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python sqlalchemy

Innocent Iguana answered on November 23, 2023 Popularity 10/10 Helpfulness 2/10

answer python sqlalchemy

python sqlalchemy

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### Way 1
from sqlalchemy import create_engine
# Connect to database
engine = create_engine('sqlite:///dbname.sqlite')
# See tables in the database
table_names = engine.table_names()
print(table_names)
# Open a connection to pass query
con = engine.connect()
# Execute query
rs = con.execute("SELECT * FROM Orders")
# Store results of all rows
results = rs.fetchall()
# Print the result as list of tuples
print(list(results))
# Column names
column_names = rs.keys()
# Store the result in a dataframe
df = pd.DataFrame(results)
df.columns = column_names
# Close the connection to pass query
con.close()

### Way 2
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite:///dbname.sqlite')
df = pd.read_sql_query("SELECT * FROM tableName", engine)

### Way 3
from sqlalchemy import create_engine, text
import pandas as pd

file1_path = 'file1.csv'
file2_path = 'file2.csv'
engine1 = create_engine(f"sqlite:///{file1_path}")
engine2 = create_engine(f"sqlite:///{file2_path}")
df1 = pd.read_sql_table('table1', con=engine1)
df2 = pd.read_sql_table('table2', con=engine2)
# Create a new SQLAlchemy engine for in-memory SQLite database
final_engine = create_engine('sqlite:///:memory:')

# Create SQL tables for each DataFrame
df1.to_sql('table1', con=final_engine, index=False)
df2.to_sql('table2', con=final_engine, index=False)

# Perform a full join using SQL queries
query = """
    SELECT *
    FROM table1
    INNER JOIN table2 ON table1.common_column = table2.common_column
"""
# Execute the SQL query and fetch the result into a DataFrame
final_result = pd.read_sql_query(text(query), final_engine)

# Print the final result
print("Final Result after Full Join:")
print(final_result)

Popularity 10/10 Helpfulness 2/10 Language python

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Tags: python sqlalchemy

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Contributed on Nov 23 2023

Innocent Iguana

0 Answers Avg Quality 2/10

python sqlalchemy

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