_threshold') |
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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 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. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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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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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_input_0_1(drop_threshold_cols, 'drop_threshold_cols') |
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_validate_input_0_1(drop_threshold_rows, 'drop_threshold_rows') |
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data = pd.DataFrame(data) |
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data = data.dropna(axis=0, how='all') |
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data = data.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, category=True, |
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cat_threshold=0.03, cat_exclude=[], show='changes'): |
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''' |
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Perform initial data cleaning tasks on a dataset, such as dropping empty rows and columns and optimizing the \ |
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datatypes. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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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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category: bool, default True |
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Change dtypes of columns to "category". Set threshold using cat_threshold. |
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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: default [] (empty list) |
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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 is printed. |
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Returns |
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------- |
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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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data = pd.DataFrame(data) |
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data_cleaned = drop_missing(data, drop_threshold_cols, drop_threshold_rows) |
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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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if show in ['changes', 'all']: |
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if show == 'all': |
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print('Before data cleaning:\n') |
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print(f'dtypes:\n{data.dtypes.value_counts()}') |
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print(f'\nNumber of rows: {data.shape[0]}') |
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print(f'Number of cols: {data.shape[1]}') |
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print(f"Missing values: {_missing_vals(data)['mv_total']}") |
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print(f'Memory usage: {_memory_usage(data)} KB') |
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print('_______________________________________________________\n') |
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print('After data cleaning:\n') |
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print(f'dtypes:\n{data_cleaned.dtypes.value_counts()}') |
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print(f'\nNumber of rows: {data_cleaned.shape[0]}') |
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print(f'Number of cols: {data_cleaned.shape[1]}') |
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print(f"Missing values: {_missing_vals(data_cleaned)['mv_total']}") |
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print(f'Memory usage: {_memory_usage(data_cleaned)} KB') |
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print('_______________________________________________________\n') |
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print( |
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f"Shape of cleaned data: {data_cleaned.shape} - Remaining NAs: {_missing_vals(data_cleaned)['mv_total']}") |
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print(f'\nChanges:') |
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print(f'Dropped rows: {data.shape[0]-data_cleaned.shape[0]}') |
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print(f'Dropped columns: {data.shape[1]-data_cleaned.shape[1]}') |
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print(f"Dropped missing values: {_missing_vals(data)['mv_total']-_missing_vals(data_cleaned)['mv_total']}") |
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mem_change = _memory_usage(data)-_memory_usage(data_cleaned) |
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print(f'Reduced memory by: {round(mem_change,2)} KB (-{round(100*mem_change/_memory_usage(data),1)}%)') |
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return data_cleaned |
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akanz1 /
klib
