``Setup Complete``
pp1 pp2 pp3 pp4 pp5 pp6
0 -94 70 -53 38 -66 -56
1 -75 18 -38 88 35 -5
2 -21 -91 14 46 43 22
3 -22 65 3 -15 57 20
4 95 -42 -18 67 81 60
``(12, 6)``

## Line Chart  ## Bar Chart ## Heatmap ## Scatter Plots

### Normal scatter chart

Use scatter plots to display the relationships between two data ### Color-coded scatter plots

We can use scatter plots to display the relationships between (not two, but...) three variables! One way of doing this is by color-coding the points. ### Regression line

Plot the line that best fits the data ### Compare two regression line

we can use the `sns.lmplot` command to add two regression lines, comparing two lines' relation strength ### Categorical scatter plot

We can adapt the design of the scatter plot to feature a categorical variable (like labels) on one of the main axes. We'll refer to this plot type as a categorical scatter plot, and we build it with the `sns.swarmplot` command. ## Distributions

### Histograms `kde=False` is something we'll always provide when creating a histogram, as leaving it out will create a slightly different plot. With`kde=True`, kernel density estimate (KDE) curve would generate at the same time ### Density plots

The next type of plot is a kernel density estimate (KDE) plot. In case you're not familiar with KDE plots, you can think of it as a smoothed histogram.

Setting `shade=True` colors the area below the curve ### 2D KDE plots ### Color-coded plots

We create a different histogram for each species by using the `sns.distplot` command three times. We use `label=` to set how each histogram will appear in the legend.  ## Changing styles with seaborn

Seaborn has five different themes: (1)"darkgrid", (2)"whitegrid", (3)"dark", (4)"white", and (5)"ticks" 