Matplotlib

Introduction

• Matplotlib is Python’s plotting library built on NumPy that can integrate directly with Pandas
• Workflow:

flowchart LR 
id1(data) --> id2(plot)
id2 --> id3(axis on figure)
id3 --> id4(customize)
id4 --> id5(save & share plot)

Tip

Figure and plot are used interchangably

Importing and getting started

%matplotlib inline # makes the notebook directly render the graphs 
import matplotlib.pyplot as plt

• plt.plot; creates a blank plot or figure (the ; gets rid of the array)
  • if the ; is annoying, you can just type plt.show() after plt.plot to get the same effect

Pyplot vs object-oriented

• According to the documentation, we should always use object-oriented when possible

Methods of getting started

# Method 1 
fig = plt.figure()
ax = fig.add_subplot()
plt.show()
 
# Method 2
fig = plt.figure()
ax = fig.add_axes([1,1,1,1])
ax.plot(x,y)
plt.show()
 
# Method 3 (recommended)
fig, ax = plt.subplots()
ax.plot(x,y)

Example workflow

# 0. Import matplotlib and get it ready for plotting in Jupyter 
%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=(5,10)) # (width, height)
 
# 3. Plot data 
ax.plot(x, y)
 
# 4. Customize plot 
ax.set(title="Simple Plot", 
	  xlabel="x-axis",
	  ylabel="y-axis")
 
# 5. Save your image 
fig.savefig("path/filename.png")

Different types of plots

• ax.scatter(data): creates a scatterplot
• ax.bar(data, data): creates a bar plot
• ax.barh(list(data), list(data)): creates a horizontal bar plot
• ax.hist(data): creates a histogram

Combining plots

# Option 1 
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2
											ncols=2
											figsize=(10,5))
ax1.plot(data)
ax2.scatter(data)
a3.bar(data)
a4.hist(data)
 
# Option 2 
fig, ax = plt.subplots(nrows=2
					  ncols=2
					  figsize=(10,5))
ax[0,0].plot(data)
ax[0,1].scatter(data)
ax[1,0].bar(data)
ax[1,1].hist(data)

Plotting from pandas DataFrames

• car_sales.plot(x="column name", y="column name"): this is the Pandas version of matplotlib
• car_sales.plot.hist()

Pyplot vs matplotlib object-oriented method

• When trying to get a quick visualization, pylot is fine but the object-oriented method is better for all other instances

Object oriented method

# create the plot 
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 Chlestrol Levels", 
	   xlabel="Age", 
	   ylabel="Cholestrol")
# Add legend 
ax.legend(*scatter.legend_elements(), title="Target")
 
# Add a horizontal line 
ax.axhline(over_50["chol"].mean(),
		  linestyle='--');

Object oriented subplots

# subplot of chol, age, thalach
fig, (ax0, ax1) = plt.subplot(nrows=2,
							  ncols=2, 
							  figsize=(10,10),
							  sharex=True)
 
# add data to a0
scatter = ax0.scatter(x=over_50["age"], 
					 y=over_50["chol"], 
					 c=over_50["target"])
 
# customize ax0
ax0.set(title="Heart Disease and Chlestrol Levels",  
	   ylabel="Cholestrol")
 
# add legend to ax0
ax0.legend(*scatter.legend_elements(), title="Target")
 
# Add a horizontal line to ax0
ax0.axhline(over_50["chol"].mean(),
		  linestyle='--');
 
# add data to ax1
scatter = ax1.scatter(x=over_50["age"], 
					 y=over_50["thalach"], 
					 c=over_50["target"],
					 cmap="winter")
 
# customize ax1
ax1.set(title="Heart Disease and Max Heart Rate", 
	   xlabel="Age", 
	   ylabel="Max Heart Rate")
 
# add legend to ax1
ax0.legend(*scatter.legend_elements(), title="Target")
 
# Add a horizontal line to ax1
ax0.axhline(over_50["thalach"].mean(),
		  linestyle='--')
 
# add title to the figure 
fit.suptitle("Heart Disease Analysis", fontsize=16, fontweight="bold");

Customizing plots

• plt.style.available: shows you what you have available to you
• plt.style.use('seaborn-whitegrid')
• plt.style.use('seaborn')
• plt.style.use('ggplot')
• ax.set(title="", xlabel="", ylabel="")
• ax.legend().set_visible(True)
• cmap='winter' — you can google different color maps
• ax.set_xlim([])