Tick labels for the xaxis are placed on top. Parameters --- A : array-like(M, N) The matrix to be displayed. fignum : None or int or False If *None*, create a new figure window with automatic numbering. If a nonzero integer, draw into the figure with the given number (create...
在当前版本的Matplotlib中,您可以这样做axis.set_xticklabels(labels, fontsize='small'). 这迫使您也指定“标签”。如果我们可以只改变字体大小就更好了。 (4认同) Kyl*_*son 18 另一种选择 我有两个并排的地块,想分别调整刻度标签. 上面的解决方案很接近,但它们并没有为我提供帮助.我从这个matplotlib...
#format_string 由颜色字符,风格字符,和标记字符 plt.plot([1,2,3,4],[1,4,9,16],'ro')plt.axis([0,6,0,20])plt.show() 有关线型和格式字符串的完整列表,请参阅plot()文档。 上例中的axis()命令采用 [xmin, xmax, ymin, ymax] 列表并指定轴的视口。 如果matplotlib仅限于使用列表,那么数字...
ax.axis('equal')#设置第一个图例要显示的元素和标签ax.legend(lines[:2], ['LineA','LineB'], loc='upper right', frameon=False)#根据一个Legend对象用add_artist方法添加第二个图例要显示的元素和标签frommatplotlib.legendimportLegend leg = Legend(ax, lines[2:], ['lineC','lineD'], loc='lowe...
from matplotlib.ticker import NullFormatter # useful for `logit` scale # Fixing random state for reproducibility np.random.seed(19680801) # make up some data in the interval ]0, 1[ y = np.random.normal(loc=0.5, scale=0.4, size=1000) y = y[(y > 0) & (y < 1)] y.sort() x =...
geopandas objects have a special set of arguments, where you can pass information to class the map and make a legend: # Make the legend not the raster scale bin_edge = np.arange(0.2,0.9,0.2) leg_args = {'loc': 'lower left', 'prop': {'size': 9}, 'title':'Prop. Water'} ...
axis([0, 4, 0, 10]) legend() If you make multiple lines with one plot command, the kwargs apply to all those lines, e.g.:: plot(x1, y1, x2, y2, antialiased=False) Neither line will be antialiased. You do not need to use format strings, which are just ...
(TrueType) # svg backend params #svg.image_inline : True # write raster image data directly into the svg file #svg.image_noscale : False # suppress scaling of raster data embedded in SVG #svg.fonttype : 'path' # How to handle SVG fonts: # 'none': Assume fonts are installed on ...
# make up some data in the open interval (0, 1) y = np.random.normal(loc=0.5, scale=0.4, size=1000) y = y[(y > 0) & (y < 1)] y.sort() x = np.arange(len(y)) # plot with various axes scales plt.figure() # linear ...
size = 3)+ scale_x_continuous(limits = c(-2, 2.5), breaks = seq(-2, 2.5, by = 0.5), expand = c(0,0.02))+ scale_y_discrete(expand = c(0.03, 0.03))+ labs(x = "Mileage", y = "Model", title = "Diverging Text Bars of Car Mileage")+ theme_bw()+ theme(axis.ticks = ...