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):

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

class SampleTask(gokart.TaskOnKart):

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

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

Easy to Load DataFrame

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

def requires(self):
    return MakeDataFrameTask()

def run(self):
    df = self.load_data_frame(required_columns={'colA', 'colB'}, drop_columns=True)

This allows us to omit reset_index and drop when loading. If there is a missing column in an example above, AssertionError will be raised. This feature is useful for pipelines based on pandas.

Please refer to load_data_frame().

Fail on empty DataFrame

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

import gokart

class EmptyTask(gokart.TaskOnKart):
    def run(self):
        df = pd.DataFrame()
$ python EmptyTask --fail-on-empty-dump true
# AssertionError
$ python 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.