Speaker 1: Welcome to the deep dive, where we take your chosen
Speaker 1: sources and well distill them down to the essential insights. Right.
Speaker 1: Think of us as your shirt cut to understanding what
Speaker 1: really matters.
Speaker 2: Yeah, cutting through the noise.
Speaker 1: Today we're plunging into the world of Python data visualization,
Speaker 1: guided by Ashwun Pajankar's book Practical Python Data Visualization, specifically for.
Speaker 2: You, the learner, exactly, the learner, someone looking for that
Speaker 2: efficient path to knowledge, those aha moments without getting you know,
Speaker 2: bogged down. So our mission today is to explore pajon
Speaker 2: Car's book, focusing on how it gets you from zero
Speaker 2: to visualizing data effectively in Python. Okay, we'll be highlighting
Speaker 2: the key concepts and practical steps from those initial chapters.
Speaker 1: Okay, so where does pajon Car start. It's got to
Speaker 1: be the foundation Python itself, right, yep? Chapter one. Now
Speaker 1: Python's been around for a while, hasn't it. The book
Speaker 1: mentions Guido van Rossum created it building on an older
Speaker 1: language called ABC. That's right, first version way back in
Speaker 1: ninety one. And it's interesting they even retired Python too
Speaker 1: fairly recently. Shows it's still evolving.
Speaker 2: Yeah, constantly. And what's fascinating about that evolution are the
Speaker 2: Python Enhancement Proposals or PEPs PEPs okay. I think of
Speaker 2: these as community driven proposals for how Python should grow
Speaker 2: and improve. Pajankar highlights PK twenty The Zen of Python.
Speaker 1: Ah, The Zen of Python. I love that. It's almost
Speaker 1: like a philosophy for coders. It really is principles like
Speaker 1: beautiful is better than ugly and readability counts. You might think, Okay,
Speaker 1: how does that relate to making charts, But it really does,
Speaker 1: doesn't it clear, Well written code just makes the whole
Speaker 1: visualization process.
Speaker 2: Smoother, absolutely easier to understand later easier to fix. These
Speaker 2: principles are fundamental to why Python is so well user
Speaker 2: friendly and why it's become such a go to language
Speaker 2: across so many fields. The book touches on this diversity,
Speaker 2: web development, gui's scientific, computing.
Speaker 1: Even system administration.
Speaker 2: Right includes a link to Python success stories, which really
Speaker 2: illustrates just how widely it's used to solve real world problems.
Speaker 1: So you could be visualizing I don't know, financial.
Speaker 2: Trends or or mapping information spread It's incredibly versatile.
Speaker 1: Now for actually getting Python onto your computer, the book
Speaker 1: walks through installation for different systems. For Windows, it involves
Speaker 1: downloading from python dot org. And this is important, making
Speaker 1: sure you add Python to your path. Ah.
Speaker 2: Yes, the path variable crucial.
Speaker 1: It basically tells your computer where to find pythons so
Speaker 1: you can run it from the command line. Then you
Speaker 1: verify it with Python dash V and PIP three dash v.
Speaker 1: That PIP three is your package installer, right, we'll need that.
Speaker 2: Definitely for installing all the visualization libraries later. And for
Speaker 2: Linux users like Ubuntu, Pujankar notes Python three and PIP
Speaker 2: three are often already installed.
Speaker 1: Convenient.
Speaker 2: Yeah, just check with Python three dash V and PIP
Speaker 2: three dash V. Knowing you have those tools ready is
Speaker 2: step one.
Speaker 1: Okay, tools ready. Then Pagencar introduces Python's different modes. There's
Speaker 1: interactive mode.
Speaker 2: Where you type code get instant results good for quick tests.
Speaker 1: And script mode where you save your code and dot
Speaker 1: wi files to run later more for actual programs exactly.
Speaker 2: He also mentions IDL, which comes with Python.
Speaker 1: Right, the Integrated Development and Learning Environment.
Speaker 2: A simple editor to start writing and running. Python works
Speaker 2: on Windows and Linux, and he shows.
Speaker 1: That classic Hello World example. In both modes. It's a
Speaker 1: small thing, but getting that first program to run always
Speaker 1: feels good.
Speaker 2: It does that solid Python foundation, then leads us straight
Speaker 2: into chapter two and Jupiter Notebook.
Speaker 1: Ah, Jupiter, okay, so way Jupiter. What's the advantage over
Speaker 1: just the basic interactive mode.
Speaker 2: Well, if you've ever been frustrated running code line by
Speaker 2: line and you make a mistake and can't easily go back,
Speaker 2: or you want to save your results with the code,
Speaker 2: Jupiter is a game.
Speaker 1: Changer, right. It fixes those issues.
Speaker 2: The gen car describes it as a server program. You
Speaker 2: run it and it lets you create these interactive notebooks
Speaker 2: right in your web browser. Okay, in the browser interesting
Speaker 2: hugely popular in research and data science because you can
Speaker 2: mix your code the output, charts and tables, and even
Speaker 2: text explanations all in one document.
Speaker 1: So it's like a dynamic report where the analysis and
Speaker 1: results live together.
Speaker 2: Precisely. Setting it up is pretty straightforward. PIP three Install.
Speaker 1: Jupiter using that PIT three again.
Speaker 2: Yep. Then you launch it from your command line with
Speaker 2: jupi notebook. It usually pops open in your browser at
Speaker 2: localhost dot eight eight eight eight.
Speaker 1: And there's sometimes a token.
Speaker 2: Yeah, Pajuancar mentions the token it's just a security measure.
Speaker 2: Make sure only you access your notebooks. Nothing to worry
Speaker 2: about usually gotcha.
Speaker 1: He gives a quick tour of the interface too.
Speaker 2: He does the files tab for navigating, running tab to
Speaker 2: see active notebooks, clusters for more advanced stuff.
Speaker 1: But the core is creating a new Python three notebook.
Speaker 2: Right and inside that you have these things.
Speaker 1: Called cells cells. Yeah, I remember first using those. It
Speaker 1: felt much more organized for trying.
Speaker 2: Things out, definitely, And Pajankar points out the different types
Speaker 2: code cells for your Python and markdown.
Speaker 1: Cell arcdown for formatting text.
Speaker 2: Exactly, for adding headings, notes, lists, making your notebook readable.
Speaker 2: He shows a quick example, and these notebooks save as
Speaker 2: ipomanbi files.
Speaker 1: Dot ipmb okay keeps everything for.
Speaker 2: A project together exactly, code, output, notes all in one place.
Speaker 1: Okay, so we have Python setup, we have this interactive
Speaker 1: Jupiter environment. Chapter three must be where the visualization starts, right.
Speaker 2: It is first taste of visualization with a library called Leather.
Speaker 1: Leather never heard of it.
Speaker 2: Pajancar introduces it as a really user friendly way to
Speaker 2: make basic charts quickly, simple syntax.
Speaker 1: How do you get it?
Speaker 2: You guessed it? PIP three install Leather huh? Okay, and
Speaker 2: what's neat here is. Pajankar shows you can run operating
Speaker 2: system commands directly from inside Jupiter using an exclamation mark
Speaker 2: before the command. So you could do dot PIP three,
Speaker 2: install leather, write in a cell or dull less to
Speaker 2: list files. Pretty handy.
Speaker 1: That is handy, let's jump around. He also mentions indentation right,
Speaker 1: that's super important in Python, crucial.
Speaker 2: He highlights it specifically. Unlike languages using curly braces, Python
Speaker 2: uses white space indentation to structure cod blocks.
Speaker 1: So it's not just for looks. It actually tells Python
Speaker 1: how the code fits together exactly.
Speaker 2: Get it wrong, you get errors.
Speaker 1: Good warning. Okay. So plotting with letter how does that work?
Speaker 2: The Junker demonstrates defining your data points and Leather is
Speaker 2: quite flexible. Accepts data in a few formats like lists
Speaker 2: of pairs.
Speaker 1: Okay.
Speaker 2: Then you create a chart object. Then you add things
Speaker 2: to it like dots using chart dot ad dots makes sense. Finally,
Speaker 2: to see it or save it, you use chart dot
Speaker 2: to SVG. It outputs a scalable mector graphic file SVG.
Speaker 1: Okay, so it's a pretty direct path. Uh, data chart
Speaker 1: object ad elements output very direct.
Speaker 2: He also shows tweaking the look, dot color size, and
Speaker 2: plotting multiple sets of data on one chart.
Speaker 1: Does leather handle colors automatically for multiple sets?
Speaker 2: It does? Yeah, picks different default colors which helps distinguish them.
Speaker 1: Nice. What else can leather do besides dots?
Speaker 2: Pajunkar introduces lines with adline bar charts with AD bars
Speaker 2: good for text categories, and column charts with AD columns.
Speaker 1: So the basic chart types.
Speaker 2: The fundamentals, Yeah, and shows simple ways to customize those two.
Speaker 1: What about axes and scales as leather handled that it does?
Speaker 2: He explains, it automatically figures out the right scale ordinal
Speaker 2: for text, linear for numbers, temporal for dates, but you
Speaker 2: can also manually set limits if you need more control,
Speaker 2: say for the II axis range.
Speaker 1: Okay, any other styling.
Speaker 2: The chapter wraps up touching on styling options, setting specific
Speaker 2: tick values on axes, changing the overall look with themes
Speaker 2: via leather dot theme themes okay, and even a cool
Speaker 2: example of making data point colors change based on their
Speaker 2: position using a custom function, a little touch of dynamic viz.
Speaker 1: Wow. Okay, So leather seems like a good starting point
Speaker 1: for simple stuff.
Speaker 2: That's the takeaway. Great entry point. But as Pajenkra points out,
Speaker 2: for more complex, highly customized charts, you'll need the broader
Speaker 2: scientific Python ecosystem.
Speaker 1: Which brings us it chapter four the Powerhouse chapter exactly.
Speaker 2: Chapter four introduces the SCIPI ecosystem. It's this collection of
Speaker 2: libraries built for math, science, engineering, and Python.
Speaker 1: Okay, so what's in this ecosystem.
Speaker 2: Pajenkra lists the core players, Python itself obviously, numb PI
Speaker 2: for numerical computing NUMBPI.
Speaker 1: Heard a lot about that one.
Speaker 2: The SIPE library itself, which has tons of mathematical routines,
Speaker 2: and matt plotlib for more advanced plotting matte plotlib.
Speaker 1: Okay, so that's the step up from Letter definitely.
Speaker 2: He also mentions other key libraries pandas for data analysis.
Speaker 1: Like working with tables, ah Pantas.
Speaker 2: SIMPI for symbolic math, psychic image for image processing, psyche
Speaker 2: learn for machine learning. It's a whole toolkit.
Speaker 1: Wow. Quite the ecosystem, and.
Speaker 2: He reminds us Jupiter notebook is often the interactive environment
Speaker 2: where you use all these together.
Speaker 1: Makes sense, So the chapter dies into numb PI.
Speaker 2: It focuses on numb PI and its core data structure.
Speaker 2: The underay n dimensional array deray.
Speaker 1: What is that exactly?
Speaker 2: Pajunker explains, it's like a super efficient container for numerical data. Importantly,
Speaker 2: all items in an under ray have the same data.
Speaker 1: Type, ah, same type. Why is that important?
Speaker 2: Speed? Yeah, That uniformity makes calculations incredibly fast compared to
Speaker 2: standard Python lists.
Speaker 1: Got it? How do you make one?
Speaker 2: He shows creating them from Python lists using np dot array.
Speaker 2: You can also specify the data type using d.
Speaker 1: Type okay, and accessing.
Speaker 2: Elements standard indexing using square brackets, both positive indices starting
Speaker 2: from zero and negative indices counting from the end, like.
Speaker 1: Myra's eer for the first, myra one for the last.
Speaker 2: Exactly, and he points out, try to access something that
Speaker 2: isn't there, you get an index error.
Speaker 1: Good to know. What about more dimensions like tables?
Speaker 2: Yet he moves on to two d arrays like matrices
Speaker 2: or tables, and even three d arrays. Shows how indexing
Speaker 2: works there too, to get specific elements or entire rows or.
Speaker 1: Columns, slicing and dicing the data.
Speaker 2: Pretty much. That ability to sele elect and manipulate parts
Speaker 2: of your data easily is a key numb Pie strength.
Speaker 1: What else about injuries properties.
Speaker 2: Briefly runs through useful properties indem for number of dimensions,
Speaker 2: shape for the size of each.
Speaker 1: Dimension like rows and columns in two D, right.
Speaker 2: D type for the data type again, size for total elements,
Speaker 2: invites for memory usage, and t for the.
Speaker 1: Transpos transpose flips, rose and columns.
Speaker 2: Yep, just useful ways to understand the structure of your array.
Speaker 1: Anything else in the numpie chapter.
Speaker 2: It finishes by listing some handy built in NUMPI constants
Speaker 2: np dot an f or infinity, np dot n n
Speaker 2: n for not a number.
Speaker 1: You see nana lot in real data.
Speaker 2: Very common and mathematical constants like np dot t i
Speaker 2: and npe useful for calculation.
Speaker 1: Okay, so chapter four gives us numb Pi for handling
Speaker 1: the numbers efficiently. Chapter five must be putting it together
Speaker 1: with mattplotlib for visualization.
Speaker 2: You got it. Chapter five data visualization with numb Pi
Speaker 2: and matt Plotlib.
Speaker 1: Finally, Matt plotlip.
Speaker 2: Pajanker introduces it as a fundamental plotting library in the
Speaker 2: siepi world. Actually inspired by Matt Labs plotting.
Speaker 1: Ah.
Speaker 2: Okay, installation kit three, install matt plotlib standard stuff and
Speaker 2: using it in code typically import mattplotlib dot pieplot as PLTKA.
Speaker 2: Giving it that short name PLT is convention. And if
Speaker 2: you're in Jupiter, that magic command percent matt plotlib inline
Speaker 2: is key.
Speaker 1: What that again tells.
Speaker 2: Jupiter to display your plots right there in the notebook
Speaker 2: output below the code cell very convenient.
Speaker 1: Right and import numpi two.
Speaker 2: Oh yeah important NumPy as np. Mattplotlib works hand in
Speaker 2: glove with numpi erase.
Speaker 1: Okay, Import's done. How do we create data to plot?
Speaker 1: He mentioned numpike creation routines.
Speaker 2: He did first up is np dot arrange creates evenly
Speaker 2: spaced numbers and arrange like.
Speaker 1: Zero, one, two three or zero point five to one
Speaker 1: point zero exactly.
Speaker 2: You have to give it the stop value, but start
Speaker 2: and step size are optional. He shows examples, including making
Speaker 2: x values for a simple y x plot.
Speaker 1: Okay, arrange what else np.
Speaker 2: Dot limb space similar, but instead of step size you
Speaker 2: give it start, stop and how many points you want
Speaker 2: in between evenly spaced ah.
Speaker 1: Useful if you need exactly, say, one hundred points across
Speaker 1: a range precisely.
Speaker 2: Pudge on car shows an example, and how to turn
Speaker 2: off plot axes with plt dot axsis just to see
Speaker 2: the generated.
Speaker 1: Points interesting any others.
Speaker 2: Briefly mentions np dot log space and np dot gm
Speaker 2: space for logarithmically or geometrically spaced values. Useful for certain
Speaker 2: plots like log scales, shows their output visually.
Speaker 1: Good to know they exist. So we have data, How
Speaker 1: do we plot it?
Speaker 2: The core function is PLT dot plot. He shows using
Speaker 2: it with both Python lists and numbpi arrays to create
Speaker 2: single line plots. Demonstrates a simple curve while by two.
Speaker 1: Okay, PLT dot plot. How about multiple lines on one chart?
Speaker 2: Two ways either call plt dot plot multiple times for
Speaker 2: each line, or you can sometimes provide multiple y value
Speaker 2: rays to a single PLT dot plot. Mattplot lib handles
Speaker 2: the different colors automatically.
Speaker 1: Nice, But plots need labels and stuff right absolutely.
Speaker 2: Pa gen car covers the essentials adding a grid PLT
Speaker 2: dot grid true. Saving the plot PLT dot save fig,
Speaker 2: myplot dot png.
Speaker 1: Save fig important. What about axis limits.
Speaker 2: PLT dot axis for setting all limits at once, or
Speaker 2: p l t dot XLM and p lt dot XLM
Speaker 2: for individual control over the x and y ranges. Good
Speaker 2: titles and axis labels PLT dot title p l T
Speaker 2: dot rabel and p l T dot label really crucial
Speaker 2: for making plots understandable.
Speaker 1: Definitely need those. What about legends If you have multiple lines.
Speaker 2: Use the label argument inside p l T dot plot
Speaker 2: for each line like p l T dot plot x
Speaker 2: y one label data set one, then call PLT dot
Speaker 2: legend to display it, and.
Speaker 1: You can control where the legend appears.
Speaker 2: Using the lock argument in PLT dot legend like upper
Speaker 2: left or best to let matplot lib decide.
Speaker 1: Okay, making it informative, Now making it look good.
Speaker 2: Colors lines big part of chapter five. He details changing
Speaker 2: colors with short codes like R for red, G, green,
Speaker 2: b blue, simple codes, changing line styles solid, dah, dash, dash, dash,
Speaker 2: dot is, adding markers to data points, et cetera. Lots
Speaker 2: of options. You can combine these format strings like row
Speaker 2: for red, circle markers dash line.
Speaker 1: Combines color marker and lin style.
Speaker 2: Hmm efficient Yeah. And then he shows more detailed control
Speaker 2: using keyword arguments in PLT dot plot like what things
Speaker 2: like colored red line style dashed line with two marcro
Speaker 2: markerface color blue marker size ten, much finer control.
Speaker 1: Wow, lots of options there. Anything else on customization.
Speaker 2: Finishes with customizing the tick marks the actual number shown
Speaker 2: on the axes. Using PLT dot x sticks and PLT
Speaker 2: dot tics, you can set the positions and even the
Speaker 2: labels for the ticks.
Speaker 1: So really fine grain control over how the axes look exactly.
Speaker 2: Matt plotlib gives you that power building on NUMPI data.
Speaker 1: So looking back at these initial chapters of practical Python
Speaker 1: data visualization, we've covered quite a bit of ground.
Speaker 2: We really have, from just getting Python running, setting up
Speaker 2: that interactive Jupiter environment, yeah, to a first taste of
Speaker 2: plotting with leather, and then diving into the core tools
Speaker 2: numbpi for the numbers and matt plotlib for them the pictures.
Speaker 1: Absolutely, and it's quite striking how even these early chapters
Speaker 1: give you a solid toolkit. You're not just looking at data,
Speaker 1: you're equipped to actually bring into life visually.
Speaker 2: Going from raw numbers to creating and customizing fundamental charts.
Speaker 2: It's a powerful first step.
Speaker 1: It really makes you think, doesn't it. How could this ability,
Speaker 1: even with these basic forms of visualization, change how you
Speaker 1: understand or communicate information in your own field?
Speaker 2: Yeah? Consider the data you see regularly, could using PLT
Speaker 2: dot plot or even simple leather charts reveal patterns or
Speaker 2: insights you just haven't noticed before. Turning numbers into stories.
Speaker 1: Definitely food for thought, and as pajunker shows, there are
Speaker 1: pathways for going Deever, those PEP docs, the full map
Speaker 1: plot lib documentation.
Speaker 2: You mentioned, there's a whole universe of more advanced techniques,
Speaker 2: different libraries, specialized charts out there.
Speaker 1: Perhaps in future deep dives we could explore some of
Speaker 1: those more advanced tools or look at specific real world applications.
Speaker 2: That'll be interesting. The possibility to these with Python visualization
Speaker 2: are well pretty exciting.