JustDoPython
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+import seaborn as sns
+import matplotlib.pyplot as plt
+from scipy import stats
+sns.set(color_codes=True)
+
+# 2.1 示例
+x = np.random.normal(size=100)
+sns.distplot(x)
+
+# 2.2 示例
+x = np.random.normal(size=100)
+sns.distplot(x, kde=False, rug=True)
+
+# 2.3 示例
+x = np.random.normal(size=100)
+sns.distplot(x, hist=False, rug=True)
+
+x = np.random.normal(0, 1, size=30)  # 初始化一组服从正态分布的随机数
+bandwidth = 1.06 * x.std() * x.size ** (-1 / 5.)  # 根据经验公式计算 KDE 的带宽
+support = np.linspace(-4, 4, 200)
+
+kernels = []
+for x_i in x:
+
+    kernel = stats.norm(x_i, bandwidth).pdf(support)  # 获取每一个观测值的核密度估计
+    kernels.append(kernel)
+    plt.plot(support, kernel, color="r") # 为每一个观测值绘制核密度估计曲线
+
+sns.rugplot(x, color=".2", linewidth=3)
+
+from scipy.integrate import trapz
+density = np.sum(kernels, axis=0)
+density /= trapz(density, support)
+plt.plot(support, density)
+
+sns.kdeplot(x, shade=True)
+
+sns.kdeplot(x)
+sns.kdeplot(x, bw=.2, label="bw: 0.2")
+sns.kdeplot(x, bw=2, label="bw: 2")
+plt.legend()
+
+sns.kdeplot(x, shade=True, cut=0)
+sns.rugplot(x)
+
+# 2.4 示例
+x = np.random.gamma(6, size=200)
+sns.distplot(x, kde=False, fit=stats.gamma)
+
+
+
+
+
+
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+import seaborn as sns
+import matplotlib.pyplot as plt
+from scipy import stats
+sns.set(color_codes=True)
+
+mean, cov = [0, 1], [(1, .5), (.5, 1)]
+data = np.random.multivariate_normal(mean, cov, 200)
+df = pd.DataFrame(data, columns=["x", "y"])
+
+# 3.1 示例
+sns.jointplot(x="x", y="y", data=df)
+
+# 3.2 示例
+x, y = np.random.multivariate_normal(mean, cov, 1000).T
+with sns.axes_style("white"):
+    sns.jointplot(x=x, y=y, kind="hex", color="k")
+
+# 3.3 示例
+sns.jointplot(x="x", y="y", data=df, kind="kde")
+
+f, ax = plt.subplots(figsize=(6, 6))
+sns.kdeplot(df.x, df.y, ax=ax)
+sns.rugplot(df.x, color="g", ax=ax)
+sns.rugplot(df.y, vertical=True, ax=ax)
+
+f, ax = plt.subplots(figsize=(6, 6))
+cmap = sns.cubehelix_palette(as_cmap=True, dark=0, light=1, reverse=True)
+sns.kdeplot(df.x, df.y, cmap=cmap, n_levels=60, shade=True)
+
+g = sns.jointplot(x="x", y="y", data=df, kind="kde", color="m")
+g.plot_joint(plt.scatter, c="w", s=30, linewidth=1, marker="+")
+g.ax_joint.collections[0].set_alpha(0)
+g.set_axis_labels("$X$", "$Y$")
+
+
+
+
+
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+import seaborn as sns
+import matplotlib.pyplot as plt
+from scipy import stats
+sns.set(color_codes=True)
+
+iris = sns.load_dataset("iris")
+sns.pairplot(iris)
+
+g = sns.PairGrid(iris)
+g.map_diag(sns.kdeplot)
+g.map_offdiag(sns.kdeplot, n_levels=6)