plt # 设置风格样式 sns.set(color_codes=True) # 构建数据 tips = sns.load_dataset("tips") """ 案例1: 绘制双变量的线性关系 """ sns.regplot....7), (.7, 1)] x, y = np.random.multivariate_normal(mean, cov, 80).T """ 案例2: 构建随机数据,绘制双变量的线性关系 """ sns.regplot...x, name="x_var"), pd.Series(y, name="y_var") """ 案例3: 构建随机数据,并对数据Series,并指定x,y对应的变量名,绘制双变量的线性关系 """ sns.regplot...构建数据 tips = sns.load_dataset("tips") """ 案例5: 根据数据的实际情况,指定按x轴进行分组, 并对x轴分组数据增加一些抖动(x_jitter=.1) """ sns.regplot...cov, 80).T x, y = pd.Series(x, name="x_var"), pd.Series(y, name="y_var") """ 案例7: 将连续变量绘制成不连续的区域 """ sns.regplot
'v_2', 'v_6', 'v_1', 'v_14'] v_12_scatter_plot = pd.concat([Y_train,Train_data['v_12']],axis = 1) sns.regplot...scatter= True, fit_reg=True, ax=ax1) v_8_scatter_plot = pd.concat([Y_train,Train_data['v_8']],axis = 1) sns.regplot...True, fit_reg=True, ax=ax3) power_scatter_plot = pd.concat([Y_train,Train_data['power']],axis = 1) sns.regplot...= True, fit_reg=True, ax=ax8) v_14_scatter_plot = pd.concat([Y_train,Train_data['v_14']],axis = 1) sns.regplot...= True, fit_reg=True, ax=ax9) v_13_scatter_plot = pd.concat([Y_train,Train_data['v_13']],axis = 1) sns.regplot
'v_2', 'v_6', 'v_1', 'v_14'] v_12_scatter_plot = pd.concat([Y_train,train_data['v_12']],axis = 1) sns.regplot...scatter= True, fit_reg=True, ax=ax1) v_8_scatter_plot = pd.concat([Y_train,train_data['v_8']],axis = 1) sns.regplot...True, fit_reg=True, ax=ax3) power_scatter_plot = pd.concat([Y_train,train_data['power']],axis = 1) sns.regplot...= True, fit_reg=True, ax=ax8) v_14_scatter_plot = pd.concat([Y_train,train_data['v_14']],axis = 1) sns.regplot...= True, fit_reg=True, ax=ax9) v_13_scatter_plot = pd.concat([Y_train,train_data['v_13']],axis = 1) sns.regplot
seaborn as sns # regplot()和lmplot()都可以绘制回归关系,推荐regplot() tips = sns.load_dataset("tips") tips.head() sns.regplot...matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() # x_jitter x轴抖动范围 sns.regplot...tips.head() anscombe = sns.load_dataset("anscombe") print(anscombe) # scatter_kws:"s" 数据点为的大小,数值越大点位越大 sns.regplot
f, ax = plt.subplots(1,2,figsize=(15,6)) sns.regplot(x="Returns", y="volume",...多项式回归 order : int, 可选 多项式回归,设定指数 sns.regplot(x="open", y="close", data=dataset.loc...sns.regplot(x= "volume", y= "Increase_Decrease", data=dataset, logistic...sns.regplot(x="open", y="volume", data=dataset.loc[dataset.Up_Down == "Up"],...sns.regplot(x="open", y="Returns", data=dataset.loc[dataset.Up_Down == "Up"],
plt.xlabel("排名") plt.ylabel("热度") plt.legend() plt.grid() plt.show() 回归散点图 import seaborn as sns sns.regplot...,y='热度',data = df, kind='hex') sns.distplot(df['热度']) 绘制单核密度图 sns.kdeplot(df['热度']) 绘制排名与热度的回归图 sns.regplot...plt.xlabel("排名") plt.ylabel("热度") plt.legend() plt.grid() plt.show() #回归散点图 import seaborn as sns sns.regplot...'热度',data = df, kind='hex') sns.distplot(df['热度']) # 绘制单核密度图 sns.kdeplot(df['热度']) #绘制排名与热度的回归图 sns.regplot
scatter_diagram(): df = pd.DataFrame(np.random.randn(100,3), columns = list('ABC')) sns.regplot
我们通过将该命令更改为 sns.regplot 来实现这一点。...输入: sns.regplot(x=insurance_data['bmi'], y=insurance_data['charges']) 输出: /opt/conda/lib/python3.6/
iris") # 构造子图 fig, ax = plt.subplots(2,2,constrained_layout=True, figsize=(8, 8)) # 增加趋势拟合线 ax_sub = sns.regplot...line_kws={"color":"r","alpha":0.7,"lw":5},ax=ax[0][0]) ax_sub.set_title('增加趋势拟合线') # 自定义标记类型 ax_sub = sns.regplot...sepal_width"], marker="+", fit_reg=False, ax=ax[0][1]) ax_sub.set_title('自定义标记类型') # 自定义标记外形 ax_sub = sns.regplot...sepal_width']>3) # 构造特殊的点 df['color']= np.where( value==True , "#9b59b6", "#3498db") # 颜色区分 ax_sub = sns.regplot...: [1, 1.5, 3, 4, 5], 'y': [5, 15, 5, 10, 2], 'group': ['A','other group','B','C','D'] }) # 绘制基本散点图 sns.regplot
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 fig,axes=plt.subplots(2,1,figsize=(12,12)) sns.regplot...(x='人均消费',y='店铺评分',data=df,color='r',marker='+',ax=axes[0]) sns.regplot(x='评论数量',y='店铺评分',data=df,color
plt.subplots(figsize=(8,4.5),dpi=200,facecolor='white',edgecolor='white') ax.set_facecolor("white") fit_line = sns.regplot...) ax.set_facecolor("white") color = [region_color[i] for i in test_data['Region_new']] fit_line = sns.regplot
sns.pairplot(df, hue='species') >>> g.map_lower(sns.kdeplot) >>> g.map_diag(sns.histplot) >>> g.map_upper(sns.regplot
006tNbRwgy1gaybj5b4z6j30um0r2acl.jpg] Analysing the relationship between energy and loudness fig = plt.subplots(figsize=(10,10)) sns.regplot...popularity fig = plt.subplots(figsize=(10,10)) plt.title('Dependence between energy and popularity') sns.regplot
c=target,cmap='spring') Out[10]: In [11]: sns.regplot..._subplots.AxesSubplot at 0x1b6698adda0> In [12]: sns.regplot(x='sepal_length',y='sepal_width',data=iris
defscatter_diagram(): df = pd.DataFrame(np.random.randn(100,3), columns = list('ABC')) sns.regplot
Analysing the relationship between energy and loudness fig = plt.subplots(figsize=(10,10)) sns.regplot...popularity fig = plt.subplots(figsize=(10,10)) plt.title('Dependence between energy and popularity') sns.regplot
time", # 列 margin_titles=True # 标题显示:True-表示行列分开,False-合并显示 )g.map(sns.regplot...size"], # 同一个y对应两个x的值 y_vars=["tip"], height=4)g.map(sns.regplot
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