Dates vs Derivatives Solution

---

Solution 1 (Object Oriented)

import matplotlib.pyplot as plt

# Instantiate a figure and an Axes
fig, ax = plt.subplots()

# Draw a scatter plot on the axes
ax.plot(derivatives, dates, c="tab:red", linestyle='None', marker='o')

# Draw the title and labels
ax.set_title("Dates vs Derivatives\n(for 30 college students surveyed about their recent year)")
ax.set_xlabel("derivates")
ax.set_ylabel("dates")

Explanation

1. Make a figure with an Axes.

   fig, ax = plt.subplots()
   

   pyplot.subplots() is a handy function for creating a figure and a set of subplots in a single line. The default behavior creates a single figure with a single Axes, but if we wanted, we could make a grid of Axes with something like this.

   fig, ax = plt.subplots(nrows=3, ncols=3)
   

   

   Alternatively, we could create the figure and axes in a less cryptic (but lengthier) way, like this.

   fig = plt.figure()  # make the figure
   fig.add_subplot()   # add the subplot
   ax = fig.axes[0]    # get the Axes object (for downstream changes)
   

   Note

   Note that ax is technically an AxesSubplot.

   type(ax)
   # <class 'matplotlib.axes._subplots.AxesSubplot'>
   

   AxesSubplot is a subclass of the more generic Axes class.

2. Draw a scatter plot on the Axes.

   ax.plot(derivatives, dates, c="tab:red", linestyle='None', marker='o')
   

   Here we use the Axes.plot() method, passing in derivatives, dates, c="tab:red" (color), linestyle='None', and marker='o'. By default, Axes.plot() connects points with lines (i.e. it draws a line plot). By setting linestyle='None' and marker='o', we get a scatter plot.

   What about Axes.scatter()

   It's true, ax.scatter(x=derivatives, y=dates, c="tab:red") works, but it's less performant than ax.plot(x=derivatives, y=dates, c="tab:red", linestyle='None').

   Use Axes.scatter() when you want to customize the points (e.g. varying their sizes or colors by another variable).

   Tip

   "tab:red" stands for Tableau red. See the Tableau color palette here.

   You can find a list of additional matplotlib color palettes here.

3. Draw the title and labels.

   title = "Dates vs Derivatives\n(for 30 college students surveyed about their recent year)"
   ax.set_title(title)        # (1)!
   ax.set_xlabel("derivates") # (2)!
   ax.set_ylabel("dates")     # (3)!
   

   1. See Axes.set_title()
   2. See Axes.set_xlabel()
   3. See Axes.set_ylabel()

---

Solution 2 (PyPlot)

# Make a scatter plot
plt.scatter(x=derivatives, y=dates, c="tab:red")

# Draw the title and labels
plt.title("Dates vs Derivatives\n(for 30 college students surveyed about their recent year)")
plt.xlabel("derivates")
plt.ylabel("dates")

Explanation

This solution uses the matplotlib.pyplot state-based interface to matplotlib, with methods:

• pyplot.scatter() (to draw the scatter plot)
• pyplot.title() (to set the title)
• pyplot.xlabel() (to set the x label)
• pyplot.ylabel() (to set the y label)

Is this better than Solution 1?

No.

Solution 1 is preferred because it provides more control over the figure and axes objects. ..but this solution is perfectly fine for ad hoc analyses .

---

Back Next