Speaker 1: Have you ever felt just buried under repetitive tasks at
Speaker 1: work or maybe just wish there was a faster I
Speaker 1: don't know, a smarter way to get things done, doesn't
Speaker 1: really matter what department you're in.
Speaker 2: Yeah, that feeling of there has to be a better
Speaker 2: way exactly.
Speaker 1: If that sounds familiar, then this deep dive is definitely
Speaker 1: for you. Today we're digging into automated recipes to obscue
Speaker 1: your business. That a really insightful book from Packed Publishing
Speaker 1: came out back in January twenty seventeen.
Speaker 2: Right, and this deep dive it's all about unlocking the well,
Speaker 2: the power of automation, specifically using Python. The book itself
Speaker 2: is set up as a practical guide, you know, packed
Speaker 2: with what they call Python recipes, basically actionable steps to
Speaker 2: help businesses and well you streamline all those everyday operations
Speaker 2: across like tons of different departments.
Speaker 1: That's a really key point our mission here. It isn't
Speaker 1: just to talk about code, right, It's more about giving
Speaker 1: you the kind of shortcut a way to understand how
Speaker 1: automation can spark those you know, aha moments.
Speaker 2: Yeah, the ones that lead to real, actual productivity gains.
Speaker 1: In your own work precisely. Yeah, and look, while the
Speaker 1: book is from twenty seventeen, the core ideas the power
Speaker 1: of Python for this stuff, it's still incredibly relevant today
Speaker 1: for automating business processes. So let's dive in. Let's look
Speaker 1: at some of the core concepts first. Okay, okay, so
Speaker 1: let's unpack this central philosophy of automate it. The main
Speaker 1: idea seems to be using Python to streamline well pretty
Speaker 1: much everything, hr, marketing, customer support, you name it, right.
Speaker 2: It's about moving away from those manual pasks, the ones
Speaker 2: you do over and over, often redundantly, and shifting towards
Speaker 2: more efficient, maybe even innovative business flows.
Speaker 1: What's really insightful, I think, is how the book frames it.
Speaker 1: It's like applying these classic problem solving patterns but to
Speaker 1: all these different business challenges exactly.
Speaker 2: It helps you think about automation not just as some
Speaker 2: tech fix, but really as a strategic tool, a way
Speaker 2: to genuinely upskill your business.
Speaker 1: As the title says, right, it empowers your operation to
Speaker 1: do more, but with less human.
Speaker 2: Drudgery, precisely less time spent on the boring stuff.
Speaker 1: Okay, so let's move into marketing and sales yeah, because
Speaker 1: this is where the book it's really interesting, I think,
Speaker 1: especially for anyone in those fields. Imagine the hours you
Speaker 1: could save. Let's talk about lead generation first.
Speaker 2: Okay.
Speaker 1: It introduces this scenario with Ryan, a marketing manager at
Speaker 1: a startup Deli Inc. Food delivery. Right, So Ryan needs
Speaker 1: this big database of London restaurants, you know, name, address,
Speaker 1: contact info, stuff to target for their platform. But searching
Speaker 1: Yelp manually forget it way too slow.
Speaker 2: Yeah, that's a classic problem. The solution the book offers
Speaker 2: Python webscraping. Oh okay, it shows you how to automatically
Speaker 2: pull out those specific details name, address, phone from Yelp
Speaker 2: search results. Basically, you teach Python to look for certain
Speaker 2: markers in the website's code, like the biz name or
Speaker 2: street address class names they mentioned, So it just grabs
Speaker 2: the data automatically pretty much. And this isn't just about
Speaker 2: finding restaurants, right, It's about systematically gathering public data at scale.
Speaker 2: Think about market research. And it turns that manual slog
Speaker 2: into automated strategic intelligence gathering.
Speaker 1: Hmmm. That opens up possibilities. And what are social media?
Speaker 1: That's another huge time sink for marketing teams.
Speaker 2: Oh, definitely The book uses the example of Joy, a
Speaker 2: marketing head. She needs to schedule product updates across different
Speaker 2: time zones, even when she's like a sleep or on vacation.
Speaker 1: That's a logistical nightmare for a lot of folks.
Speaker 2: Totally, So for Joy, the book shows how to use
Speaker 2: Twitter's rest APIs with Python. Specifically, it mentions a tool
Speaker 2: called twython. Okay, twython, which is essentially, you know, a
Speaker 2: Python library that lets your program talk directly to Twitter,
Speaker 2: so you can programmatically post tweets schedule them based on
Speaker 2: content time and this is key time zone. It even
Speaker 2: mentions using the pits module for getting those time zones right,
Speaker 2: like for an Australian audience.
Speaker 1: Wow, okay, So it takes social media from being this
Speaker 1: constant real time burden to something you can plan out
Speaker 1: and optimize.
Speaker 2: Before forehand, exactly pre planned optimized outreach.
Speaker 1: Now for digging even deeper, there's Judy, a data scientist
Speaker 1: set a magazine. Her challenge is different. She needs to
Speaker 1: collect and analyze social media data, mostly from Twitter, to
Speaker 1: find insights for articles.
Speaker 2: Right, So the solution there involves automating that analysis. It
Speaker 2: shows using Python tools like tweepee that lets you tap
Speaker 2: into Twitter's live data stream. The streaming APIs, so you're
Speaker 2: getting real time tweets yep. And then for making sense
Speaker 2: of it all, it brings in pandas. Think of pandas
Speaker 2: as like a superpowered spreadsheet for code. It can analyze
Speaker 2: the sentiment of those tweets, you know, figure out if
Speaker 2: they're positive, negative, or neutral about a product like an
Speaker 2: iPhone or a Samsung Note.
Speaker 1: Okay, sentiment analysis.
Speaker 2: Yeah, and if we connect this back to the bigger picture, right,
Speaker 2: this kind of automation really shifts marketing. You move from
Speaker 2: just reacting to things to being proactive using data to
Speaker 2: drive your strategy.
Speaker 1: Yeah, that makes sense.
Speaker 2: It really raises a big question, doesn't it. How much
Speaker 2: more targeted could marketing actually be if you had these
Speaker 2: kinds of insights automated and like instantly available.
Speaker 1: Good question. Okay, let's let's pivot now, let's talk HR
Speaker 1: and ADMIN. Anyone who's dealt with onboarding new hires knows
Speaker 1: the sheer volume of paperwork and repetitive steps involved.
Speaker 2: Oh, absolutely, the endless forms.
Speaker 1: Right. The book has this great example of an HR
Speaker 1: team automating new higher orientation. I can just picture the relief. Yeah,
Speaker 1: you know, dealing with fifteen twenty new hires every month.
Speaker 2: Yeah, that manual process is killer. The book describes an
Speaker 2: HR manager sending out personalized orientation documents based on department
Speaker 2: a whole month after someone joins. Super tedious, So what's
Speaker 2: a Python fix? The solution is generating personalized word documents,
Speaker 2: you know, the dot dox files programmatically. It uses a
Speaker 2: Python module called Python.
Speaker 1: Docs Okay, python docks, right, It's.
Speaker 2: A way to create and change word docs using code.
Speaker 2: The script in the book pulls employee data. Okay, it
Speaker 2: uses a simple dictionary in the example, but you could
Speaker 2: easily hook it up to a real database and merges
Speaker 2: it with an agenda template, and it just spits up
Speaker 2: personalized word files exactly with the right titles, addresses department
Speaker 2: specific sessions, all filled in automatically. It's a perfect case
Speaker 2: study for automating something highly repetitive but also really important.
Speaker 1: And it's not just onboarding, is it. I mean, think
Speaker 1: about managing all sorts of office records, employee info, financial statements.
Speaker 1: Doing that manually in CSVS or Excel. So much room for.
Speaker 2: Error, absolutely, and so time consuming. The book gives you
Speaker 2: Python recipes specifically for that simplifying and automating tasks with
Speaker 2: CSV and Excel.
Speaker 1: Files like what specifically, well.
Speaker 2: Reading and writing CSV files, even setting up custom CSV dialects,
Speaker 2: which basically means you can handle weirdly formatted data files smoothly.
Speaker 2: For Excel, it covers retrieving data, putting new data, in
Speaker 2: formatting cells, doing calculations with formulas, even inserting charts automatically.
Speaker 1: Wow.
Speaker 2: Yeah. It highlights automating income statement analysis across different years
Speaker 2: for a finance team. Imagine taking hours of manual spreadsheet
Speaker 2: work down to like seconds.
Speaker 1: That's a huge efficiency game. Okay, let's shift again. Customer support. Yeah,
Speaker 1: here it's all about being efficient, being responsive, and the
Speaker 1: book has some interesting ideas here too. Take Kelly, she's
Speaker 1: the director of customer support, her team, her engineers. They
Speaker 1: spend way too much time on what they call level
Speaker 1: one requests, you know, customers asking for info that's already
Speaker 1: sitting on the FAQ page.
Speaker 2: Right, and they're just manually copying and pasting links exactly.
Speaker 1: So what's the automated approach?
Speaker 2: The book outlines building an automated email response system. It
Speaker 2: automatically acknowledges the support ticket and sends back a link
Speaker 2: to the relevant FAQ section, like straight away, how does
Speaker 2: it do that? It details using specific Python libraries awesomet
Speaker 2: club for sending email and immaplub for fetching email, so
Speaker 2: the script can check the inbox for unread support requests
Speaker 2: and fire off those auto responses.
Speaker 1: That frees up the support team for the harder problem.
Speaker 2: Precisely reduces the load lets them focus on more complex issues.
Speaker 1: The book all So talks about playing with SMS and
Speaker 1: voice notifications using cloud, telefany VoIP.
Speaker 2: Yeah, that opens up a whole other area for automation.
Speaker 2: Think about instant updates via text or even automated voice messages.
Speaker 1: What kind of examples do they give?
Speaker 2: Well, things like registering with cloud to lefhany providers like
Speaker 2: Twilio's a big one, sending and receiving texts automatically. It
Speaker 2: even mentions SMS workflows like the ones Domino's Pizza uses
Speaker 2: for order tracking.
Speaker 1: Oh yeah, I get those right.
Speaker 2: And beyond texts, sending automated voice messages or even building
Speaker 2: parts of customer customer service software. It just extends your
Speaker 2: reach and responsiveness way beyond manual limits.
Speaker 1: So if we pull back a bit, what does all
Speaker 1: this mean for how businesses interact with customers. More broadly,
Speaker 1: I mean think about chatbots everywhere now, Pizza hut on Facebook, Messenger,
Speaker 1: CNN giving you news headlines.
Speaker 2: Bots are definitely a big part of it, and the
Speaker 2: book explains why they're so relevant now. They arrange from
Speaker 2: simple rule based ones, you know, if customer types. X
Speaker 2: Spot says, why all the way to really smart AI
Speaker 2: powered bots.
Speaker 1: And why are they so relevant now?
Speaker 2: Several reasons. One is just changing habits people spend more
Speaker 2: time in chat apps. Bots are also cost effective, right,
Speaker 2: reduces the need for as many human agents for simple stuff.
Speaker 2: They allow for massive scale, reaching millions on platforms like
Speaker 2: Facebook or Telegram, and the text got better Cheaper advances
Speaker 2: in AI and natural language processing make them smarter.
Speaker 1: That makes sense. Does the book get into the nitty
Speaker 1: gritty of actually building these bots? Yeah, or maybe some
Speaker 1: of the challenges.
Speaker 2: Oh yeah, it gets practical. It shows how to build
Speaker 2: Telegram bots using a library called Python telegram Bot, and
Speaker 2: for Facebook Messenger it uses flask, which is a Python
Speaker 2: web framework along with the Request library for talking to
Speaker 2: Facebook systems.
Speaker 1: Okay, so tangible tools exactly.
Speaker 2: And for the smart bots, it introduces concepts like AI
Speaker 2: driven sentiment analysis which we mentioned earlier, and using something
Speaker 2: called AM artificial intelligence markup language. Yeah, it's a way
Speaker 2: to structure the bot's knowledge so it can understand content
Speaker 2: better and respond in a more well human like way.
Speaker 2: The example they use is a bot for a book
Speaker 2: publishing website that can have a decent conversation with customers.
Speaker 2: It really moves beyond just theory.
Speaker 1: Interesting, Okay. Finally, let's talk about data, making sense of
Speaker 1: all the information we collect and maybe even visual information.
Speaker 1: The book has this concept of imaging as a business process.
Speaker 2: Right. Think about Peter who needs to digitize stacks of
Speaker 2: financial documents their image based maybe scan PD apps or photos,
Speaker 2: and he needs to index them. Doing that manually scanning
Speaker 2: typing in data is slow, expensive, error.
Speaker 1: Prone definitely, So how does automation help there?
Speaker 2: The solution involves automating both the spanning and the indexing
Speaker 2: using Python. It leverages powerful libraries like OpenCV and psychic
Speaker 2: image for image processing stuff like finding the document edges
Speaker 2: in a photo okay, and then it uses ocr tools
Speaker 2: optical character recognition like tesseract and its Python wrapper py
Speaker 2: Tesseract basically teaching the computer to read the text with
Speaker 2: it the image, so it.
Speaker 1: Can turn a picture of a document into actual text exactly.
Speaker 2: The book shows a recipe take an image, say a
Speaker 2: newspaper clipping, detect its edges, identify the text areas, digitally
Speaker 2: scan it to make it clean black and white, and
Speaker 2: then extract the actual text content.
Speaker 1: Wow.
Speaker 2: Yeah, and this really brings up a key question about efficiency. Right,
Speaker 2: imagine going from boxes of paper records to a fully indexed,
Speaker 2: instantly searchable digital archive for compliance for research, all automated.
Speaker 2: What stands out to you as the biggest win there?
Speaker 1: Oh, that's a good win for me. I think it's
Speaker 1: exactly that compliance and archives taking that massive headache of
Speaker 1: finding specific paper documents and turning it into like a
Speaker 1: simple keyword search. That time save for audits or just
Speaker 1: finding old information seems enormous.
Speaker 2: Totally agree, speed and accessibility, And.
Speaker 1: You know, speaking of data, the book doesn't just stop
Speaker 1: at images. It lays out a solid process for general
Speaker 1: data analysis and visualization too, the steps you need for
Speaker 1: making decisions based on data.
Speaker 2: Yes, it covers the whole workflow, starting with Okay, what's
Speaker 2: your hypothesis? What question are you trying to answer? Then
Speaker 2: finding the data sources, collecting it, cleaning it up which
Speaker 2: is super important, removing duplicates, dealing with weird outliers.
Speaker 1: Right, the data wrangling part.
Speaker 2: Exactly, then the actual analysis and finally visualizing it so
Speaker 2: people can understand it.
Speaker 1: What tools does it recommend for that?
Speaker 2: Python tools naturally, pandas again for handling data and tables, filtering, summarizing,
Speaker 2: NumPy for heavy duty math, and then for creating charts
Speaker 2: and graphs matplotlib and seaborn, and the use cases things
Speaker 2: like reading and interpreting data. It uses an example of
Speaker 2: tech Crunch funding data, looking at it by investment category,
Speaker 2: funding round city, generating insights by filtering and aggregating like
Speaker 2: which funding rounds were most common each month. And as
Speaker 2: we talked about with Judy, automating social media analysis fits
Speaker 2: here too. It really gives you a blueprint for becoming
Speaker 2: more data driven.
Speaker 1: Okay. And just to tie up the administrative side, the
Speaker 1: book tackles time in the zone, specifically for things like
Speaker 1: automatic invoice generation.
Speaker 2: Right, because businesses always struggle with dates and times, especially
Speaker 2: across time zones, for scheduling reports financial stuff like invoicing,
Speaker 2: it gets messy fast.
Speaker 1: Yeah, deal, I saving leap years exactly.
Speaker 2: So. The solution uses pythons built in date time and
Speaker 2: calendar modules, plus that PET library again for handling time
Speaker 2: zones properly. The book gives a recipe for generating personalized
Speaker 2: invoices automatically, like for a specific customer YEP, using customer
Speaker 2: ID name the billing month, and it handles all the
Speaker 2: date math correctly, like figuring out past dates even across
Speaker 2: leap years ensures accuracy timeliness, even if you're dealing with
Speaker 2: clients worldwide.
Speaker 1: Okay, wow, so we've really only scratched the surface here,
Speaker 1: but you get a sense of the breadth of automating,
Speaker 1: generating leads, scheduling tweets, onboarding new hires, handling customer support,
Speaker 1: digitizing documents, analyzing.
Speaker 2: Data, and it's all powered by these Python rescids. This
Speaker 2: deep dive, it really does offer you a shortcut, a
Speaker 2: way to grasp how these practical techniques can solve actual
Speaker 2: business problems and hopefully spark some innovation in your own work.
Speaker 1: Yeah, it gives you powerful tools and helps you see
Speaker 1: where those efficiencies might be hiding in your own processes. Absolutely,
Speaker 1: so here's something to think about as we wrap up
Speaker 1: If businesses can automate so much of the how repetitive tasks,
Speaker 1: the data processing, What new frontiers and what does that
Speaker 1: open up? What becomes possible when we can focus more
Speaker 1: human brain power on strategy and creativity instead of just
Speaker 1: the execution. How might that truly transform the future of work,