Python Tutorial: Working with Excel, Word, and CSV Files Using xlrd/xlwt/xlutils, python-docx, and csv Modules

Python is widely used for data processing, and interacting with common office formats like Excel, Word, and CSV is essential. This article introduces the most popular libraries for each format and provides practical code snippets.

Excel Operations

For Excel handling, the xlrd , xlwt , xlutils , XlsxWriter , and OpenPyXL libraries are commonly used. The table below compares their capabilities:

Feature

xlrd&xlwt&xlutils

XlsxWriter

OpenPyXL

Excel COM Interface

Read

Supported

Not supported

Supported

Supported

Write

Supported

Supported

Supported

Supported

Modify

Supported

Not supported

Supported

Supported

xls format

Supported

Not supported

Not supported

Supported

xlsx format

High‑version only

Supported

Supported

Supported

Large files

Not supported

Supported

Supported

Not supported

Efficiency

Fast

Fast

Fast

Very slow

Functionality

Weak

Powerful

Average

Very powerful

Below are concise examples for writing, reading, and modifying Excel files using xlwt and xlrd together with xlutils :

<code># Install the libraries
$ pip install xlrd xlwt xlutils

# Write an .xls file
import xlwt
wb = xlwt.Workbook()
sh1 = wb.add_sheet('成绩')
sh1.write(0, 0, '姓名')
sh1.write(0, 1, '专业')
sh1.write(0, 2, '科目')
sh1.write(0, 3, '成绩')
# ... (additional rows) ...
wb.save('test.xls')
</code>

<code># Read the .xls file
import xlrd
wb = xlrd.open_workbook('test.xls')
sh1 = wb.sheet_by_index(0)
print('Rows:', sh1.nrows, 'Cols:', sh1.ncols)
print('First row, second column:', sh1.cell_value(0, 1))
</code>

<code># Modify using xlutils
import xlrd
from xlutils.copy import copy
rb = xlrd.open_workbook('test.xls')
wb = copy(rb)
sh = wb.get_sheet(0)
sh.write(4, 0, '王欢')
sh.write(4, 1, '通信工程')
sh.write(4, 2, '机器学习')
sh.write(4, 3, 89)
wb.save('test_modified.xls')
</code>

Word Operations

The python-docx library (current version 0.8.10) enables creation and manipulation of Word documents. Installation is performed with pip install python-docx .

<code># Create a new document with a title
from docx import Document
doc = Document()
doc.add_heading('How to Create and Manipulate Word with Python', 0)
doc.save('example.docx')
</code>

Further examples show how to add headings, paragraphs, styled text, lists, images, and tables, as well as how to read existing documents and iterate over paragraphs and tables.

<code># Add formatted text and a quote
paragraph = doc.add_paragraph('This is a paragraph.')
run = paragraph.add_run(' (font size 20)')
run.font.size = Pt(20)
run.bold = True
run.italic = True
run.underline = True
run.font.color.rgb = RGBColor(0xFF, 0x00, 0x00)
doc.add_paragraph('A quoted line.', style='Intense Quote')
</code>

CSV Operations

Python’s built‑in csv module provides reader and writer classes, as well as DictReader and DictWriter for dictionary‑based I/O. It also offers the Sniffer class to detect dialects.

Key constants for quoting behavior:

Constant

Description

QUOTE_ALL

Quote all fields

QUOTE_MINIMAL

Quote only fields containing special characters

QUOTE_NONNUMERIC

Quote all non‑numeric fields

QUOTE_NONE

No quoting

<code># Write a CSV file
import csv
with open('test.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(['id', 'name', 'age'])
    data = [('1001', '张三', '21'), ('1002', '李四', '31')]
    writer.writerows(data)
</code>

<code># Read a CSV file
import csv
with open('test.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    for row in reader:
        print(', '.join(row))
</code>

<code># Detect dialect automatically
import csv
with open('test.csv', newline='') as csvfile:
    sample = csvfile.read(1024)
    dialect = csv.Sniffer().sniff(sample)
    csvfile.seek(0)
    reader = csv.reader(csvfile, dialect)
    for row in reader:
        print(row)
</code>

Finally, the article lists common file‑mode strings (e.g., 'r' , 'w' , 'a' , and their + variants) for reading, writing, and appending both text and binary files.

Overall, the guide equips readers with practical code snippets and comparative information to efficiently handle Excel, Word, and CSV files in Python projects.