When to Use a Scatter Plot¶

Date published: 2018-06-10

Category: Data Visualizations

Subcategory: Best Practices

Tags: scatter plot

Scatter plots help visually illustrate the relationship between two or more variables of paired data samples.

What are paired samples? Think of them as two or more measurements for each observation. For example, you could measure the number of steps taken by someone in a day and the number of calories burned in a day. In that instance, the person is considered the observation. The data samples, as we'll call variables, are the quantitative measurements. In that instance, steps taken in one variable, and number of calories burned is another.

The data for the activity measurements recorded on a single day would look like this in a table:

Observation	Daily Steps	Daily Calories Burned
Maddie	7000	2800
Jake	8000	2900
Stephanie	9500	3100

On a 2-D plot, each point/dot in our scatter plot represents a single observation and it corresponding .

For the activity measurements instance, the x-axis can be the values for steps taken and the y-axis can be values for daily calories burned.

Below, I'll walk through several practical examples to illustrate the proper use of scatter plots.

Import Modules¶

In [5]:

import matplotlib.pyplot as plt
%matplotlib inline

import matplotlib.pyplot as plt %matplotlib inline

Example: Steps Versus Calories Burned¶

Here is my actual Fitbit data of measurements of daily steps take and daily calories burned. I took 15 days' worth of data.

Record Fitbit Data in Python Lists¶

In [22]:

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]

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]

Scatter Plot of Fitbit Data¶

In [13]:

plt.figure(figsize=(8, 8))
plt.scatter(steps, calories_burned, s=70, c='lightseagreen')
plt.title("Steps Versus Calories Burned from Fitbit Data", fontsize=18, y=1.03)
plt.xlabel("steps", fontsize=14, labelpad=15)
plt.ylabel("calories burned", fontsize=14, labelpad=15)
plt.tick_params(labelsize=12, pad=6);

plt.figure(figsize=(8, 8)) plt.scatter(steps, calories_burned, s=70, c='lightseagreen') plt.title("Steps Versus Calories Burned from Fitbit Data", fontsize=18, y=1.03) plt.xlabel("steps", fontsize=14, labelpad=15) plt.ylabel("calories burned", fontsize=14, labelpad=15) plt.tick_params(labelsize=12, pad=6);

Explanation of Fitbit Data¶

Generally, the more steps taken on a single day, the more calories that were likely burned.

Example: Height Versus Mass¶

I took measurements from 15 of my friends on their height (inches) and mass (pounds).

A sample of data collected in a table is:

Observation	Height (inches)	Mass (pounds)
Ed	68	130
Matt	73	170
Lauren	63	125

Record Physical Trait Data in Python Lists¶

In [17]:

height_inches = [68, 73, 63, 63, 72, 79, 58, 73, 70, 72, 68, 56, 70, 68, 70]

mass_pounds = [130, 170, 125, 119, 165, 185, 115, 192, 195, 210, 143, 140, 190, 182, 173]

height_inches = [68, 73, 63, 63, 72, 79, 58, 73, 70, 72, 68, 56, 70, 68, 70] mass_pounds = [130, 170, 125, 119, 165, 185, 115, 192, 195, 210, 143, 140, 190, 182, 173]

Scatter Plot of Physical Trait Data¶

In [20]:

plt.figure(figsize=(8, 8))
plt.scatter(height_inches, mass_pounds, s=70, c='sandybrown')
plt.title("Height Versus Mass of My Friends", fontsize=18, y=1.03)
plt.xlabel("Height (inches)", fontsize=14, labelpad=15)
plt.ylabel("Mass (pounds)", fontsize=14, labelpad=15)
plt.tick_params(labelsize=12, pad=6);

plt.figure(figsize=(8, 8)) plt.scatter(height_inches, mass_pounds, s=70, c='sandybrown') plt.title("Height Versus Mass of My Friends", fontsize=18, y=1.03) plt.xlabel("Height (inches)", fontsize=14, labelpad=15) plt.ylabel("Mass (pounds)", fontsize=14, labelpad=15) plt.tick_params(labelsize=12, pad=6);

Explanation of Physical Trait Plot¶

Generally, an increase in height leads to an increase in mass.

Example: Minutes Played Versus Turnovers for Lebron James¶

Below is actual data from a sample of games played by Lebron James in the regular season of 2017-2018. In each game, Lebron was recorded to play a number of minutes and had recorded turnovers (lost possession of the ball to the other team).

A sample of data in a table would look like:

Game Date	Play time (minutes)	Turnovers
3/30/2018	42	1
3/21/2018	39	0
3/19/2018	40	6

Record Data in Python Lists¶

In [23]:

play_time_minutes = [42, 39, 40, 40, 31, 39, 29, 41, 39, 37, 48, 38, 38, 40, 38, 32, 27, 33, 41]

turnovers = [1, 0, 6, 6, 2, 5, 3, 6, 3, 3, 5, 7, 4, 11, 2, 2, 3, 4, 7]

play_time_minutes = [42, 39, 40, 40, 31, 39, 29, 41, 39, 37, 48, 38, 38, 40, 38, 32, 27, 33, 41] turnovers = [1, 0, 6, 6, 2, 5, 3, 6, 3, 3, 5, 7, 4, 11, 2, 2, 3, 4, 7]

Plot Lebron's Minutes Versus Turnover Data¶

In [27]:

plt.figure(figsize=(8, 8))
plt.scatter(play_time_minutes, turnovers, s=70, c='darkred')
plt.title("Play Time Versus Turnovers for Lebron in 2017-2018 Season Games", fontsize=15, y=1.015)
plt.xlabel("Play Time (minutes)", fontsize=14, labelpad=15)
plt.ylabel("Turnovers", fontsize=13, labelpad=15)
plt.tick_params(labelsize=12, pad=6);

plt.figure(figsize=(8, 8)) plt.scatter(play_time_minutes, turnovers, s=70, c='darkred') plt.title("Play Time Versus Turnovers for Lebron in 2017-2018 Season Games", fontsize=15, y=1.015) plt.xlabel("Play Time (minutes)", fontsize=14, labelpad=15) plt.ylabel("Turnovers", fontsize=13, labelpad=15) plt.tick_params(labelsize=12, pad=6);

Explanation of Lebron's NBA Data Plot¶

There isn't a clear relationship between minutes played for Lebron and the number of turnovers in games.