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Intro to Seaborn

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Categorical Distribution Plots

4:21 with AJ Tran

The box plot function gives us a summary of the spread of data grouped by a categorical variable. Another way of visualizing the distribution is by using the violin plot. The violin plot is like a mix of a box plot and a KDE. It is analogous to the box plot.

Box plot

sns.catplot(kind='box', data=pokemon, x='Type', y='Attack', aspect=2)

Violin plot

sns.catplot(kind='violin', data=pokemon, x='Type', y='Attack', aspect=2)

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    What is the distribution of Attack for each type? 0:00
    The catplot function has another subfamily of plots that will help us 0:04
    visualize the distribution of data with a categorical variable. 0:08
    We'll make some box plots to look at that. 0:12
    sns.catplot, and 0:16
    this time we'll call the kind box, 0:19
    data=pokemon, x='Type', 0:25
    and y='Attack'. 0:31
    And again, let's fix the aspect so 0:35
    that the x-axis labels are better spaced apart for 0:39
    ease of reading, aspect=2. 0:44
    Recall that a box plot gives us a summary of the spread of data. 0:48
    By using the catplot function, we are able to get the spread of data for 0:53
    each type of Pokemon all on one plot. 0:58
    Notice that the diamond markers represent outliers. 1:00
    And where there's a line, instead of a box plot, 1:04
    that means that there's only one observation for that type of Pokemon. 1:08
    For each of these box and whisker plots, we have a five-number summary. 1:12
    The line in the middle of the box represents the median value, or 1:17
    their central tendency of Attack points. 1:21
    Then we have the first and third quartiles, and 1:24
    then the whiskers, which represent the max and minimum values. 1:28
    Another way of visualizing the distribution is by using the violin plot. 1:34
    The violin plot is like a mix of a box and whisker plot and a KDE. 1:39
    Violin plots are analogous to box plots, but recall that the KDE 1:44
    lets us make inferences about the data based on a probability curve. 1:49
    We can easily make a violin plot by copying cell 20, 1:54
    and changing the kind parameter from box to violin. 1:59
    Notice that the violin plot includes part of the box and whiskers that 2:09
    are found in the box plot, the median and the first and third quartiles. 2:14
    So it provides a similar summary of the spread of data. 2:19
    That's one of the joys of using Seaborn. 2:23
    There are different plot types that can give us similar findings for our data. 2:25
    Let's take a look at our question again. 2:29
    What is the distribution of Attack for each type of Pokemon? 2:32
    For this example, I'll answer it for the Normal Type of Pokemon. 2:36
    According to our box and whiskers plot, 2:41
    the minimum Attack points lies between 0 and 10, 2:45
    let's say 5, while the maximum goes all the way up to 110. 2:51
    The median Attack points for Normal Type Pokemon looks to be about 75. 2:58
    And the first and third quartiles 3:04
    look to be around 55 and 105. 3:09
    Now let's record our observations in a new markdown cell. 3:13
    For the Normal Type Pokemon, 3:25
    the minimum Attack is 5, 3:33
    the maximum is 110, 3:40
    median is 75. 3:47
    The first and 3:51
    third quartiles are, 3:54
    say, 55 and 105, 3:59
    respectively. 4:04
    Awesome, now try practicing using the Defense stats of the Pokemon. 4:14

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