Python中openpyxl和pandas的使用示例详解

pip install openpyxl

pip install openpyxl[charts]

from openpyxl import workbook
# 创建一个新工作簿
wb = workbook()
# 获取活动的工作表
ws = wb.active
# 设置工作表标题
ws.title = "第一个工作表"
# 创建新的工作表
ws1 = wb.create_sheet("第二个工作表")  # 默认插入到最后
ws2 = wb.create_sheet("第三个工作表", 0)  # 插入到第一个位置
# 保存工作簿
wb.save("新工作簿.xlsx")

from openpyxl import load_workbook
# 打开一个已存在的工作簿
wb = load_workbook('example.xlsx')
# 获取所有工作表名称
print(wb.sheetnames)
# 通过名称获取工作表
ws = wb['sheet1']
# 检查工作表是否存在
if 'sheet1' in wb.sheetnames:
    print("sheet1存在")

# 复制工作表
source = wb['sheet1']
target = wb.copy_worksheet(source)
target.title = "sheet1的副本"
# 删除工作表
del wb['sheet1的副本']
# 遍历所有工作表
for sheet in wb:
    print(sheet.title)

# 获取单元格
cell = ws['a1']
# 写入值
ws['a1'] = "hello"
ws['b1'] = "world"
# 使用单元格坐标
ws.cell(row=1, column=3, value="!")
# 读取值
print(ws['a1'].value)  # 输出: hello
# 单元格坐标
print(cell.row, cell.column)  # 输出: 1, 1
print(cell.coordinate)  # 输出: a1

# 批量写入数据
for row in range(1, 6):
    for col in range(1, 5):
        ws.cell(row=row, column=col, value=f"r{row}c{col}")
# 批量读取数据
for row in ws.iter_rows(min_row=1, max_row=3, min_col=1, max_col=3):
    for cell in row:
        print(cell.value, end="\t")
    print()
# 使用values_only参数只获取值
for row in ws.iter_rows(values_only=true):
    print(row)

# 合并单元格
ws.merge_cells('a1:d1')
ws['a1'] = "合并的标题"
# 取消合并
ws.unmerge_cells('a1:d1')
# 设置行高和列宽
ws.row_dimensions[1].height = 30
ws.column_dimensions['a'].width = 20
# 隐藏行列
ws.column_dimensions['b'].hidden = true
ws.row_dimensions[2].hidden = true

from openpyxl.styles import font, color
# 设置字体
bold_font = font(name='arial', size=12, bold=true, italic=false, color='ff0000')
ws['a1'].font = bold_font
# 修改现有单元格字体
for row in ws.iter_rows(min_row=2, max_row=5):
    for cell in row:
        cell.font = font(size=10, color=color(rgb='0000ff'))

from openpyxl.styles import alignment
# 设置对齐方式
center_aligned = alignment(horizontal='center', vertical='center', wrap_text=true)
ws['a1'].alignment = center_aligned
# 应用到范围
for row in ws.iter_rows(min_row=1, max_row=10):
    for cell in row:
        cell.alignment = alignment(horizontal='center')

from openpyxl.styles import border, side
# 定义边框样式
thin_border = border(left=side(style='thin'), 
                     right=side(style='thin'),
                     top=side(style='thin'),
                     bottom=side(style='thin'))
# 应用边框
for row in ws.iter_rows(min_row=1, max_row=5):
    for cell in row:
        cell.border = thin_border

from openpyxl.styles import patternfill, gradientfill
# 纯色填充
yellow_fill = patternfill(start_color='ffff00', end_color='ffff00', fill_type='solid')
ws['a1'].fill = yellow_fill
# 渐变填充
gradient_fill = gradientfill(stop=('ffffff', '0000ff'))
ws['b1'].fill = gradient_fill

from openpyxl.styles import numbers
# 设置数字格式
ws['c1'].value = 3.1415926
ws['c1'].number_format = numbers.format_number_00  # 显示两位小数
# 自定义格式
ws['d1'].value = 0.85
ws['d1'].number_format = '0.00%'  # 显示为百分比

# 写入公式
ws['e1'] = '=sum(a1:d1)'
ws['e2'] = '=average(a2:d2)'
ws['e3'] = '=if(a3>b3, "a大", "b大")'
# 读取公式
print(ws['e1'].value)  # 输出: =sum(a1:d1)
# 计算数据
ws['f1'] = '结果'
ws['f2'] = '=e2*100'

from openpyxl.chart import barchart, reference
# 准备数据
for i in range(1, 6):
    ws[f'a{i}'] = i
    ws[f'b{i}'] = i*i
# 创建柱状图
chart = barchart()
data = reference(ws, min_col=2, min_row=1, max_row=5)
categories = reference(ws, min_col=1, min_row=2, max_row=5)
chart.add_data(data, titles_from_data=true)
chart.set_categories(categories)
# 添加图表到工作表
ws.add_chart(chart, "d1")

from openpyxl.drawing.image import image
# 插入图像
img = image('logo.png')
ws.add_image(img, 'a10')
# 调整图像大小
img.width = 100
img.height = 100

from openpyxl.worksheet.datavalidation import datavalidation
# 创建数据验证
dv = datavalidation(type="list", formula1='"男,女"', allow_blank=true)
dv.add('a1:a10')  # 应用到a1:a10范围
ws.add_data_validation(dv)
# 数字范围验证
dv_num = datavalidation(type="whole", operator="between", formula1=1, formula2=100)
dv_num.error = "输入必须在1到100之间"
dv_num.add('b1:b10')
ws.add_data_validation(dv_num)

from openpyxl.formatting.rule import colorscalerule, formularule
# 色阶条件格式
color_scale_rule = colorscalerule(start_type='min', start_color='ff0000',
                                 mid_type='percentile', mid_value=50, mid_color='ffff00',
                                 end_type='max', end_color='00ff00')
ws.conditional_formatting.add('c1:c10', color_scale_rule)
# 公式条件格式
formula_rule = formularule(formula=['isblank(c1)'], stopiftrue=true, font=font(color='ff0000'))
ws.conditional_formatting.add('c1:c10', formula_rule)

# 保护工作表
ws.protection.sheet = true
ws.protection.password = 'password'
ws.protection.enable()
# 解锁特定单元格
for row in ws.iter_rows(min_row=1, max_row=5):
    for cell in row:
        cell.protection = protection(locked=false)

# 冻结第一行和第一列
ws.freeze_panes = 'b2'
# 解冻
ws.freeze_panes = none

pip install pandas openpyxl xlrd

import pandas as pd
# 从字典创建
data = {
    '姓名': ['张三', '李四', '王五'],
    '年龄': [25, 30, 35],
    '城市': ['北京', '上海', '广州']
}
df = pd.dataframe(data)
# 从列表创建
data = [
    {'姓名': '张三', '年龄': 25, '城市': '北京'},
    {'姓名': '李四', '年龄': 30, '城市': '上海'},
    {'姓名': '王五', '年龄': 35, '城市': '广州'}
]
df = pd.dataframe(data)
# 显示dataframe
print(df)

# 查看前几行
print(df.head(2))
# 查看后几行
print(df.tail(1))
# 查看形状
print(df.shape)  # 输出: (3, 3)
# 查看列名
print(df.columns)  # 输出: index(['姓名', '年龄', '城市'], dtype='object')
# 查看索引
print(df.index)  # 输出: rangeindex(start=0, stop=3, step=1)
# 查看数据类型
print(df.dtypes)

# 选择列
print(df['姓名'])  # 选择单列
print(df[['姓名', '年龄']])  # 选择多列
# 选择行
print(df.iloc[0])  # 通过位置选择
print(df.loc[0])  # 通过索引选择
print(df[1:3])  # 切片选择
# 条件选择
print(df[df['年龄'] > 28])
print(df[(df['年龄'] > 25) & (df['城市'] == '上海')])

# 添加列
df['性别'] = ['男', '女', '男']
# 修改值
df.loc[0, '年龄'] = 26
df['年龄'] = df['年龄'] + 1
# 删除列
df = df.drop('性别', axis=1)
# 删除行
df = df.drop(0, axis=0)

# 读取整个excel文件
df = pd.read_excel('data.xlsx', sheet_name='sheet1')
# 读取指定范围
df = pd.read_excel('data.xlsx', sheet_name='sheet1', usecols='a:c', nrows=10)
# 读取多个工作表
with pd.excelfile('data.xlsx') as xls:
    df1 = pd.read_excel(xls, 'sheet1')
    df2 = pd.read_excel(xls, 'sheet2')
# 处理缺失值
df = pd.read_excel('data.xlsx', na_values=['na', 'n/a', '缺失'])

# 写入单个dataframe
df.to_excel('output.xlsx', sheet_name='数据', index=false)
# 写入多个dataframe
with pd.excelwriter('output.xlsx') as writer:
    df1.to_excel(writer, sheet_name='sheet1')
    df2.to_excel(writer, sheet_name='sheet2')
# 追加模式写入
with pd.excelwriter('output.xlsx', mode='a') as writer:
    df3.to_excel(writer, sheet_name='sheet3')
# 设置格式
df.to_excel('output.xlsx', 
            sheet_name='数据',
            index=false,
            float_format="%.2f",  # 浮点数格式
            freeze_panes=(1, 0))  # 冻结首行

# csv文件
df.to_csv('data.csv', index=false)
df = pd.read_csv('data.csv')
# json
df.to_json('data.json', orient='records')
df = pd.read_json('data.json')
# sql数据库
from sqlalchemy import create_engine
engine = create_engine('sqlite:///data.db')
df.to_sql('table_name', engine, if_exists='replace')
df = pd.read_sql('select * from table_name', engine)

# 检测缺失值
print(df.isnull().sum())
# 删除缺失值
df_cleaned = df.dropna()  # 删除任何包含缺失值的行
df_cleaned = df.dropna(subset=['年龄'])  # 只删除年龄列有缺失的行
# 填充缺失值
df_filled = df.fillna(0)  # 用0填充
df_filled = df.fillna(df.mean())  # 用均值填充数值列
df_filled = df.fillna(method='ffill')  # 用前一个值填充

# 检测重复行
print(df.duplicated().sum())
# 删除重复行
df_unique = df.drop_duplicates()
# 基于某些列删除重复
df_unique = df.drop_duplicates(subset=['姓名', '城市'])

# 查看数据类型
print(df.dtypes)
# 转换数据类型
df['年龄'] = df['年龄'].astype('float64')
df['日期'] = pd.to_datetime(df['日期'])
# 分类数据
df['城市'] = df['城市'].astype('category')

# 字符串方法
df['姓名'] = df['姓名'].str.upper()  # 转为大写
df['城市'] = df['城市'].str.replace('京', '都')  # 替换
# 提取信息
df['姓氏'] = df['姓名'].str[0]  # 提取第一个字符
df['名字'] = df['姓名'].str[1:]  # 提取第二个字符之后
# 拆分列
df[['姓', '名']] = df['姓名'].str.split(expand=true)

# 解析日期
df['日期'] = pd.to_datetime(df['日期'], format='%y-%m-%d')
# 提取日期部分
df['年'] = df['日期'].dt.year
df['月'] = df['日期'].dt.month
df['日'] = df['日期'].dt.day
df['星期'] = df['日期'].dt.day_name()
# 日期运算
df['年龄天数'] = (pd.to_datetime('today') - df['出生日期']).dt.days
df['年龄'] = df['年龄天数'] // 365

# 应用简单函数
df['年龄加10'] = df['年龄'].apply(lambda x: x + 10)
# 应用复杂函数
def age_group(age):
    if age < 20: return '少年'
    elif age < 40: return '青年'
    else: return '中年'
df['年龄段'] = df['年龄'].apply(age_group)
# 向量化操作
df['bmi'] = df['体重'] / (df['身高']/100)**2

# 基本分组
grouped = df.groupby('城市')
# 聚合函数
print(grouped['年龄'].mean())  # 每个城市的平均年龄
print(grouped.agg({'年龄': ['mean', 'min', 'max'], '收入': 'sum'}))
# 多级分组
grouped = df.groupby(['城市', '性别'])
print(grouped['年龄'].mean())

# 简单透视表
pivot = pd.pivot_table(df, values='年龄', index='城市', aggfunc='mean')
# 复杂透视表
pivot = pd.pivot_table(df, 
                      values=['年龄', '收入'],
                      index=['城市', '性别'],
                      columns=['教育程度'],
                      aggfunc={'年龄': 'mean', '收入': ['sum', 'count']},
                      fill_value=0,
                      margins=true)

# 合并两个dataframe
df1 = pd.dataframe({'key': ['a', 'b', 'c'], 'value': [1, 2, 3]})
df2 = pd.dataframe({'key': ['a', 'b', 'd'], 'value': [4, 5, 6]})
# 内连接
pd.merge(df1, df2, on='key', how='inner')
# 左连接
pd.merge(df1, df2, on='key', how='left')
# 外连接
pd.merge(df1, df2, on='key', how='outer')
# 纵向合并
pd.concat([df1, df2], axis=0)

# 创建时间序列
date_rng = pd.date_range(start='1/1/2020', end='1/10/2020', freq='d')
df = pd.dataframe(date_rng, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng)))
# 设置为索引
df = df.set_index('date')
# 重采样
df.resample('w').mean()  # 按周平均
df.resample('m').sum()  # 按月求和
# 滚动窗口
df.rolling(window=3).mean()  # 3天移动平均

# 描述性统计
print(df.describe())
# 相关性
print(df.corr())
# 协方差
print(df.cov())
# 唯一值计数
print(df['城市'].value_counts())
# 交叉表
pd.crosstab(df['城市'], df['性别'])

import matplotlib.pyplot as plt
# 线图
df.plot.line()
# 柱状图
df.plot.bar()
# 直方图
df['年龄'].plot.hist(bins=20)
# 箱线图
df.plot.box()
# 散点图
df.plot.scatter(x='年龄', y='收入')
plt.show()

from openpyxl import load_workbook
from openpyxl.utils.dataframe import dataframe_to_rows
# 创建dataframe
df = pd.dataframe({
    '产品': ['a', 'b', 'c', 'd'],
    '销量': [120, 150, 90, 200],
    '单价': [25.5, 32.0, 18.0, 40.5]
})
# 加载现有工作簿或创建新工作簿
try:
    wb = load_workbook('report.xlsx')
except filenotfounderror:
    wb = workbook()
# 选择或创建工作表
if '销售报告' in wb.sheetnames:
    ws = wb['销售报告']
else:
    ws = wb.create_sheet('销售报告')
# 清空现有内容
ws.delete_rows(1, ws.max_row)
# 写入dataframe数据
for r in dataframe_to_rows(df, index=false, header=true):
    ws.append(r)
# 添加公式
ws['e1'] = '销售额'
for row in range(2, ws.max_row + 1):
    ws[f'e{row}'] = f'=b{row}*c{row}'
# 设置格式
header_font = font(bold=true, color='ffffff')
header_fill = patternfill(start_color='4f81bd', end_color='4f81bd', fill_type='solid')
for cell in ws[1]:
    cell.font = header_font
    cell.fill = header_fill
# 保存工作簿
wb.save('report.xlsx')

from openpyxl import load_workbook
# 加载工作簿
wb = load_workbook('data.xlsx')
ws = wb.active
# 将工作表数据转换为列表
data = []
for row in ws.iter_rows(values_only=true):
    data.append(row)
# 转换为dataframe
df = pd.dataframe(data[1:], columns=data[0])
# 处理数据
df['日期'] = pd.to_datetime(df['日期'])
df['销售额'] = df['数量'] * df['单价']
print(df.head())

import pandas as pd
from openpyxl import workbook
from openpyxl.styles import font, alignment, border, side, patternfill
from openpyxl.chart import barchart, reference
from openpyxl.drawing.image import image
from datetime import datetime
# 1. 准备数据
sales_data = {
    '月份': ['1月', '2月', '3月', '4月', '5月', '6月'],
    '产品a': [1200, 1500, 1800, 2100, 2400, 2700],
    '产品b': [800, 950, 1100, 1250, 1400, 1550],
    '产品c': [500, 600, 700, 800, 900, 1000]
}
df = pd.dataframe(sales_data)
# 2. 创建excel工作簿
wb = workbook()
ws = wb.active
ws.title = "销售报告"
# 3. 写入标题
ws['a1'] = "2023年上半年销售报告"
ws['a1'].font = font(size=16, bold=true, name='微软雅黑')
ws.merge_cells('a1:d1')
# 4. 写入数据
# 写入列标题
columns = list(df.columns)
for col_num, column_title in enumerate(columns, 1):
    cell = ws.cell(row=3, column=col_num, value=column_title)
    cell.font = font(bold=true)
    cell.alignment = alignment(horizontal='center')
# 写入数据
for row_num, row_data in enumerate(df.values, 4):
    for col_num, cell_value in enumerate(row_data, 1):
        ws.cell(row=row_num, column=col_num, value=cell_value)
# 5. 添加汇总行
last_row = ws.max_row + 1
ws.cell(row=last_row, column=1, value="总计").font = font(bold=true)
for col_num in range(2, 5):
    col_letter = chr(64 + col_num)
    ws.cell(row=last_row, column=col_num, 
           value=f"=sum({col_letter}4:{col_letter}{last_row-1})")
# 6. 设置格式
# 设置边框
thin_border = border(left=side(style='thin'), 
                     right=side(style='thin'),
                     top=side(style='thin'),
                     bottom=side(style='thin'))
for row in ws.iter_rows(min_row=3, max_row=last_row, min_col=1, max_col=4):
    for cell in row:
        cell.border = thin_border
        if cell.row > 3 and cell.column > 1:  # 数据单元格
            cell.number_format = '#,##0'
# 设置对齐
for row in ws.iter_rows(min_row=3, max_row=last_row, min_col=1, max_col=4):
    for cell in row:
        cell.alignment = alignment(horizontal='center')
# 7. 创建图表
chart = barchart()
chart.type = "col"
chart.style = 10
chart.title = "产品销售趋势"
chart.y_axis.title = "销售额"
chart.x_axis.title = "月份"
data = reference(ws, min_col=2, max_col=4, min_row=3, max_row=last_row-1)
categories = reference(ws, min_col=1, min_row=4, max_row=last_row-1)
chart.add_data(data, titles_from_data=true)
chart.set_categories(categories)
ws.add_chart(chart, "f3")
# 8. 添加页脚
footer_row = last_row + 2
ws.cell(row=footer_row, column=1, 
       value=f"报告生成时间: {datetime.now().strftime('%y-%m-%d %h:%m:%s')}")
# 9. 调整列宽
ws.column_dimensions['a'].width = 12
ws.column_dimensions['b'].width = 12
ws.column_dimensions['c'].width = 12
ws.column_dimensions['d'].width = 12
# 10. 保存文件
filename = f"销售报告_{datetime.now().strftime('%y%m%d')}.xlsx"
wb.save(filename)
print(f"报表已生成: {filename}")

from openpyxl import load_workbook
from openpyxl.utils.dataframe import dataframe_to_rows
import pandas as pd
# 1. 加载模板文件
template_path = 'report_template.xlsx'
wb = load_workbook(template_path)
ws = wb['data']
# 2. 准备数据
data = {
    'region': ['north', 'south', 'east', 'west'],
    'q1': [1200, 1500, 1800, 900],
    'q2': [1300, 1600, 1900, 950],
    'q3': [1400, 1700, 2000, 1000],
    'q4': [1500, 1800, 2100, 1050]
}
df = pd.dataframe(data)
# 3. 清空模板中的数据区域 (保留格式)
for row in ws.iter_rows(min_row=3, max_row=100, min_col=1, max_col=6):
    for cell in row:
        cell.value = none
# 4. 写入新数据
for r_idx, row in enumerate(dataframe_to_rows(df, index=false, header=false), 3):
    for c_idx, value in enumerate(row, 1):
        ws.cell(row=r_idx, column=c_idx, value=value)
# 5. 更新公式 (假设模板中已有公式)
last_data_row = 3 + len(df) - 1
for row in range(3, last_data_row + 1):
    ws[f'f{row}'] = f'=sum(b{row}:e{row})'
# 6. 更新汇总公式
ws['b20'] = f'=sum(b3:b{last_data_row})'
ws['c20'] = f'=sum(c3:c{last_data_row})'
ws['d20'] = f'=sum(d3:d{last_data_row})'
ws['e20'] = f'=sum(e3:e{last_data_row})'
ws['f20'] = f'=sum(f3:f{last_data_row})'
# 7. 更新报告日期
ws['a1'] = f"销售报告 - {pd.timestamp.today().strftime('%y-%m-%d')}"
# 8. 保存新文件
output_path = 'quarterly_report.xlsx'
wb.save(output_path)
print(f"报告已生成: {output_path}")

wb = load_workbook('large_file.xlsx', read_only=true)

wb = workbook(write_only=true)
ws = wb.create_sheet()
for row in data:
    ws.append(row)

wb = load_workbook('file.xlsx', data_only=false)
wb.calculation = false
# ... 写入公式 ...
wb.calculation = true
wb.save('file.xlsx')

del large_df
del wb

import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import font, alignment, numbers
from datetime import datetime
def generate_financial_report(input_path, output_path):
    # 1. 使用pandas读取和处理数据
    df = pd.read_excel(input_path, sheet_name='transactions')
    # 数据清洗
    df = df.dropna(subset=['amount'])
    df['date'] = pd.to_datetime(df['date'])
    df['month'] = df['date'].dt.to_period('m')
    # 分类汇总
    income = df[df['type'] == 'income'].groupby('month')['amount'].sum()
    expense = df[df['type']