Learn Matplotlib in 1 hour! 📊
https://www.youtube.com/watch?v=c9vhHUGdav0
[00:00] Hey everybody.
[00:01] In this video, I'm going to teach you everything you need to know to get started working with Mattplot Lib.
[00:07] So, I encourage you to sit back, relax, and enjoy the show.
[00:12] Mattplot Lib is a Python library for creating charts and graphs.
[00:18] You can take plain numbers and turn them into visuals you can actually understand.
[00:22] With just a couple lines of code, you can make your first graph.
[00:25] Boom. Instant line graph.
[00:28] You can create line charts, bar charts, scatter plots, histograms, and my favorite, pie charts, because I like pie.
[00:34] You can customize the appearance of what your charts look like, too.
[00:37] Here, I went a little crazy with the customization, but I think you get the idea.
[00:41] What I would recommend before starting the series, be familiar with all Python subjects up to dictionaries.
[00:46] Knowing numpy and pandas are helpful, but not necessary for this series.
[00:50] I do have some free courses on YouTube to catch you up to speed if you're interested.
[00:53] Anyways, let's begin.
[00:56] Oh yeah, let's do this.
[00:58] To start working with mattplot lib, we have to be sure
[01:00] with matplotlib, we have to be sure that we have the library installed.
[01:02] One easy way to check that at the top of your IDE, if you were to import matplotlib.
[01:09] If it's underlined red, you can hover your cursor over it and install the package by clicking this link.
[01:17] Now, if this popup doesn't appear, if you're using PyCharm, you can open up a terminal.
[01:22] Alt F12 is the shortcut.
[01:26] Then use pip to install Matplotlib.
[01:31] Pip install Matplotlib.
[01:32] Enter.
[01:36] Give it a moment to install.
[01:38] And that's all you got to do.
[01:41] We can close out of this terminal.
[01:43] And that red underline should go away.
[01:47] To check the current version within a print statement, we will print access the library of matplotlib.
[01:55] Dot dunder meaning double underscore version dunder.
[01:59] Again, the current
[02:02] version dunder.
[02:05] Again, the current version of mattplot lib that I'm using is 3.10.6.
[02:09] We can delete this print statement.
[02:12] We can delete this print statement.
[02:15] There's a module called pipplot.
[02:17] It's found within the library of mattplot liib.
[02:19] It provides a userfriendly interface for plotting.
[02:22] We're going to import that module by following mattplot lib with pi plot.
[02:27] So al together import mattplot lib do import matt import mattplot.pipplot.
[02:39] That's a tongue twister.
[02:41] Just so we don't have to type this long name every time.
[02:45] We're going to give it an alias or nickname as plt for short.
[02:51] So in order to access here we go mattplot lib.pipplot.
[02:55] We're just going to give it a nickname of plt.
[02:59] Here's how we can create a basic plot.
[03:02] We'll need a set of x and ycoordinates.
[03:05] We'll need a set of x and y coordinates.
[03:05] We'll start with a basic Python list.
[03:07] We'll start with a basic Python list.
[03:07] For the x coordinates, we'll say x equals a Python list.
[03:13] A Python list.
[03:13] Here you can type in some numbers.
[03:16] I'm going to type in some years.
[03:18] 2023, 2024, 2025, and 2026.
[03:25] And then for the y-coordinates, this will be a separate Python list.
[03:27] Make up some numbers.
[03:30] Uh in this scenario, let's pretend that we have different class sizes each year.
[03:33] Starting with 2023, there's a class size of 15 students.
[03:34] Then for 2024, we have 25 students, then 30 and 20.
[03:38] Feel free to pick different numbers if you would like.
[03:42] We have a list of x coordinates and a list of y-coordinates.
[03:47] Each set of these elements is a pair of x and y coordinates.
[03:50] We have to be sure that they match.
[03:52] Now to create a basic plot, we're going
[04:06] Now to create a basic plot, we're going to access plt.
[04:09] To access plt, call the plot function and then pass in our coordinates.
[04:15] X, y.
[04:17] The first argument is for the points on the x-axis.
[04:21] The second argument is for the coordinates on the y-axis.
[04:25] Now, if I were to run this, nothing displays.
[04:27] You also need to call the show function of your plot.
[04:30] Here's how.
[04:34] When we would like to show our plot, we will access plt, call the show function in order to show our plot.
[04:44] Now I'm using PyCharm.
[04:44] My plot appears on the right hand side in a separate window called plots.
[04:49] Here's my x-axis with all of my years, but we'll have to do some customization in a future topic.
[04:58] Then here are all the y-coordinates.
[05:01] For 2023, we have 15.
[05:04] 2024 is 25.
[05:07] 2024 is 25.
[05:07] 2025 is 30.
[05:07] And 2026 is 20.
[05:12] And you can 2025 is 30.
[05:12] And 2026 is 20.
[05:12] And you can change these coordinates, too.
[05:14] I don't change these coordinates, too.
[05:14] I don't know.
[05:17] I'll set 30 to instead be a,000.
[05:17] Let's see what this looks like.
[05:20] And we get a different plot,
[05:22] but I'll change that back.
[05:25] You can also close out of past versions
[05:27] of plots by hitting this X button next
[05:29] to each.
[05:32] Now, for the plot function, if you only
[05:34] pass in a single parameter, this
[05:37] argument would be for points on the Y
[05:39] ais.
[05:40] And here's what that would look like.
[05:44] The default behavior for plot is to
[05:47] start at zero and place a point at each
[05:50] increment of one.
[05:50] 1 2 3.
[05:55] But each of these ticks, they increment by.5 each.
[05:59] The points on the xaxis will be provided
[06:01] to you.
[06:03] But we'll pass in both x and y.
[06:07] But we'll pass in both x and y.
[06:09] Now, if you're familiar with numpy, we could instead use numpy arrays because they're faster than python lists.
[06:14] To set that up, we will import numpy as np for short.
[06:22] We'll assign x to be a numpy array.
[06:25] nparray function and then pass in your Python list.
[06:31] Then do the same thing with y.
[06:35] nparray pass in your Python list.
[06:38] And that's all you have to do.
[06:41] Numpy arrays are faster than Python lists and give you more functionality.
[06:46] Your homework is in the comment section.
[06:48] either post two lists or two numpy arrays.
[06:51] Try and think of something creative if you can.
[06:53] All right, everybody.
[06:54] And that is an introduction to creating basic plots in mattplot lib.
[07:05] In this video, I'm going to show you how we can start customizing our plots.
[07:07] More
[07:09] We can start customizing our plots.
[07:11] More specifically, we'll be customizing the lines and markers.
[07:13] Here's how.
[07:15] Within our plot function, we can pass one of a few keyword arguments.
[07:18] One of which is marker.
[07:20] Marker equals a given character.
[07:22] Depending on the character that you set for marker, your marker will be one of a few symbols.
[07:28] For example, if your marker set to be a period, a point, each set of coordinates has a point, although it's a little hard to see.
[07:35] I'll try and zoom in.
[07:38] You can see that each set of coordinates has a point.
[07:41] Another is circle.
[07:44] That would be an O.
[07:47] Triangle down is a V.
[07:51] For a star, you can use an asterisk.
[07:53] These are a few markers you can use, but not all of them.
[07:55] If you read the documentation, there's a fairly extensive list for our project.
[07:58] For the marker, we'll set it to be a dot, a period.
[08:02] But then you can change the marker size if this is too small.
[08:04] That will be a separate keyword argument.
[08:09] For the keyword argument of marker size,
[08:13] For the keyword argument of marker size, pick a size.
[08:13] Here's what 10 looks like.
[08:17] Here's 20.
[08:20] Here's 20.
[08:20] 30.
[08:23] 30.
[08:23] What's a th00and look like?
[08:23] I'm curious.
[08:28] No, go back.
[08:28] Go back.
[08:28] Go back.
[08:31] No, go back.
[08:31] Go back.
[08:31] Go back.
[08:31] We'll set it to be 30.
[08:33] it to be 30.
[08:33] For the marker size, it can be abbreviated to MS.
[08:35] For the marker size, it can be abbreviated to MS.
[08:38] abbreviated to MS.
[08:38] MS meaning marker size.
[08:40] But for teaching purposes, I'll keep this as marker size just because it's easier to read and understand.
[08:43] purposes, I'll keep this as marker size just because it's easier to read and understand.
[08:44] just because it's easier to read and understand.
[08:44] Here's how to change the marker colors.
[08:46] understand. Here's how to change the marker colors.
[08:46] This is a separate keyword argument.
[08:48] marker colors. This is a separate keyword argument.
[08:52] Marker face color.
[08:53] Marker face color.
[08:53] And here you can pick a color.
[08:57] face color. And here you can pick a color.
[08:57] You can select the name of a color.
[08:59] color. You can select the name of a color.
[08:59] You can use RGB values, hexodimal values, or HSL values.
[09:02] color. You can use RGB values, hexodimal values, or HSL values.
[09:02] Let's start with something simple like the name of a color such as cyan.
[09:05] values, or HSL values. Let's start with something simple like the name of a color such as cyan.
[09:07] something simple like the name of a color such as cyan.
[09:11] The markers are now this color, cyan.
[09:14] The markers are now this color, cyan.
[09:16] But for better customization, I recommend looking up a color picker.
[09:19] So, here's a color picker that we could use.
[09:21] I would like a very specific shade of blue.
[09:24] Something like that's pretty good.
[09:29] I'll just select this hexadimal value.
[09:34] We're going to paste that hexadeimal value and rerun this program.
[09:36] And now we have a very specific shade of blue for the marker face color for this keyword.
[09:39] You can also shorten this to MFC.
[09:41] It's shorter but not as readable for a beginner.
[09:46] I'll keep it as is. Marker face color.
[09:48] Then for the edges, you can use marker edge color.
[09:50] I'll set it to be the same color, or you could pick a different color if you would like.
[09:52] We'll keep it the same.
[09:55] Now, the edges of each marker are the same color as the face.
[10:14] are the same color as the face.
[10:16] The abbreviated version of this word is M EC, in case you see it.
[10:20] Now, we'll start working with the lines.
[10:23] You can select a line style.
[10:26] The default is solid.
[10:28] Let's use a dashed line.
[10:32] Here's what it looks like.
[10:36] for the line.
[10:38] It's now dashed.
[10:41] Or we could do dotted dotted.
[10:44] dotted.
[10:47] We have a dotted line now.
[10:49] Or dash dot dash dot.
[10:53] dash dot.
[10:56] It's a combination of dashes and dots.
[10:58] Or for no line, if you just want the points, the markers, you can set this to be none.
[11:04] Then we have no line,
[11:08] but let's set it to be solid.
[11:11] Solid.
[11:15] Here's how you can change the line width.
[11:17] Here's how you can change the line width cuz right now it's fairly skinny.
[11:20] cuz right now it's fairly skinny.
[11:20] We will pass in a keyword argument of
[11:22] We will pass in a keyword argument of line width.
[11:24] Normally it's one, but here's what two looks like.
[11:29] I'll set this to be three.
[11:33] I'll set this to be three.
[11:33] four.
[11:35] four.
[11:35] And I don't know what's 10 look like.
[11:39] And I don't know what's 10 look like.
[11:39] That's a little too much.
[11:41] Let's set it to be four.
[11:44] to be four.
[11:44] Yeah, that's pretty decent.
[11:46] Here's how to change the line color.
[11:50] to change the line color.
[11:50] Set the color to be then pick a color.
[11:55] Set the color to be then pick a color.
[11:55] I'll use that color picker again.
[11:58] I'll use that color picker again.
[11:58] Let's see.
[12:01] I'll select something kind of dark blue like that.
[12:04] dark blue like that.
[12:04] Copy and paste the color value.
[12:07] color value.
[12:07] And here's what we got.
[12:11] Our line color is now a very specific shade.
[12:14] is now a very specific shade.
[12:14] Now, let's say we have more than one
[12:16] Now, let's say we have more than one line to customize.
[12:18] I'm going to create another numpy array.
[12:20] We'll rename y as y1.
[12:20] Plot x and y1.
[12:25] Then we will also have y 2.
[12:25] And I'll just do some copying and pasting.
[12:28] Y 2 will have some different values.
[12:31] Make up some values.
[12:33] I'll say, I don't know, 17, 23, 38, and I don't know, five.
[12:37] Again, your values don't have to be the same as mine.
[12:45] Feel free to pick some different numbers.
[12:48] We're plotting X and Y1, but we'll also plot X and Y 2.
[12:51] That set of coordinates.
[12:56] access plt.
[12:56] Call the plot function.
[13:00] Pass in x and y 2.
[13:05] So now we have two lines, but our second line doesn't have the same customizations as our first line.
[13:08] Here's one option, although I don't recommend it.
[13:10] You could copy and paste all of these keyword arguments and pass them
[13:17] these keyword arguments and pass them in.
[13:19] in.
[13:21] This would work, although here's a much better option.
[13:24] You could instead create a dictionary with all of these keyword arguments.
[13:26] And then when we plot a line, we can just unpack our dictionary.
[13:31] Here's how we can do that.
[13:33] Let's create a dictionary which we'll name line style.
[13:39] This will equal call the dictionary function.
[13:41] To create a dictionary, we'll cut all of these keyword arguments and then place them within this dictionary function.
[13:54] Now we could just pass in this dictionary when we plot a line.
[14:00] We just have to unpack it using double asterisks.
[14:05] Unpack the line style dictionary.
[14:08] Then it's as if we're passing in all of these individual keyword arguments.
[14:12] We'll do this for the second line as well.
[14:16] unpack our line style dictionary
[14:19] unpack our line style dictionary and it's the same.
[14:23] Let's create a third line.
[14:24] We'll name this line Y3.
[14:27] This will be another numpy array.
[14:31] Make up some values 13, then 15, 20, and 30.
[14:38] Then we'll have to plot this line.
[14:41] Access plt.
[14:44] Call the plot function. pass in our new coordinates x and y3.
[14:47] Then we will unpack our dictionary of line style.
[14:50] Then we should have a third line,
[14:53] which we do.
[14:55] All of these lines are the same color.
[14:57] That's because with our dictionary of lines style, we've set all the colors to be the same, but you could pass in an individual color.
[15:01] Think of this as the default color for all lines if we use the line style dictionary.
[15:04] Another option is that we can cut this keyword attribute of color and then pass that as a separate keyword argument as
[15:21] that as a separate keyword argument as well as the line style dictionary after unpacking it.
[15:23] So let's pass in a color then pick a color.
[15:27] I'll keep the first line to be blue.
[15:29] For the second line I'll pick a different color.
[15:32] Let's go with something green.
[15:34] That's good.
[15:37] We'll pass in a different color for the second line.
[15:40] Then for the third line, I'll pass in a color that's red.
[15:46] That's good.
[15:51] And now each of our lines has a different color.
[15:58] All right, everybody.
[16:00] So that is some basic plot customization.
[16:02] In the next video, we'll discuss labels.
[16:03] To help increase engagement with this video, comment down below what your favorite food is.
[16:05] And well everybody, that is an introduction to plot customization using Mattplot LIIB.
[16:07] And well everybody, that is an introduction to plot customization using Mattplot LIIB.
[16:09] And well everybody, that is an introduction to plot customization using Mattplot LIIB.
[16:11] And well everybody, that is an introduction to plot customization using Mattplot LIIB.
[16:13] And well everybody, that is an introduction to plot customization using Mattplot LIIB.
[16:15] And well everybody, that is an introduction to plot customization using Mattplot LIIB.
[16:22] All right, everybody.
[16:24] In this next section, we're going to customize various labels, our title, and the ticks of our chart.
[16:30] Here's how we can customize those.
[16:32] In this demonstration, I have various class sizes yearbyear, the years 2023 through 2026.
[16:38] These are all numpy arrays, but you can use Python lists, too.
[16:43] To set a title, we're going to access plt.
[16:46] Call the title function, and then we can pass in a title as a string.
[16:51] We'll set the title to be class size.
[16:55] Then we will run this, and we should have a title.
[17:00] class size. Although it's kind of small and hard to read.
[17:02] Here's how we can customize it further.
[17:04] We can pass in some keyword arguments.
[17:07] One of which is font size.
[17:09] To change the font size, I'll set that to be 20.
[17:15] Here's the new title.
[17:17] You can also change the font family, and it's just family.
[17:20] I'll pick Ariel.
[17:20] That's a
[17:24] family.
[17:24] I'll pick Ariel.
[17:24] That's a professional looking font.
[17:27] professional looking font.
[17:27] You can set the font weight to be bold.
[17:31] You can set the font weight to be bold.
[17:31] Font weight equals the word bold.
[17:38] Let me make the title a little bit
[17:39] Let me make the title a little bit bigger.
[17:39] Let's say 25.
[17:43] bigger.
[17:43] Let's say 25.
[17:46] That's better.
[17:46] Let's also set a color for our title.
[17:49] Let's also set a color for our title.
[17:49] Color equals.
[17:49] Here you can pick the name
[17:52] of a color.
[17:52] You can use RGB values.
[17:52] You
[17:56] can use RGB values.
[17:56] You can use hexodimal values.
[17:58] can use hexodimal values.
[17:58] I'll pick something kind of dark blue
[18:02] like that.
[18:02] I'll copy the hex value and
[18:07] then set the keyword argument of color
[18:08] to be that hexodimal value.
[18:08] And now our
[18:11] title should be a specific shade of
[18:13] blue.
[18:13] Now we'll set a label for the x-axis.
[18:15] Now we'll set a label for the x-axis.
[18:19] We will access plt call the function of
[18:22] x label.
[18:25] x label.
[18:25] We'll set the label to be year.
[18:30] And we should have a label on the x-axis.
[18:33] And we should have a label on the x-axis for year.
[18:33] Although it's kind of small.
[18:35] for year.
[18:35] Although it's kind of small.
[18:35] We'll change the font size.
[18:37] We'll change the font size.
[18:37] We'll copy the font size from our title,
[18:40] We'll copy the font size from our title, but set it to be 20.
[18:44] Here's the new font size.
[18:48] Let's change the font family.
[18:50] Let's change the font family.
[18:50] I'll pick Ariel again.
[18:54] I'll set the font weight to be bold.
[19:00] And then I'll pick a color.
[19:03] And then I'll pick a color.
[19:03] I'll pick a lighter shade of blue.
[19:07] I'll pick a lighter shade of blue.
[19:07] Let's try that.
[19:10] Color equals
[19:13] Color equals that specific shade of blue.
[19:16] that specific shade of blue.
[19:16] Yeah, not bad.
[19:19] Yeah, not bad.
[19:19] The label is kind of close to the border.
[19:21] We'll be adjusting the margin a little bit later in this topic.
[19:23] To create the label on the Y-axis, I'm just going to copy
[19:27] Y-axis, I'm just going to copy everything within this X label function.
[19:30] Everything within this X label function. Paste it. Change X to Y.
[19:33] Paste it. Change X to Y.
[19:36] We'll say the text will be let's change the text of the label to be students as in number of students.
[19:41] In number of students. We should have a similar label on the Y axis.
[19:46] Ais. Now we're going to customize the ticks of our graph.
[19:47] Now we're going to customize the ticks of our graph. Each of these ticks is incrementing by.5 each.
[19:53] Incrementing by.5 each. You can kind of see that here.
[19:55] You can kind of see that here. You can force ticks to only display at these values for the x-axis.
[19:58] You can force ticks to only display at these values for the x-axis. Here's how.
[20:03] Here's how. We'll access plt. Call the x tick function.
[20:07] We'll access plt. Call the x tick function.
[20:09] Function. X tick.
[20:11] X tick. Then pass in our list or numpy array that we're using for the x-axis.
[20:13] Then pass in our list or numpy array that we're using for the x-axis.
[20:16] That we're using for the x-axis. So now we have ticks only at each given year 2023, 2024, 2025, 2026.
[20:19] So now we have ticks only at each given year 2023, 2024, 2025, 2026.
[20:25] Now to customize the ticks, we will
[20:28] Now to customize the ticks, we will access PLT.
[20:30] Access PLT call the tick params function, meaning call the tick params function, meaning tick parameters.
[20:34] Tick parameters.
[20:37] To select an axis, you'll select the keyword argument of access.
[20:39] You can select X, you can select Y, or you can select both.
[20:41] We'll select both.
[20:44] We're going to select colors plural, not singular.
[20:48] I'll select the same color as my labels.
[20:53] Now, the ticks should be blue.
[20:58] All right, everybody.
[21:01] So, that is how to add various labels to your plots in Mattplot Lib.
[21:02] Hey everybody, I have a super quick video today on how to create grid lines using the grid function using Mattplot LIIB.
[21:04] The grid function creates grid lines.
[21:12] They help make plots easier to read by adding reference lines.
[21:13] We'll need some sample data to work with.
[21:15] We'll just make something up.
[21:18] On the
[21:29] We'll just make something up.
[21:29] On the xaxis, we'll have 1 2 3 4 5.
[21:35] On the xaxis, we'll have 1 2 3 4 5.
[21:35] On the y-axis, we'll have y-axis, we'll have 5 10 15 20 25.
[21:38] It really doesn't matter.
[21:42] 5 10 15 20 25.
[21:42] It really doesn't matter.
[21:42] We're more focused on the grid lines We're more focused on the grid lines today.
[21:44] We'll need a plot access plt.
[21:47] today.
[21:47] We'll need a plot access plt.
[21:51] Call the plot function.
[21:51] Pass in x.
[21:51] Pass in y.
[21:55] Then access plot.
[21:55] Call the show function to show our plot.
[21:59] Here's our sample plot.
[21:59] Now, let's say you would like to create some grid you would like to create some grid lines.
[22:02] For better readability, here's how.
[22:03] lines.
[22:06] For better readability, here's how.
[22:08] how.
[22:08] We're going to access plt call the grid function.
[22:11] function.
[22:11] Let's see what happens if we don't pass in any arguments.
[22:13] Let's see what happens if we don't pass in any arguments.
[22:14] These are the grid lines with no customization.
[22:17] We have grid lines on the x-axis and the y-axis.
[22:19] customization.
[22:19] We have grid lines on the x-axis and the y-axis.
[22:21] If you would just like a single axis, you can specify that with the keyword argument axis equals.
[22:25] If you would just like a single axis, you can specify that with the keyword argument axis equals.
[22:27] you can specify that with the keyword argument axis equals.
[22:28] argument axis equals.
[22:32] argument axis equals.
[22:32] We'll pick the x-axis to begin with.
[22:36] We'll pick the x-axis to begin with.
[22:36] We will only have vertical lines then on the x-axis by setting the axis to x.
[22:42] If we were to set this to y,
[22:44] we would have grid lines on the y-axis.
[22:48] You can change the line width.
[22:51] Line width equals.
[22:51] Normally, it's one, but here's two.
[22:57] The lines are a little bit thicker.
[22:59] Here's three.
[23:02] Let's set that back to two.
[23:05] You can change the color.
[23:08] Color equals, then select a color.
[23:08] I'll just pick light gray to keep it simple.
[23:17] The lines are now light gray.
[23:21] You can specify a line style.
[23:23] Normally, it's solid.
[23:26] Let's try dashed.
[23:29] Here's dashed.
[23:32] Here's dashed.
[23:32] Here's dotted.
[23:35] Here's dotted.
[23:35] Here's dash dot.
[23:41] But let's set this back to dashed.
[23:46] All right, everybody.
[23:48] That is a super quick lesson on how to create grid lines using mattplot lib.
[23:58] All right, everybody.
[24:00] For this topic, we're going to create a bar chart.
[24:02] A bar chart compares categories of data by representing each category with a bar.
[24:07] Here's an example that I thought of.
[24:09] You know the food pyramid?
[24:11] We'll create a bar chart to represent daily consumption of categories of foods.
[24:16] Here are the different categories.
[24:17] This will be a Python list.
[24:22] Categories equals.
[24:24] We'll create a Python list.
[24:26] The first element within this list will be grains such as bread.
[24:30] Then fruit,
[24:33] Then fruit, vegetables, protein, dairy, dairy, and sweets.
[24:48] We'll create another Python list of values for each of these categories.
[24:55] What value should each of them have?
[24:56] Let's pretend that these values are for daily servings.
[25:00] How many in each category did I consume today?
[25:05] We'll make up some numbers.
[25:05] For grains, this will be four.
[25:09] Fruit, three.
[25:09] Vegetables, I don't eat very many vegetables, that will be two.
[25:12] Lots of protein, that's a five.
[25:15] Dairy, three.
[25:15] And sweets, one.
[25:18] But feel free to make up your own numbers.
[25:21] Now, although it's not necessary, I'm going to convert these Python lists to be numpy arrays.
[25:31] All right, we are ready to make a bar
[25:33] All right, we are ready to make a bar chart.
[25:33] Categories will be on the xaxis.
[25:36] chart. Categories will be on the xaxis, values will be on the y-axis.
[25:39] Rather values will be on the y-axis.
[25:39] Rather than using a plot such as plt called the plot function, we're instead going to use bar to make a bar chart.
[25:47] use bar to make a bar chart.
[25:49] First pass in what's going to be used for the x-axis categories, comma, then the y-axis of values.
[25:56] the y-axis of values.
[25:56] And then you do have to show your bar chart.
[25:57] And then you do have to show your bar chart.
[26:03] chart. plt call the show function.
[26:03] And let's see what we have.
[26:07] Here we go.
[26:07] A bar chart compares categories of data by representing each category with a bar.
[26:12] by representing each category with a bar.
[26:14] Here are the categories that we've stated.
[26:17] stated. grains, fruit, vegetables, protein, dairy, sweets.
[26:19] And here are the values on the lefth hand side for each.
[26:23] values on the lefth hand side for each.
[26:23] Let's add a title and two labels to our bar chart.
[26:27] bar chart.
[26:27] PLT call the title function, then pass in a title as a string.
[26:30] PLT call the title function, then pass in a title as a string.
[26:30] We'll
[26:33] then pass in a title as a string.
[26:33] We'll say daily consumption.
[26:37] Let's make sure that works.
[26:40] Yes, it does.
[26:42] We'll add a label to the x-axis.
[26:45] We'll add a label to the x-axis.
[26:46] plt x label.
[26:49] x label.
[26:49] We'll say the word food.
[26:53] Yep, it says food.
[26:53] And you can change the size if you want for these labels,
[26:58] but that might be overkill for this lesson.
[27:01] We've already covered that in the previous topic.
[27:03] PLT call the Y label function.
[27:09] We'll pass in a string that says quantity
[27:14] and it should say that on the lefth hand side.
[27:15] Now let's change the color of our bar chart.
[27:18] When we call the bar function, we can set the color to be a color of your choice.
[27:21] You can pick a color name.
[27:23] You can use hexodimal values.
[27:25] I'll just say sky blue.
[27:27] And the bar colors should change.
[27:34] And the bar colors should change.
[27:36] Now, if you would like a horizontal bar chart, I'll comment this line of code out.
[27:43] Copy it, paste it, you're going to instead call the bar H function for a horizontal bar chart, which looks a little something like this.
[27:51] But honestly, for me, I prefer the vertical bar chart.
[27:57] All right, everybody. So that is how to create a basic bar chart using mattplot lib.
[28:08] All right everybody we are now going to create a bar chart using mattplot liib.
[28:13] A bar chart it's a circular chart divided into slices to show percentages of the total.
[28:18] It's good for visualizing distribution among categories.
[28:24] In this demonstration, we're going to create a fictional college.
[28:26] There will be four classes, freshman, sophomore, junior, senior.
[28:30] We'll create a Python list of categories.
[28:36] We'll have freshman, sophomores, I think I misspelled that, juniors, and seniors.
[28:57] We'll also create a Python list of the number of students in each class.
[29:02] Values equals a Python list. We'll have 300 freshmen in our college, 250 sophomores, 275 juniors, and 225 seniors.
[29:21] If you're familiar with numpy, you do have the option to convert any numerical Python lists to numpy arrays.
[29:26] Although for a list of four values, it's not really too beneficial.
[29:31] If this were a larger data set, we would benefit from numpy arrays more, but it would be a good habit for us to develop.
[29:37] good habit for us to develop.
[29:39] Now, Python list of strings don't really benefit from numpy arrays.
[29:41] We'll keep that as is.
[29:44] We have the two collections that we need, categories and values.
[29:47] To create a pie chart, we will access plt,
[29:50] call the pi function,
[29:52] pass in our values.
[29:55] Let me show you what just passing in values will look like.
[29:57] We will also call the show function.
[30:00] plt.show function.
[30:03] We do have the distributions, but we don't have any labels.
[30:07] Here's how to set the labels.
[30:10] When we call the pi function, we will pass in a few keyword arguments.
[30:14] The first will be labels.
[30:16] What are the labels of our pie chart?
[30:19] The labels will be the categories that we have.
[30:22] Labels equals categories.
[30:24] And we should have some categories for each of these pie slices.
[30:29] Freshman, sophomores, juniors, seniors.
[30:32] You can add some percentages to each of these pie slices.
[30:36] Here's how.
[30:36] That's going to be another keyword argument.
[30:39] going to be another keyword argument.
[30:44] The keyword argument is auto PCT.
[30:47] This means auto percentage.
[30:48] We have to set this equal to a formatting instruction.
[30:50] We're going to use percent one f.
[30:55] Let's see what we have.
[31:00] All right. We don't need all these digits after the decimal.
[31:02] Let's limit that.
[31:05] After one, we'll add 0.1 to display one digit after the decimal.
[31:12] 28.6 23.8 8 26.2 21.4.
[31:17] To add a percent sign after, we'll use double percent signs cuz normally using one percent sign is a format specifier.
[31:26] Okay, now there's a percent sign after each of these numbers.
[31:29] Here's how to set the colors for each pie slice.
[31:33] We'll create another list for convenience, which I will name colors.
[31:35] Colors equals.
[31:38] Then in order of your categories,
[31:40] categories, place a color within here.
[31:42] You can place a color within here.
[31:46] You can choose a color name, RGB values, choose a color name, RGB values, hexodimal values.
[31:48] We'll just keep it simple and pick some color names.
[31:50] simple and pick some color names.
[31:53] Freshman will be red, sophomores will be yellow,
[31:55] yellow, juniors will be blue,
[31:58] juniors will be blue, seniors will be green.
[32:01] seniors will be green.
[32:02] And then we do have to pass that in as a keyword argument.
[32:04] keyword argument.
[32:09] Set colors equal to our list of colors.
[32:11] And the colors should change.
[32:11] Freshman are red, sophomores are yellow, juniors are red, sophomores are yellow, juniors are blue, seniors are green.
[32:18] are blue, seniors are green.
[32:21] Let's add a title to our pie chart.
[32:23] PLT called the title function. Pass in a title.
[32:27] My college will be named Broode College because I think I'm cool.
[32:32] College because I think I'm cool.
[32:35] You can explode one of these pie slices.
[32:38] Explode means to displace it slightly.
[32:38] If you need to highlight a pie slice,
[32:40] If you need to highlight a pie slice, we'll explode seniors.
[32:45] For the keyword argument of explode, we'll set this equal to a Python list.
[32:52] We have four categories. We'll need four values.
[32:54] I'll set each of these to be zero.
[32:56] I need to explode seniors. I'll set this to a value that's not one.
[32:58] Let's start with 0.1.
[33:00] The greater the number, the further the distance that pi slice will explode.
[33:08] Here's 0.1.
[33:11] Let's change that to 0.2.
[33:13] The distance traveled should be a little bit greater.
[33:16] Here's.3.
[33:17] We'll set that to be 02.
[33:21] That's pretty good.
[33:23] And 0.1.
[33:26] We can also add a drop shadow.
[33:29] shadow equals true.
[33:33] And there should be a drop shadow for each pie slice.
[33:37] To rotate your pie
[33:41] each pie slice.
[33:46] To rotate your pie chart, you'll set start angle to be some angle.
[33:49] If I were to set this to be 180 for 180°, my pie chart is going to rotate by 180°.
[33:56] I'll just set that to be let's check out 90.
[34:00] That's pretty good.
[34:02] All right, everybody.
[34:04] So that is how to create a basic pie chart using mattplot liib.
[34:12] All right everybody in this video we're going to discuss scatter graphs in mattplot lib.
[34:16] Scatter graphs show the relationship between two variables.
[34:19] They help to identify a correlation positive negative or no correlation.
[34:21] For example the more hours you study the higher your test scores tend to be.
[34:24] That's a positive correlation.
[34:27] Scatters help us identify those correlations.
[34:30] In this demonstration, I'll create the following data set.
[34:32] You can either follow along and type what I'm about to type or look
[34:42] and type what I'm about to type or look in the comment section.
[34:44] I'll try and post this data set.
[34:47] We'll have X, which will be a Python list.
[34:50] These will be hours studied for various students.
[34:52] We'll have 10 values.
[34:55] The first student didn't study at all.
[34:57] They studied zero hours.
[34:57] Another studied 1 hour.
[35:04] 1 2 3 4 5 6 7 7 8.
[35:09] Feel free to make up some numbers too.
[35:11] It really doesn't make a difference.
[35:14] Then y will be the test scores.
[35:15] This will be another Python list.
[35:18] The student that didn't study at all, they got a 55% on their test score.
[35:21] The first value will be 55.
[35:24] Then another got 60.
[35:24] 65.
[35:28] 62.
[35:30] 68
[35:32] 70
[35:34] 75
[35:36] 78
[35:38] 82
[35:40] 85
[35:40] 87.
[35:40] That's good enough for now.
[35:44] 87. That's good enough for now.
[35:45] To create a scatter graph, we're going to access PLT.
[35:48] Call the scatter function.
[35:50] Pass in X.
[35:55] Then pass in Y.
[35:59] X is for the hours studied.
[36:03] Y is for the grades.
[36:04] What grade did they get on their test score?
[36:06] Then in order to show this graph, we have to call the show function.
[36:10] plt.show function.
[36:14] Let's see what we have.
[36:17] Here is our scatter graph.
[36:19] You can see that there's a positive correlation between our studied and the exam scores.
[36:22] Let's add some labels to our scatter graph.
[36:27] For the X label access plt, call the X label function.
[36:33] Then pass in a string to serve as the label.
[36:36] Hours studied is good.
[36:37] Let's make sure that that works.
[36:40] Yes, it does.
[36:43] We have a label for hours studied.
[36:45] For the y-axis, call the y label function.
[36:48] Then pass in a string.
[36:52] We'll say grades or grade singular.
[36:56] Let's make sure that that works.
[36:59] Yes, it does. Here's the grade.
[37:02] Then let's add a title plt.
[37:05] Call the title function.
[37:07] I'm going to say test scores just as a general title for our scatter graph.
[37:11] Let's add a little bit of customization.
[37:15] Let's change the color.
[37:17] This will be a keyword argument.
[37:20] color equals some color.
[37:23] You can pick a color name.
[37:25] You can use RGB values.
[37:28] I'll just say sky blue to keep it simple.
[37:31] And the color should change, which it does.
[37:34] You can also change the alpha.
[37:36] Alpha is related to the transparency.
[37:39] If I set alpha to be 0.5, that's 50%.
[37:42] The transparency is going to change, although it's a little hard to see.
[37:44] If I
[37:46] although it's a little hard to see.
[37:48] If I set alpha to be 0.1, then the transparency is 10% and you can barely see it.
[37:51] I'll keep it at 50%.
[37:54] Let's change the size of our dots on our scatter graph.
[37:56] That would be S for size.
[37:59] Let's check out 100.
[38:02] That's bigger.
[38:07] Here's 200.
[38:09] Yeah, we can keep it at 200.
[38:12] That's a good size.
[38:14] Okay, let's create another data set.
[38:16] The following set of scores will be for one class.
[38:19] We'll add another class of scores.
[38:21] Although it's not necessary, I forgot to make these numpy arrays.
[38:23] You can if you would like.
[38:25] Let's rename x to be x1, y to be y1.
[38:26] Pass in x1, and y1.
[38:33] We're going to create a whole another set.
[38:37] Let's copy and paste x1 and y1.
[38:39] Rename them as X2 and Y2.
[38:48] And Y2. And let's change some of these values.
[38:50] And let's change some of these values around.
[38:53] For the second set, we'll have 0 1 2 2 3 4 5 6 7 8.
[39:02] Then for the exam scores for the second class, we'll have 50 58.
[39:09] 65 70 72.
[39:11] 70 72 78.
[39:14] 78 83.
[39:15] 83 88.
[39:17] 88 92.
[39:19] 92 95.
[39:21] 95 97.
[39:22] 97.
[39:25] We're going to call the scatter function.
[39:26] We're going to call the scatter function again.
[39:28] Again.
[39:30] We'll copy and paste what we had previously, but pass in X2 and Y2, the second set of coordinates.
[39:34] Second set of coordinates.
[39:36] Let's change the color to be red.
[39:41] Let's change the color to be red.
[39:44] The blue set of dots is for class one.
[39:47] The red set of dots is for class 2.
[39:48] Based on this graph, we can't tell without a legend.
[39:51] The legend will tell you which class is which.
[39:55] But we'll need some labels.
[39:56] We'll pass those in as keyword arguments.
[39:59] For class one, we'll set label equal to class A.
[40:06] And then for our second class, this will be class B.
[40:10] Label equals class B.
[40:14] But then we have to show the legend because it's not visible.
[40:21] Access PLT.
[40:21] Call the legend function.
[40:28] Then we should have a legend within our scatter graph.
[40:31] Class A is the blue dots.
[40:34] Class B is the red dots.
[40:34] All right, everybody.
[40:36] So those are scatter graphs.
[40:38] They show relationships between two variables.
[40:41] For example, hours studied and exam grades.
[40:44] The more hours you study, the better grade you tend to get.
[40:46] That's a positive correlation, which we
[40:49] That's a positive correlation, which we can see from this graph here.
[40:51] And well everybody those are scatter graphs using mattplot liib.
[40:59] Yo hey everybody in this video I'm going to explain histograms in mattplot lib.
[41:03] Histograms are a visual representation of the distribution of quantitative data.
[41:09] In short we can visualize the distribution of data.
[41:11] Histograms group values into bins.
[41:14] Think of these like intervals.
[41:17] will count how many fall in each range in each bucket or bin.
[41:20] Histograms are great for statistics and probability.
[41:22] In many cases, you'll see a bell curve distribution.
[41:25] Now, in this demonstration, I recommend using NumPy cuz we'll need a lot of data to work with.
[41:29] With NumPy, we can generate some random numbers.
[41:32] Here's how we can do that.
[41:33] We're going to create some test scores.
[41:35] We're a teacher and we have 100 students that each took a test.
[41:37] We'll create a visual to display the distribution of scores.
[41:39] We'll create a numpy array of scores.
[41:42] Scores equals np
[41:53] numpy array of scores.
[41:56] Scores equals np accessis random. Then call the normal function for a normal distribution.
[41:59] There's a few keyword arguments here we'll pass in.
[42:01] The first is loc.
[42:06] LOC equals loc is the location of the center, the median of our data.
[42:09] For the center, we'll say that the median is 80.
[42:11] 80%. All of the test scores are going to cluster around this score of 80.
[42:14] Most students will do pretty well.
[42:17] If this were a lower score like 50, that means most of the students failed.
[42:19] That's because the most frequent score is going to be around 50%.
[42:21] We'll set that to be 80.
[42:23] Now, we'll need a scale.
[42:26] This is another keyword argument.
[42:27] Scale equals scale is the standard deviation, the spread.
[42:30] How far away are the scores going to deviate from the center?
[42:32] The higher the number, the greater the deviation.
[42:36] Let's start with 10.
[42:38] And then we need a size.
[42:40] How many numbers do we want to generate?
[42:43] We'll set size equal to 100.
[42:45] We have 100 test scores.
[42:48] Let's
[42:57] to 100. We have 100 test scores.
[42:59] Let's test what we have currently. We'll test what we have currently.
[43:03] We'll access plt. Call the hist function for a histogram.
[43:06] Pass in our numpy array of scores.
[43:09] Then we do have to call the show function to show our histogram.
[43:12] access plt called call the show function.
[43:16] Here's what a histogram looks like.
[43:18] We set the center to be 80.
[43:21] Scores around that number are going to be the most frequent.
[43:22] And we've set the scale to be 10.
[43:25] 10 is the standard deviation.
[43:28] That's a statistics and probability term.
[43:30] How far away will the scores deviate from the center?
[43:32] Now, if I set the scale to be a small number like one,
[43:35] the scores are going to cluster more closely to 80.
[43:39] The lowest score was 77.
[43:41] The highest score was 82.
[43:44] If I set scale to be a big number, like 50.
[43:47] Well, then the scores really deviate from the center of 80.
[43:49] We have some students that scored below zero and
[43:59] students that scored below zero and others above 200.
[44:02] others above 200.
[44:02] Let's set that back to 10.
[44:07] Just to ensure that no scores go below
[44:09] Just to ensure that no scores go below zero or above 100, we can use the clip
[44:13] zero or above 100, we can use the clip function.
[44:15] function.
[44:15] We'll reassign our numpy array of
[44:17] We'll reassign our numpy array of scores.
[44:17] Access numpy.
[44:17] Call the clip
[44:21] scores.
[44:21] Access numpy.
[44:21] Call the clip function.
[44:24] function.
[44:24] We have to pass in our numpy array of
[44:27] We have to pass in our numpy array of scores.
[44:28] scores.
[44:28] For any scores below zero, we'll set
[44:30] For any scores below zero, we'll set them to be zero.
[44:30] We'll clip them.
[44:30] For
[44:33] them to be zero.
[44:33] We'll clip them.
[44:33] For any scores above 100, we'll clip them
[44:36] any scores above 100, we'll clip them down to 100.
[44:39] down to 100.
[44:39] It's kind of like we're setting the
[44:40] It's kind of like we're setting the bounds, the minimum and the maximum.
[44:47] All scores below zero will be set to
[44:49] All scores below zero will be set to zero.
[44:49] All scores above 100 will be set
[44:51] zero.
[44:51] All scores above 100 will be set to 100.
[44:53] to 100.
[44:53] Let's set the scale back to something
[44:55] Let's set the scale back to something massive like 50.
[44:58] massive like 50.
[44:58] Yep, there's no scores below zero and no
[45:00] Yep, there's no scores below zero and no scores above 100.
[45:03] scores above 100.
[45:03] But we don't need a scale that big.
[45:07] But we don't need a scale that big.
[45:07] Let's add some customization.
[45:09] You can set the number of bins that you want as another keyboard argument.
[45:11] This will be within the hist function for a histogram.
[45:17] I'll set the number of bins to be 10.
[45:22] We should now have 10 bins or intervals.
[45:25] 1 2 3 4 5 6 7 8 9 10.
[45:30] If I were to set this to 20, we would now have 20.
[45:33] But I think 10 is a good number.
[45:38] Let's change the color.
[45:41] That's another keyword argument.
[45:44] Color equals pick a color.
[45:47] I'll just set my histogram to be light green.
[45:52] There we go.
[45:52] To better define the edges, you can set an edge color because right now it kind of looks like one massive blob.
[46:02] Edge color equals I'll set it to be black.
[46:08] We should get a nice outline around each interval or bin.
[46:11] Let's add some labels because well, I forgot to do that.
[46:16] Let's add a title.
[46:18] plt called the title function.
[46:20] We'll say exam scores.
[46:25] Let's make sure that that works.
[46:27] Yes, it does.
[46:31] Let's set a label for the x-axis.
[46:35] plt call the x label function.
[46:37] We'll say score.
[46:41] Here we go. Score.
[46:46] And for the y-axis, plt call the y label function.
[46:51] Number of students.
[46:54] Number of students is on the y- axis.
[46:55] All right, everybody.
[46:59] So, that's a histogram.
[47:01] It's a visual representation of the distribution of quantitative data.
[47:04] data.
[47:04] We're displaying the distribution of data.
[47:07] And well, everybody, those are histograms in Mattplot Lib.
[47:13] Yo.
[47:15] Hey everybody.
[47:15] In this video, I'm going to show you how we can create subplots using Mattplot Lib.
[47:19] There's a couple definitions we'll need to cover.
[47:21] One of which is a figure.
[47:24] A figure is the entire canvas in which we add a plot to.
[47:27] Then we have an ax.
[47:29] An ax is also known as a subplot.
[47:32] It's a single plot within a figure.
[47:35] A figure can contain multiple axes or subplots.
[47:38] We're going to call a subplots function.
[47:40] It's going to return a tpple that has a few things inside of it.
[47:45] Just to demonstrate temporarily, I'm going to print access plt call the subplots function.
[47:53] Within the subplots function, we're going to determine the layout of our figure.
[47:57] How many rows and columns of subplots will be within our figure?
[48:00] For example, we could have a 2x two, two
[48:06] example, we could have a 2x two, two rows, two columns.
[48:06] Or 2x3, two rows, three columns, or a 3x two, three rows, two columns.
[48:16] two columns.
[48:16] Let's stick with the 2x two for now.
[48:19] I want to show you what this function returns exactly.
[48:22] It's a little complex, but we'll break it down.
[48:27] The subplots function, it will return a tpple.
[48:30] The tpple has two entities, a figure object, which is our canvas, and a two-dimensional array of axes objects.
[48:37] When we call this function, we're going to unpack this tupler right away.
[48:43] Rather than use print, we're going to assign what's returned to us and unpack it.
[48:49] We'll create a figure object and an axis object.
[48:55] figure and axes equals call the subplots function.
[49:06] Figure is our canvas.
[49:08] Figure is our canvas.
[49:10] We won't actually be using it in this topic, but we're provided with it by the subplots function.
[49:12] We'll print this two-dimensional list right away.
[49:14] Here's how we'll plot each of these subplots, but we won't add any data quite yet.
[49:16] Not until later.
[49:19] If I was to call the show function, we can see all the subplots that we have.
[49:22] access plt.
[49:24] Call the show function.
[49:26] We have four subplots, two rows, two columns.
[49:29] If this were a 1x two, one row, two columns, we have two subplots.
[49:32] Or a 2x 1, which would be two rows, one column.
[49:35] A 3x3 would be three rows, three columns.
[49:37] We don't need that many subplots, but I think you get the idea.
[49:40] We'll have a 2x two.
[49:43] Two rows, two columns.
[49:46] We'll fill each of these individual subplots in.
[49:48] Here's how.
[49:50] We'll start with the top left corner.
[50:06] We'll start with the top left corner.
[50:06] Row 0, column 0.
[50:09] Row 0, column 0.
[50:09] We'll access our 2D list of axes at row 0, column 0, and then plot it calling the plot function.
[50:19] We will need some data.
[50:19] We'll keep it simple.
[50:24] We'll say x equals a numpy array with a list that contains 1 2 3 4 5.
[50:34] We'll plot x.
[50:34] But to keep it simple, we'll just say y will be x * 2.
[50:44] Let's take a look.
[50:48] We've selected the subplot at row 0, column 0.
[50:53] So only this one is filled in with our data.
[50:56] Let's customize it.
[50:56] Let's set the color to be red with a keyword argument.
[51:04] The color is now red.
[51:06] The color is now red.
[51:06] And let's add a title.
[51:09] And let's add a title.
[51:09] We need to select that specific subplot.
[51:11] We need to select that specific subplot using a set of indices.
[51:13] using a set of indices axes at row 0, column 0.
[51:18] Call the set title function.
[51:18] Then pass it a title.
[51:21] title function.
[51:21] Then pass it a title.
[51:21] We'll say x 2 for the title.
[51:25] We'll say x 2 for the title.
[51:25] And there's the title for this specific subplot.
[51:27] And there's the title for this specific subplot.
[51:29] subplot.
[51:29] Now we'll plot the top right subplot.
[51:33] Now we'll plot the top right subplot.
[51:33] We need to select that specific one.
[51:36] We need to select that specific one.
[51:36] That would be row 0, column 1.
[51:39] We'll call the plot function.
[51:39] Pass in x.
[51:44] call the plot function.
[51:44] Pass in x.
[51:44] We'll say x to the^ of two for the y-axis.
[51:46] We'll say x to the^ of two for the y-axis.
[51:48] y-axis.
[51:48] Select a color.
[51:48] I'll select blue.
[51:53] Select a color.
[51:53] I'll select blue.
[51:53] Then set the title axis at row 0 column 1.
[51:56] Then set the title axis at row 0 column 1.
[51:56] We will set the title to be x to the^ of two.
[52:01] at row 0 column 1.
[52:01] We will set the title to be x to the^ of two.
[52:04] to be x to the^ of two.
[52:07] x to the of two.
[52:09] The top right subplot should now be plotted.
[52:11] All right. Let's select the bottom left corner that will have different indices.
[52:17] axes at row one, column zero. We'll call the plot function.
[52:22] Pass in x. For y, we'll say x to the power of three.
[52:25] We'll set the color to be green.
[52:33] Then set a title axis at indices of 1, 0.
[52:38] Call the set title function.
[52:42] This will be x to the of three.
[52:50] Oops, looks like I misspelled color.
[52:53] There we go.
[52:55] Some of these subplots are overlapping.
[52:59] You can add a tight layout so everything fits.
[53:02] Access plt. Call the tight layout function. And now they should all fit in
[53:09] function.
[53:10] And now they should all fit in place, place, which they do.
[53:13] Let's finish this bottom right corner.
[53:15] I'll just copy and paste the last two lines.
[53:18] That would be axes at row one, column 1.
[53:22] This will be x ^ 4.
[53:26] The color will be purple.
[53:30] Axes at row 1, column 1.
[53:33] We'll set the title to be x to the^ of 4.
[53:36] There we go.
[53:39] We have now filled in the bottom right corner.
[53:44] Now, if this were a 3x3, well, then only these four subplots are still filled in, but we really don't need that many.
[53:50] Now, you don't necessarily need to use plots.
[53:53] You could use different graphs instead such as a bar chart or whatever else you would like.
[54:00] All right, everybody.
[54:02] And that is how to create subplots using mattplot lib.
[54:09] Yo.
[54:09] Hey everybody.
[54:11] In this video, we're going to integrate both pandas and mattplot lib.
[54:16] In my panda series, we've been working with the CSV file that contains the stats for the original 150 Pokémon.
[54:24] We'll be reusing that in this series.
[54:26] Or if there's another set of data that you would like to work with, feel free to use that, too.
[54:29] I'll post this in the comment section on YouTube and you can just copy and paste it if you would like.
[54:34] Just be sure to put it in a CSV file.
[54:37] To work with pandas, we'll need to import it.
[54:37] Import pandas.
[54:41] We'll give it a nickname as pd for short.
[54:47] When we read a CSV file, we're going to assign it to be a dataf frame, which we will name df.
[54:53] df equals access pandas as pd.
[54:58] call the read CSV function and then pass in the name of that file or a file path.
[55:04] The name of my file is data.csv.
[55:07] And it's right next to my main Python file.
[55:09] I don't need a file path beforehand.
[55:11] beforehand.
[55:13] Let's be sure that it works.
[55:13] I'm going to print my data frame.
[55:17] Here are all the stats of the original 150 Pokémon.
[55:21] It's a truncated view though, but that's good enough.
[55:24] We'll create a bar chart that shows the quantity of the primary types.
[55:29] For example, there's grass types, there's fire types,
[55:34] dragon types, so on and so forth.
[55:37] We'll be selecting only a single column, a single series, type one.
[55:42] There is a secondary type of type two, but we won't be concerned with that in this video.
[55:47] To select a single column, we'll be using the subscript operator, which is a set of straight brackets.
[55:54] Then select the column.
[55:57] We will select type one.
[55:59] That is the primary type.
[56:02] We'll be returned with a single series, a single column.
[56:06] There is a built-in function of series.
[56:09] It's called value counts.
[56:09] And this is a function.
[56:13] counts. And this is a function.
[56:15] This function will return the number in each category.
[56:17] each category.
[56:20] So, if I were to run this, we would have a total of 28 water types, 22 normal types, 14 poison types, 12 fire types, and a whole bunch more.
[56:27] fire types, and a whole bunch more.
[56:31] We'll plot the quantity of each type.
[56:34] The index we'll put on the y-axis with a horizontal bar chart, and the values we'll place on the x-axis.
[56:37] horizontal bar chart, and the values we'll place on the x-axis.
[56:43] Now for convenience I'm going to assign our value counts.
[56:46] our value counts.
[56:48] We'll say type count equals this series.
[56:51] count equals this series.
[56:54] this series.
[56:57] Type count will be a series.
[56:59] It will hold the amount of each type.
[57:03] Now to create a bar chart, we'll access plt.
[57:05] plt.
[57:07] Call the bar function.
[57:10] We'll create a horizontal bar chart in just a moment.
[57:12] For the x-axis, we'll
[57:15] Just a moment.
[57:15] For the x-axis, we'll access our series of type count, but I would like just the index.
[57:22] We don't need the entire series.
[57:25] And for the y-axis, we'll need the values.
[57:31] Take our series of type count. Access the values.
[57:34] Let's see what we have currently.
[57:37] We'll do a test run.
[57:37] Be sure to show your plot because I forgot to do that.
[57:42] There we go.
[57:44] Here's our bar chart.
[57:46] But we have a little bit of work to do.
[57:48] All of these index names are grouped together.
[57:51] Let's place them on the x-axis with a horizontal bar chart.
[57:55] We'll use a horizontal bar chart with bar h.
[57:59] That's a lot better.
[58:02] Now they're in ascending order.
[58:04] I would like the values with the most on the top rather than the bottom.
[58:09] Well, when we create our type count series, we can pass in a keyword argument.
[58:14] Ascending equals true.
[58:17] Ascending equals true.
[58:17] And they should now be in reverse order.
[58:20] And they should now be in reverse order.
[58:21] You can change the colors if you would like.
[58:25] Pass in a keyword argument of color.
[58:28] Then pick a color.
[58:30] I'll use a hex color picker.
[58:32] I'll pick something blue.
[58:35] That's pretty decent.
[58:38] I'll copy the hex value and then paste it within that color keyword argument.
[58:41] color keyword argument.
[58:43] Not bad.
[58:47] I'll also add an edge color of black.
[58:54] Edge color equals black.
[58:56] Let's add a title and some labels.
[58:59] PLT call the title function.
[59:02] This title will be number of Pokemon by primary type.
[59:07] Let's make sure that it works.
[59:10] Yes, it does.
[59:12] For the x-axis, we'll say count.
[59:12] plt. Call the x label function.
[59:18] count. plt. Call the x label function.
[59:18] Pass in the word of count.
[59:23] There we are.
[59:23] Then for the y-axis axis.
[59:26] There we are.
[59:26] Then for the y-axis axis plt call the y label function.
[59:29] Pass in a plt call the y label function.
[59:29] Pass in a word of type.
[59:35] Just to make sure everything fits.
[59:37] We're going to call the tight layout function.
[59:39] so that everything fits.
[59:39] plt call the tight layout function.
[59:43] so that everything fits.
[59:43] plt call the tight.
[59:44] tight layout function.
[59:47] layout function.
[59:47] And now everything should fit within our figure.
[59:51] figure.
[59:51] All right everybody.
[59:51] So that served as a final project for us.
[59:52] final project for us.
[59:52] That's how to use both pandas and mapplot lib together.
[59:55] final project for us.
[59:55] That's how to use both pandas and mapplot lib together.