python - 在同一单元格中绘制图

Question

此代码呈现两个正态分布:

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=3)
title = "Fit results: mu = %.2f,  std = %.2f" % (mu, std)

plt.title(title)
fig = plt.gcf()
fig.set_size_inches(4, 3)

plt.show()

data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)

plt.plot(x, p, 'k', linewidth=2)
title = "Fit results: mu = %.2f,  std = %.2f" % (mu, std)
plt.title(title)
fig = plt.gcf()
fig.set_size_inches(4, 3)

plt.show()

:

如何并排呈现这些分布？

我尝试过使用子图:

fig, axs = plt.subplots(1,2)

所以之前的代码变成:

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(1,2)

data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=3)
title = "Fit results: mu = %.2f,  std = %.2f" % (mu, std)

plt.title(title)
fig = plt.gcf()
fig.set_size_inches(4, 3)

plt.show()

data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)

plt.plot(x, p, 'k', linewidth=2)
title = "Fit results: mu = %.2f,  std = %.2f" % (mu, std)
plt.title(title)
fig = plt.gcf()
fig.set_size_inches(4, 3)

plt.show()

但这并不正确:

如何并排渲染两个或更多图？

更新:

按照 @Varun Balupuri 答案使用代码:

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=3)
title = "Fit results: mu = %.2f,  std = %.2f" % (mu, std)

plt.title(title)
fig = plt.gcf()
fig.set_size_inches(4, 3)

# plot in the first subplot
plt.subplot(1,2,1)

data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')


xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)

# plot in the second subplot
plt.subplot(1, 2, 2)

plt.plot(x, p, 'k', linewidth=2)
title = "Fit results: mu = %.2f,  std = %.2f" % (mu, std)
plt.title(title)
fig = plt.gcf()
fig.set_size_inches(4, 3)

plt.show()

并排渲染图但重叠，左侧图表中缺少线条，右侧图表中缺少直方图:

Answer 1

使用方法fig, axs = plt.subplots(1,2)是正确的。它会给你一个数字 fig和轴数组 axs .
接下来您需要做的是明确使用这些轴。而不是plt.plot你可以打电话 axs[0].plot()绘制到第一个轴和 axs[1].plot()绘制到第二个轴。 .hist 也一样打电话。

最后，您还需要单独为每个子图设置标题，axs[0].set_title(title)而不是plt.title(title) .

此外，下面的代码更正了 pdf 的数据限制，以使用子图的轴限制。

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(1,2, figsize=(5,3))

# first subplot is axs[0]
data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
axs[0].hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = axs[0].get_xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
axs[0].plot(x, p, 'k', linewidth=2)
title = "Fit results:\n mu = %.2f,\n  std = %.2f" % (mu, std)
axs[0].set_title(title)

# second subplot is axs[1]
data = norm.rvs(10.0, 2.5, size=500)
mu, std = norm.fit(data)
axs[1].hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = axs[1].get_xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
axs[1].plot(x, p, 'k', linewidth=2)
title = "Fit results:\n mu = %.2f,\n  std = %.2f" % (mu, std)
axs[1].set_title(title)

plt.tight_layout()
plt.show()