For Pandas

Gokart has several features for Pandas.

Pandas Type Config

Pandas has a feature that converts the type of column(s) automatically. This feature sometimes cause wrong result. To avoid unintentional type conversion of pandas, we can specify a column name to check the type of Task input and output in gokart.

from typing import Any, Dict
import pandas as pd
import gokart


# Please define a class which inherits `gokart.PandasTypeConfig`.
class SamplePandasTypeConfig(gokart.PandasTypeConfig):

    @classmethod
    def type_dict(cls) -> Dict[str, Any]:
        return {'int_column': int}


class SampleTask(gokart.TaskOnKart[pd.DataFrame]):

    def run(self):
        # [PandasTypeError] because expected type is `int`, but `str` is passed.
        df = pd.DataFrame(dict(int_column=['a']))
        self.dump(df)

This is useful when dataframe has nullable columns because pandas auto-conversion often fails in such case.

Easy to Load DataFrame

The load() method is used to load input pandas.DataFrame.

def requires(self):
    return MakeDataFrameTask()

def run(self):
    df = self.load()

Please refer to load().

Fail on empty DataFrame

When the fail_on_empty_dump parameter is true, the dump() method raises EmptyDumpError on trying to dump empty pandas.DataFrame.

import gokart


class EmptyTask(gokart.TaskOnKart):
    def run(self):
        df = pd.DataFrame()
        self.dump(df)

$ python main.py EmptyTask --fail-on-empty-dump true
# EmptyDumpError
$ python main.py EmptyTask
# Task will be ran and outputs an empty dataframe

Empty caches sometimes hide bugs and let us spend much time debugging. This feature notifies us some bugs (including wrong datasources) in the early stage.

Please refer to fail_on_empty_dump.