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A Step-by-Step Tutorial on How to Download and Complete a Free W9 PDF
If you are a freelancer or an independent contractor, you may be familiar with the W9 form. This form is essential for tax purposes, as it provides your clients with the necessary information to report payments made to you. In the past, obtaining a W9 form may have involved printing, filling out by hand, and mailing it back. However, thanks to technology advancements, you can now download and complete a free W9 PDF online. In this tutorial, we will guide you through the process of downloading and completing a free W9 PDF effortlessly.
Downloading a Free W9 PDF
To begin with, you need to find a reliable website that offers free W9 forms in PDF format. You can easily find such websites by doing a quick search on any search engine. Look for reputable sources such as government websites or trusted tax-related platforms.
Once you have found a suitable website, navigate to the page where the free W9 PDF is available for download. Most websites will have clear instructions or links directing you to download the form. Click on the provided link or button to initiate the download process.
Familiarizing Yourself with the Form
After successfully downloading the free W9 PDF form, open it using any compatible PDF reader software installed on your device. Take some time to familiarize yourself with the different sections and fields present in the form.
The W9 form typically consists of several sections that require your personal information such as your name, business name (if applicable), address, taxpayer identification number (TIN), and other relevant details. It is crucial to fill in these sections accurately as any discrepancies could lead to issues later on.
Completing Your Personal Information
Once you are comfortable with the layout of the form and understand what information is required from you, proceed to fill in your personal details. Start by entering your full legal name in the designated field. If you have a business name, provide it in the appropriate section.
Next, input your mailing address, including street address, city, state, and zip code. It is important to ensure that this information matches the address on file with the Internal Revenue Service (IRS) to avoid any confusion or delays in processing.
Providing Your Taxpayer Identification Number (TIN)
The most critical part of completing a W9 form is providing your taxpayer identification number (TIN). Depending on your situation, this could be either your Social Security Number (SSN) or an Employer Identification Number (EIN).
Carefully input your TIN in the designated field and double-check for accuracy. Any errors or incorrect information can lead to complications when it comes time for tax reporting and filing.
Once you have completed all the necessary sections of the W9 form, save a copy of the filled-out PDF on your device for future reference. It is also advisable to print a hard copy as backup.
In conclusion, downloading and completing a free W9 PDF can be done easily with just a few simple steps. By following this step-by-step tutorial, you can ensure that you provide accurate and complete information on the form. Remember to keep copies of both digital and physical versions for your records.
This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.
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pip install pdf-reports Copy PIP instructions
Released: Jul 21, 2023
Create nice-looking PDF reports from HTML content.
- Open issues:
View statistics for this project via Libraries.io , or by using our public dataset on Google BigQuery
Tags PDF, report, web, jinja, weasyprint
PDF Reports (complete documentation here ) is a Python library to create nice-looking PDF reports from HTML or Pug templates. It features modern-looking components (via the Semantic UI framework) and provides routines to embed tables or plots in the documents.
Example of use
Your Pug template file template.pug may look like this (see a full example ):
Your Python code will be as follows:
And your final result may look like this ( PDF file ):
License: MIT, Copyright Edinburgh Genome Foundry
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Python PDF Reports in Python/v3
How to make PDF reports with Python and Plotly Graphs.
Creating PDF Reports with Plotly Graphs and Python ¶
Since Plotly graphs can be embedded in HTML or exported as a static image, you can embed Plotly graphs in reports suited for print and for the web.
This notebook is a primer on creating PDF reports with Python from HTML with Plotly graphs. This notebook uses:
- Plotly for interactive, web native graphs
- IPython Notebook to create this notebook, combining text, HTML, and Python code
- xhtml2pdf to convert HTML to PDF in Python
Part 1 - Create the HTML Template ¶
Display the interactive report, suited for the web ¶.
This version contains the interactive version of the Plotly graphs, served from Plotly's server
Display the static report, to be converted to PDF ¶
This version contains the static version of the Plotly graphs, also served from Plotly's server
Part 2 - Convert the HTML to PDF with xhtml2pdf ¶
Generating images on the fly ¶.
The static report in the example above uses graphs that were already created in Plotly. Sometimes it's helpful to use graph images that are created on-the-fly. For example, if you're using plotly.js to create the web-reports you might not be saving the graphs to accounts on plot.ly .
To create static images of graphs on-the-fly, use the plotly.plotly.image class. This class generates images by making a request to the Plotly image server.
Here's an alternative template that uses py.image.get to generate the images and template them into an HTML and PDF report.
Generate PDF report in Python
High-speed python via .net library to build pdf reports and for document automation using pdf templates and custom data.
This report generator provides efficient platform-independent API. Use our Reporting API to develop high-level software for Python via .NET platform. By integrating our solution into your software, you can generate stunning reports from PDF templates and custom data using Python.
Reporting for PDF using Python
Create appealing reports from PDF templates and custom data with Python. Empower your reports with lists, tables, charts, images, barcodes, and other document elements with Python via .NET. With this LINQ reporting engine for Python via .NET, you can generate reports seamlessly.
See how easy it is to build PDF report in Python by taking the following steps:
- Choose your data source such as JSON, XML, CSV, databases, or objects of custom types.
- Prepare a PDF template document. Using LINQ -based syntax, sort, filter, and group your data directly in PDF templates.
- Use our Python via .NET reporting engine to bind the PDF template and data from your data source using LINQ syntax and get a report in the format of your choice.
Automate PDF document generation in Python
Using this Python via .NET solution, you can create reports in many popular document formats with professional quality. Not only reports, generate PDF documents of any type such as invoices, resumes, contracts, letters, and others using Python.
PDF Report Generation in Python
Our reporting engine is based on the technology of dynamically binding a data source to fields in PDF template using LINQ syntax. Such a reporting engine will significantly increase labor productivity when preparing documents of the same type by automating routine operations.
Create PDF report in Python
To see how to generate a report in Python and how our programming API works, load a PDF template document and file with your data. Specify the name of a data source object, if used in the PDF template. After running the code, download a report in a convenient format generated with our Python via .NET library.
How to generate PDF report in Python
- Install 'Aspose.Words for Python via .NET'
- Add a library reference (import the library) to your Python project
- Create a PDF template marked up with LINQ based syntax
- Load the PDF template document
- Load your data from the data source: files, databases, or custom objects
- Build a report by passing your PDF template and data to a 'ReportingEngine' instance
- Save the generated report as a separate file
Python library to build PDF report
We host our Python packages in PyPi repositories. Please follow the step-by-step instructions on how to install "Aspose.Words for Python via .NET" to your developer environment.
This package is compatible with Python ≥3.5 and <3.12. If you develop software for Linux, please have a look at additional requirements for gcc and libpython in Product Documentation .
Other supported document formats for Reporting
You can generate reports and automate documents for other file formats:
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Generating PDF Reports from a Python Script
New to Anvil?
Build web apps with nothing but Python
Create a PDF with Python
So, you’re doing some data analysis in Python, and you want to generate a PDF report. If you Google around, you’ll find a bunch of jerry-rigged ways of doing it, by generating HTML. Working with HTML and CSS is a pain – wouldn’t it be easier if we could just design our PDFs with a drag-and-drop designer?
We’re going to do just that, using Anvil . Anvil is a platform for building web UIs, but today we’ll just be using it to make PDFs.
In this example, we’re going to take two CSVs, representing sales data from this month and last month, and create a PDF report that looks like this:
We’ll build up to this in a few stages:
- Preparing our data with Pandas
- Designing our first PDF with Anvil’s drag-and-drop designer
Passing data into Anvil
- Displaying a table on our PDF
- Displaying a total in our PDF table
- Plotting graphs on our PDF using Python and Plotly
Follow along to build the app yourself, or you can open the full example app and script here:
Open in Anvil Download local script
And you can download our sample CSV files here:
Preparing our data
Let’s say we have two CSVs, describing our company’s revenue, by source, for this month and last month. They look like this:
We can use pandas to load and join our two CSVs. (You’ll need this_month.csv and last_month.csv saved in your working directory):
That will produce a data frame like this:
Designing our first PDF
To design our PDF, we first open the Anvil cloud editor , and create a new app, choosing the ‘Material Design’ theme. We’ll want to create a “Form” - that’s a piece of web UI - which we will then turn into a PDF.
For our PDF, we don’t want any headers, or navigation, so we’ll create a new “Blank Panel” form, and call it ReportForm :
Note: This guide includes screenshots of the Classic Editor . Since we created this guide, we've released the new Anvil Editor , which is more powerful and easier to use.
All the code in this guide will work, but the Anvil Editor will look a little different to the screenshots you see here!
We can use the drag-and-drop editor to put a title on our page. We’ll use a Label component, then adjust its properties to display a centred title with large text:
Rendering it from Python
Before we go any further, let’s generate that PDF from Python. We’ll use the Uplink to connect our local code to this Anvil app.
Then we install the Uplink library:
And then we paste that connection code into our script, and add code to create a PDF file:
Now we run this code. Anvil will produce a PDF, containing just our title.
You’ve just created a PDF and written it to your local filesystem!
Displaying data on our PDF
We want to display more than just a title: We want to show our data! The first step is to pass our data into our Form’s code, that runs inside Anvil.
We can’t pass Pandas data frames directly to Anvil, so we turn our data into a list of dicts first:
Here’s what that looks like: It’s a list, with a dictionary for each row of the data frame:
We can pass this data as an extra argument to render_form() , which will in turn be passed into the __init__ function of our Anvil form.
We edit our script to say:
Displaying data on our form
Now, we’ll make ReportForm display this data. The first step is to go into the Code for the form, and add the extra argument to our __init__ function:
Edit the definition of the __init__ function to accept our data. It now looks like this:
Displaying a table
We want to display a table, with each category of revenue and its growth/decline since last month. So we drag a Data Grid onto our page, and give it three columns: Category, Revenue, and Change, displaying the dictionary keys category , revenue and change .
Inside this DataGrid is a RepeatingPanel, and we can display rows in our table by filling out its item property. Edit our ReportForm ’s __init__ method as follows:
Now, when you run your script, it will generate a PDF with a table:
Displaying a total
We want to display a “total” row for this table. So we add a new DataRowPanel to our grid, underneath the automatic rows from the RepeatingPanel, and display our totals there.
We can do this entirely in code, by adding this to our __init__ function:
Voila! Our table is looking spiffy. If we run our script, we get this PDF:
The final piece is to display a graph! We’ll be summarising our data with two graphs: a pie chart displaying the proportion of revenue from each source, and a bar chart comparing each category’s performance with last month.
We then use the popular Plotly API to plot our data on these components. We add the following code to our __init__ function:
If we run our script, we have created a complete sales report in PDF form!
That’s all, folks!
You now have a Python script that will generate a beautiful PDF report – and you know how to design more!
Again, you can open the full example app and script here:
Generate PDF reports #
The Python ecosystem has everything needed to create PDF reports with a custom layout.
This shows how to generate a PDF report extracting data from an Atoti session and displaying it as a table spanning over multiple pages and a chart.
Creating the session #
Let’s start by creating an Atoti session and loading some data into it:
Querying the session #
The first query will retrieve the values of a few measures for each Sub category and Brand .
Styling tables #
The result of this first query will be displayed as a table in our report. We’ll use three different techniques to style it:
Defining cell properties in the MDX query #
In the query below, we use calculated measures to style the table cells. In particular, the following portion of the query ensures that the cells for the contributors.COUNT measure are colored red if their value is less than 300: WITH MEMBER [Measures].[Count] as [Measures].[contributors.COUNT], FORE_COLOR=IIf([Measures].[Count] < 300, RGB(255, 135, 135), NULL)
Applying style with pandas #
pandas provides many ways to style a DataFrame .
Here, we’ll apply a background color to a specified row:
Adding style with CSS #
Before being a PDF, our report will be an HTML file so we can use CSS to style it:
Creating the chart #
Many Python libraries can create charts from pandas DataFrames. In this how-to, we’ll use seaborn which is a wrapper around Matplotlib :
Creating the HTML report #
We’ll use Jinja to render this template:
With this data:
Exporting the report as a PDF #
WeasyPrint can render HTML as a PDF:
Going further #
The report could be sent by email periodically using the schedule library or:
Task Scheduler on Windows
cron on Linux or macOS
A Lambda function in the cloud