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Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,1\N00:00:04,510 --> 00:00:05,680\NWelcome back. This video,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,2\N00:00:05,680 --> 00:00:10,480\NI'm going to walk you through some of the different types of charts that we're going to be learning how to create outcomes or
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,3\N00:00:10,480 --> 00:00:16,720\Nbe able to identify the appropriate type of chart for data in a question and understand key rules to avoid common errors.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,4\N00:00:16,720 --> 00:00:20,880\NI'm not going to be showing the detailed code for these chart types in the video.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,5\N00:00:20,880 --> 00:00:24,640\NYou're going to be able to find that in the documentation link from here. And also,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,6\N00:00:24,640 --> 00:00:28,600\NI'm going to be preparing a notebook that demonstrates various of these charting
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,7\N00:00:28,600 --> 00:00:34,330\Ntypes with the actual code to create them using the software we discussing.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,8\N00:00:34,330 --> 00:00:44,470\NSo common software for this or Seabourne and matplotlib, those are going to be the primary ones that we're working with this semester.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,9\N00:00:44,470 --> 00:00:49,330\NWhen I'm showing the function names, Seabourne is commonly input imported S.A.S.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,10\N00:00:49,330 --> 00:00:52,870\NSo as an ascot function is going to be a seabourne function PLDT,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,11\N00:00:52,870 --> 00:00:59,860\Nthe function is going to be a matplotlib of function and also showing the function you can use in plot nine or Ares g.G plot too,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,12\N00:00:59,860 --> 00:01:04,600\Nif you want to use those instead. I often use plot nine for a lot of my graphics.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,13\N00:01:04,600 --> 00:01:10,060\NThat's just for reference though. We're not going to be getting into much detail on Plot nine in the course of this course.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,14\N00:01:10,060 --> 00:01:16,130\NSo there's a variety of different types of charts. Some of them are showing relative proportions.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,15\N00:01:16,130 --> 00:01:23,860\NSome of them are showing how different amounts relate to each other. Some of them are showing positions and an x y coordinate space.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,16\N00:01:23,860 --> 00:01:30,070\NA bar chart is a very common type of chart that shows numeric values grouped by a categorical or ordinal variable.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,17\N00:01:30,070 --> 00:01:31,780\NSometimes they're grouped by New America as well.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,18\N00:01:31,780 --> 00:01:37,600\NBut usually our x axis is a categorical variable of some kind best with a moderate number of categories.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,19\N00:01:37,600 --> 00:01:41,950\NWe can use a second categorical variable to say color the bars.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,20\N00:01:41,950 --> 00:01:50,170\NSo this chart shows the survival rates of Titanic passengers or the X axis is the passage class for second or third class.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,21\N00:01:50,170 --> 00:01:55,660\NAnd then the bars are colored based on the gender of the of the passenger.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,22\N00:01:55,660 --> 00:01:58,000\NAnd so we can see the different survival rates.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,23\N00:01:58,000 --> 00:02:06,250\NThe y axis on a bar chart is often a mean or a sum or a count within the cap of the group determined by our categorical variables.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,24\N00:02:06,250 --> 00:02:12,970\NSometimes these will be horizontal. So the horizontal bar chart, the categorical is on the Y and the bars run horizontally.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,25\N00:02:12,970 --> 00:02:17,080\NThis also shows some whiskers that come from a confidence interval.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,26\N00:02:17,080 --> 00:02:24,760\NIt's very easy to generate a default, relatively good confidence interval with Seabourne so tough to pluck to plot
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,27\N00:02:24,760 --> 00:02:30,730\Nthese Seabourne has the count plot function which lets which does a quick,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,28\N00:02:30,730 --> 00:02:36,970\Nbasically categorical histogram. How many observations are in each are in each category.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,29\N00:02:36,970 --> 00:02:41,830\NThe cap plot variable will plot by default a mean value for each category.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,30\N00:02:41,830 --> 00:02:47,860\NAnd if you have it, do the mean plotting. It will also compute. Ninety five percent confidence intervals.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,31\N00:02:47,860 --> 00:02:53,350\NThat's what's being shown in this in this plot here.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,32\N00:02:53,350 --> 00:03:00,050\NAnd then you can also use the bat, the bar function or the plot nine Geon Bar.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,33\N00:03:00,050 --> 00:03:04,340\NSo if you rules about bar charts first is never start the Y axis on a bar chart.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,34\N00:03:04,340 --> 00:03:13,320\NAnything but zero. And so the reason for this we can see here is that.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,35\N00:03:13,320 --> 00:03:17,270\NSo the top one. So these are these are looking at the mean average ratings.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,36\N00:03:17,270 --> 00:03:22,770\NWe take you to movies, mean rating, and then we compute the mean of the average ratings within a genre.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,37\N00:03:22,770 --> 00:03:33,570\NWhat is that? So if we look here, the difference between horror and IMAX, it's a notable difference, but it's a difference of about point five or so.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,38\N00:03:33,570 --> 00:03:40,080\NThe difference between sci fi and short is a difference of a little under one, probably.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,39\N00:03:40,080 --> 00:03:46,170\NBut when we start the Y axis at 2.5 instead of zero,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,40\N00:03:46,170 --> 00:03:51,840\Nwhat happens is the differences look much larger than they are because the human eye, naturally it's not.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,41\N00:03:51,840 --> 00:04:01,050\NWe not only want to see the difference, but we want to it's very natural for us to compare the difference to the bar length because these are bars.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,42\N00:04:01,050 --> 00:04:06,620\NThey have length, they have an area since they're all the same with the length is proportional to the area.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,43\N00:04:06,620 --> 00:04:12,660\NBraking length area. Proportionality is a good way to confuse your readers,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,44\N00:04:12,660 --> 00:04:20,910\Nbut it looks like IMAX movies have twice as high an average rating as horror movies because the bar is twice as high, but they don't.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,45\N00:04:20,910 --> 00:04:25,770\NIt's really a shift from about 2.8 to three point three or three point four.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,46\N00:04:25,770 --> 00:04:33,090\NAnd so it creates a distortion that makes the different like it highlights the differences, but it makes the differences look larger than they are.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,47\N00:04:33,090 --> 00:04:39,210\NSo when I talked about integrity and avoiding deception, when I was introducing statistical graphics,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,48\N00:04:39,210 --> 00:04:45,750\Nthis is what I was talking about, the differences there. It's just not as big as it looks like it is.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,49\N00:04:45,750 --> 00:04:47,460\NAnd we truncate our bar charts.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,50\N00:04:47,460 --> 00:04:55,050\NSo if you have the general rule here to generalize beyond bar charts is if something has a length that varies based on the data,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,51\N00:04:55,050 --> 00:05:04,470\Nthat length needs to actually represent the value, not the value, minus something because you started the axis somewhere else.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,52\N00:05:04,470 --> 00:05:06,960\NSo if you're including Whiskers, like I did in the previous chart,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,53\N00:05:06,960 --> 00:05:14,040\Ndefine how they're computed and also as one thing to just be careful of seaboard's cat platen count plot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,54\N00:05:14,040 --> 00:05:21,270\NIf you aren't using the color for a second variable, they will just make every bar a different color for no particular reason,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,55\N00:05:21,270 --> 00:05:25,010\Nwhich it creates something that's different when it doesn't need to be so.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,56\N00:05:25,010 --> 00:05:29,520\NIt causes the reader to look for a difference that isn't actually their best avoided.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,57\N00:05:29,520 --> 00:05:32,640\NYou can fix that by just specifying the color.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,58\N00:05:32,640 --> 00:05:40,050\NWe saw histograms last week in a histogram as a bar chart, but a categorical was Binz or ranges of a numerical value.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,59\N00:05:40,050 --> 00:05:46,800\NAlso, though, if we have a bar chart that's showing the relative frequency of categorical variables that can also be called a histogram,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,60\N00:05:46,800 --> 00:05:50,730\Nthe Y axis is either the number or the fraction of occurrences in this case.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,61\N00:05:50,730 --> 00:05:58,170\NSo we can that. The key thing, though, is the different heights of the bars that I see visually, the relative frequency of different values.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,62\N00:05:58,170 --> 00:06:01,680\NSo it really makes it visually clear how the data is shaped.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,63\N00:06:01,680 --> 00:06:07,950\NWe can see Skewes and things like that. Is there one way to graphically describe a distribution?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,64\N00:06:07,950 --> 00:06:12,120\NA scatterplot shows two numeric variables. So each observation is a dot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,65\N00:06:12,120 --> 00:06:16,800\NEach observation has two numeric variables. And we put the one variable on the x axis.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,66\N00:06:16,800 --> 00:06:22,290\NThe other variable on the Y axis and put the dot at where its variable values would intersect.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,67\N00:06:22,290 --> 00:06:26,700\NThis is really useful for seeing how two variables relate. Does one increase with the other Duplin?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,68\N00:06:26,700 --> 00:06:30,840\NDo points clump or cluster in an interesting way? Other interesting patterns.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,69\N00:06:30,840 --> 00:06:43,050\NIt helps us find outliers. So this this is scatterplot is showing the tip versus the total bill for a bunch of restaurant bills.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,70\N00:06:43,050 --> 00:06:45,540\NAnd each each observation is a bill.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,71\N00:06:45,540 --> 00:06:55,380\NAnd then the x axis is the the under the total bill on the Y axis is the tip that the that the the customer added to the bill.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,72\N00:06:55,380 --> 00:07:02,790\NAnd we a couple of refinements we can do here. We can color or change to the point tight by a categorical variable.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,73\N00:07:02,790 --> 00:07:08,140\NSo on this one, we've changed it so that the points are different color.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,74\N00:07:08,140 --> 00:07:13,310\NSo those dinners are blue circles and the lunches are orange AXA's.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,75\N00:07:13,310 --> 00:07:19,130\NWe could also add a trend line or some other kind of a line to show some context, for example, on this chart,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,76\N00:07:19,130 --> 00:07:27,170\Nwe might want to plot a line that shows that the 20 percent point and that let us easily see where we're going over 20 percent,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,77\N00:07:27,170 --> 00:07:33,650\Nhow that the tips are distributed relative to it to a 20 percent mark.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,78\N00:07:33,650 --> 00:07:41,170\NWe can also X can be a categorical variable when that happens. We call this a point plot or a strip plot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,79\N00:07:41,170 --> 00:07:47,020\NFunctions for doing this are scatter scatterplot and then plotlines John Point,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,80\N00:07:47,020 --> 00:07:50,800\Nthe Seabourne documentation has some examples of more of these align plot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,81\N00:07:50,800 --> 00:07:59,350\NIt's like a scatterplot that we have to numeric variables. However, we it emphasizes the progression or continuity from one variable to the next.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,82\N00:07:59,350 --> 00:08:04,930\NBy combining them with a line, it really works best. We have one Y per X value that we want to plot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,83\N00:08:04,930 --> 00:08:11,260\NIf we've got more than one, it really starts getting very, very jagged. It's very common for Time series.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,84\N00:08:11,260 --> 00:08:15,430\NSo this is another example from the Seabourne tutorial not labeled super well.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,85\N00:08:15,430 --> 00:08:22,510\NI don't know what the value actually is, but it shows that we have some kind of a value that's changing over time and it's going negative.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,86\N00:08:22,510 --> 00:08:29,710\NThat was zero. The Y axis is at the top and the values otherwise our negative functions to create.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,87\N00:08:29,710 --> 00:08:36,370\NThese are line plot from seabourne, line from a matplotlib and Gyeom line from plot nine.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,88\N00:08:36,370 --> 00:08:41,380\NA box plot shows the distribution of a numeric variable grouped by a categorical.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,89\N00:08:41,380 --> 00:08:46,630\NSo the bar chart just showed us, say, the average value, maybe with confidence interval.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,90\N00:08:46,630 --> 00:08:51,820\NThe box plot actually shows us the distribution and it does so in a way that's based on the median.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,91\N00:08:51,820 --> 00:09:01,050\NSo the median, the the horizontal line in the middle of the box is the median value, the top and bottom of the box.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,92\N00:09:01,050 --> 00:09:06,750\NAre the first and third quarter close to the bottom of the first quartile and the top as the third quartile.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,93\N00:09:06,750 --> 00:09:12,630\NAnd what that means is twenty five percent of the values are below the bottom of the box.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,94\N00:09:12,630 --> 00:09:17,700\NTwenty five percent in the bottom half. Twenty five percent here and then twenty five percent above.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,95\N00:09:17,700 --> 00:09:21,390\NWe then show these these whiskers that extend out to the minimum,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,96\N00:09:21,390 --> 00:09:27,390\Na maximum of the data and a number of plotting packages will do some kind of an outlier detection.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,97\N00:09:27,390 --> 00:09:37,470\NThis is using seabourne default outlier detection. So if the max is very high and what the rule it uses by default is it allows the whisker to be.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,98\N00:09:37,470 --> 00:09:44,460\NSo you've got the IQ are the inter quartile range. That's the height of the box. It allows the whisker to be one point five times that tall.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,99\N00:09:44,460 --> 00:09:50,880\NAnd if you have any data points that are further away than that, it plots them as individual points, makes it easy to see outliers.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,100\N00:09:50,880 --> 00:09:57,930\NYou can change. It's that the whisker goes all the way up to the max, but it lets you quickly see and compare between different groups.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,101\N00:09:57,930 --> 00:10:03,210\NThe median, the first and third quartiles and the men in the max to the data.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,102\N00:10:03,210 --> 00:10:11,520\NVery useful for comparing observations of a variable when you're grouped by some categorical functions
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,103\N00:10:11,520 --> 00:10:19,680\Nfor doing this or box plot from both Seabourne and matplotlib and then Gyeom block box from plot nine.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,104\N00:10:19,680 --> 00:10:25,710\NA few more plots, a violin plot. It's like a box plot, except it's based around the mean and has curved sides.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,105\N00:10:25,710 --> 00:10:30,120\NThe swarm plot is a kind of another kind of a categorical scatterplot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,106\N00:10:30,120 --> 00:10:38,860\NIt's usually best to avoid pie charts, especially 3D pie charts, or a lot of the of our software is not going to produce 3D charts very easily.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,107\N00:10:38,860 --> 00:10:46,620\NDon't try to go make a 3D chart. They're almost always more confusing, especially like the 3D bars that you have from vintage PowerPoint.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,108\N00:10:46,620 --> 00:10:57,750\NBut even a pie chart, just because the human perception is not super great at accurately comparing angular areas.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,109\N00:10:57,750 --> 00:11:00,120\NSo usually a bar chart,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,110\N00:11:00,120 --> 00:11:07,680\Nrestacked bar chart is going to be a better option than a pie chart or a donut chart is sometimes a better option where you've got to circle.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,111\N00:11:07,680 --> 00:11:14,250\NThis is one place where I disagree with the reading. The reading that I gave you recommends pie charts for showing relative proportions.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,112\N00:11:14,250 --> 00:11:19,170\NI recommend usually avoiding those use a bar chart is a stacked bar chart if you need to show you
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,113\N00:11:19,170 --> 00:11:25,230\Nwant to show multiple proportions of different or relative proportions within different categories.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,114\N00:11:25,230 --> 00:11:29,670\NThere's another kind of plot that's not a plot on its own, but it's combined with other kinds of plots.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,115\N00:11:29,670 --> 00:11:34,830\NThat's a rug plot useful for just displaying distributions at a margin.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,116\N00:11:34,830 --> 00:11:40,500\NSo to learn more, I've gone I've taken a whirlwind tour through a number of different plot types, the class readings.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,117\N00:11:40,500 --> 00:11:46,170\NSo the paper that I assigned you to read, it talks through the use cases for a number of different plot types.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,118\N00:11:46,170 --> 00:11:50,160\NI'm going to be providing tutorial notebooks that walk you through different plot types.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,119\N00:11:50,160 --> 00:11:54,660\NThe textbook talks about graph plotting and data visualization.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,120\N00:11:54,660 --> 00:11:58,190\NThe Seabourne and matplotlib docs are extensive. And for what?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,121\N00:11:58,190 --> 00:12:04,230\NIf you're using another plodding library, its documentation as well. Most plotting libraries also have a gallery student.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,122\N00:12:04,230 --> 00:12:10,740\NGo through the gallery, look for a plot that has a feature you want in your plot or that you think might be useful for displaying your data.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,123\N00:12:10,740 --> 00:12:14,580\NClick on it and they'll give you the code to show you how they made that plot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,124\N00:12:14,580 --> 00:12:22,650\NYou might want to combine pieces from multiple plots. In practice, it takes a lot of trial and error to really get the hang of your plot and library
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,125\N00:12:22,650 --> 00:12:27,630\Nand figure out how to make it show you the data in the way you really want it to.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,126\N00:12:27,630 --> 00:12:32,730\NLearning one plotting library really deep is useful for a lot of the a lot of the python ones,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,127\N00:12:32,730 --> 00:12:37,350\Nespecially the ones that are oriented towards static charts. They're built on top of matplotlib.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,128\N00:12:37,350 --> 00:12:41,910\NSo Seabourne is a convenience API on top of matplotlib. If you're using Seabourne,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,129\N00:12:41,910 --> 00:12:49,500\Nyou're also going to need to use matplotlib calls a lot of the time when the seabourne gets you 90 percent of the way there,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,130\N00:12:49,500 --> 00:12:55,470\Nbut not quite all the way. So to wrap up, there are many different types of charts that have different use cases.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,131\N00:12:55,470 --> 00:13:01,020\NLearning graphics techniques takes time and practice takes some of the example notebooks that I'm providing.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,132\N00:13:01,020 --> 00:13:05,730\NTake some of the galleries from the examples from, say, the Seabourne Gallery.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,133\N00:13:05,730 --> 00:13:11,670\NPlay with them, play with them with some data that I'm giving you, play with them with some data that you have elsewhere.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,134\N00:13:11,670 --> 00:13:30,957\NBut it takes time and practice and spend some time with the galleries of the of the the plotting libraries you're using.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,