Python利用xlrd和xlwt模块进行自动化操作读写Excel的完整指南

pip install xlrd

import xlrd

# 打开Excel文件
workbook = xlrd.open_workbook('example.xlsx')

# 获取第一个工作表
sheet = workbook.sheet_by_index(0)

# 获取行数和列数
num_rows = sheet.nrows
num_cols = sheet.ncols

# 遍历打印每个单元格的值
for row in range(num_rows):
    for col in range(num_cols):
        cell_value = sheet.cell(row, col).value
        print(f'({row+1}, {col+1}): {cell_value}')

pip install xlwt

import xlwt

# 创建一个Excel工作簿
workbook = xlwt.Workbook()

# 在工作簿中添加一个工作表
sheet = workbook.add_sheet('Sheet1')

# 写入数据
data = [
    ['Name', 'Age', 'City'],
    ['John', 25, 'New York'],
    ['Alice', 30, 'London'],
    ['Bob', 22, 'Tokyo']
]

for row_index, row in enumerate(data):
    for col_index, value in enumerate(row):
        sheet.write(row_index, col_index, value)

# 保存文件
workbook.save('output.xls')

import xlrd
from datetime import datetime

workbook = xlrd.open_workbook('dates.xlsx')
sheet = workbook.sheet_by_index(0)

# 读取日期数据并进行格式化输出
date_cell = sheet.cell_value(1, 0)
date_value = xlrd.xldate_as_datetime(date_cell, workbook.datemode)
formatted_date = date_value.strftime('%Y-%m-%d %H:%M:%S')
print(f'Formatted Date: {formatted_date}')

import xlwt

workbook = xlwt.Workbook()
sheet = workbook.add_sheet('StyledSheet')

# 创建样式
style = xlwt.XFStyle()
font = xlwt.Font()
font.bold = True
font.color_index = xlwt.Style.colour_map['red']
style.font = font

# 在单元格应用样式
sheet.write(0, 0, 'Styled Text', style)

# 保存文件
workbook.save('styled_output.xls')

import xlrd
import xlwt

# 读取原始数据
input_workbook = xlrd.open_workbook('input_data.xlsx')
input_sheet = input_workbook.sheet_by_index(0)

# 创建新的工作簿和工作表
output_workbook = xlwt.Workbook()
output_sheet = output_workbook.add_sheet('ProcessedData')

# 处理数据并写入新的工作表
for row_index in range(input_sheet.nrows):
    for col_index in range(input_sheet.ncols):
        cell_value = input_sheet.cell_value(row_index, col_index)

        # 进行数据处理，这里简单示范，可以根据实际需求进行更复杂的操作
        processed_value = cell_value * 2

        # 写入新的工作表
        output_sheet.write(row_index, col_index, processed_value)

# 保存结果
output_workbook.save('processed_output.xls')

import xlrd

file_path = 'example.xlsx'

try:
    workbook = xlrd.open_workbook(file_path)
except FileNotFoundError:
    print(f"Error: File '{file_path}' not found.")

import xlrd

workbook = xlrd.open_workbook('example.xlsx')
sheet_index = 0

try:
    sheet = workbook.sheet_by_index(sheet_index)
except IndexError:
    print(f"Error: Sheet at index {sheet_index} not found.")

import xlrd

file_path = 'example.xlsx'

try:
    with xlrd.open_workbook(file_path) as workbook:
        sheet = workbook.sheet_by_index(0)
        # 进行其他操作
except FileNotFoundError:
    print(f"Error: File '{file_path}' not found.")

import xlrd
from datetime import datetime

def read_excel_date(cell):
    try:
        date_value = xlrd.xldate_as_datetime(cell, 0)  # 0表示使用1900年日期基准
        return date_value.strftime('%Y-%m-%d')
    except xlrd.xldate.XLDateError:
        return "Invalid Date"

workbook = xlrd.open_workbook('dates.xlsx')
sheet = workbook.sheet_by_index(0)

date_cell = sheet.cell_value(1, 0)
formatted_date = read_excel_date(date_cell)
print(f'Formatted Date: {formatted_date}')

pip install pandas

import pandas as pd

# 读取 Excel 文件
df = pd.read_excel('example.xlsx', sheet_name='Sheet1')

# 打印前几行数据
print(df.head())

# 在现有数据框上进行处理
df['New_Column'] = df['Old_Column'] * 2

# 写入到新的 Excel 文件
df.to_excel('output_with_pandas.xlsx', index=False)

# 创建虚拟环境
python -m venv myenv

# 激活虚拟环境
source myenv/bin/activate  # 在 Linux/macOS 中
myenv\Scripts\activate.bat  # 在 Windows 中

# 安装依赖
pip install xlrd xlwt pandas

# 生成 requirements.txt 文件
pip freeze > requirements.txt

# 安装依赖
pip install -r requirements.txt

import matplotlib.pyplot as plt
import seaborn as sns

# 示例：绘制柱状图
sns.barplot(x='Category', y='Value', data=df)
plt.show()

pip install tensorflow pytorch

pip install numpy scipy

pip install scikit-learn

pip install beautifulsoup4 scrapy