<jupyter_start><jupyter_text>Automated data exploration<jupyter_code>import pandas as pd # !pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip
from pandas_profiling import ProfileReport
df = pd.read_csv('data/iris2.csv', index_col=False, header=0)
profile = ProfileReport(df, progress_bar=True, minimal=False) #plot={'histogram': {'bins': 20}}
# profile.to_file("output.html")
# profile<jupyter_output><empty_output><jupyter_text>Automated feature preprocessing for transactional and time series data<jupyter_code>import featuretools as ft
es = ft.EntitySet(id="data")
es = es.entity_from_dataframe(entity_id="data",
                               dataframe=df.reset_index()
                             )
feature_matrix, feature_defs = ft.dfs(entityset=es,
                                       target_entity="data",
#                                       agg_primitives=["mean", "max", "min", "std", "skew"] #,necessitates a parent-child relation to work
                                     trans_primitives = ['add_numeric', 'multiply_numeric'] # for ts: cus_sum  -  list of primitive: ft.list_primitives()
                                     )
# feature_matrix<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11129163281884646, 'fraction_numerical': 0.003249390739236393, 'mean_word_length': 2.8742138364779874, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '196674', 'license': 'MIT', 'n_tokens_mistral': 376, 'n_tokens_neox': 358, 'n_words': 68, 'path': 'src/utils/data exploration and feature engineering.ipynb', 'repo_name': 'P-mir/Assess', 'size': '1231'}