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deduplication on pandas dataframe with custom function
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import pandas as pd | |
from tqdm import tqdm | |
def jaccard_sim(doc1, doc2, thres=0.9): | |
return len(doc1 & doc2) / len(doc1 | doc2) > thres | |
def duplicated(df, textcol, func, **kwargs): | |
"""Simlarly to pd.duplicated it finds the duplicated rows | |
based on a text column using a custom function. | |
Textcol parameter is assumed to be a single column name cotaining | |
textual data. The texts are first turned into a set of raw tokens and | |
then the comparison function is ran against every candidate rows. | |
Parameters: | |
----------- | |
df : pd.DataFrame | |
dataframe to find duplicates in | |
subset : str | |
target text column | |
func : function with signiture (doc1, doc2) -> {True|False} | |
comparison function, assumed to return True if the two | |
documents are the same or False otherwise | |
kwargs : dict | |
keyword arguments to pass to comparison function. | |
Returns: | |
-------- | |
duplicates : pd.Series | |
Series of boolean values with the same indeces as df | |
""" | |
df['comp'] = df[textcol].apply(lambda x: tuple(set(x.split()))) | |
duplicates = df.duplicated(subset='comp') | |
df['comp'] = df.comp.apply(lambda x: set(x)) | |
df = df[['comp']] | |
for i in tqdm(range(len(df.index)-1)): | |
if duplicates[i]: | |
continue | |
subdf = df[i+1:] | |
subdup = duplicates[i+1:] | |
base = df.loc[df.index[i], 'comp'] | |
candidate_indices = subdup.loc[~subdup].index | |
candidates = ( | |
subdf | |
.loc[subdf.index.isin(candidate_indices), 'comp'] | |
.apply(lambda x: func(base, x, **kwargs))) | |
duplicates = duplicates | candidates | |
return duplicates | |
def drop_duplicates(df, textcol, func, **kwargs): | |
"""Drops the duplicated rows from a dataframe based on | |
a textual column using a custom function. | |
Parameters: | |
----------- | |
See duplicated function. | |
Retuns: | |
------- | |
df : pd.DataFrame | |
Deduplicated dataframe | |
""" | |
duplicates = duplicated(df, subset, func, **kwargs) | |
return df.loc[~duplicates] | |
if __name__ == '__main__': | |
df = pd.read_csv('random_csv_with_text_col.csv') | |
print(df.shape) | |
df_deduped = drop_duplicates(df, 'textcol', jaccard_sim, thres=0.9) | |
print(df_deduped.shape) |
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