sns.kdeplot(df.loc[df['cyl'] == 6, "cty"], shade=True, color="dodgerblue", label="Cyl=6", alpha=.7) sns.kdeplot(df.loc[df['cyl'] == 8, "cty"], shade=True, color="orange", label="Cyl=8", alpha=.7) # Decoration plt.title('Density Plot of City Mileage by n_Cylinder...
35 时间序列图 (Time Series Plot)36 带波峰波谷标记的时序图 (Time Series with Peaks and Troughs Annotated)37 自相关和部分自相关图 (Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot)38 交叉相关图 (Cross Correlation plot)39 时间序列分解图 (Time Series Decomposition Plot)40 多个时间序列...
10),dpi=80)fig,axes=joypy.joyplot(mpg,column=['hwy','cty'],by="class",ylim='own',figsize=(14,10))# Decorationplt.title('Joy Plot of City and Highway Mileage by Class',fontsize=22)plt.show()
sns.kdeplot(df.loc[df['cyl'] == 6, "cty"], shade=True, color="dodgerblue", label="Cyl=6", alpha=.7) sns.kdeplot(df.loc[df['cyl'] == 8, "cty"], shade=True, color="orange", label="Cyl=8", alpha=.7) # Decoration plt.title('Density Plot of City Mileage by n_Cylinder...
19 哑铃图 (Dumbbell Plot) 哑铃图表传达了各种项目的“前”和“后”位置以及项目的等级排序。 如果您想要将特定项目/计划对不同对象的影响可视化,那么它非常有用。 图19 四、分布 (Distribution) 20 连续变量的直方图 (Histogram for Continuous Variable) 直方图显示给定变量的频率分布。 下面的图表示基于类型变量...
cm.inferno_r(i/float(n_categories)) for i in range(n_categories)] # Draw Plot and Decoratefig = plt.figure( FigureClass=Waffle, plots={ '111': { 'values': df['counts'], 'labels': ["{0} ({1})".format(n[0], n[1]) for n in df[['class', 'counts']].itertuples()],...
四、分布 (Distribution) 20. 连续变量的直方图 (Histogram for Continuous Variable) 21. 类型变量的直方图 (Histogram for Categorical Variable) 22. 密度图 (Density Plot) 23. 直方密度线图 (Density Curves with Histogram) 24. Joy Plot 25. 分布式包点图 (Distributed Dot Plot) 26. 箱形图 (Box Plot...
df.sort_values('mpg_z', inplace=True) df.reset_index(inplace=True) # Draw plot plt.figure(figsize=(14,14), dpi= 80) plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z) for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z): t = plt.text(x, y, round(tex, 2), horizon...
ax.boxplot(norm_reviews['RT_user_norm']) ax.set_xticklabels(['Rotten Tomatoes']) ax.set_ylim(0, 5) plt.show() 盒图1.png #多个盒图 num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue'] ...
1num_cols = ['RT_user_norm','Metacritic_user_nom','IMDB_norm','Fandango_Ratingvalue']2fig, ax =plt.subplots()3ax.boxplot(norm_reviews[num_cols].values)#箱线图4ax.set_xticklabels(num_cols, rotation=20)5ax.set_ylim(0,5)6plt.show() ...