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Pandas provides rich export functionalities, allowing you to export DataFrames to various common formats, including CSV, Excel, SQL databases, JSON, and more. This section details the usage methods and considerations for various export methods.

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Export to CSV

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CSV is the most universal data exchange format. Exporting can be affected by the environment's default encoding, so attention must be paid to Chinese encoding issues.

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Basic Export

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Example

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import pandas as pd\\n\\n# Prepare test data\\ndf = pd.DataFrame({\\n    "Name": ["Zhang San","Li Si","Wang Wu","Zhao Liu"],\\n    "Age": [25,30,28,35],\\n    "City": ["Beijing","Shanghai","Guangzhou","Shenzhen"],\\n    "Salary": [12000,15000,11000,18000]\\n})\\n\\n# Most basic export (includes index)\\ndf.to_csv("output.csv")\\n\\n# Exclude index\\ndf.to_csv("output.csv", index=False)\\n

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Chinese Encoding Handling

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Example

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import pandas as pd\\n\\ndf = pd.DataFrame({\\n    "Name": ["Zhang San","Li Si","Wang Wu"],\\n    "City": ["Beijing","Shanghai","Guangzhou"]\\n})\\n\\n# UTF-8 Encoding (recommended)\\ndf.to_csv("output_utf8.csv", encoding="utf-8")\\n\\n# UTF-8 with BOM（Excel Open without garbled text)\\ndf.to_csv("output_utf8_bom.csv", encoding="utf-8-sig")\\n\\n# GBK Encoding (suitable for legacy systems)\\ndf.to_csv("output_gbk.csv", encoding="gbk")\\n\\n# Verify encoding\\nimport os\\nprint("File size comparison:")\\nfor f in ["output_utf8.csv","output_utf8_bom.csv","output_gbk.csv"]:\\n    print(f"{f}: {os.path.getsize(f)} bytes")\\n

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Export Options Details

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Example

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import pandas as pd\\n\\ndf = pd.DataFrame({\\n    "Name": ["Zhang San","Li Si"],\\n    "Age": [25,30]\\n})\\n\\n# Specify delimiter (default is comma)\\ndf.to_csv("output.tsv", sep="\\\\t")  # TSV Format\\n\\n# Do not write header\\ndf.to_csv("output.csv", header=False)\\n\\n# Customize column names (When header=False Hour）\\ndf.to_csv("output.csv", header=False, columns=["Name","Age"])\\n\\n# Export onlySpecific column\\ndf.to_csv("output.csv", columns=)  # Export only"Name"column\\n\\n# Missing value handling\\nimport numpy as np\\ndf_with_na = pd.DataFrame({\\n    "A": [1,2, np.nan,4],\\n    "B": ["a",None,"c","d"]\\n})\\ndf_with_na.to_csv("output.csv", na_rep="NULL")  # Specify missing value representation\\n\\n# Floating-point precision\\ndf = pd.DataFrame({"value": [1.23456789,2.3456789]})\\ndf.to_csv("output.csv", float_format="%.2f")  # Keep 2 decimal places\\n

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Export to Excel

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The Excel format is suitable for manual viewing and editing, but exporting large files can be slow.

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Install Dependencies

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pip install openpyxl xlwt\\n

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Basic Export

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Example

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import pandas as pd\\n\\ndf = pd.DataFrame({\\n    "Name": ["Zhang San","Li Si","Wang Wu"],\\n    "Age": [25,30,28],\\n    "City": ["Beijing","Shanghai","Guangzhou"],\\n    "Salary": [12000,15000,11000]\\n})\\n\\n# Export as Excel (.xlsx Format，Requires openpyxl)\\ndf.to_excel("output.xlsx", index=False)\\n\\n# Export as older version .xls Format（Requires xlwt)\\ndf.to_excel("output.xls", index=False)\\n

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Multi-Sheet Export

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Example

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import pandas as pd\\n\\n# Create multiple DataFrames\\ndf1 = pd.DataFrame({"A": [1,2,3],"B": [4,5,6]})\\ndf2 = pd.DataFrame({"C": [7,8,9],"D": [10,11,12]})\\n\\n# Export to different sheets in the same Excel file\\nwith pd.ExcelWriter("output.xlsx", engine="openpyxl") as writer:\\n    df1.to_excel(writer, sheet_name="Sheet1", index=False)\\n    df2.to_excel(writer, sheet_name="Sheet2", index=False)\\n\\nprint("Multi Sheet Export successful")\\n

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Formatted Export

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Example

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import pandas as pd\\nfrom openpyxl import Workbook\\nfrom openpyxl.styles import Font, PatternFill, Alignment\\n\\n# Create DataFrame\\ndf = pd.DataFrame({\\n    "Name": \\n

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