Using Python, you presumably can import information from different file codecs, similar to CSV, Excel, Textual content material, JSON, and SQL. As quickly as imported, you presumably can manipulate the knowledge using libraries like pandas after which export the knowledge to codecs like CSV, HTML, JSON, and SQL. Beneath are the detailed steps and code examples for each import and export operation.
1. Import CSV
To import a CSV file, you need to use the pandas library.
import pandas as pd# Import CSV file
df_csv = pd.read_csv('path_to_your_file.csv')
print(df_csv.head())
Documentation : https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
2. Import Excel
To import an Excel file, you need to use the pandas library.
import pandas as pd# Import Excel file
df_excel = pd.read_excel('path_to_your_file.xlsx', sheet_name='Sheet1')
print(df_excel.head())
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html
3. Import Textual content material
To import a textual content material file, you need to use the pandas library. If the textual content material file is delimited (e.g., tab-delimited), specify the delimiter.
import pandas as pd# Import Textual content material file
df_text = pd.read_csv('path_to_your_file.txt', delimiter='t')
print(df_text.head())
4. Import JSON
To import a JSON file, you need to use the pandas library.
import pandas as pd# Import JSON file
df_json = pd.read_json('path_to_your_file.json')
print(df_json.head())
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html
5. Import SQL
To import information from a SQL database, you need to use the pandas library along with SQLAlchemy.
import pandas as pd
from sqlalchemy import create_engine# Create a SQLAlchemy engine
engine = create_engine('sqlite:///path_to_your_database.db')
# Import information from SQL
df_sql = pd.read_sql('SELECT * FROM your_table_name', con=engine)
print(df_sql.head())
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html
1. Export to CSV
To export information to a CSV file, you need to use the pandas library.
# Export DataFrame to CSV
df_csv.to_csv('output_file.csv', index=False)
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html
2. Export to HTML
To export information to an HTML file, you need to use the pandas library.
# Export DataFrame to HTML
df_csv.to_html('output_file.html')
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_html.html
3. Export to JSON
To export information to a JSON file, you need to use the pandas library.
# Export DataFrame to JSON
df_csv.to_json('output_file.json')
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html
4. Export to SQL
To export information to a SQL database, you need to use the pandas library along with SQLAlchemy.
# Export DataFrame to SQL
df_csv.to_sql('your_table_name', con=engine, if_exists='alternate', index=False)
Documentation: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html
This info ensures you may need clear steps for importing and exporting information using Python using different file codecs, with each export operation following its corresponding import operation for greater readability and group.
