Crunching Large Datasets Made Fast and Easy: the Polars Library | Hacker Noon

'Crunching Large Datasets Made Fast and Easy: the Polars Library' datascience bigdata

Back to our task, this is the script implementing the logic described above for processing one file.

def process_file: global basepath, stopwords not_word=r'' # define what we are reading df=pl.read_csv # filter out terms with non alphabetical characters ... df=df.filter.str.contains.is_not) # ... and eliminate terms shorter than 3 chars df=df.filter.str.lengths > 2) # ... and also stop words df["word"]=df["word"].str.to_lowercase df=df.filter.is_in.is_not) # sum unique counts and sort by sum desc df=df.groupby['count'].sum.

The syntax of working with data frames in polars bears similarity with the syntax in pandas, but only to a certain extent. Polars has a chained expression syntax that makes it very … well, expressive. I liked that a lot. I must admit, though that without stackoverflow I would have never come up withWhat makes Polars so fast?

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