It's common to want to modify your fdl.DatasetInfo object in the case where something was inferred incorrectly by fdl.DatasetInfo.from_dataframe.

Let's walk through an example of how to do this.

Suppose you've loaded in a dataset as a pandas DataFrame.

import pandas as pd

df = pd.read_csv('example_dataset.csv')

Below is an example of what is displayed upon inspection.

Suppose you create a fdl.DatasetInfo object by inferring the details from this DataFrame.

dataset_info = fdl.DatasetInfo.from_dataframe(df)

Below is an example of what is displayed upon inspection.

But upon inspection, you notice a few things are wrong.

1. The value range of output_column is set to [0.01, 0.99], when it should really be [0.0, 1.0].
2. There are no possible values set for feature_3.
3. The data type of feature_3 is set to fdl.DataType.STRING, when it should really be fdl.DataType.CATEGORY.

Let's see how we can address these issues.

Modifying a column’s value range

Let's say we want to modify the range of output_column in the above fdl.DatasetInfo object to be [0.0, 1.0].

You can do this by setting the value_range_min and value_range_max of the output_column column.

dataset_info['output_column'].value_range_min = 0.0
dataset_info['output_column'].value_range_max = 1.0

Modifying a column’s possible values

Let's say we want to modify the possible values of feature_3 to be ['Yes', 'No'].

You can do this by setting the possible_values of the feature_3 column.

dataset_info['feature_3'].possible_values = ['Yes', 'No']

Modifying a column’s data type

Let's say we want to modify the data type of feature_3 to be fdl.DataType.CATEGORY.

You can do this by setting the data_type of the feature_3 column.

dataset_info['feature_3'].data_type = fdl.DataType.CATEGORY