5 Best Ways to Convert a CSV Table to PDF in Python

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💡 Problem Formulation: Python developers often need to convert data from CSV (Comma-Separated Values) files into formatted PDF (Portable Document Format) files. Whether it’s for report generation, data sharing, or archival purposes, the conversion from a plain text CSV file to a styled and portable PDF is a common task. This article tackles the problem of transforming a simple CSV table, with columns and rows, into a PDF document, maintaining the table’s structure and content.

Method 1: Using pandas and ReportLab

The pandas library in combination with ReportLab offers a powerful method to convert CSV files into PDFs. Pandas reads the CSV into a DataFrame, which ReportLab can then use to generate a PDF. This approach allows for complex data manipulation before the conversion and provides extensive styling options for the PDF output.

Here’s an example:

import pandas as pd
from reportlab.pdfgen import canvas

# Load CSV into a DataFrame
data = pd.read_csv('data.csv')
# Creating canvas
c = canvas.Canvas('data.pdf')

# Add your custom PDF generation logic here using data

c.save()

The output is a ‘data.pdf’ file with the content from ‘data.csv’.

This snippet shows the basic structure of using pandas to load the CSV and ReportLab to create a PDF. The user must add custom logic to format and draw the table onto the canvas object before saving the PDF.

Method 2: Using pandas, matplotlib, and PdfPages

This method utilizes pandas for data handling, matplotlib for creating a visual representation of the table, and PdfPages for saving the output as PDF. It is particularly useful when the PDF requires the inclusion of plots or other visual elements alongside tabular data.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

# Load CSV into a DataFrame
df = pd.read_csv('data.csv')

# Plotting DataFrame and saving to PDF
with PdfPages('data.pdf') as pdf:
    df.plot(kind='bar')
    plt.savefig(pdf, format='pdf')
    pdf.close()

The output is a ‘data.pdf’ file displaying a bar chart of the DataFrame loaded from ‘data.csv’.

This code reads in a CSV file to a DataFrame and then uses matplotlib to plot the data. The resulting figure is saved into the ‘data.pdf’ file via PdfPages. It’s useful for direct data visualization conversions.

Method 3: Using PyFPDF or FPDF2

The FPDF libraries, PyFPDF for Python 2 or FPDF2 for Python 3, provide an easy way to create PDF files, allowing for precise control over the layout and styling of the PDF content. After reading the CSV file, the FPDF library is used to add content to the PDF page by page.

Here’s an example:

import fpdf
import csv

pdf = fpdf.FPDF(format='letter')
pdf.add_page()
pdf.set_font("Arial", size=12)

with open('data.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    for row in reader:
        pdf.cell(200, 10, txt=str(row), ln=True)

pdf.output('data.pdf')

The output is a ‘data.pdf’ file with the rows from ‘data.csv’ placed on individual lines.

This code block utilizes the FPDF library to iterate over rows in a CSV file and outputs each row to a PDF in a simple, line-by-line manner, giving developers control over the PDF layout.

Method 4: Using WeasyPrint

WeasyPrint is a visual rendering engine for HTML and CSS that can output to PDF. It allows the developer to convert a CSV file into an HTML table and then renders it to PDF. This is useful when web technologies are preferred for layout and style.

Here’s an example:

import pandas as pd
from weasyprint import HTML

data = pd.read_csv('data.csv')
html_string = data.to_html(index=False)
HTML(string=html_string).write_pdf('data.pdf')

The output is a ‘data.pdf’ file, which presents the CSV data in an HTML table format.

This snippet converts the CSV data into a pandas DataFrame, then to an HTML string, and finally uses WeasyPrint to convert the HTML table into a PDF file. WeasyPrint empowers developers to use CSS for PDF styling, which can be advantageous.

Bonus One-Liner Method 5: Using Tabula

For quickly turning a CSV file into a simple PDF table, tabula-py can provide an efficient one-liner solution. Note that Tabula is better known for extracting tables from PDFs, but it can also generate basic PDF tables from CSV data.

Here’s an example:

import tabula

tabula.convert_into('data.csv', 'data.pdf', output_format='pdf')

The output is a ‘data.pdf’ file containing a basic table structure from ‘data.csv’.

This method demonstrates how tabula can directly convert a CSV file into a PDF with minimal coding effort. It is quick and straightforward, though options for customization are limited as compared to other methods.

Summary/Discussion

  • Method 1: pandas and ReportLab. Strength: High customization. Weakness: Steeper learning curve and more verbose code.
  • Method 2: pandas, matplotlib, and PdfPages. Strength: Good for including data visualizations. Weakness: Limited to matplotlib’s graphing capabilities.
  • Method 3: PyFPDF or FPDF2. Strength: Precise PDF layout control. Weakness: Requires manual positioning of elements.
  • Method 4: WeasyPrint. Strength: Uses HTML/CSS for layout and styling. Weakness: Additional complexity due to HTML/CSS usage.
  • Method 5: Tabula. Strength: Quick and simple. Weakness: Limited customization options.