# Scatter Plots using Matplotlib

- March 4, 2018 • 16 min read
- Key Terms: scatter plot

#### Import Modules¶

```
import matplotlib.pyplot as plt
% matplotlib inline
```

#### What is Matplotlib¶

In Python, we utilize the module `matplotlib`

to generate plots. We can create scatter plots, line plots, bar charts, histograms and more.

`pyplot`

is an interface to `matplotlib`

to provide easier syntax for plotting.

`% matplotlib inline`

allows us to immediately see these plots inline in our Jupyter Notebook.

#### Generate a Simple Scatter Plot¶

We call the `scatter`

function and must pass in at least two arguments, the first our list of x-coordinates, and the second our list of y-coordinates.

```
plt.scatter([1, 2, 3, 4], [1, 2, 3, 4]);
```

We plotted 4 scatter points.

#### Generate a Scatter Plot with My Fitbit Activity Data¶

Below is my Fitbit data of daily steps taken and daily calories burned over a 15-day period.

```
calories_burned = [3092, 2754, 2852, 2527, 3199, 2640, 2874, 2649,
2525, 2858, 2530, 2535, 2487, 2534, 2668]
```

```
steps = [15578, 8769, 14133, 8757, 18045, 9087, 14367, 11326, 6776,
14359, 10428, 9296, 8177, 8705, 9426]
```

```
plt.scatter(steps, calories_burned)
plt.xlabel("steps")
plt.ylabel("calories burned");
```

In order to more easily see the variation, by default, Matplotlib labels the smallest y-axis tick at 2500 instead of simply 0.

We can see a positive relationship between these two variables. The more steps I took in day, the more calories I burned.