Great question! Let’s break down which library is best suited for your SQLite-to-Excel export task, based on what you need to accomplish:

Pandas – The Go-To for Quick, Simple Exports #

If your main goal is to get data from SQLite into Excel with minimal code and no fancy formatting, Pandas is your best bet. It abstracts away all the low-level details of connecting to the database and writing to Excel, letting you do the whole job in just a few lines.

• Why it works: Pandas can directly read SQL query results into a DataFrame (its tabular data structure), then export that DataFrame to Excel in one command. It automatically handles data types, headers, and basic formatting.
• Bonus: It uses either Openpyxl or XlsxWriter under the hood, so you can still tap into more advanced features if needed.

Example code:

import pandas as pd
import sqlite3

# Connect to your SQLite database
conn = sqlite3.connect("your_database.db")

# Pull data into a DataFrame
df = pd.read_sql_query("SELECT * FROM your_target_table", conn)

# Export to Excel (index=False removes the extra index column)
df.to_excel("exported_data.xlsx", index=False)

# Clean up the database connection
conn.close()

Openpyxl – For Fine-Grained Excel Control #

If you need to customize the Excel file after exporting (like setting cell colors, merging cells, adding comments, or modifying existing sheets), Openpyxl is the way to go. It’s a full-featured library for reading and writing .xlsx files, giving you complete control over every element of the spreadsheet.

• Why it works: You can fetch data from SQLite manually, then build the Excel file row by row, styling or structuring it exactly how you want.
• Note: It’s more verbose than Pandas, but that verbosity gives you flexibility.

Example code:

import sqlite3
from openpyxl import Workbook

# Connect to SQLite and fetch data
conn = sqlite3.connect("your_database.db")
cursor = conn.cursor()
cursor.execute("SELECT * FROM your_target_table")
data_rows = cursor.fetchall()
column_names = [desc[0] for desc in cursor.description]

# Create a new Excel workbook and sheet
wb = Workbook()
ws = wb.active

# Write column headers
ws.append(column_names)

# Write each data row
for row in data_rows:
    ws.append(row)

# Save the file (you can add formatting steps here if needed)
wb.save("custom_export.xlsx")

conn.close()

XlsxWriter – For Complex, Feature-Rich Reports #

If you need to generate Excel files with advanced features like charts, pivot tables, conditional formatting, or formulas, XlsxWriter is ideal. It’s optimized for creating new .xlsx files with these professional-grade elements, though it doesn’t support reading or modifying existing files.

• Pro tip: You can pair it with Pandas to get the best of both worlds—use Pandas to handle the data extraction, then XlsxWriter to add the fancy formatting.

Example code (Pandas + XlsxWriter):

import pandas as pd
import sqlite3

conn = sqlite3.connect("your_database.db")
df = pd.read_sql_query("SELECT * FROM your_target_table", conn)

# Set up ExcelWriter with XlsxWriter as the engine
writer = pd.ExcelWriter("formatted_report.xlsx", engine="xlsxwriter")
df.to_excel(writer, index=False, sheet_name="Sales Data")

# Access the workbook and worksheet objects to add formatting
workbook = writer.book
worksheet = writer.sheets["Sales Data"]

# Create a bold, highlighted format for headers
header_format = workbook.add_format({"bold": True, "bg_color": "#F2F2F2"})

# Apply the format to each header cell
for col_num, value in enumerate(df.columns.values):
    worksheet.write(0, col_num, value, header_format)

# Save and close the writer
writer.close()
conn.close()

Final Recommendations #

• Quick, no-frills export: Use Pandas—it’s fast, simple, and covers 90% of common use cases.
• Custom formatting/editing: Use Openpyxl when you need to tweak the Excel file beyond basic exports.
• Advanced reports with charts/pivot tables: Use XlsxWriter (often alongside Pandas) for polished, feature-rich spreadsheets.

内容的提问来源于stack exchange，提问作者Nenad Ristov