Method to split a Pandas dataset into train, validation and test splits

get_dataset_partitions_pd.py

def get_dataset_partitions_pd(df, train_split=0.8, val_split=0.1, test_split=0.1):
    assert (train_split + test_split + val_split) == 1
    
    # Only allows for equal validation and test splits
    assert val_split == test_split 

    # Specify seed to always have the same split distribution between runs
    df_sample = df.sample(frac=1, random_state=12)
    indices_or_sections = [int(train_split * len(df)), int((1 - val_split - test_split) * len(df))]
    
    train_ds, val_ds, test_ds = np.split(df_sample, indices_or_sections)
    
    return train_ds, val_ds, test_ds

Hi Angel,
I think there is a little mistake in your code:
Using the default values, the expression [int(train_split * len(df)), int((1 - val_split - test_split) * len(df))]
would result [len(df)*(1-0.8),len(df)*(1-0.1-0.1)], which is [len(df)*0.8,len(df)*0.8], so the val_ds would not have any data in it.
I think the correct line would be:
indices_or_sections = [int(train_split * len(df)), int((1 - test_split) * len(df))]
Please correct me if I'm wrong. Thank you for your work, I came here be following this site.
Cheers, Marcell