这里有一个简单的解决办法

fig, ax = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=False)
for sp in fig.axes:
    sp.plot(range(10))

阅读文档:matplotlib.pyplot.subplots

Pyplot.subplots()返回一个元组图ax，它使用表示法在两个变量中解包

fig, axes = plt.subplots(nrows=2, ncols=2)

代码:

fig = plt.figure()
axes = fig.subplots(nrows=2, ncols=2)

不能工作，因为subplots()是pyplot中的函数，而不是对象图的成员。

将坐标轴数组转换为1D

Generating subplots with plt.subplots(nrows, ncols), where both nrows and ncols is greater than 1, returns a nested array of <AxesSubplot:> objects. It’s not necessary to flatten axes in cases where either nrows=1 or ncols=1, because axes will already be 1 dimensional, which is a result of the default parameter squeeze=True The easiest way to access the objects, is to convert the array to 1 dimension with .ravel(), .flatten(), or .flat. .ravel vs. .flatten flatten always returns a copy. ravel returns a view of the original array whenever possible. Once the array of axes is converted to 1-d, there are a number of ways to plot. This answer is relevant to seaborn axes-level plots, which have the ax= parameter (e.g. sns.barplot(…, ax=ax[0]). seaborn is a high-level API for matplotlib. See Figure-level vs. axes-level functions and seaborn is not plotting within defined subplots

import matplotlib.pyplot as plt
import numpy as np  # sample data only

# example of data
rads = np.arange(0, 2*np.pi, 0.01)
y_data = np.array([np.sin(t*rads) for t in range(1, 5)])
x_data = [rads, rads, rads, rads]

# Generate figure and its subplots
fig, axes = plt.subplots(nrows=2, ncols=2)

# axes before
array([[<AxesSubplot:>, <AxesSubplot:>],
       [<AxesSubplot:>, <AxesSubplot:>]], dtype=object)

# convert the array to 1 dimension
axes = axes.ravel()

# axes after
array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],
      dtype=object)

遍历扁平数组 如果子图比数据多，这将导致IndexError: list索引超出范围 尝试选择3。或者选择坐标轴的子集(例如坐标轴[:-2])

for i, ax in enumerate(axes):
    ax.plot(x_data[i], y_data[i])

按索引访问每个轴

axes[0].plot(x_data[0], y_data[0])
axes[1].plot(x_data[1], y_data[1])
axes[2].plot(x_data[2], y_data[2])
axes[3].plot(x_data[3], y_data[3])

索引数据和坐标轴

for i in range(len(x_data)):
    axes[i].plot(x_data[i], y_data[i])

压缩轴和数据，然后遍历元组列表。

for ax, x, y in zip(axes, x_data, y_data):
    ax.plot(x, y)

Ouput

An option is to assign each axes to a variable, fig, (ax1, ax2, ax3) = plt.subplots(1, 3). However, as written, this only works in cases with either nrows=1 or ncols=1. This is based on the shape of the array returned by plt.subplots, and quickly becomes cumbersome. fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for a 2 x 2 array. This option is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.

有几种方法可以做到这一点。subplots方法创建图形和子图，然后存储在ax数组中。例如:

import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig, ax = plt.subplots(nrows=2, ncols=2)

for row in ax:
    for col in row:
        col.plot(x, y)

plt.show()

然而，像这样的东西也可以工作，虽然它不是那么“干净”，因为你创建了一个带有子图的图形，然后在它们上面添加:

fig = plt.figure()

plt.subplot(2, 2, 1)
plt.plot(x, y)

plt.subplot(2, 2, 2)
plt.plot(x, y)

plt.subplot(2, 2, 3)
plt.plot(x, y)

plt.subplot(2, 2, 4)
plt.plot(x, y)

plt.show()

如果你真的想使用循环，请执行以下操作:

def plot(data):
    fig = plt.figure(figsize=(100, 100))
    for idx, k in enumerate(data.keys(), 1):
        x, y = data[k].keys(), data[k].values
        plt.subplot(63, 10, idx)
        plt.bar(x, y)  
    plt.show()

import matplotlib.pyplot as plt

fig, ax = plt.subplots(2, 2)

ax[0, 0].plot(range(10), 'r') #row=0, col=0
ax[1, 0].plot(range(10), 'b') #row=1, col=0
ax[0, 1].plot(range(10), 'g') #row=0, col=1
ax[1, 1].plot(range(10), 'k') #row=1, col=1
plt.show()