<jupyter_start><jupyter_text>df1.to_json()<jupyter_code>df1.to_json(orient='records')
df
df.to_json(orient="records")
df2.to_json(orient="records")
url = 'https://www.fdic.gov/bank/individual/failed/banklist.html'

df = pd.read_html(url)
type(df)
df[0]
url_1= 'https://en.wikipedia.org/wiki/Mobile_country_code#:~:text=The%20mobile%20country%20code%20consists,0%3A%20Test%20networks'
dfs = pd.read_html(url_1,match='Country',header=0)
dfs
df_excel=pd.read_excel('abc.xlsx')
df_excel.head()
df_excel[index==66]
df_excel.to_pickle('df_excel')
df = pd.read_pickle('df_excel')
df.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.184640522875817, 'fraction_numerical': 0.03758169934640523, 'mean_word_length': 10.351851851851851, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, 'lorem ipsum': 0, 'www.': 1, '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': '66602', 'license': 'MIT', 'n_tokens_mistral': 286, 'n_tokens_neox': 274, 'n_words': 22, 'path': 'Pandas.ipynb', 'repo_name': 'parad13/ML_basics', 'size': '612'}