创建 2 个图，但它们重叠成一个图。如何使用 matplotlib 创建 2 个单独的图形？
Creating 2 plots but they're overlapping into one graph. How can I create 2 separate graphs using matplotlib?
我正在使用 matplotlib 创建密度图和 blox 图，但是当我 运行 我的代码时，我得到一个图，其中两个图相互重叠。如何重构我的代码以输出两个单独的图形？
link 到图形图像：
https://ibb.co/6bCK9MZ
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def make_t_distribution(sample_size, mean, sd):
t_sample = stats.t.rvs(sample_size - 1, mean, sd, sample_size) # Random t-distribution sample
sample_mean = np.mean(t_sample) # sample mean
sample_std = np.std(t_sample) # sample standard deviation
t_dist = stats.t(df = sample_size - 1, loc = sample_mean, scale = sample_std) # make a t-distribution based on the sample
x_axis = np.linspace(t_dist.ppf(0.0001), t_dist.ppf(0.9999), 500) # Generate an x-axis based on t-quantile values
return t_dist, x_axis
def make_prob_plot():
ax = plt.axes()
tdist1, x1=make_t_distribution(10,0,2)
tdist2, x2=make_t_distribution(100,0,2)
tdist3, x3=make_t_distribution(1000,0,2)
tdist4, x4=make_t_distribution(10000,0,2)
tdist5, x5=make_t_distribution(500,0,2)
# density plot
plt.xlim(-7.5,7.5)
y1=ax.plot(x1,tdist1.pdf(x1), '-', label="$df=9$")
y2=ax.plot(x2,tdist2.pdf(x2), ':', label="$df=99$")
y3=ax.plot(x3,tdist3.pdf(x3), '--' ,label="$df=999$")
y4=ax.plot(x4,tdist4.pdf(x4), '-.', label="$df=9999$")
y5=ax.plot(x5,tdist5.pdf(x5), '.', label="$Normal$")
plt.xlabel("Value")
plt.ylabel("Density")
plt.title("PDF Distribution Comparison $N(\mu=0$, $\sigma=2$)")
plt.legend()
# boxplot
dist1 = np.random.normal(0,2,10)
dist2 = np.random.normal(0,2,100)
dist3 = np.random.normal(0,2,1000)
dist4 = np.random.normal(0,2,10000)
distributions = (dist1, dist2, dist3, dist4)
plt.boxplot(distributions, labels = ("df=9","df=99","df=999","df=9999"));
plt.boxplot(distributions, widths= .7);
green_diamond = dict(markerfacecolor='g', marker='D')
plt.boxplot(distributions, flierprops=green_diamond);
return plt
make_prob_plot()
最简洁的方法是使用 plt.subplot.
并排使用数字
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import scipy.stats as stats
def make_t_distribution(sample_size, mean, sd):
t_sample = stats.t.rvs(sample_size - 1, mean, sd, sample_size) # Random t-distribution sample
sample_mean = np.mean(t_sample) # sample mean
sample_std = np.std(t_sample) # sample standard deviation
t_dist = stats.t(df=sample_size - 1, loc=sample_mean, scale=sample_std) # make a t-distribution based on the sample
x_axis = np.linspace(t_dist.ppf(0.0001), t_dist.ppf(0.9999), 500) # Generate an x-axis based on t-quantile values
return t_dist, x_axis
def make_prob_plot():
figure, axis = plt.subplots(2,1)
tdist1, x1 = make_t_distribution(10, 0, 2)
tdist2, x2 = make_t_distribution(100, 0, 2)
tdist3, x3 = make_t_distribution(1000, 0, 2)
tdist4, x4 = make_t_distribution(10000, 0, 2)
tdist5, x5 = make_t_distribution(500, 0, 2)
# density plot
plt.xlim(-7.5, 7.5)
y1 = axis[0].plot(x1, tdist1.pdf(x1), '-', label="$df=9$")
y2 = axis[0].plot(x2, tdist2.pdf(x2), ':', label="$df=99$")
y3 = axis[0].plot(x3, tdist3.pdf(x3), '--', label="$df=999$")
y4 = axis[0].plot(x4, tdist4.pdf(x4), '-.', label="$df=9999$")
y5 = axis[0].plot(x5, tdist5.pdf(x5), '.', label="$Normal$")
plt.xlabel("Value")
plt.ylabel("Density")
plt.title("PDF Distribution Comparison $N(\mu=0$, $\sigma=2$)")
axis[0].legend()
# boxplot
dist1 = np.random.normal(0, 2, 10)
dist2 = np.random.normal(0, 2, 100)
dist3 = np.random.normal(0, 2, 1000)
dist4 = np.random.normal(0, 2, 10000)
distributions = (dist1, dist2, dist3, dist4)
axis[1].boxplot(distributions, labels=("df=9", "df=99", "df=999", "df=9999"));
axis[1].boxplot(distributions, widths=.7);
green_diamond = dict(markerfacecolor='g', marker='D')
axis[1].boxplot(distributions, flierprops=green_diamond);
return plt
make_prob_plot()
我正在使用 matplotlib 创建密度图和 blox 图，但是当我 运行 我的代码时，我得到一个图，其中两个图相互重叠。如何重构我的代码以输出两个单独的图形？
link 到图形图像： https://ibb.co/6bCK9MZ
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def make_t_distribution(sample_size, mean, sd):
t_sample = stats.t.rvs(sample_size - 1, mean, sd, sample_size) # Random t-distribution sample
sample_mean = np.mean(t_sample) # sample mean
sample_std = np.std(t_sample) # sample standard deviation
t_dist = stats.t(df = sample_size - 1, loc = sample_mean, scale = sample_std) # make a t-distribution based on the sample
x_axis = np.linspace(t_dist.ppf(0.0001), t_dist.ppf(0.9999), 500) # Generate an x-axis based on t-quantile values
return t_dist, x_axis
def make_prob_plot():
ax = plt.axes()
tdist1, x1=make_t_distribution(10,0,2)
tdist2, x2=make_t_distribution(100,0,2)
tdist3, x3=make_t_distribution(1000,0,2)
tdist4, x4=make_t_distribution(10000,0,2)
tdist5, x5=make_t_distribution(500,0,2)
# density plot
plt.xlim(-7.5,7.5)
y1=ax.plot(x1,tdist1.pdf(x1), '-', label="$df=9$")
y2=ax.plot(x2,tdist2.pdf(x2), ':', label="$df=99$")
y3=ax.plot(x3,tdist3.pdf(x3), '--' ,label="$df=999$")
y4=ax.plot(x4,tdist4.pdf(x4), '-.', label="$df=9999$")
y5=ax.plot(x5,tdist5.pdf(x5), '.', label="$Normal$")
plt.xlabel("Value")
plt.ylabel("Density")
plt.title("PDF Distribution Comparison $N(\mu=0$, $\sigma=2$)")
plt.legend()
# boxplot
dist1 = np.random.normal(0,2,10)
dist2 = np.random.normal(0,2,100)
dist3 = np.random.normal(0,2,1000)
dist4 = np.random.normal(0,2,10000)
distributions = (dist1, dist2, dist3, dist4)
plt.boxplot(distributions, labels = ("df=9","df=99","df=999","df=9999"));
plt.boxplot(distributions, widths= .7);
green_diamond = dict(markerfacecolor='g', marker='D')
plt.boxplot(distributions, flierprops=green_diamond);
return plt
make_prob_plot()
最简洁的方法是使用 plt.subplot.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import scipy.stats as stats
def make_t_distribution(sample_size, mean, sd):
t_sample = stats.t.rvs(sample_size - 1, mean, sd, sample_size) # Random t-distribution sample
sample_mean = np.mean(t_sample) # sample mean
sample_std = np.std(t_sample) # sample standard deviation
t_dist = stats.t(df=sample_size - 1, loc=sample_mean, scale=sample_std) # make a t-distribution based on the sample
x_axis = np.linspace(t_dist.ppf(0.0001), t_dist.ppf(0.9999), 500) # Generate an x-axis based on t-quantile values
return t_dist, x_axis
def make_prob_plot():
figure, axis = plt.subplots(2,1)
tdist1, x1 = make_t_distribution(10, 0, 2)
tdist2, x2 = make_t_distribution(100, 0, 2)
tdist3, x3 = make_t_distribution(1000, 0, 2)
tdist4, x4 = make_t_distribution(10000, 0, 2)
tdist5, x5 = make_t_distribution(500, 0, 2)
# density plot
plt.xlim(-7.5, 7.5)
y1 = axis[0].plot(x1, tdist1.pdf(x1), '-', label="$df=9$")
y2 = axis[0].plot(x2, tdist2.pdf(x2), ':', label="$df=99$")
y3 = axis[0].plot(x3, tdist3.pdf(x3), '--', label="$df=999$")
y4 = axis[0].plot(x4, tdist4.pdf(x4), '-.', label="$df=9999$")
y5 = axis[0].plot(x5, tdist5.pdf(x5), '.', label="$Normal$")
plt.xlabel("Value")
plt.ylabel("Density")
plt.title("PDF Distribution Comparison $N(\mu=0$, $\sigma=2$)")
axis[0].legend()
# boxplot
dist1 = np.random.normal(0, 2, 10)
dist2 = np.random.normal(0, 2, 100)
dist3 = np.random.normal(0, 2, 1000)
dist4 = np.random.normal(0, 2, 10000)
distributions = (dist1, dist2, dist3, dist4)
axis[1].boxplot(distributions, labels=("df=9", "df=99", "df=999", "df=9999"));
axis[1].boxplot(distributions, widths=.7);
green_diamond = dict(markerfacecolor='g', marker='D')
axis[1].boxplot(distributions, flierprops=green_diamond);
return plt
make_prob_plot()