# plot the same data on both axes ax.plot(pts) ax2.plot(pts) # zoom-in / limit the view to different portions of the data ax.set_ylim(.78, 1.) # outliers only ax2.set_ylim(0, .22) # most of the data # hide the
In [1]: import numba In [2]: numba.set_num_threads(1) In [3]: df = pd.DataFrame(np.random.randn(10_000, 100)) In [4]: roll = df.rolling(100) # 默认使用单Cpu进行计算 In [5]: %timeit roll.mean(engine="numba", engine_kwargs={"parallel": True}) 347 ms ± 26 ms per ...
longitude], zoom_start=12) # Display the map of San Francisco san_map早期使用Python绘制地图主要...
axes[1, 1].plot(df['Mes'], df['deep learning'])结果如下:我们可以为每个变量的点绘制具有不同样式的图形: plt.plot(df ['Mes'],df ['data science'],'r-')plt.plot(df ['Mes'],df ['data science'] * 2,'bs')plt .plot(df ['Mes'],df ['data science'] * 3,'g ^')结果如下...
42 # Create map and display it san_map = folium.Map(location=[latitude, longitude], zoom_...
axins= zoomed_inset_axes(ax, 2, loc=1)#zoom = 6axins.plot(x,y)#sub region of the original imagex1, x2, y1, y2 = 2.5, 7.5, 2.5, 7.5axins.set_xlim(x1, x2) axins.set_ylim(y1, y2)#fix the number of ticks on the inset axesaxins.yaxis.get_major_locator().set_params(...
axins= zoomed_inset_axes(ax, 2, loc=1)#zoom = 6axins.plot(x,y)#sub region of the original imagex1, x2, y1, y2 = 2.5, 7.5, 2.5, 7.5axins.set_xlim(x1, x2) axins.set_ylim(y1, y2)#fix the number of ticks on the inset axesaxins.yaxis.get_major_locator().set_params(...
在2比2中可以看到四个数据gridplot:得分,助攻,篮板和失误。在创建四个图形并配置其各自的图表时,属性中存在大量冗余。因此,为了简化代码,for可以使用循环 # Create a dict with the stat name and its corresponding column in the datastat_names = {'Points': 'teamPTS', 'Assists': 'teamAST', 'Rebound...
volcano_map=folium.Map(zoom_start=10)groups=folium.FeatureGroup('')# 将每一行火山的数据添加进来foriinrange(0,df_volcano.shape[0]):volcano=df_volcano.iloc[i]groups.add_child(folium.CircleMarker([volcano['latitude'],volcano['longitude']],popup=volcano['volcano_name'],radius=3,color='blue'...
这个图是可以互动的，zoom in/out啊，拖动选择啥的，感兴趣的可以到demo这里看看。然后你就可以慢慢研究...