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peperoncino: A library for easy data processing for pandas

Project description

peperoncino: A library for easy data processing for pandas


$ pip install peperoncino

How to use

Processing DataFrame

import peperoncino as pp

pipeline = pp.Pipeline(
    # query data
    pp.Query("bar <= 3"),
    # assign new feature
    pp.Assign(hoge="foo * bar"),
    # generate combination feature
    pp.Combinations(["foo", "baz"], ["*", "/"]),
    # target encoding
    pp.TargetEncoding(["baz"], "y", ref=0),
    # select features
        ["hoge", "*_foo_baz", "TARGET_ENC_baz_BY_y", "y"],

# execute the processing
train_df, val_df, test_df = \
    pipeline.process([train_df, val_df, test_df])

Predefined processings

name description
ApplyColumn Apply a function to a column.
AsCategory Assign category dtype to columns.
Assign Assign a feature by a formula.
Combinations Create combination features.
DropColumns Drop columns.
DropDuplicates Drop duplicate rows.
Pipeline Chain processings.
Query Query rows by a given condition.
RenameCOlumns Rename columns.
Select Select columns.
StatsEncoding Encode columns by statistical values of another column.
TargetEncoding Target Encoding with smoothing.

Define processing

All processings are subclass of pp.BaseProcessing.
All you need is define the _process(self, dfs: List[pd.DataFrame]) -> List[pd.DataFrame] function.

class ExampleProcessing(pp.BaseProcessing):
    def _process(self, dfs: List[pd.DataFrame]) -> List[pd.DataFrame]:
        return [df + 1 for df in dfs]

If your processing doesn't depent on each other data frames, then use pp.SeparatedProcessing.

class ExampleProcessing(pp.SeparatedProcessing):
    def sep_process(self, df: pd.DataFrame) -> pd.DataFrame:
        return df * 2

If you need to merge all dataframes and then apply your processing, use pp.MergedProcessing.

class ExampleProcessing(pp.SeparatedProcessing):
    def simul_process(self, df: pd.DataFrame) -> pd.DataFrame:
        return df.assign(col1_mean=df['col1'].mean())

Project details

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