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Matplotlib Cheat Sheet

Table of contents

Introduction to Matplotlib

  • Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack
  • Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned
  • Hence, nowadays, cleaner, more modern APIs, for example, Seaborn, ggpy, HoloViews, Altai, has been developed to drive Matplotlib
import matplotlib.pyplot as plt
  • The plt interface is what we will use most often

Setting Styles

# See the different styles avail
plt.style.available
# Set Style
plt.style.use('seaborn-whitegrid')

Plotting from an IPython notebook

  • %matplotlib notebook will lead to interactive plots embedded within the notebook
  • %matplotlib inline will lead to static images of your plot embedded in the notebook

matplotlib-anatomy-of-a-plot

  • Figure can contains multiple Subplot
  • Axes 0 and Axes 1 are AxesSubplot stacked together

Matplotlib Two Interfaces

Pyplot API vs Object-Oriented API

  • Quickly → use Pyplot Method
  • Advanced → use Object-Oriented Method
  • In general, try to use the object-oriented interface (more flexible) over the pyplot interface (i.e: plt.plot())
x = [1,2,3,4]
y = [11,22,33,44]
  • MATLAB-style or PyPlot API: Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact
# Pyplot API
plt.plot(x,y, color='blue')

plt.title("A Sine Curve") #in OO, use the ax.set() method to set all these properties at once
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.xlim([1,3])
plt.ylim([20,])
  • Object-oriented: plotting functions are methods of explicit Figure and Axes objects.
# [Recommended] Object-oriented interface 
fig, ax = plt.subplots() #create figure + set of subplots, by default, nrow =1, ncol=1
ax.plot(x,y) #add some data
plt.show()
Matplotlib Gotchas

While most plt functions translate directly to ax methods (such as plt.plot() → ax.plot(), plt.legend() → ax.legend(), etc.), this is not the case for all commands. In particular, functions to set limits, labels, and titles are slightly modified. For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:

  • plt.xlabel() → ax.set_xlabel()
  • plt.ylabel() → ax.set_ylabel()
  • plt.xlim() → ax.set_xlim()
  • plt.ylim() → ax.set_ylim()
  • plt.title() → ax.set_title() In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ax.set()
ax.set(xlim=(0, 10), ylim=(-2, 2),
       xlabel='x', ylabel='sin(x)',
       title='A Simple Plot');

Matplotlib Workflow

# 0. Import and get matplotlib ready
%matplotlib inline
import matplotlib.pyplot as plt

# 1. Prepare data
x = [1, 2, 3, 4]
y = [11, 22, 33, 44]

# 2. Setup plot
fig, ax = plt.subplots(figsize=(5,5)) #Figure size = Width & Height of the Plot

# 3. Plot data
ax.plot(x, y)

# 4. Customize plot
ax.set(title="Sample Simple Plot", 
       xlabel="x-axis", 
       ylabel="y-axis", 
       xlim=(0, 10), ylim=(-2, 2))

# 5. Save & Show
fig.savefig("../images/simple-plot.png")

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Subplots

  • Option #1: to plot multiple subplots in same figure
# Option 1: Create multiple subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, 
                                             ncols=2, 
                                             figsize=(10, 5))
# Plot data to each axis
ax1.plot(x, x/2);
ax2.scatter(np.random.random(10), np.random.random(10));
ax3.bar(nut_butter_prices.keys(), nut_butter_prices.values());
ax4.hist(np.random.randn(1000));

Screenshot 2021-03-23 at 8 52 20 AM

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Scatter Bar and Histogram Plot

Scatter

#<--- Method 1: Pytlot --->: 
df.plot(kind = 'scatter',
             x = 'age',
             y = 'chol',
             c = 'target', #c = color the dot based on over_50['target'] columns
             figsize=(10,6));
#<--- Method 2: OO --->: 
## OO Method from Scratch
fig, ax = plt.subplots(figsize=(10,6))

## Plot the data
scatter = ax.scatter(x=over_50["age"],
                     y=over_50["chol"],
                     c=over_50["target"]);
# Customize the plot
ax.set(title="Heart Disease and Cholesterol Levels",
       xlabel="Age",
       ylabel="Cholesterol");
# Add a legend
ax.legend(*scatter.legend_elements(), title="target"); # * to unpack all the values of Title="target"

#Add a horizontal line
ax.axhline(over_50["chol"].mean(), linestyle = "--");

Bar

  • Vertical
  • Horizontal
#<--- Method 1: Pytlot --->: 
df.plot.bar();
#<--- Method 2: OO --->: 
fig, ax = plt.subplots()
ax.bar(x, y)
ax.set(title="Dan's Nut Butter Store", ylabel="Price ($)");

Histogram

# Create Histogram of Age to see the distribution of age

heart_disease["age"].plot.hist(bins=10);

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