Learn Data Visualization with Python: Course OverviewCourse Overview

Introduction to Matplotlib

Master a commonly used Python graphing module, Matplotlib. Soon, you will be producing high-quality plots to visualize your data.

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Key Concepts

Review core concepts you need to learn to master this subject

Pyplot functions

Setting Linestyle, Color in Matplotlib

Pyplot-axis

Figures in Matplotlib

Subplots in Matplotlib

Adjusting Subplot Margins in Matplotlib

X-ticks and Y-ticks in Matplotlib

Pyplot functions

The Python library Matplotlib contains the pyplot module, which provides users with an interface for graphing data. Pyplot contains over 100 functions, from acorr to yticks. You must import the module, and plt is the standard variable name used.

from matplotlib import pyplot as plt

Here are some of the most common pyplot functions:

Function	Description
Plot	plots y versus x as lines and/or markers
Show	displays a figure
Axis	sets some axis properties
Xlabel	sets the label for the x-axis
Ylabel	sets the label for the y-axis
Title	sets a title for the axes
Subplot	adds a subplot to the current figure
Subplots_adjust	tunes the subplot layout
Legend	places a legend on the axes
Figure	creates a new figure
Savefig	saves the current figure

Setting Linestyle, Color in Matplotlib

In Python’s Matplotlib, the pyplot.plot() function can accept parameters to set the color(color), linestyle(linestyle) and marker(marker) for line graph. Color values can be HTML color names or HEX codes. Line styles can be dashed('--') or dotted('..'). Markers can be circles('o'), squares('s'), stars('*'), or other shapes.

Pyplot-axis

Matplotlib’s pyplot.axis function takes one parameter, which must be a four-item array, and returns the current axes’ limits. The four items should contain enough info to define an x axis and a y axis by minimum and maximum values. The array must order these values as follows: x-axis minimum, x-axis maximum, y-axis minimum, y-axis maximum.

https://author.codecademy.com/learning-standards/5cc9c73faf246d6f7689df89

Figures in Matplotlib

In Python’s Matplotlib, a figure is a container that holds plots. It can hold a single plot or multiple plots. When a figure holds multiple separate plots, those are called subplots.

Subplots in Matplotlib

In Python, the Matplotlib’s pyplot.subplot() function can be used to create a figure with a grid of subplots. The function accepts number of rows, number of columns, and the current index as arguments.

Adjusting Subplot Margins in Matplotlib

In Python’s Matplotlib, subplots can overlap, either horizontally or vertically. The function pyplot.subplots_adjust() can set better spacing around each subplot in a figure. It can set values for left, right, bottom and top margins, plus the horizontal(wspace) and vertical(hspace) spaces between adjacent subplots.

X-ticks and Y-ticks in Matplotlib

In Python’s Matplotlib, the x-tick and y-tick marks of the plot can be changed using functions ax.set_xticks() and ax.set_yticks(). These functions accepts an array of values representing tick mark positions.

Line Graphs in Matplotlib

Lesson 1 of 3

1
Matplotlib is a Python library used to create charts and graphs. In this first lesson, you will get an overview of the basic commands necessary to build and label a line graph. The concepts you wi…
2
Line graphs are helpful for visualizing how a variable changes over time. Some possible data that would be displayed with a line graph: * average prices of gasoline over the past decade * weight o…
3
We can also have multiple line plots displayed on the same set of axes. This can be very useful if we want to compare two datasets with the same scale and axis categories. Matplotlib will automati…
4
We can specify a different color for a line by using the keyword color with either an HTML color name or a HEX code : plt.plot(days, money_spent, color=’green’) plt.plot(days, money_spent_2, col…
5
Sometimes, it can be helpful to zoom in or out of the plot, especially if there is some detail we want to address. To zoom, we can use plt.axis(). We use plt.axis() by feeding it a list as input. T…
6
Eventually, we will want to show these plots to other people to convince them of important trends in our data. When we do that, we’ll want to make our plots look as professional as possible. The…
7
Sometimes, we want to display two lines side-by-side, rather than in the same set of x- and y-axes. When we have multiple axes in the same picture, we call each set of axes a subplot. The pictu…
8
Sometimes, when we’re putting multiple subplots together, some elements can overlap and make the figure unreadable: ![overlapping](https://s3.amazonaws.com/codecademy-content/courses/matplotlib/ov…
9
When we have multiple lines on a single graph we can label them by using the command plt.legend(). The legend method takes a list with the labels to display. So, for example, we can call: plt.pl…
10
In all of our previous exercises, our commands have started with plt.. In order to modify tick marks, we’ll have to try something a little bit different. Because our plots can have multiple subpl…
11
When we’re making lots of plots, it’s easy to end up with lines that have been plotted and not displayed. If we’re not careful, these “forgotten” lines will show up in your new plots. In order to b…
12
Now you’ve played around with several two-dimensional line plots in Matplotlib. You’ve seen how you can create simple, readable plots with few commands. You’ve also learned some commands to style a…

1
In the previous lesson, you learned how to create and label a line plot using Matplotlib! In this lesson, you’ll learn how to create and when to use different types of plots: * Compare categories…
2
The plt.bar function allows you to create simple bar charts to compare multiple categories of data. Some possible data that would be displayed with a bar chart: * x-axis — famous buildings, …
3
When we create a bar chart, we want each bar to be meaningful and correspond to a category of data. In the drinks chart from the last exercise, we could see that sales were different for different …
4
We can use a bar chart to compare two sets of data with the same types of axis values. To do this, we plot two sets of bars next to each other, so that the values of each category can be compared. …
5
If we want to compare two sets of data while preserving knowledge of the total between them, we can also stack the bars instead of putting them side by side. For instance, if someone was plotting t…
6
In the previous exercise, you learned to represent data as bars of different heights. Sometimes, we need to visually communicate some sort of uncertainty in the heights of those bars. Here are so…
7
We’ve learned how to display errors on bar charts using error bars. Let’s take a look at how we might do this in an aesthetically pleasing way on line graphs. In Matplotlib, we can use plt.fill_be…
8
If we want to display elements of a data set as proportions of a whole, we can use a pie chart. Pie charts are helpful for displaying data like: * Different ethnicities that make up a school distr…
9
We also want to be able to understand what each slice of the pie represents. To do this, we can either: 1. use a legend to label each color, or 2. put labels on the chart itself. #### Method 1 b…
10
Sometimes we want to get a feel for a large dataset with many samples beyond knowing just the basic metrics of mean, median, or standard deviation. To get more of an intuitive sense for a dataset, …
11
If we want to compare two different distributions, we can put multiple histograms on the same plot. This could be useful, for example, in comparing the heights of a bunch of men and the heights of …
12
In helping MatplotSip visualize their data, you’ve learned a bunch of new plot types that you can use in Matplotlib. Congratulations on adding these new plotting abilities to your repertoire: * Ho…

1
Let’s imagine that you are working as a high school math teacher. You want to display important metrics about your current class and the ones you have taught in previous years, in order to understa…
2
First, we are going to look at the chart called bar_chart.png. The data you will need to recr…
3
Now, we are going to look at the chart called side-by-side.png. This displays the differences in average test scores between students who went to two different middle schools before enrolling in yo…
4
Now, we are going to look at the chart called stacked-bars.png. This graph displays the breakdown of students who got As, Bs, Cs, Ds, and Fs in each unit. ![stacked](https://s3.amazonaws.com/codec…
5
Now, we are going to look at the chart called histograms.png. This displays the breakdown of final exam scores between your first year of teaching vs your second year of teaching. ![histogram](htt…
6
Now, we are going to look at the chart called pie.png. This displays what students think the hardest topic covered throughout your math course is. ![pie](https://s3.amazonaws.com/codecademy-conten…
7
Now, we are going to look at the chart called line_graph.png. This displays the relationship between the time students say they studied and the scores they received on their final exams. ![fill_be…