使用pandas轻松将数据导出为PDF格式从基础操作到高级技巧全面解析让你的数据分析报告更专业

威震华夏关云长 · 发表于 2025-9-17 15:20:05

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在数据分析工作中，我们经常需要将分析结果以专业、易读的格式呈现给他人。PDF格式因其跨平台兼容性和格式稳定性，成为报告分享的首选。Pandas作为Python数据分析的核心库，提供了强大的数据处理能力，但本身并不直接支持PDF导出功能。本文将详细介绍如何结合pandas和其他Python库，将数据轻松导出为专业的PDF格式报告。

准备工作

在开始之前，我们需要安装一些必要的Python库。主要需要的库包括：

• pandas：用于数据处理
• matplotlib和seaborn：用于数据可视化
• reportlab：用于PDF生成
• PyPDF2：用于PDF操作
• tabulate：用于表格格式化
• weasyprint：用于HTML/CSS到PDF的转换

可以通过以下命令安装这些库：

pip install pandas matplotlib seaborn reportlab PyPDF2 tabulate weasyprint

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基础操作

使用DataFrame.to_string()和reportlab

最简单的方法是使用DataFrame的to_string()方法将数据转换为字符串，然后使用reportlab将其写入PDF。

import pandas as pd
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.units import inch
# 创建示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, 30, 35, 40],
'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']}
df = pd.DataFrame(data)
# 将DataFrame转换为字符串
df_string = df.to_string()
# 创建PDF
pdf_file = "basic_output.pdf"
c = canvas.Canvas(pdf_file, pagesize=letter)
# 设置字体
c.setFont("Helvetica", 12)
# 添加标题
c.drawString(inch, 10*inch, "Basic DataFrame Export")
# 添加DataFrame内容
text_object = c.beginText(inch, 9*inch)
text_object.textLines(df_string)
c.drawText(text_object)
# 保存PDF
c.save()

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使用tabulate和reportlab

tabulate库可以帮助我们创建更美观的表格格式：

import pandas as pd
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.units import inch
from tabulate import tabulate
# 创建示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, 30, 35, 40],
'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']}
df = pd.DataFrame(data)
# 使用tabulate格式化DataFrame
table = tabulate(df, headers='keys', tablefmt='grid')
# 创建PDF
pdf_file = "tabulate_output.pdf"
c = canvas.Canvas(pdf_file, pagesize=letter)
# 设置字体
c.setFont("Helvetica", 10)
# 添加标题
c.drawString(inch, 10*inch, "DataFrame Export with Tabulate")
# 添加表格
text_object = c.beginText(inch, 9*inch)
text_object.textLines(table)
c.drawText(text_object)
# 保存PDF
c.save()

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使用matplotlib和PDF后端

matplotlib支持将图形直接保存为PDF格式，我们可以利用这一点来导出表格：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# 创建示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, 30, 35, 40],
'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']}
df = pd.DataFrame(data)
# 创建图形
fig, ax = plt.subplots(figsize=(8, 6))
ax.axis('off') # 隐藏坐标轴
# 创建表格
table = ax.table(cellText=df.values,
colLabels=df.columns,
cellLoc='center',
loc='center')
# 设置表格样式
table.auto_set_font_size(False)
table.set_fontsize(12)
table.scale(1, 1.5)
# 添加标题
plt.title("DataFrame Export with Matplotlib", fontsize=16, pad=20)
# 保存为PDF
plt.savefig("matplotlib_output.pdf", bbox_inches='tight')
plt.close()

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中级技巧

使用reportlab的Table类

reportlab提供了更高级的表格功能，可以让我们更好地控制表格的样式和布局：

import pandas as pd
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
# 创建示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, 30, 35, 40],
'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']}
df = pd.DataFrame(data)
# 准备数据
data_for_table = [df.columns.tolist()] + df.values.tolist()
# 创建PDF文档
doc = SimpleDocTemplate("reportlab_table.pdf", pagesize=letter)
elements = []
# 添加标题
styles = getSampleStyleSheet()
title = Paragraph("DataFrame Export with ReportLab Table", styles['Title'])
elements.append(title)
# 创建表格
table = Table(data_for_table)
# 设置表格样式
style = TableStyle([
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 14),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
('BACKGROUND', (0, 1), (-1, -1), colors.beige),
('FONTNAME', (0, 1), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 1), (-1, -1), 12),
('GRID', (0, 0), (-1, -1), 1, colors.black)
])
table.setStyle(style)
# 添加表格到文档
elements.append(table)
# 构建PDF
doc.build(elements)

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添加样式和格式化

我们可以进一步自定义表格的样式，包括颜色、字体、边框等：

import pandas as pd
from reportlab.lib.pagesizes import letter, landscape
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
from reportlab.lib.units import inch
# 创建示例DataFrame
data = {'Product': ['A', 'B', 'C', 'D', 'E'],
'Sales': [1200, 1800, 1500, 2100, 1900],
'Profit': [300, 450, 375, 525, 475],
'Margin': [0.25, 0.25, 0.25, 0.25, 0.25]}
df = pd.DataFrame(data)
# 格式化数值列
df['Sales'] = df['Sales'].map('${:,.2f}'.format)
df['Profit'] = df['Profit'].map('${:,.2f}'.format)
df['Margin'] = df['Margin'].map('{:.2%}'.format)
# 准备数据
data_for_table = [df.columns.tolist()] + df.values.tolist()
# 创建PDF文档（横向布局）
doc = SimpleDocTemplate("styled_table.pdf", pagesize=landscape(letter))
elements = []
# 添加标题
styles = getSampleStyleSheet()
title = Paragraph("Sales Report with Styled Table", styles['Title'])
elements.append(title)
elements.append(Spacer(1, 0.25*inch))
# 添加描述
description = Paragraph("This report shows the sales performance of different products.", styles['Normal'])
elements.append(description)
elements.append(Spacer(1, 0.25*inch))
# 创建表格
table = Table(data_for_table, colWidths=[1.5*inch, 1.5*inch, 1.5*inch, 1.5*inch])
# 设置表格样式
style = TableStyle([
# 标题行样式
('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
('ALIGN', (0, 0), (-1, 0), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 14),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
# 数据行样式
('ALIGN', (0, 1), (-1, -1), 'CENTER'),
('FONTNAME', (0, 1), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 1), (-1, -1), 12),
# 交替行颜色
('BACKGROUND', (0, 1), (-1, 1), colors.lightgrey),
('BACKGROUND', (0, 2), (-1, 2), colors.white),
('BACKGROUND', (0, 3), (-1, 3), colors.lightgrey),
('BACKGROUND', (0, 4), (-1, 4), colors.white),
('BACKGROUND', (0, 5), (-1, 5), colors.lightgrey),
# 边框
('GRID', (0, 0), (-1, -1), 1, colors.black),
('BOX', (0, 0), (-1, -1), 2, colors.black),
# 特定单元格样式（高亮最高销售额）
('BACKGROUND', (1, 3), (1, 3), colors.lightgreen),
])
table.setStyle(style)
# 添加表格到文档
elements.append(table)
# 添加页脚
footer = Paragraph("Generated on: " + pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"), styles['Normal'])
elements.append(Spacer(1, 0.5*inch))
elements.append(footer)
# 构建PDF
doc.build(elements)

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使用HTML和CSS与weasyprint

weasyprint允许我们使用HTML和CSS来设计PDF，这为那些熟悉Web开发的人提供了更灵活的选项：

import pandas as pd
import weasyprint
# 创建示例DataFrame
data = {'Product': ['A', 'B', 'C', 'D', 'E'],
'Sales': [1200, 1800, 1500, 2100, 1900],
'Profit': [300, 450, 375, 525, 475],
'Margin': [0.25, 0.25, 0.25, 0.25, 0.25]}
df = pd.DataFrame(data)
# 格式化数值列
df['Sales'] = df['Sales'].map('${:,.2f}'.format)
df['Profit'] = df['Profit'].map('${:,.2f}'.format)
df['Margin'] = df['Margin'].map('{:.2%}'.format)
# 将DataFrame转换为HTML表格
html_table = df.to_html(classes='styled-table', index=False)
# 创建完整的HTML文档
html_content = f"""
<!DOCTYPE html>
<html>
<head>
<title>Sales Report</title>
<style>
body {{
font-family: Arial, sans-serif;
margin: 20px;
}}
h1 {{
color: #2c3e50;
text-align: center;
}}
.styled-table {{
border-collapse: collapse;
margin: 25px 0;
font-size: 0.9em;
font-family: sans-serif;
min-width: 400px;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
}}
.styled-table thead tr {{
background-color: #009879;
color: #ffffff;
text-align: left;
}}
.styled-table th,
.styled-table td {{
padding: 12px 15px;
text-align: center;
}}
.styled-table tbody tr {{
border-bottom: 1px solid #dddddd;
}}
.styled-table tbody tr:nth-of-type(even) {{
background-color: #f3f3f3;
}}
.styled-table tbody tr:last-of-type {{
border-bottom: 2px solid #009879;
}}
.styled-table tbody tr.active-row {{
font-weight: bold;
color: #009879;
}}
.footer {{
text-align: right;
font-size: 10px;
color: #777;
margin-top: 30px;
}}
</style>
</head>
<body>
<h1>Sales Report</h1>
{html_table}
<div class="footer">
Generated on: {pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S")}
</div>
</body>
</html>
"""
# 使用weasyprint将HTML转换为PDF
weasyprint.HTML(string=html_content).write_pdf("html_css_output.pdf")

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高级技巧

创建多页PDF

当数据量很大时，我们需要将表格分布在多个页面上：

import pandas as pd
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, PageBreak
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
from reportlab.lib.units import inch
import numpy as np
# 创建大型示例DataFrame
np.random.seed(42)
products = [f'Product-{i}' for i in range(1, 101)]
sales = np.random.randint(100, 5000, size=100)
profit = np.random.randint(10, 1000, size=100)
margin = profit / sales
df = pd.DataFrame({
'Product': products,
'Sales': sales,
'Profit': profit,
'Margin': margin
})
# 格式化数值列
df['Sales'] = df['Sales'].map('${:,.2f}'.format)
df['Profit'] = df['Profit'].map('${:,.2f}'.format)
df['Margin'] = df['Margin'].map('{:.2%}'.format)
# 创建PDF文档
doc = SimpleDocTemplate("multipage_table.pdf", pagesize=letter)
elements = []
# 添加标题
styles = getSampleStyleSheet()
title = Paragraph("Multi-Page Sales Report", styles['Title'])
elements.append(title)
# 每页显示的行数（不包括标题行）
rows_per_page = 20
# 计算总页数
total_pages = (len(df) - 1) // rows_per_page + 1
# 分页创建表格
for page_num in range(total_pages):
start_idx = page_num * rows_per_page
end_idx = min((page_num + 1) * rows_per_page, len(df))
# 获取当前页的数据
page_data = df.iloc[start_idx:end_idx]
# 准备表格数据
if page_num == 0: # 第一页包含标题行
data_for_table = [df.columns.tolist()] + page_data.values.tolist()
else:
data_for_table = page_data.values.tolist()
# 创建表格
table = Table(data_for_table, colWidths=[1.5*inch, 1.5*inch, 1.5*inch, 1.5*inch])
# 设置表格样式
style = TableStyle([
# 标题行样式（仅第一页）
*([('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
('ALIGN', (0, 0), (-1, 0), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 14),
('BOTTOMPADDING', (0, 0), (-1, 0), 12)] if page_num == 0 else []),
# 数据行样式
('ALIGN', (0, 0 if page_num > 0 else 1), (-1, -1), 'CENTER'),
('FONTNAME', (0, 0 if page_num > 0 else 1), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 0 if page_num > 0 else 1), (-1, -1), 10),
# 交替行颜色
*[('BACKGROUND', (0, i), (-1, i), colors.lightgrey)
for i in range(1 if page_num == 0 else 0, len(data_for_table), 2)],
# 边框
('GRID', (0, 0 if page_num > 0 else 1), (-1, -1), 1, colors.black),
('BOX', (0, 0 if page_num > 0 else 1), (-1, -1), 2, colors.black),
])
table.setStyle(style)
# 添加表格到文档
elements.append(table)
# 添加页码
page_info = Paragraph(f"Page {page_num + 1} of {total_pages}", styles['Normal'])
elements.append(page_info)
# 如果不是最后一页，添加分页符
if page_num < total_pages - 1:
elements.append(PageBreak())
# 构建PDF
doc.build(elements)

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添加图表和可视化

在报告中添加图表可以使数据更加直观：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Image, Spacer
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
from reportlab.lib.units import inch
import io
# 创建示例DataFrame
np.random.seed(42)
categories = ['Category A', 'Category B', 'Category C', 'Category D', 'Category E']
values = np.random.randint(10, 100, size=5)
df = pd.DataFrame({
'Category': categories,
'Value': values
})
# 创建图表
plt.figure(figsize=(8, 6))
bars = plt.bar(df['Category'], df['Value'], color='skyblue')
plt.title('Category Values', fontsize=16)
plt.xlabel('Category', fontsize=12)
plt.ylabel('Value', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
# 在柱状图上添加数值标签
for bar in bars:
height = bar.get_height()
plt.text(bar.get_x() + bar.get_width()/2., height,
f'{height}',
ha='center', va='bottom')
# 将图表保存到内存中
img_buffer = io.BytesIO()
plt.savefig(img_buffer, format='png', dpi=300, bbox_inches='tight')
plt.close()
# 将缓冲区位置重置到开始
img_buffer.seek(0)
# 创建PDF文档
doc = SimpleDocTemplate("report_with_chart.pdf", pagesize=letter)
elements = []
# 添加标题
styles = getSampleStyleSheet()
title = Paragraph("Report with Chart and Table", styles['Title'])
elements.append(title)
elements.append(Spacer(1, 0.25*inch))
# 添加图表
chart = Image(img_buffer, width=6*inch, height=4.5*inch)
elements.append(chart)
elements.append(Spacer(1, 0.5*inch))
# 准备表格数据
data_for_table = [df.columns.tolist()] + df.values.tolist()
# 创建表格
table = Table(data_for_table, colWidths=[3*inch, 2*inch])
# 设置表格样式
style = TableStyle([
# 标题行样式
('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
('ALIGN', (0, 0), (-1, 0), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 14),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
# 数据行样式
('ALIGN', (0, 1), (-1, -1), 'CENTER'),
('FONTNAME', (0, 1), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 1), (-1, -1), 12),
# 交替行颜色
('BACKGROUND', (0, 1), (-1, 1), colors.lightgrey),
('BACKGROUND', (0, 2), (-1, 2), colors.white),
('BACKGROUND', (0, 3), (-1, 3), colors.lightgrey),
('BACKGROUND', (0, 4), (-1, 4), colors.white),
('BACKGROUND', (0, 5), (-1, 5), colors.lightgrey),
# 边框
('GRID', (0, 0), (-1, -1), 1, colors.black),
('BOX', (0, 0), (-1, -1), 2, colors.black),
])
table.setStyle(style)
# 添加表格到文档
elements.append(table)
# 添加分析文本
analysis_text = """
This report presents the values across different categories. As shown in the chart and table above,
Category D has the highest value, while Category A has the lowest. The data suggests a varied distribution
of values across all categories.
"""
analysis = Paragraph(analysis_text, styles['Normal'])
elements.append(Spacer(1, 0.5*inch))
elements.append(analysis)
# 添加页脚
footer = Paragraph("Generated on: " + pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"), styles['Normal'])
elements.append(Spacer(1, 0.5*inch))
elements.append(footer)
# 构建PDF
doc.build(elements)

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创建自定义模板和自动化报告

我们可以创建一个函数来自动化报告生成过程：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from reportlab.lib.pagesizes import letter, landscape
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Image, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.lib import colors
import io
from datetime import datetime
def generate_sales_report(data, output_filename, company_name="Your Company"):
"""
生成销售报告的函数
参数:
data -- 包含销售数据的DataFrame
output_filename -- 输出PDF文件名
company_name -- 公司名称
"""
# 创建PDF文档
doc = SimpleDocTemplate(output_filename, pagesize=landscape(letter))
elements = []
# 获取样式
styles = getSampleStyleSheet()
# 创建自定义标题样式
title_style = ParagraphStyle(
'CustomTitle',
parent=styles['Title'],
fontSize=24,
textColor=colors.darkblue,
alignment=1, # 居中
spaceAfter=30
)
# 添加标题
title = Paragraph(f"{company_name} Sales Report", title_style)
elements.append(title)
# 添加日期
date_paragraph = Paragraph(f"Report Date: {datetime.now().strftime('%Y-%m-%d')}", styles['Normal'])
elements.append(date_paragraph)
elements.append(Spacer(1, 0.5*inch))
# 创建总销售额图表
plt.figure(figsize=(10, 6))
plt.bar(data['Product'], data['Sales'], color='skyblue')
plt.title('Sales by Product', fontsize=16)
plt.xlabel('Product', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
# 在柱状图上添加数值标签
for i, v in enumerate(data['Sales']):
plt.text(i, v + max(data['Sales']) * 0.01, f"${v:,.0f}", ha='center')
# 将图表保存到内存中
img_buffer = io.BytesIO()
plt.savefig(img_buffer, format='png', dpi=300, bbox_inches='tight')
plt.close()
img_buffer.seek(0)
# 添加图表
chart = Image(img_buffer, width=7*inch, height=4*inch)
elements.append(chart)
elements.append(Spacer(1, 0.5*inch))
# 准备表格数据
table_data = [data.columns.tolist()] + data.values.tolist()
# 创建表格
table = Table(table_data, colWidths=[2*inch, 1.5*inch, 1.5*inch, 1.5*inch])
# 设置表格样式
style = TableStyle([
# 标题行样式
('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
('ALIGN', (0, 0), (-1, 0), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 14),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
# 数据行样式
('ALIGN', (0, 1), (-1, -1), 'CENTER'),
('FONTNAME', (0, 1), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 1), (-1, -1), 12),
# 交替行颜色
*[('BACKGROUND', (0, i), (-1, i), colors.lightgrey)
for i in range(1, len(table_data), 2)],
# 边框
('GRID', (0, 0), (-1, -1), 1, colors.black),
('BOX', (0, 0), (-1, -1), 2, colors.black),
])
table.setStyle(style)
# 添加表格到文档
elements.append(table)
elements.append(Spacer(1, 0.5*inch))
# 添加摘要信息
total_sales = data['Sales'].sum()
total_profit = data['Profit'].sum()
avg_margin = data['Margin'].mean()
summary_text = f"""
Summary:
Total Sales: ${total_sales:,.2f}
Total Profit: ${total_profit:,.2f}
Average Margin: {avg_margin:.2%}
Top Performing Product: {data.loc[data['Sales'].idxmax(), 'Product']} (${data['Sales'].max():,.2f})
"""
summary = Paragraph(summary_text, styles['Normal'])
elements.append(summary)
elements.append(Spacer(1, 0.5*inch))
# 创建利润率图表
plt.figure(figsize=(10, 6))
plt.bar(data['Product'], data['Margin'], color='lightgreen')
plt.title('Profit Margin by Product', fontsize=16)
plt.xlabel('Product', fontsize=12)
plt.ylabel('Margin (%)', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
# 在柱状图上添加数值标签
for i, v in enumerate(data['Margin']):
plt.text(i, v + max(data['Margin']) * 0.01, f"{v:.1%}", ha='center')
# 将图表保存到内存中
img_buffer2 = io.BytesIO()
plt.savefig(img_buffer2, format='png', dpi=300, bbox_inches='tight')
plt.close()
img_buffer2.seek(0)
# 添加图表
chart2 = Image(img_buffer2, width=7*inch, height=4*inch)
elements.append(chart2)
elements.append(Spacer(1, 0.5*inch))
# 添加页脚
footer_text = f"This report was automatically generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} by {company_name}."
footer = Paragraph(footer_text, styles['Normal'])
elements.append(footer)
# 构建PDF
doc.build(elements)
print(f"Report generated successfully: {output_filename}")
# 示例使用
if __name__ == "__main__":
# 创建示例数据
np.random.seed(42)
products = [f'Product-{chr(65+i)}' for i in range(8)]
sales = np.random.randint(10000, 50000, size=8)
profit = np.random.randint(1000, 15000, size=8)
margin = profit / sales
sales_data = pd.DataFrame({
'Product': products,
'Sales': sales,
'Profit': profit,
'Margin': margin
})
# 生成报告
generate_sales_report(sales_data, "automated_sales_report.pdf", "ABC Corporation")

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实际应用案例

销售数据分析报告

让我们创建一个完整的销售数据分析报告，包括数据汇总、趋势分析和可视化：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from reportlab.lib.pagesizes import letter, landscape
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Image, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.lib import colors
import io
from datetime import datetime, timedelta
# 创建模拟销售数据
np.random.seed(42)
date_range = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
products = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E']
regions = ['North', 'South', 'East', 'West']
# 生成数据
data = []
for date in date_range:
for product in products:
for region in regions:
# 基础销量，带有季节性和随机性
base_sales = np.random.randint(10, 100)
# 季节性因素（夏季销量更高）
seasonal_factor = 1 + 0.3 * np.sin(2 * np.pi * date.dayofyear / 365)
# 周末因素
weekend_factor = 1.2 if date.dayofweek >= 5 else 1.0
# 计算最终销量
sales = int(base_sales * seasonal_factor * weekend_factor)
# 计算收入和利润
price = np.random.uniform(10, 50)
cost = price * np.random.uniform(0.4, 0.7)
revenue = sales * price
profit = sales * (price - cost)
data.append([date, product, region, sales, price, cost, revenue, profit])
# 创建DataFrame
columns = ['Date', 'Product', 'Region', 'Sales', 'Price', 'Cost', 'Revenue', 'Profit']
sales_df = pd.DataFrame(data, columns=columns)
# 按月汇总数据
sales_df['Month'] = sales_df['Date'].dt.to_period('M')
monthly_summary = sales_df.groupby(['Month', 'Product']).agg({
'Sales': 'sum',
'Revenue': 'sum',
'Profit': 'sum'
}).reset_index()
# 按产品和地区汇总数据
product_region_summary = sales_df.groupby(['Product', 'Region']).agg({
'Sales': 'sum',
'Revenue': 'sum',
'Profit': 'sum'
}).reset_index()
# 创建PDF报告
doc = SimpleDocTemplate("sales_analysis_report.pdf", pagesize=letter)
elements = []
# 获取样式
styles = getSampleStyleSheet()
# 添加标题
title = Paragraph("2023 Sales Analysis Report", styles['Title'])
elements.append(title)
elements.append(Spacer(1, 0.25*inch))
# 添加报告日期
date_paragraph = Paragraph(f"Report Date: {datetime.now().strftime('%Y-%m-%d')}", styles['Normal'])
elements.append(date_paragraph)
elements.append(Spacer(1, 0.5*inch))
# 添加执行摘要
exec_summary = """
Executive Summary:
This report provides a comprehensive analysis of sales performance for the year 2023.
Key findings include:
- Total revenue for the year was ${:,.2f}
- Total profit was ${:,.2f}
- The best performing product was {} with ${:,.2f} in revenue
- Sales showed strong seasonal patterns, with peak performance in the summer months
""".format(
sales_df['Revenue'].sum(),
sales_df['Profit'].sum(),
sales_df.groupby('Product')['Revenue'].sum().idxmax(),
sales_df.groupby('Product')['Revenue'].sum().max()
)
summary = Paragraph(exec_summary, styles['Normal'])
elements.append(summary)
elements.append(PageBreak())
# 添加月度销售趋势图表
plt.figure(figsize=(10, 6))
monthly_trend = sales_df.groupby('Month')['Revenue'].sum()
plt.plot(monthly_trend.index.astype(str), monthly_trend.values, marker='o', linewidth=2)
plt.title('Monthly Revenue Trend', fontsize=16)
plt.xlabel('Month', fontsize=12)
plt.ylabel('Revenue ($)', fontsize=12)
plt.grid(True, linestyle='--', alpha=0.7)
plt.xticks(rotation=45)
plt.tight_layout()
# 将图表保存到内存中
img_buffer = io.BytesIO()
plt.savefig(img_buffer, format='png', dpi=300, bbox_inches='tight')
plt.close()
img_buffer.seek(0)
# 添加图表
chart = Image(img_buffer, width=7*inch, height=4*inch)
elements.append(chart)
elements.append(Spacer(1, 0.5*inch))
# 添加产品销售比较图表
plt.figure(figsize=(10, 6))
product_sales = sales_df.groupby('Product')['Revenue'].sum()
plt.bar(product_sales.index, product_sales.values, color=sns.color_palette('viridis', len(product_sales)))
plt.title('Revenue by Product', fontsize=16)
plt.xlabel('Product', fontsize=12)
plt.ylabel('Revenue ($)', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
# 在柱状图上添加数值标签
for i, v in enumerate(product_sales.values):
plt.text(i, v + max(product_sales.values) * 0.01, f"${v:,.0f}", ha='center')
plt.tight_layout()
# 将图表保存到内存中
img_buffer2 = io.BytesIO()
plt.savefig(img_buffer2, format='png', dpi=300, bbox_inches='tight')
plt.close()
img_buffer2.seek(0)
# 添加图表
chart2 = Image(img_buffer2, width=7*inch, height=4*inch)
elements.append(chart2)
elements.append(Spacer(1, 0.5*inch))
# 添加地区销售热图
region_product_pivot = sales_df.pivot_table(
values='Revenue',
index='Region',
columns='Product',
aggfunc='sum'
)
plt.figure(figsize=(10, 6))
sns.heatmap(region_product_pivot, annot=True, fmt='.0f', cmap='YlGnBu', linewidths=.5)
plt.title('Revenue Heatmap by Region and Product', fontsize=16)
plt.tight_layout()
# 将图表保存到内存中
img_buffer3 = io.BytesIO()
plt.savefig(img_buffer3, format='png', dpi=300, bbox_inches='tight')
plt.close()
img_buffer3.seek(0)
# 添加图表
chart3 = Image(img_buffer3, width=7*inch, height=4*inch)
elements.append(chart3)
elements.append(PageBreak())
# 添加产品月度销售表格
# 准备表格数据
product_month_pivot = sales_df.pivot_table(
values='Revenue',
index='Product',
columns='Month',
aggfunc='sum'
)
# 格式化数据
product_month_pivot = product_month_pivot.applymap(lambda x: f"${x:,.0f}")
# 添加总计行
product_month_pivot.loc['Total'] = product_month_pivot.sum(axis=0)
product_month_pivot['Total'] = product_month_pivot.sum(axis=1)
# 转换为列表格式
table_data = [product_month_pivot.index.tolist()] + [
[product_month_pivot.index[i]] + product_month_pivot.iloc[i].tolist()
for i in range(len(product_month_pivot))
]
# 创建表格
table = Table(table_data)
# 设置表格样式
style = TableStyle([
# 标题行样式
('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
('ALIGN', (0, 0), (-1, 0), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 10),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
# 产品列样式
('BACKGROUND', (0, 1), (0, -1), colors.lightgrey),
('ALIGN', (0, 1), (0, -1), 'LEFT'),
# 总计行样式
('BACKGROUND', (0, -1), (-1, -1), colors.darkblue),
('TEXTCOLOR', (0, -1), (-1, -1), colors.white),
('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'),
# 数据单元格样式
('ALIGN', (1, 1), (-2, -2), 'RIGHT'),
('FONTNAME', (1, 1), (-2, -2), 'Helvetica'),
('FONTSIZE', (1, 1), (-2, -2), 9),
# 边框
('GRID', (0, 0), (-1, -1), 1, colors.black),
('BOX', (0, 0), (-1, -1), 2, colors.black),
])
table.setStyle(style)
# 添加表格标题
table_title = Paragraph("Monthly Revenue by Product ($)", styles['Heading2'])
elements.append(table_title)
elements.append(Spacer(1, 0.25*inch))
# 添加表格
elements.append(table)
elements.append(PageBreak())
# 添加地区销售表格
# 准备表格数据
region_summary = sales_df.groupby('Region').agg({
'Sales': 'sum',
'Revenue': 'sum',
'Profit': 'sum'
}).reset_index()
# 计算利润率
region_summary['Profit Margin'] = region_summary['Profit'] / region_summary['Revenue']
# 格式化数据
region_summary['Sales'] = region_summary['Sales'].map('{:,.0f}'.format)
region_summary['Revenue'] = region_summary['Revenue'].map('${:,.2f}'.format)
region_summary['Profit'] = region_summary['Profit'].map('${:,.2f}'.format)
region_summary['Profit Margin'] = region_summary['Profit Margin'].map('{:.2%}'.format)
# 转换为列表格式
table_data2 = [region_summary.columns.tolist()] + region_summary.values.tolist()
# 创建表格
table2 = Table(table_data2, colWidths=[1.5*inch, 1.5*inch, 1.5*inch, 1.5*inch, 1.5*inch])
# 设置表格样式
style2 = TableStyle([
# 标题行样式
('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
('ALIGN', (0, 0), (-1, 0), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 12),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
# 数据行样式
('ALIGN', (0, 1), (-1, -1), 'CENTER'),
('FONTNAME', (0, 1), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 1), (-1, -1), 11),
# 交替行颜色
*[('BACKGROUND', (0, i), (-1, i), colors.lightgrey)
for i in range(1, len(table_data2), 2)],
# 边框
('GRID', (0, 0), (-1, -1), 1, colors.black),
('BOX', (0, 0), (-1, -1), 2, colors.black),
])
table2.setStyle(style2)
# 添加表格标题
table_title2 = Paragraph("Sales Summary by Region", styles['Heading2'])
elements.append(table_title2)
elements.append(Spacer(1, 0.25*inch))
# 添加表格
elements.append(table2)
elements.append(Spacer(1, 0.5*inch))
# 添加结论和建议
conclusions = """
Conclusions and Recommendations:
Based on the analysis of 2023 sales data, we have identified several key insights and opportunities:
1. Seasonal Trends: Sales show clear seasonal patterns with peak performance during summer months.
We recommend increasing marketing efforts and inventory levels before these peak periods.
2. Product Performance: {} has been the top performer throughout the year. Consider allocating
more resources to this product line and analyzing its success factors for application to other products.
3. Regional Differences: The {} region shows the highest profit margins. Investigate the factors
contributing to this success and consider applying these strategies to underperforming regions.
4. Growth Opportunities: The {} region shows potential for growth. Develop targeted marketing
campaigns and consider introducing product bundles tailored to this market.
""".format(
sales_df.groupby('Product')['Revenue'].sum().idxmax(),
region_summary.loc[region_summary['Profit Margin'].astype(str).str.strip('%').astype(float).idxmax(), 'Region'],
region_summary.loc[region_summary['Profit Margin'].astype(str).str.strip('%').astype(float).idxmin(), 'Region']
)
conclusion_paragraph = Paragraph(conclusions, styles['Normal'])
elements.append(conclusion_paragraph)
elements.append(Spacer(1, 0.5*inch))
# 添加页脚
footer_text = f"This report was automatically generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}."
footer = Paragraph(footer_text, styles['Normal'])
elements.append(footer)
# 构建PDF
doc.build(elements)
print("Sales analysis report generated successfully: sales_analysis_report.pdf")

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常见问题和解决方案

问题1：表格内容超出页面边界

当表格内容太多时，可能会超出PDF页面的边界。解决方案包括：

# 调整表格大小和字体
table = Table(data_for_table, colWidths=[1*inch, 1*inch, 1*inch]) # 减小列宽
style = TableStyle([
('FONTSIZE', (0, 0), (-1, -1), 8), # 减小字体大小
# 其他样式设置...
])
table.setStyle(style)
# 或者使用自动调整列宽
table = Table(data_for_table)
style = TableStyle([
('FONTSIZE', (0, 0), (-1, -1), 10),
# 其他样式设置...
])
table.setStyle(style)
# 让表格自动调整大小以适应内容
table._argW[0] = 1.5*inch # 设置第一列宽度
table._argW[1] = 1.5*inch # 设置第二列宽度
# 以此类推...

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问题2：中文字符显示问题

在PDF中显示中文字符时可能会遇到编码问题。解决方案：

from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
# 注册中文字体
pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf')) # 确保字体文件存在
# 在样式中使用中文字体
style = TableStyle([
('FONTNAME', (0, 0), (-1, -1), 'SimSun'),
# 其他样式设置...
])

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问题3：图像分辨率过低

在PDF中插入的图像可能分辨率过低，影响打印质量。解决方案：

# 提高保存图像时的DPI
plt.savefig(img_buffer, format='png', dpi=600, bbox_inches='tight')
# 或者使用矢量图形格式（如SVG）
plt.savefig(img_buffer, format='svg', bbox_inches='tight')

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问题4：大型DataFrame处理缓慢

处理大型DataFrame时可能会遇到性能问题。解决方案：

# 分批处理数据
chunk_size = 1000 # 每批处理的行数
for i in range(0, len(df), chunk_size):
chunk = df.iloc[i:i+chunk_size]
# 处理当前批次的数据
process_chunk(chunk)
# 或者使用数据采样
sample_df = df.sample(n=1000) # 随机采样1000行
# 处理采样后的数据
process_sample(sample_df)

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问题5：PDF文件过大

生成的PDF文件可能过大，不利于分享和存储。解决方案：

# 压缩图像
from PIL import Image
def compress_image(input_buffer, quality=85):
img = Image.open(input_buffer)
output_buffer = io.BytesIO()
img.save(output_buffer, format='JPEG', quality=quality, optimize=True)
output_buffer.seek(0)
return output_buffer
# 使用压缩后的图像
compressed_buffer = compress_image(img_buffer)
chart = Image(compressed_buffer, width=7*inch, height=4*inch)

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结论和最佳实践

在本文中，我们详细介绍了如何使用pandas和其他Python库将数据导出为PDF格式，从基础操作到高级技巧。以下是一些最佳实践建议：

1. 选择合适的工具：根据需求选择最适合的库。对于简单的表格导出，reportlab是一个不错的选择；对于复杂的布局和样式，weasyprint提供了更大的灵活性。
2. 规划布局：在开始编写代码之前，先规划好PDF的布局，包括标题、表格、图表和文本的位置。
3. 保持一致性：在整个报告中保持一致的样式，包括字体、颜色和间距，以创建专业的外观。
4. 优化性能：对于大型数据集，考虑分批处理或采样，以提高性能。
5. 添加元数据：在PDF中添加标题、作者、创建日期等元数据，以便于管理和搜索。
6. 测试不同设备：在不同的设备和PDF阅读器上测试生成的PDF，确保兼容性和显示效果。
7. 自动化流程：创建可重用的函数和模板，以自动化报告生成过程。
8. 错误处理：添加适当的错误处理机制，以应对数据格式问题或生成过程中的异常。

选择合适的工具：根据需求选择最适合的库。对于简单的表格导出，reportlab是一个不错的选择；对于复杂的布局和样式，weasyprint提供了更大的灵活性。

规划布局：在开始编写代码之前，先规划好PDF的布局，包括标题、表格、图表和文本的位置。

保持一致性：在整个报告中保持一致的样式，包括字体、颜色和间距，以创建专业的外观。

优化性能：对于大型数据集，考虑分批处理或采样，以提高性能。

添加元数据：在PDF中添加标题、作者、创建日期等元数据，以便于管理和搜索。

测试不同设备：在不同的设备和PDF阅读器上测试生成的PDF，确保兼容性和显示效果。

自动化流程：创建可重用的函数和模板，以自动化报告生成过程。

错误处理：添加适当的错误处理机制，以应对数据格式问题或生成过程中的异常。

通过遵循这些最佳实践，您可以创建专业、美观且信息丰富的PDF报告，有效地展示您的数据分析结果。

希望本文能帮助您掌握使用pandas将数据导出为PDF的技巧，提升您的数据分析报告质量！

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