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Intro to Seaborn

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Relationship Plots

5:36 with AJ Tran

There are two types of plots for charting out relationships in Seaborn: scatter plots and line plots.

Scatter Plot

sns.scatterplot(data=pokemon, x='HP', y='Attack')

With hue:

sns.scatterplot(data=pokemon, x='HP', y='Attack', hue='Type')

Line Plot

sns.lineplot(data=pokemon, x='HP', y='Attack')

relplot

Make many scatter plots based on the Type of Pokemon:

sns.relplot(kind='scatter', data=pokemon, x='HP', y='Attack', hue='Type', col='Type', col_wrap=3)

Independent Practice

Can you infer a relationship between HP and Defense?

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    What is the relationship between Attack and Health Points? 0:01
    There are two types of plots for charting out relationships in Seaborn. 0:07
    Scatterplots and lineplots. 0:12
    Let's start with a scatterplot. 0:15
    Seaborn uses a declarative approach to plotting, 0:19
    which means that the function calls are self-descriptive. 0:22
    To make a scatterplot, 0:26
    we call the scatterplot function, 0:29
    sns.scatterplot, and it takes three parameters, 0:33
    data=pokemon, x=HP and y=Attack. 0:40
    Shift+Enter to run the cell. 0:46
    The scatterplot shows us that there may be a general positive 0:49
    correlation between HP and Attack, with one outlier. 0:53
    Generally as HP increases, so does Attack. 0:57
    Pokemon with larger health points tend to be stronger. 1:01
    Now let's try a lineplot. 1:06
    We call the lineplot function to make one. 1:10
    sns.lineplot. 1:14
    It takes the same three parameters, 1:17
    data=Pokemon, x=HP, and y= Attack. 1:23
    Shift+Enter to run the cell. 1:30
    The lineplot doesn't do a great job at showing us information that can be 1:33
    easily inferred. 1:38
    Recall, that a lineplot is better at showing an x-axis that 1:39
    follows a continuous variable like time. 1:44
    In this example, we're plotting out a discrete variable HP. 1:47
    So what happens is that the lineplot goes all over the place. 1:52
    And it's harder to infer a trend compared to the scatterplot, that's okay. 1:56
    A part of exploratory data analysis is trying out different things to see 2:02
    what works well. 2:07
    It's normal for some plots to show us better insights than other kinds. 2:08
    So let's explore more of the scatterplot. 2:14
    We can introduce the hue keyword to look at a breakdown of how different types 2:17
    are distributed in the scatterplot. 2:22
    Remember that the type of Pokemon is a categorical variable like water, 2:24
    grass and electric. 2:30
    sns.scatterplot, data=pokemon, 2:31
    x = HP, y = Attack. 2:38
    And hue = Type. 2:47
    I've got an AttributeError, 2:54
    module 'seaborn' has no attribute 'scatterplt' with no o. 2:56
    So let's fix that typo scatterplot, and 3:02
    now I've got a scatterplot with color codes. 3:06
    And this takes us into analyzing the categorical aspects of our data. 3:11
    We can break this scatterplot down further by using the relplot function and 3:17
    introducing the "col" Column keyword argument, 3:22
    to make many scatterplots based on the type of Pokemon. 3:25
    Sns.relplot is short for relationship plot. 3:32
    Starts with the same parameters 3:37
    data=pokemon, x=HP and y= Attack. 3:41
    We will pass it the hue keyword argument for Type, and 3:50
    now the "col" column keyword argument which also takes Type 3:55
    Shift+Enter to run the cell. 4:04
    And right now it's really difficult to see these new plots. 4:07
    So we'll add one more parameter, 4:11
    the col_wrap keyword argument, col_wrap=3. 4:15
    And now we have a scatterplot for 4:26
    each type of Pokemon that describes the relationship between HP and Attack. 4:28
    It's nice to have a breakdown of types like this because for some types, 4:34
    there are very few observations. 4:39
    Looking at the types that have many observations, we can infer that generally, 4:41
    HP and Attack are somewhat positively correlated. 4:46
    Now try practicing with the defense style of Pokemon. 4:50
    Can you infer a relationship between HP and defense? 4:53
    Before we move on to making more plots, 5:02
    let's write down our observations to record them. 5:04
    We'll use a mark down cell and say that generally, HP and 5:10
    Attack are somewhat correlated, somewhat positively correlated. 5:16
    Pokemon with more HP tend to be stronger. 5:26
    Awesome. 5:35

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