Chart Types & Reasons to Use6:00 with Ken Alger
Let's take a look at some of the more common chart types you will come across and the reasons you would choose one over another.
Welcome back. 0:00 Now that we have seen a little bit about how Matplotlib works, 0:01 let's talk briefly about different chart types. 0:05 As we work through the rest of this course, 0:07 you will get a better sense of how and when to use each type. 0:10 Charts come in a wide range of visual styles and 0:13 are used to represent different things. 0:16 Matpotlib supports a lot of options. 0:18 Line, bar, pie, scatter plot, histogram, box plot, 0:22 heat maps and candlestick charts are some of the more common charts. 0:26 In this course, we'll be showcasing scatter, histogram and 0:32 box plots with Matplotlib. 0:36 I'll touch on some of the others as well, and 0:38 included links in the teachers notes to other charts for further reading. 0:40 Let's talk about the when's and why's of some of these chart types. 0:44 A line chart is used to identify trends or patterns in data and 0:49 commonly used for exploring trends over time. 0:53 They can be used to compare multiple groups by using different lines. 0:56 For example, the total sales of several products over a period of time. 0:59 A bar chart is most effective when comparing categories of data. 1:04 They can also be used, like a line chart, for tracking changes over time. 1:08 When used in this fashion they, are best applied to large changes in the data. 1:12 A bar chart will have two axes. 1:17 One typically with numerical data and the other with a category. 1:20 For example, 1:23 the number of different types of apples sold each week at a farmers' market. 1:24 Or for a time example, the total population of the world since 1000 BCE. 1:29 A pie chart is best used when comparing parts of a whole at a snapshot in time, 1:34 instead of a change over time. 1:39 A pie chart would answer questions like, what is 1:41 the percentage of each type of apples sold last week at our farmer's market. 1:44 A pie chart will easily show that, for example, 23% were red delicious, 1:49 18% were golden delicious and 15% were granny Smith. 1:54 Scatter plots are similar to line charts in that they both have a horizontal and 1:59 vertical axis. 2:04 However, scatter plots are used to show how much one variable is 2:04 impacting by another or is correlation. 2:09 We use scatter plots to show relationships between values. 2:12 The plotter points are markers, can be different sizes to showcase importance, 2:16 and different color to show specific data buckets. 2:21 It allows us to quickly visualize the distribution of the data and 2:24 notice any outliers. 2:28 We can see if there's a positive, negative, or 2:29 non-existent correlation between data based on the scatter plot results. 2:32 Histograms look like bar charts, however, looks can be deceiving as they 2:37 are not the same, and are indeed used for different purposes. 2:42 Histograms are used to show distributions of variables while bar charts 2:45 are used to compare the variables. 2:50 Unlike a bar chart a histogram won't have gaps between data. 2:52 Empty values may be possible however if there are no data points for 2:56 particular value. 3:00 In a histogram chart, the data are split into different intervals or 3:02 bends, to show the frequency of distribution of continuous data. 3:06 This allows for the inspection of the data distribution, and 3:10 will show outliers or skewed data. 3:15 When using a histogram, choosing an appropriate number of bins and 3:17 their width is important for meaningful and accurate reporting. 3:21 Box plots, sometimes called box and whisker plots. 3:25 Combine the functionality, the bar chart, with a histogram. 3:29 They allow for the quick examination of and comparison between different sets of 3:33 data while displaying statistical information about the data. 3:38 It allows for visualizing the minimum first quartile, 3:42 medium, third quartile, and maximum values of a data set. 3:45 Wow, that's a lot like a high school statistics class. 3:49 Put more simply it allows us to see the overall distribution, 3:53 central value and variability of a data set. 3:57 Much like a histogram choosing the number of bins and 4:00 their width is an important consideration for reporting. 4:03 Finally, I'd also like to briefly touch on heatmaps and candlestick charts. 4:07 Or we won't be using them in this course. 4:12 You're likely to come across them. 4:14 A heatmap is a chart in which the area inside recognized boundaries is shaded in 4:16 proportion to the data being represented. 4:20 For example you could have a heatmap representing population density. 4:23 Countries with higher populations will be represented with different colors than 4:27 countries with lower populations. 4:31 Candlestick charts are heavily used in a financial sector. 4:33 While they bear resemblance to box charts, that where their similarities end. 4:37 Each candlestick will typically show one day of price movement of a stock, 4:41 currency or derivative. 4:46 It's like a combination of a line and bar chart showing 4:47 an overtime trend while also showing the daily information for the data. 4:51 Specifically they show the open, close, high, and low values for 4:55 security and are a cornerstone of financial technical analysis. 5:00 Wow, that's a lot of charting options, and 5:04 it only scratches the surface of the charts available in Matplotlib. 5:07 It's also just the beginning of the when and why to use each chart. 5:11 Choosing the chart type is predominantly determined by the questions about your 5:15 data and how to best represent your data for the intended audience. 5:19 In addition to picking the proper chart type, 5:23 there is one other aspect of reporting that is important to remember, scale. 5:25 This is the value you mark on the axis to show the relationship between 5:30 the units that are being measured. 5:34 Often, we may want to utilize multiple chart types to showcase our data. 5:36 When doing so, we need to keep scale in mind across our charts, so 5:40 that our data biz efforts aren’t misleading. 5:44 We’ll examine this a bit more as we look at more charting options with matplotlib. 5:47 Now is a good time to take a short break. 5:51 Get up and stretch a bit before we look at a real world dataset and 5:54 see how to visualize it, using a variety of charts. 5:57
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