WEBVTT 1 00:00:00.000 --> 00:00:03.399 All right, let's kick off this deep dive. Today. We're 2 00:00:03.439 --> 00:00:07.240 diving headfirst into a challenge that's probably well, pretty familiar 3 00:00:07.280 --> 00:00:10.839 if you're an engineer. The sheer volume of calculations, right, 4 00:00:11.000 --> 00:00:14.359 the endless streams of data, and sometimes just feeling like 5 00:00:14.800 --> 00:00:17.519 the tools you've always used, maybe spreadsheets, they kind of 6 00:00:17.559 --> 00:00:18.079 hit a wall. 7 00:00:18.559 --> 00:00:22.039 They absolutely do, especially when problems get really complex, or 8 00:00:22.320 --> 00:00:25.600 you know, you find yourself doing the same tedious thing over. 9 00:00:25.480 --> 00:00:26.039 And over again. 10 00:00:26.160 --> 00:00:28.640 Yeah, exactly. Every engineer is looking for that edge. You 11 00:00:28.679 --> 00:00:33.320 want more speed, more flexibility, and just rock solid accuracy 12 00:00:33.359 --> 00:00:34.200 in your work. 13 00:00:34.039 --> 00:00:37.759 And that's really where programming starts to look very appealing. 14 00:00:37.799 --> 00:00:40.039 It offers a different way to tackle those challenges. 15 00:00:40.200 --> 00:00:43.439 And specifically today we're doing a deep dive into Python. 16 00:00:44.039 --> 00:00:46.399 You've probably heard the name. It's a programming language that's 17 00:00:46.560 --> 00:00:47.799 just incredibly versatile. 18 00:00:47.880 --> 00:00:49.439 Yeah, it's popping up everywhere, and. 19 00:00:49.399 --> 00:00:54.359 It's rapidly becoming well, almost indispensable in civil and structural engineering. 20 00:00:54.920 --> 00:00:57.560 We've been digging into a guide that's literally written for 21 00:00:57.840 --> 00:00:59.520 engineers wanting to jump into this. 22 00:00:59.479 --> 00:01:02.200 World, right, and our goal here in this deep dive 23 00:01:02.280 --> 00:01:04.439 is to take that source material and really pull out 24 00:01:04.439 --> 00:01:07.680 the core concepts. We want to show you how Python, 25 00:01:08.200 --> 00:01:12.000 especially when you pair it with some really powerful specialized libraries, 26 00:01:12.400 --> 00:01:16.120 can just fundamentally change how you work. So streamlining workflows, 27 00:01:16.159 --> 00:01:20.040 streamlining workflows, tackling problems that may be felt impossible before 28 00:01:20.560 --> 00:01:22.920 automating those tasks that just eat up your time. 29 00:01:23.120 --> 00:01:29.480 Yeah yeah, So why should you our listener care? Because 30 00:01:29.840 --> 00:01:32.640 what we learn diving into this guide, it feels like 31 00:01:32.719 --> 00:01:36.840 it offers a genuine shortcut, a shortcut to what though, 32 00:01:36.959 --> 00:01:39.879 a way to handle those real world engineering problems faster, 33 00:01:40.120 --> 00:01:43.319 more efficiently, and honestly with a level of flexibility that's 34 00:01:43.480 --> 00:01:45.519 just tough to get with the traditional methods alone. 35 00:01:45.640 --> 00:01:46.640 Okay, let's unpack that. 36 00:01:46.680 --> 00:01:48.000 Then where do you even begin? 37 00:01:48.359 --> 00:01:50.120 Well, the guide we look at starts right there with 38 00:01:50.200 --> 00:01:52.879 the motivations the why bother for an engineer to learn 39 00:01:52.920 --> 00:01:55.439 Python right, and one of the big ones is exactly 40 00:01:55.519 --> 00:01:59.000 what you said, moving beyond the limits of spreadsheets, especially 41 00:01:59.040 --> 00:02:02.200 when data gets big or the calculations get intricate, you 42 00:02:02.280 --> 00:02:04.040 need something faster, more adaptable. 43 00:02:04.079 --> 00:02:07.439 So it's not like ditch Excel immediately. It's more like 44 00:02:07.680 --> 00:02:09.639 adding a new powerful. 45 00:02:09.240 --> 00:02:10.560 Tool exactly right. 46 00:02:10.800 --> 00:02:13.919 The source material is pretty clear you can still use 47 00:02:13.960 --> 00:02:16.840 Python in a professional setting, even when you need to 48 00:02:16.879 --> 00:02:20.719 share data in common formats like Excel files. Okay, we'll 49 00:02:20.719 --> 00:02:23.800 see how a library called pandas makes that surprisingly seamless. 50 00:02:23.800 --> 00:02:25.800 Actually, and another motivation. 51 00:02:25.599 --> 00:02:29.879 Learning practical engineering applications. This isn't just abstract coding theory. 52 00:02:29.919 --> 00:02:33.360 It's directly applicable. We're talking about solving the kinds of 53 00:02:33.400 --> 00:02:35.960 structural or civil problems you face day to day. 54 00:02:36.080 --> 00:02:39.840 Yeah, and who wouldn't want to say, automate creating reports, 55 00:02:40.280 --> 00:02:44.280 maybe getting them into professional formats like latex or PDF. 56 00:02:44.080 --> 00:02:48.840 Or generating really sharp publication quality plots straight from your analysis. 57 00:02:48.919 --> 00:02:51.960 Exactly. These are all things Python enables. It's not about 58 00:02:51.960 --> 00:02:53.080 replacing everything, No. 59 00:02:53.400 --> 00:02:53.800 Not at all. 60 00:02:53.879 --> 00:02:57.120 It's about empowering you with this adaptable tool set that 61 00:02:57.240 --> 00:02:59.520 really complements the skills in the software. 62 00:02:59.120 --> 00:02:59.800 You already use. 63 00:03:00.120 --> 00:03:03.319 Okay, so Python it is. But where do you literally start, 64 00:03:03.360 --> 00:03:06.479 like installing it, typing the code? The guide gives us 65 00:03:06.520 --> 00:03:07.479 those first steps. 66 00:03:07.800 --> 00:03:11.240 Yeah, for beginners, the usual recommendation is anaconda. It's a 67 00:03:11.280 --> 00:03:16.039 distribution basically a package okay that bundles Python itself along 68 00:03:16.080 --> 00:03:18.280 with most of the essential libraries you'll need. It just 69 00:03:18.319 --> 00:03:20.280 saves a ton of set up headaches right, gets you 70 00:03:20.319 --> 00:03:23.560 going faster, and the environment for actually writing the code 71 00:03:23.919 --> 00:03:27.680 that material mention Jupiter notebooks. Jupiter notebooks are fantastic, especially 72 00:03:27.680 --> 00:03:29.599 for this kind of work. They let you mix things up. 73 00:03:29.680 --> 00:03:32.240 How So you have code cells where you write and 74 00:03:32.319 --> 00:03:35.800 run your Python code, right, Yeah, but you also have markdown. 75 00:03:35.319 --> 00:03:39.039 Cells ah for text and explanations. 76 00:03:38.400 --> 00:03:42.639 Exactly, write explanations, put in equations, add images, basically document 77 00:03:42.680 --> 00:03:45.879 your whole thought process alongside the actual calculations. 78 00:03:46.080 --> 00:03:49.199 So it's like a living document your analysis and your 79 00:03:49.240 --> 00:03:51.240 explanation all in one place. 80 00:03:51.080 --> 00:03:54.439 Which is just invaluable for checking your work later or 81 00:03:54.479 --> 00:03:56.960 sharing it with colleagues. Makes things reproducible. 82 00:03:57.039 --> 00:04:01.439 Okay, environment set up. Now, what about the absolute bedrock, 83 00:04:01.719 --> 00:04:05.039 the foundational Python concepts an engineer needs to. 84 00:04:05.000 --> 00:04:08.080 Get so the source runs through the core syntax pretty quickly. 85 00:04:08.599 --> 00:04:11.719 Things like comments in your code using the hash symbol 86 00:04:11.759 --> 00:04:16.319 hashtag for a single line or triple quotes for multiple lines. 87 00:04:16.560 --> 00:04:18.560 Essential for clarity, absolutely essential. 88 00:04:18.680 --> 00:04:20.439 It's not just telling the computer what to do, but 89 00:04:20.800 --> 00:04:23.759 reminding yourself or telling others why you're doing. 90 00:04:23.680 --> 00:04:24.160 It that way. 91 00:04:24.279 --> 00:04:27.279 Good practice. Easy to skip when you're in a hurry, though. 92 00:04:27.360 --> 00:04:30.720 Oh definitely. Then you've got variables obviously, and understanding the 93 00:04:30.720 --> 00:04:31.680 basic data. 94 00:04:31.439 --> 00:04:33.879 Types like numbers and texts right in. 95 00:04:33.959 --> 00:04:38.360 For integers whole numbers, float for numbers with decimal points, 96 00:04:39.120 --> 00:04:42.720 booll for true or false conditions, which are key for logic, 97 00:04:43.399 --> 00:04:46.759 and string for text. Think of them like labeled boxes 98 00:04:46.800 --> 00:04:48.560 for different kinds of information. 99 00:04:48.199 --> 00:04:49.240 And grouping data. 100 00:04:49.600 --> 00:04:50.279 Collections. 101 00:04:50.480 --> 00:04:53.040 Yeah, Python has built in collections. The list is probably 102 00:04:53.079 --> 00:04:55.519 the most common. It's like a flexible array or vector 103 00:04:55.680 --> 00:04:58.199 flexible hot. It's ordered, you can change things in it, 104 00:04:58.439 --> 00:05:01.680 and you grab elements using an index starting from zero. 105 00:05:01.720 --> 00:05:04.560 That's important, index zero. It can even hold different types 106 00:05:04.560 --> 00:05:07.360 of data like numbers and strings together, okay. 107 00:05:07.160 --> 00:05:10.399 And dictionaries a dick Yeah, that's more like a lookup table. 108 00:05:11.240 --> 00:05:15.040 It stores data as key value pairs, so you find 109 00:05:15.040 --> 00:05:18.759 a value using its unique key, not its position like 110 00:05:18.800 --> 00:05:19.279 in a list. 111 00:05:19.839 --> 00:05:21.920 Un ordered gotcha and tupuls tuple. 112 00:05:22.000 --> 00:05:24.240 It's ordered like a list, but the big difference is 113 00:05:24.279 --> 00:05:25.720 you can't change it once you create it. 114 00:05:26.000 --> 00:05:26.639 Immutable. 115 00:05:26.879 --> 00:05:27.399 Why use that? 116 00:05:27.480 --> 00:05:27.680 Then? 117 00:05:27.879 --> 00:05:31.639 They're often used when a function needs to return multiple results. 118 00:05:31.839 --> 00:05:33.879 You bottle them in a tuple and then you can 119 00:05:33.920 --> 00:05:35.920 easily unpack them back into separate variables. 120 00:05:36.279 --> 00:05:40.439 H okay, makes sense. Basic math is obvious plus velmad 121 00:05:40.560 --> 00:05:43.600 But what about powers exponents. 122 00:05:43.120 --> 00:05:46.639 Right, Engineers use those all the time. Python uses double asterisks, 123 00:05:47.160 --> 00:05:49.519 so two in three is too cubed. Not the care 124 00:05:49.639 --> 00:05:50.920 ensemble you might see elsewhere. 125 00:05:50.920 --> 00:05:51.720 That's a common. 126 00:05:51.480 --> 00:05:53.160 Gotcha good tip for powers. 127 00:05:53.839 --> 00:05:54.759 Also useful are. 128 00:05:54.639 --> 00:05:57.839 The assignment operators like plus or make just shorthand ways 129 00:05:57.839 --> 00:06:00.399 to update of variables of value like x plus one 130 00:06:00.439 --> 00:06:01.600 is the same as x plus one. 131 00:06:01.959 --> 00:06:03.120 There's a bit of typing. 132 00:06:02.839 --> 00:06:04.879 And making decisions logic flow. 133 00:06:05.000 --> 00:06:07.959 That's your if yes, which is short for l if 134 00:06:08.000 --> 00:06:11.079 and l statements. They work based on conditions that evaluate 135 00:06:11.120 --> 00:06:11.879 too true or. 136 00:06:11.839 --> 00:06:15.800 False, using comparison operators exactly for checking equality. Is this 137 00:06:15.879 --> 00:06:18.920 equal to that? For not equal, then your standard edge. 138 00:06:18.839 --> 00:06:22.279 A and I remember the source flag A really common mistake. 139 00:06:21.959 --> 00:06:25.160 Here, Oh yeah, the classic versus Everyone does it at first. 140 00:06:25.480 --> 00:06:29.360 Single equals is assignment right, x equals five, assigns five 141 00:06:29.399 --> 00:06:31.879 to x, double equals x and equals five, so this 142 00:06:32.000 --> 00:06:34.800 is equal to five. Huge difference in meaning. Get that 143 00:06:34.879 --> 00:06:36.600 wrong and your logic goes haywire. 144 00:06:36.680 --> 00:06:40.439 And you combine conditions with and or not standard logic yep. 145 00:06:40.560 --> 00:06:44.839 No Python structure It's all about the spacing, isn't it indentation? 146 00:06:45.120 --> 00:06:49.000 Yes, this is fundamental Python. Where other languages might use 147 00:06:49.040 --> 00:06:52.600 curly braces or keywords like ndif to show code blocks 148 00:06:52.639 --> 00:06:55.519 belonging to an if statement or a loop, Python uses 149 00:06:55.600 --> 00:06:57.959 indentation consistent spacing. 150 00:06:58.240 --> 00:06:59.319 And it's not just for looks. 151 00:06:59.480 --> 00:07:03.040 No, it's syntactically mandatory. If your indentation is wrong, your 152 00:07:03.040 --> 00:07:05.399 code won't run, or worse, it'll run in correctly. It 153 00:07:05.480 --> 00:07:07.879 forces a certain level of visual clarity, which is actually 154 00:07:07.920 --> 00:07:08.360 a good thing. 155 00:07:08.439 --> 00:07:10.279 Okay, what about doing things repeatedly? 156 00:07:10.439 --> 00:07:10.839 Loops? 157 00:07:11.079 --> 00:07:13.399 Four loops are the main tool here. You use them 158 00:07:13.439 --> 00:07:16.279 to iterate over sequences like the items in a list, 159 00:07:16.720 --> 00:07:19.199 or to just repeat an action a specific. 160 00:07:18.800 --> 00:07:19.519 Number of times. 161 00:07:19.560 --> 00:07:21.000 And that range function comes up. 162 00:07:20.959 --> 00:07:23.839 A lot very often with four loops. Dot range n 163 00:07:24.000 --> 00:07:26.560 gives you a sequence of numbers from zero up two, 164 00:07:26.639 --> 00:07:27.680 but not including n. 165 00:07:27.839 --> 00:07:31.480 So range five is zero, one, two, three, four exactly. 166 00:07:31.879 --> 00:07:34.040 That n one end point is crucial to remember. 167 00:07:34.120 --> 00:07:37.680 Can you control the loop while it's running like stop early, yep. 168 00:07:37.720 --> 00:07:40.879 Break, exits the loop, immediately, continue, skips the rest of 169 00:07:40.879 --> 00:07:42.879 the current iteration, and jump straight to the next one. 170 00:07:43.360 --> 00:07:44.639 Very handy sometimes. 171 00:07:44.319 --> 00:07:48.480 Okay, last basic concept functions key for organization totally. 172 00:07:48.759 --> 00:07:51.480 Think of a function as a named, reusable chunk of 173 00:07:51.480 --> 00:07:54.240 code that does one specific job. You define it with 174 00:07:54.279 --> 00:07:57.399 a deaf keyword, give it a name, give it inputs, right, 175 00:07:57.600 --> 00:08:01.079 specify any inputs it needs in parentheses. Then it performs 176 00:08:01.079 --> 00:08:04.759 its operations and usually uses the return keyword to send 177 00:08:04.800 --> 00:08:05.480 back a result. 178 00:08:05.560 --> 00:08:08.800 An output keeps your code cleaner, reusable. 179 00:08:08.279 --> 00:08:12.000 Exactly avoids repeating yourself. The source also mentioned variable scope. 180 00:08:12.040 --> 00:08:15.040 Ah, yes, local versus global important stuff. 181 00:08:15.480 --> 00:08:19.560 Variables created inside of function are local. They only exist 182 00:08:19.600 --> 00:08:22.279 within that function and disappear when it finishes. 183 00:08:22.000 --> 00:08:23.800 So they don't mess with things outside. 184 00:08:24.120 --> 00:08:24.319 Right. 185 00:08:24.639 --> 00:08:27.879 Global variables are defined outside functions and can generally be 186 00:08:27.959 --> 00:08:32.799 accessed from anywhere. Understanding scope helps prevent unexpected side effects. 187 00:08:32.879 --> 00:08:36.440 The trapezoid area function example and the source was pretty clear. 188 00:08:36.639 --> 00:08:39.840 Yeah, good, simple example. You pass in the bases in height, 189 00:08:40.320 --> 00:08:43.440 The function calculates the area using the formula and returns 190 00:08:43.600 --> 00:08:44.759 that single value. 191 00:08:44.879 --> 00:08:47.679 And you mentioned tiples before for returning multiple things. 192 00:08:47.960 --> 00:08:48.240 Yep. 193 00:08:48.399 --> 00:08:50.759 The source showed how you can set default values for 194 00:08:50.799 --> 00:08:54.600 function inputs, making them optional, and how returning a twopole 195 00:08:54.919 --> 00:08:58.000 lets a function give back say both the area and perimeter, 196 00:08:58.279 --> 00:09:01.000 which you can then easily unpack, and two separate variables 197 00:09:01.039 --> 00:09:01.559 where you call. 198 00:09:01.440 --> 00:09:01.919 It the function. 199 00:09:02.080 --> 00:09:05.240 Okay, that covers the Python fundamentals. Now, the real power 200 00:09:05.279 --> 00:09:08.279 boost for engineers comes from the libraries, right or absolutely. 201 00:09:09.000 --> 00:09:12.000 This is where Python transforms from a general purpose language 202 00:09:12.120 --> 00:09:15.919 into a specialized engineering powerhouse. The guide really focuses on 203 00:09:16.039 --> 00:09:18.960 four core libraries that are well game changers. 204 00:09:19.159 --> 00:09:21.320 Let's start with the big one for numbers, numb PI. 205 00:09:21.600 --> 00:09:22.000 Numb PI. 206 00:09:22.120 --> 00:09:25.840 It's fundamental if you're doing numerical work in Python, especially 207 00:09:25.840 --> 00:09:28.879 with the rays. This is the standard. The core element 208 00:09:29.000 --> 00:09:30.679 is the end array, the ind dimensional array. 209 00:09:30.759 --> 00:09:32.720 How is it different from a standard Python list. 210 00:09:33.000 --> 00:09:36.679 It's like a list, but highly optimized for math, generally 211 00:09:36.759 --> 00:09:40.559 much faster for calculations. Crucially, all elements in a NUMBPI 212 00:09:40.639 --> 00:09:42.799 array usually have to be the same data type, like 213 00:09:42.840 --> 00:09:44.399 all floats or all integers. 214 00:09:44.799 --> 00:09:46.000 Lists can mix types. 215 00:09:46.080 --> 00:09:47.399 Got it? How do you use it? 216 00:09:47.480 --> 00:09:51.080 Standard practice is import nump is NP. Everyone uses NP 217 00:09:51.279 --> 00:09:52.039 as the shorthand. 218 00:09:52.080 --> 00:09:53.919 Okay, creating a raise easy. 219 00:09:54.360 --> 00:09:57.080 NP dot zeros makes an array full of zeros mp 220 00:09:57.159 --> 00:09:59.279 dot range is like Python's range, but gives you an 221 00:09:59.279 --> 00:10:02.440 actual numbpire right, and np dot lends space is super 222 00:10:02.559 --> 00:10:03.559 useful for engineers. 223 00:10:03.639 --> 00:10:04.200 Lends space. 224 00:10:04.279 --> 00:10:06.879 Yeah, np dot lends space, go start stopping them. It 225 00:10:06.919 --> 00:10:10.159 creates an array with numb evenly spaced points between start 226 00:10:10.200 --> 00:10:13.039 and stop, including the stop point, perfect for creating X 227 00:10:13.080 --> 00:10:15.399 coordinates along a beam or points for plotting. 228 00:10:15.679 --> 00:10:18.279 Okay, and doing math with these arrays. 229 00:10:18.120 --> 00:10:21.279 Numb pi shines here. You can get properties like dimensions, 230 00:10:21.360 --> 00:10:24.720 want to be in shape, shape shape sized, data type 231 00:10:24.799 --> 00:10:28.399 d type basic math plus a at even for powers 232 00:10:28.879 --> 00:10:32.080 works element wisele limit wise, meaning if you have two 233 00:10:32.200 --> 00:10:35.039 arrays A and B the same shape A plus B, 234 00:10:35.120 --> 00:10:37.000 adds the first element of A to the first of B, 235 00:10:37.279 --> 00:10:39.279 the second to the second, and so on same. For 236 00:10:39.399 --> 00:10:42.759 multiplication to B, it applies the operation to corresponding elements. 237 00:10:42.799 --> 00:10:47.559 But wait, matrix multiplication, that's different, right, crucial for structural analysis. 238 00:10:47.000 --> 00:10:48.720 Absolutely crucial. This trists people up. 239 00:10:48.840 --> 00:10:52.720 The standard asterisk is element wise multiplication. For true matrix 240 00:10:52.799 --> 00:10:56.279 multiplication like you need for transformations or solving systems, use 241 00:10:56.320 --> 00:10:59.120 the AT symbol an b okay. 242 00:10:58.840 --> 00:11:01.159 At for matrix math element wise got it? 243 00:11:01.240 --> 00:11:02.000 Remember at B. 244 00:11:02.919 --> 00:11:05.919 You can also slice arrays like lists using start end 245 00:11:05.960 --> 00:11:08.559 dot step, but it works across multiple dimensions too, and 246 00:11:08.600 --> 00:11:11.919 you can reshape them, stack them vertically, v stack a pend, 247 00:11:12.039 --> 00:11:14.399 or insert elements. Lots of manipulation tools. 248 00:11:14.600 --> 00:11:18.080 The guide had a great practical example calculating moment and 249 00:11:18.120 --> 00:11:19.399 sheer along a simple beam. 250 00:11:19.639 --> 00:11:22.519 Yeah, that really shows the element wise power. You create 251 00:11:22.519 --> 00:11:25.399 your X ray along the beam using np dot line space. 252 00:11:25.600 --> 00:11:28.559 Then you just write the standard beam equations for moment 253 00:11:28.799 --> 00:11:31.679 M and sheer V, but using the entire x ray 254 00:11:31.679 --> 00:11:31.919 in the. 255 00:11:31.840 --> 00:11:35.159 Formula like M equals l x two w x two 256 00:11:35.200 --> 00:11:36.039 two exactly. 257 00:11:36.360 --> 00:11:40.440 Numpi automatically applies that calculation to every single value in 258 00:11:40.480 --> 00:11:42.879 the X ray all at once. You get the entire 259 00:11:43.120 --> 00:11:46.720 M diagram or v diagram as a Numpi array instantly, 260 00:11:46.840 --> 00:11:47.559 super efficient. 261 00:11:47.799 --> 00:11:50.639 Wow. Okay, so numb pie handles the heavy numerical lifting. 262 00:11:50.919 --> 00:11:52.440 What if you need to deal with the math before 263 00:11:52.440 --> 00:11:55.000 you have numbers, like solving equations symbolically. 264 00:11:55.120 --> 00:11:57.720 That's where simp comes in. It's a library for symbolic mathematics. 265 00:11:57.840 --> 00:11:59.879 Think of it as a computer algebra system or see 266 00:12:00.600 --> 00:12:01.440 right inside Python. 267 00:12:01.559 --> 00:12:03.519 So algebra with symbols, not just numbers. 268 00:12:03.519 --> 00:12:07.360 Percisely, you import simpsky. Then you define symbols like xsp 269 00:12:07.480 --> 00:12:11.279 dot symbols x or maybe EI lsp dot symbols EIL 270 00:12:11.759 --> 00:12:12.840 for engineering properties. 271 00:12:13.200 --> 00:12:14.039 Then you can write. 272 00:12:13.799 --> 00:12:17.240 Equations using these symbols. Simps functions like sp dot solve 273 00:12:17.240 --> 00:12:19.799 set to solve an equation for a specific symbol, or 274 00:12:19.919 --> 00:12:22.559 sp dot lens solve to solve systems of linear equations 275 00:12:22.600 --> 00:12:25.360 symbolically calculus two differentiation integration. 276 00:12:25.519 --> 00:12:29.000 The guide mentioned a display function for making it look nice. 277 00:12:29.200 --> 00:12:33.480 Yeah, especially in Jupiter notebooks. Display your SIMP expression often 278 00:12:33.519 --> 00:12:37.960 renders the output using latex math formatting, so complicated formulas 279 00:12:38.000 --> 00:12:40.639 look like they do in a textbook, much easier to 280 00:12:40.679 --> 00:12:41.320 read and check. 281 00:12:41.679 --> 00:12:44.039 How does this help with a real problem? The source 282 00:12:44.120 --> 00:12:47.200 used a two span beam example, again perfect case. 283 00:12:47.639 --> 00:12:51.360 A continuous beam like that is often statically indeterminate. Basic 284 00:12:51.399 --> 00:12:54.720 equilibrium equations aren't enough to find all the support reactions, right. 285 00:12:54.600 --> 00:12:58.720 You need compatibility equations considering deflections or slopes Exactly. 286 00:12:59.080 --> 00:13:01.759 With SIMP, you can say up both the equilibrium equations 287 00:13:01.799 --> 00:13:05.600 and the compatibility equations using your defined symbols for unknown 288 00:13:05.600 --> 00:13:10.399 reactions like va VC and maybe an unknown internal moment MX. Okay, 289 00:13:10.679 --> 00:13:13.360 Then you use simply solvers like sp dot len solve 290 00:13:13.559 --> 00:13:16.200 to solve that system of symbolic equations. It gives you 291 00:13:16.240 --> 00:13:20.039 the algebraic expressions for those unknown reactions in terms of 292 00:13:20.080 --> 00:13:22.720 the symbolic loads and lengths WL, etc. 293 00:13:23.399 --> 00:13:28.039 So you solve the underlying indeterminate structure problem algebraically first. 294 00:13:28.080 --> 00:13:31.080 Yes, before you even plug in a single number. It 295 00:13:31.080 --> 00:13:34.200 helps you verify the fundamental mechanics and let simply handle 296 00:13:34.240 --> 00:13:37.039 the messy algebra. Then you can substitute numbers into those 297 00:13:37.080 --> 00:13:39.480 symbolic solutions later using numb pie. 298 00:13:39.519 --> 00:13:44.000 Perhaps that's powerful. Okay, From symbolic math to data wrangling. 299 00:13:44.320 --> 00:13:47.279 Pandas If numbpie is for a numerical arrays, PANDAS is 300 00:13:47.279 --> 00:13:52.399 for tabular data think spreadsheets, tables with rows and columns. 301 00:13:52.559 --> 00:13:56.080 It's built for handling potentially large structured data sets, especially 302 00:13:56.120 --> 00:13:58.399 reading and writing common file formats. 303 00:13:58.080 --> 00:14:00.000 Like Excel and CSV files. 304 00:13:59.639 --> 00:14:04.039 Exactly dot xlsx, dot csv. PANDAS makes working with those 305 00:14:04.080 --> 00:14:06.440 super easy. The core structure in PANDAS is. 306 00:14:06.440 --> 00:14:08.960 The data frame day frame like a table. 307 00:14:08.679 --> 00:14:12.279 Pretty much imagine a two D table, but with powerful extras. 308 00:14:12.360 --> 00:14:15.879 It has labeled columns like spreadsheet headers, and labeled rose 309 00:14:16.080 --> 00:14:19.759 called the index. It's designed for easily selecting, filtering, cleaning, 310 00:14:19.799 --> 00:14:21.360 and analyzing that kind of data. 311 00:14:21.360 --> 00:14:22.799 What kind of things can you do? Easily? 312 00:14:23.080 --> 00:14:26.919 Create them from scratch or from numpire rays access data 313 00:14:26.919 --> 00:14:29.879 by column name or row index, lots of data cleaning 314 00:14:29.919 --> 00:14:34.399 tools built in sorting, sort values, renaming columns or the index, rename, 315 00:14:34.679 --> 00:14:37.879 dropping rose or columns, drop handling missing data, resetting the 316 00:14:37.879 --> 00:14:40.440 index if it gets messed up, reset index. You can 317 00:14:40.440 --> 00:14:42.440 also combine data frames CONCAT. 318 00:14:42.639 --> 00:14:45.639 The guide really highlighted working with external files. That sounds 319 00:14:45.639 --> 00:14:46.679 incredibly useful. 320 00:14:46.759 --> 00:14:49.279 It is probably one of the most common uses for engineers. 321 00:14:49.679 --> 00:14:53.879 PDA dot redexl mydata dot xl sx or pd dot 322 00:14:53.919 --> 00:14:57.320 recsv results dot csv one line and your data isn't 323 00:14:57.320 --> 00:14:57.919 a data. 324 00:14:57.679 --> 00:15:00.360 Frame, even if the files a bit messy like multiple 325 00:15:00.440 --> 00:15:01.080 header rows. 326 00:15:01.159 --> 00:15:03.919 Yeah canas has options for that. You can specify header 327 00:15:04.039 --> 00:15:06.039 zero one if the headers pans the first two rows, 328 00:15:06.039 --> 00:15:08.720 for example, or index col zero if the first column 329 00:15:08.799 --> 00:15:11.240 the file should be used as the roll labels the index. 330 00:15:10.960 --> 00:15:13.559 And cleaning that imported data, getting rid of empty rows 331 00:15:13.639 --> 00:15:14.320 or junk. 332 00:15:14.159 --> 00:15:16.679 PANDAS makes that much easier than doing it manually. In Excel, 333 00:15:17.120 --> 00:15:19.240 you can drop those extra header levels that sometimes come 334 00:15:19.279 --> 00:15:22.200 in DF dot columns DOT drop level, remove rows with 335 00:15:22.240 --> 00:15:26.080 any missing values, DF dot drop and selectively drop rows. 336 00:15:25.919 --> 00:15:29.639 Or columns and getting data back out to share with colleagues. 337 00:15:29.639 --> 00:15:32.559 Maybe just as easy df dot to excel output dot 338 00:15:32.679 --> 00:15:36.480 XLSX or df dot t s sv cleanda dot csv. 339 00:15:36.919 --> 00:15:40.039 You can control whether the index is written and other formatting. 340 00:15:40.279 --> 00:15:42.840 It bridges the gap between your Python analysis and standard 341 00:15:42.840 --> 00:15:43.600 office formats. 342 00:15:43.639 --> 00:15:44.159 Really well. 343 00:15:44.279 --> 00:15:47.639 How did the two span beam example use pandas it. 344 00:15:47.720 --> 00:15:50.919 Showed using a data frame to manage results from multiple 345 00:15:50.960 --> 00:15:51.600 load cases. 346 00:15:51.759 --> 00:15:52.919 So you use simply. 347 00:15:52.720 --> 00:15:55.600 Numpi to calculate the M and V diagrams for dead lows, 348 00:15:55.639 --> 00:15:58.120 live load, windload, et cetera. Okay, you can store each 349 00:15:58.279 --> 00:16:01.519 MRA and vra as a in a Panda's data frame 350 00:16:01.600 --> 00:16:04.559 with columns labeled maybe mddvdad, MLIV. 351 00:16:04.200 --> 00:16:04.840 Live and so on. 352 00:16:04.960 --> 00:16:06.919 Ah, so everything is organized together. 353 00:16:06.720 --> 00:16:09.399 Exactly, all results in one table. Makes it super easy 354 00:16:09.399 --> 00:16:13.200 to then compare load cases, find maximums or minimums across cases, envelopes, 355 00:16:13.440 --> 00:16:14.799 or apply load combinations. 356 00:16:14.879 --> 00:16:19.120 Right, Okay, we've calculated, we've solved symbolically, we've organized the data. 357 00:16:19.279 --> 00:16:22.799 Now we need to see it visualization with matt plotlib. 358 00:16:22.879 --> 00:16:27.399 Mat plotlib it's the workhorse. Plotting library of Python very versatile. 359 00:16:27.399 --> 00:16:30.240 You can create anything of simple line graphs to complex 360 00:16:30.320 --> 00:16:32.240 publication quality technical figures. 361 00:16:32.559 --> 00:16:33.399 How do you get started? 362 00:16:33.519 --> 00:16:38.679 The standard import is importmattplotlib dot piplot as PLT. Everyone 363 00:16:38.759 --> 00:16:42.240 uses PLT. The key concept is the figure and axis 364 00:16:42.440 --> 00:16:43.320 figure and axis. 365 00:16:43.480 --> 00:16:44.399 The figure is like. 366 00:16:44.360 --> 00:16:47.320 The overall window or canvas. The axis usually just one. 367 00:16:47.399 --> 00:16:50.120 AX is the actual plot area within that figure where 368 00:16:50.120 --> 00:16:53.000 your data gets drawn, the part with the xy axis, 369 00:16:53.039 --> 00:16:54.200 the lines, the points, and. 370 00:16:54.200 --> 00:16:56.639 The guide recommended A specific way to set this up. 371 00:16:56.559 --> 00:16:59.440 Yeah, the object oriented approach is generally preferred for control. 372 00:17:00.039 --> 00:17:03.519 You start with fig AX, it goes PLT dot subplots. 373 00:17:04.079 --> 00:17:07.519 This creates both a figure FIG and a single axis. 374 00:17:07.200 --> 00:17:10.119 Object AX, and then you plot on AX exactly. 375 00:17:10.160 --> 00:17:12.799 You call messods directly on the AX object AC dot 376 00:17:12.839 --> 00:17:15.720 plot x data white data for a line plot, x 377 00:17:15.759 --> 00:17:19.000 dot scatter for scatterplots, et cetera. Then you use PLT 378 00:17:19.160 --> 00:17:21.519 dot show at the end to actually display the figure. 379 00:17:21.640 --> 00:17:24.039 Why that way instead of just PLT dot plot. 380 00:17:23.960 --> 00:17:26.359 Using the AX object gives you much finer control over 381 00:17:26.400 --> 00:17:30.359 everything on that specific plot labels, titles, ticks, legends, colors. 382 00:17:30.559 --> 00:17:32.720 It's much more robust, especially if you end up wanting 383 00:17:32.759 --> 00:17:35.000 multiple plots in the same figure later makes sense. 384 00:17:35.160 --> 00:17:38.559 Customization is obviously key for engineering plots totally. 385 00:17:38.480 --> 00:17:41.519 And map plot labelts. You customize almost anything at access 386 00:17:41.559 --> 00:17:44.839 labels with AX dot sets label and act dot set 387 00:17:44.839 --> 00:17:47.200 the label. Set a title with ax dot set title 388 00:17:47.519 --> 00:17:50.559 at a legend if you have multiple lines using ax dot. 389 00:17:50.400 --> 00:17:52.279 Legend line styles colors. 390 00:17:52.039 --> 00:17:55.319 YEP control line styles, solid dash colors, ad markers. 391 00:17:55.359 --> 00:17:56.039 At data points. 392 00:17:56.279 --> 00:17:58.880 You can fill areas between curves using ax dot fill 393 00:17:58.960 --> 00:18:02.559 between really useful for showing moment envelopes or maybe the 394 00:18:02.599 --> 00:18:05.160 feasible region in an interaction diagram. 395 00:18:05.160 --> 00:18:07.160 Oh nice, ticks and labels too. 396 00:18:07.119 --> 00:18:07.799 Full control. 397 00:18:07.920 --> 00:18:10.160 Customize the locations and labels of the tick marks on 398 00:18:10.200 --> 00:18:13.359 both axis sex sticks set tis. You can even rotate 399 00:18:13.400 --> 00:18:17.359 the dick labels sixtick labels rotation if they overlap essential 400 00:18:17.400 --> 00:18:18.680 for clear technical plots. 401 00:18:18.880 --> 00:18:22.920