arange(len(bars1)) r2 = [x + barWidth for x in r1] r3 = [x + barWidth for x in r2] # 绘制分组条形图 plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='g1') plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label=...
import matplotlib.pyplot as plt import numpy as np def scatterplot(x_data, y_data, x_label="", y_label="", title="", color = "r", yscale_log=False): # Create the plot object _, ax = plt.subplots() # Plot the data, set the size (s), color and transparency (alpha) # of...
Plot type plotly seaborn Simple bar graph express bar barplot Grouped bar graph color attribute and barmode=’group’ hue attribute Stacked bar graph color attribute label and color attributes with multiple plots Simple line graph express line lineplot Multiple line graph color and symbol attributes ...
ax.bar(x_data + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width) ax.set_ylabel(y_label) ax.set_xlabel(x_label) ax.set_title(title) ax.legend(loc = 'upper right') # 调用绘图函数 groupedbarplot(x_data = mean_by_reg_co_day....
ax.bar(x_data,y_data_list[i],color=colors[i],bottom=y_data_list[i-1],align='center',label=y_data_names[i])ax.set_ylabel(y_label)ax.set_xlabel(x_label)ax.set_title(title)ax.legend(loc='upper right')defgroupedbarplot(x_data,y_data_list,colors,y_data_names="",x_label="",...
其中,theta为极角,rho为极径,选项的内容与plot函数相同。 (2)统计图 ①条形图:bar函数(垂直条形图)和barh函数(水平条形图) bar(y,style),参数y是数据,选项style用于指定分组排列模式(“grouped”,簇状分组;“stacked”,堆积分组) bar(x,y,style),其中x存储横坐标,y存储数据 ...
bar_plot1 = sns.barplot(x='number', y='count', data=df, label="count", color="red") bar_plot2 = sns.barplot(x='number', y='select', data=df, label="select", color="green") plt.legend(ncol=2, loc="upper right",
ax.set_title('Boxplot Grouped by Data Sources') # 显示绘图结果 plt.show() 运行完整的代码,你会看到一个箱型图组图,其中每组数据都有自己的箱型图,你可以通过观察箱型图的形状来比较每组数据的分布情况。 完整代码如下: import numpy as np
consideration_grouped = important_consideration.groupby('age_group').agg('count') consideration_grouped 注意groupby和其他聚合函数如何自动忽略NaNs的。这大大简化了生活。 目前不分析30岁以下的消费者,所以只绘制其他年龄组。 consideration_grouped[:-1].sort_index(ascending=False).plot( kind='barh', figsi...
grouped_df = train_df.groupby('floor')['price_doc'].aggregate(np.median).reset_index() plt.figure(figsize=(12,8)) sns.pointplot(grouped_df.floor.values, grouped_df.price_doc.values, alpha=0.8, color=color[2]) plt.ylabel('Median Price', fontsize=12) ...