Data Visualizations Matplotlib Plotting Tutorial

# Scatter Plots using Matplotlib

#### 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.

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```