Matplotlib子图自定义布局问询：6张子图按指定行列排列实现

Hey there! The problem with your current code is that plt.subplots(6) creates 6 subplots arranged in a single vertical column—totally not what you want for that custom 3-row layout. To build the setup you described:

• Column 1 is occupied entirely by subplot a1 (spanning all 3 rows)
• Column 2 has a2 (row 1) and a3 (row 2)
• Column 3 has c1 (row 1), c2 (row 2), and c3 (row 3)

you'll need to use Matplotlib's tools for non-uniform subplot grids. Below are two straightforward methods to implement this:

Method 1: Using gridspec (Flexible and Recommended) #

gridspec lets you define a grid of rows and columns, then assign subplots to span specific sections of that grid:

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

# Set up the figure and grid (3 rows, 3 columns)
fig = plt.figure(figsize=(12, 8))  # Adjust size to fit your needs
gs = gridspec.GridSpec(3, 3)  # 3 rows, 3 columns

# Assign each subplot to its position with proper spans
a1 = fig.add_subplot(gs[:, 0])  # Column 0, all 3 rows
a2 = fig.add_subplot(gs[0, 1])  # Row 0, Column 1
a3 = fig.add_subplot(gs[1, 1])  # Row 1, Column 1
c1 = fig.add_subplot(gs[0, 2])  # Row 0, Column 2
c2 = fig.add_subplot(gs[1, 2])  # Row 1, Column 2
c3 = fig.add_subplot(gs[2, 2])  # Row 2, Column 2

# Your existing plotting code goes here
# Compounding Amount being plotted
a1.plot(x_indexes, Amount_list)
c1.plot(x_indexes, Non_compounding_list)

L = 1 
S = 1 
x_long = [] 
x_short = [] 
for i in L_Amount_list:
    x_long.append(L)
    L += 1
for i in S_Amount_list:
    x_short.append(S)
    S += 1 

a2.plot(x_short, S_Amount_list)
a3.plot(x_long, L_Amount_list)
c2.plot(x_short, S_Non_compounding_list)
c3.plot(x_long, L_Non_compounding_list)

# Optional: Add titles and clean up spacing
a1.set_title('a1: Compounding Amount')
a2.set_title('a2: Short Compounding')
a3.set_title('a3: Long Compounding')
c1.set_title('c1: Non-Compounding Amount')
c2.set_title('c2: Short Non-Compounding')
c3.set_title('c3: Long Non-Compounding')

plt.tight_layout()  # Automatically adjusts subplot spacing
plt.show()

Method 2: Using subplot2grid (More Intuitive for Beginners) #

subplot2grid uses a grid shape and location coordinates to place subplots, with options to set how many rows/columns each subplot spans:

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12, 8))

# Syntax: subplot2grid((total_rows, total_cols), (start_row, start_col), rowspan, colspan)
a1 = plt.subplot2grid((3, 3), (0, 0), rowspan=3)  # Spans 3 rows, 1 column
a2 = plt.subplot2grid((3, 3), (0, 1))  # Starts at row 0, column 1
a3 = plt.subplot2grid((3, 3), (1, 1))  # Starts at row 1, column 1
c1 = plt.subplot2grid((3, 3), (0, 2))  # Starts at row 0, column 2
c2 = plt.subplot2grid((3, 3), (1, 2))  # Starts at row 1, column 2
c3 = plt.subplot2grid((3, 3), (2, 2))  # Starts at row 2, column 2

# Your plotting code (same as above)
a1.plot(x_indexes, Amount_list)
c1.plot(x_indexes, Non_compounding_list)

L = 1 
S = 1 
x_long = [] 
x_short = [] 
for i in L_Amount_list:
    x_long.append(L)
    L += 1
for i in S_Amount_list:
    x_short.append(S)
    S += 1 

a2.plot(x_short, S_Amount_list)
a3.plot(x_long, L_Amount_list)
c2.plot(x_short, S_Non_compounding_list)
c3.plot(x_long, L_Non_compounding_list)

# Add titles and adjust layout
a1.set_title('a1: Compounding Amount')
a2.set_title('a2: Short Compounding')
a3.set_title('a3: Long Compounding')
c1.set_title('c1: Non-Compounding Amount')
c2.set_title('c2: Short Non-Compounding')
c3.set_title('c3: Long Non-Compounding')

plt.tight_layout()
plt.show()

Key Notes #

• Both methods will create exactly the layout you described. gridspec is more powerful for complex grids, while subplot2grid is easier to read for simple non-uniform layouts.
• The figsize parameter lets you adjust the overall size of your figure to make sure all subplots are readable.
• plt.tight_layout() is crucial to prevent overlapping labels and titles.

内容的提问来源于stack exchange，提问作者selcuk egese