<jupyter_start><jupyter_text>A partial solution excercise to transfrom categorical feature dataset containing n unique values to categorical column<jupyter_code># create a dictionary of data1 unique values to number of unique values
data1_class = dict(zip(df['data1'].unique().tolist(), range(1,df['data1'].nunique()+1)))
# try to map every single unique data1 value to its unique value number from dictionary 
df['data1_categorical_class'] = df['data1'].apply(lambda x : data1_class[x])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10444874274661509, 'fraction_numerical': 0.019342359767891684, 'mean_word_length': 7.779661016949152, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '201670', 'license': 'MIT', 'n_tokens_mistral': 156, 'n_tokens_neox': 153, 'n_words': 50, 'path': 'TransformCategoricalFeature.ipynb', 'repo_name': 'tarun-jethwani/quickrepo', 'size': '517'}