<jupyter_start><jupyter_text>DATA INPUT AND OUTPUT  * CSV * EXEL * HTML * SQL<jupyter_code>pwd
pd.read_csv("example")
df = pd.read_csv("example")
df
df.to_csv("My_output",index=False)
pd.read_csv("My_output")
pd.read_excel("Excel_Sample.xlsx",sheet_name='Sheet1')
df.to_excel("Excel_Sample2.xlsx",sheet_name="newSheet")
pd.read_excel("Excel_Sample2.xlsx",sheet_name="newSheet")
data = pd.read_html('https://www.fdic.gov/bank/individual/failed/banklist.html')
type(data)
data[0].head()
from sqlalchemy import create_engine
engine = create_engine("sqlite:///:memory:")
df.to_sql("my_tabletest",engine)
sqldf = pd.read_sql("my_tabletest",con=engine)
sqldf<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16862170087976538, 'fraction_numerical': 0.005865102639296188, 'mean_word_length': 9.348484848484848, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '34354', 'license': 'MIT', 'n_tokens_mistral': 282, 'n_tokens_neox': 274, 'n_words': 31, 'path': 'Pandas/pandas_Data_in_out_put.ipynb', 'repo_name': 'DEKKAR-AHMED/data_Science_Learning', 'size': '682'}