# Unlocking Python Gigs: Earning Up to $15K Monthly
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Chapter 1: My Journey with Python
I began my journey with Python more than a decade ago and quickly fell in love with its capabilities. Over the years, I've engaged in various projects that have allowed me to leverage Python professionally, especially after I shifted away from my original career in chemical engineering.
Photo by Hitesh Choudhary on Unsplash
Throughout this journey, I have consistently earned an average of around $3,000 monthly, with peaks reaching up to $15,000. One crucial lesson I've learned is to steer clear of platforms like Upwork and Fiverr for finding gigs unless you're using them to outsource work. However, always ensure you can manage the gig if issues arise.
In this article, I'll delve into the various Python gigs I've undertaken over the years, excluding development work with Excel, Access, and similar tools.
Section 1.1: Lead Generation
Although it may be frowned upon by some, email marketing remains a significant avenue for generating leads. Many businesses still seek leads to expand their operations. Occasionally, I purchase lists and resell segments to clients, with one notable sale netting me $6,000 for a subset of my list. Remarkably, it took me less than ten minutes to write the query and extract the necessary leads. I'm afraid I can't disclose my source.
Section 1.2: Bot Development
Bots are highly sought after, and clients often require them for various purposes. For example, a midwestern client wanted to quickly purchase items from Supreme New York due to their popularity. I developed a bot that would automatically navigate the site, select his desired product, and make a purchase if it fell below a specific price. He valued the ability to fully automate the process or halt it before finalizing his order. This project, which I completed swiftly using Python, earned me $500.
Subsection 1.2.1: UCC Data Scraping
My introduction to Uniform Commercial Codes (UCC) came a few years back when I created around ten scrapers to gather UCC data from various states, each with its unique structure. To support these searches, I also developed web scrapers for Bizapedia, Dun & Bradstreet, and Google to supplement the UCC information. This gig earned me approximately $10,000. Interestingly, my writings about this endeavor on Medium have led to additional projects, with two follow-up gigs already secured.
Subsection 1.2.2: Keyword Mapping
I have also scraped websites to identify specific keywords. For instance, I analyzed over 5,000 .ai websites, tracking the occurrence of targeted keywords. Although the writing part was quick, the actual scraping took considerable time and yielded about $500 in earnings.
Chapter 2: PDF and Legal Data Handling
Section 2.1: PDF Data Extraction
While not a standalone gig, I also worked on extracting data from PDFs related to Texas UCC sites, where the UCC data is embedded in scanned documents. This involved downloading PDFs and extracting information. Below is a snippet of the code I use to extract text from image-based PDFs:
def Get_text_from_image(pdf_path):
global processed
if processed % 100 == 0:
print(processed, time.time())processed += 1
try:
pdf = wi(filename=pdf_path, resolution=300)
pdfImg = pdf.convert('jpeg')
imgBlobs = []
extracted_text = []
for img in pdfImg.sequence:
page = wi(image=img)
imgBlobs.append(page.make_blob('jpeg'))
for imgBlob in imgBlobs:
im = Image.open(io.BytesIO(imgBlob))
text = pytesseract.image_to_string(im, lang='eng')
im.close()
extracted_text.append(text)
except:
return []return extracted_text
Section 2.2: Automating Legal Data
In the legal domain, there's significant potential for data science. I once came across a law firm advertising on Craigslist for someone to extract garnishment data from three different websites. I proposed an automated solution, building a tool with Python that saved them time and earned me $1,500.
Section 2.3: Labor Law Analysis
Another legal firm approached me to analyze employee hours and assess whether an employer was exploiting rounding errors to underpay employees. Using predefined rules, I crafted a Python program with Pandas to evaluate their data for fair compensation.
Section 2.4: PDF Automation for Contracts
A cemetery owner required his sales team to complete contracts, but the existing PDF forms were inefficient. I utilized a Python package—whose name escapes me—to generate numbers for each input on a PDF, allowing for data entry through Excel and automating the completion of the PDF files. I've collaborated with this client for four to five years on various projects.
Conclusion: The Potential of Python
This overview is just a glimpse into the myriad of gigs I've tackled over the years. Python is an exceptional tool for generating supplementary income and can also lead to new career opportunities. In my professional life, I've employed Python to access extensive databases and create impressive data visualizations. The only boundaries with Python are those of your imagination. Most of my projects come through referrals, Craigslist, and advertisements.
Happy Coding!