# Plot function arguments in relationship

### Visualizing Data with Pairs Plots in Python – Towards Data Science

Bivariate descriptive displays or plots are designed to reveal the relationship A scatter plot displays the values of two variables at a time using symbols, Plot annual temperature tann on the y-axis as a function of elevation elev on the x- axis. Graphing is a pictorial way of representing relationships between various quantities, A graph can give one an idea about how these variables change relative to one another. For this Simple Graphs: Lines, Periodic Functions, and More. A pairs plot allows us to see both distribution of single variables and relationships between two variables. Pair plots are a great method to.

When doing this, you cannot use a row variable. Using the pointplot function without specifying order is likely to produce an incorrect plot. There are also a number of methods on the FacetGrid object for manipulating the figure at a higher level of abstraction.

The most general is FacetGrid. When making a figure without row or column faceting, you can also use the ax attribute to directly access the single axes. However, to work properly, any function you use must follow a few rules: This will be true of functions in the matplotlib. It must accept the data that it plots in positional arguments.

Internally, FacetGrid will pass a Series of data for each of the named positional arguments passed to FacetGrid. It must be able to accept color and label keyword arguments, and, ideally, it will do something useful with them.

This function will just take a single vector of data for each facet: For example, this approach will allow use to map plt. In a PairGrideach row and column is assigned to a different variable, so the resulting plot shows each pairwise relationship in the dataset. In the former, each facet shows the same relationship conditioned on different levels of other variables.

Here the slope seems constant. Our slope is a constant positive value. So once again, our slope here is a constant positive line.

## The graphical relationship between a function & its derivative (part 1)

Let me be careful here because at this point, our slope won't really be defined, because our slope, you could draw multiple tangent lines at this little pointy point. So let me just draw a circle right over there. But then as we get right over here, the slope seems to be positive. So let's draw that. The slope seems to be positive, although it's not as positive as it was there. So the slope looks like it is-- I'm just trying to eyeball it-- so the slope is a constant positive this entire time.

We have a line with a constant positive slope. So it might look something like this.

### Data Frames and Plotting

And let me make it clear what interval I am talking about. I want these things to match up. So let me do my best. So this matches up to that. This matches up over here.

## Bivariate Plots

And we just said we have a constant positive slope. So let's say it looks something like that over this interval.

And then we look at this point right over here. So right at this point, our slope is going to be undefined. There's no way that you could find the slope over-- or this point of discontinuity.

But then when we go over here, even though the value of our function has gone down, we still have a constant positive slope. While there are an almost overwhelming number of methods to use in EDA, one of the most effective starting tools is the pairs plot also called a scatterplot matrix.

A pairs plot allows us to see both distribution of single variables and relationships between two variables. Pair plots are a great method to identify trends for follow-up analysis and, fortunately, are easily implemented in Python! In this article we will walk through getting up and running with pairs plots in Python using the seaborn visualization library.

**Linear Functions**

We will see how to create a default pairs plot for a rapid examination of our data and how to customize the visualization for deeper insights. The code for this project is available as a Jupyter Notebook on GitHub.

We will explore a real-world dataset, comprised of country-level socioeconomic data collected by GapMinder. We can load in the socioeconomic data as a pandas dataframe and look at the columns: Each row of the data represents an observation for one country in one year and the columns hold the variables data in this format is known as tidy data.

There are 2 categorical columns country and continent and 4 numerical columns. The columns are fairly self-explanatory: