<jupyter_start><jupyter_text>Nulls<jupyter_code># null values
df_sample.dropna(inplace=True)<jupyter_output><empty_output><jupyter_text>data types<jupyter_code># df.astype({'col1': 'int32'}).dtypes
new_types = {'VendorID':'int32',
            'tpep_pickup_datetime':'datetime64',
            'tpep_dropoff_datetime':'datetime64',
            'passenger_count':'int32',
            'RatecodeID':'int32',
            'store_and_fwd_flag':'category'
            }

df = df_sample.astype(new_types)
df.info()
df.sample(2)<jupyter_output><empty_output><jupyter_text>export data<jupyter_code>df.to_pickle(EXPORT_PATH)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14664586583463338, 'fraction_numerical': 0.0218408736349454, 'mean_word_length': 5.294117647058823, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '113207', 'license': 'FTL', 'n_tokens_mistral': 261, 'n_tokens_neox': 243, 'n_words': 19, 'path': 'notebooks/01a_clean.ipynb', 'repo_name': 'tarekyehya/NYC-Taxi-Trips', 'size': '641'}