matplotlib是基于python语言的开源项目，其旨在为python提供一个数据绘图包，本文简单介绍如何使用该程序包绘制漂亮的柱状图。
导入命令
1）设置工作环境%cd "F:\Dropbox\python"2）导入程序包import matplotlib.pyplot as pltimport numpy as npfrom matplotlib.image import BboxImagefrom matplotlib._png import read_pngimport matplotlib.colorsfrom matplotlib.cbook import get_sample_dataimport pandas as pd3）读取数据data=pd.read_csv("CAR.csv")4）定义并绘制图像class RibbonBox(object):original_image = read_png(get_sample_data("Minduka_Present_Blue_Pack.png",asfileobj=False))cut_location = 70b_and_h = original_image[:,:,2]color = original_image[:,:,2] - original_image[:,:,0]alpha = original_image[:,:,3]nx = original_image.shape[1]def __init__(self, color):rgb = matplotlib.colors.colorConverter.to_rgb(color)im = np.empty(self.original_image.shape,self.original_image.dtype)im[:,:,:3] = self.b_and_h[:,:,np.newaxis]im[:,:,:3] -= self.color[:,:,np.newaxis]*(1.-np.array(rgb))im[:,:,3] = self.alphaself.im = imdef get_stretched_image(self, stretch_factor):stretch_factor = max(stretch_factor, 1)ny, nx, nch = self.im.shapeny2 = int(ny*stretch_factor)stretched_image = np.empty((ny2, nx, nch),self.im.dtype)cut = self.im[self.cut_location,:,:]stretched_image[:,:,:] = cutstretched_image[:self.cut_location,:,:] = self.im[:self.cut_location,:,:]stretched_image[-(ny-self.cut_location):,:,:] = self.im[-(ny-self.cut_location):,:,:]self._cached_im = stretched_imagereturn stretched_imageclass RibbonBoxImage(BboxImage):zorder = 1def __init__(self, bbox, color,cmap = None,norm = None,interpolation=None,origin=None,filternorm=1,filterrad=4.0,resample = False,**kwargs):BboxImage.__init__(self, bbox,cmap = cmap,norm = norm,interpolation=interpolation,origin=origin,filternorm=filternorm,filterrad=filterrad,resample = resample,**kwargs)self._ribbonbox = RibbonBox(color)self._cached_ny = Nonedef draw(self, renderer, *args, **kwargs):bbox = self.get_window_extent(renderer)stretch_factor = bbox.height / bbox.widthny = int(stretch_factor*self._ribbonbox.nx)if self._cached_ny != ny:arr = self._ribbonbox.get_stretched_image(stretch_factor)self.set_array(arr)self._cached_ny = nyBboxImage.draw(self, renderer, *args, **kwargs)if 1:from matplotlib.transforms import Bbox, TransformedBboxfrom matplotlib.ticker import ScalarFormatterfig, ax = plt.subplots()years = np.arange(2001,2008)box_colors = [(0.8, 0.2, 0.2),(0.2, 0.8, 0.2),(0.2, 0.2, 0.8),(0.7, 0.5, 0.8),(0.3, 0.8, 0.7),(0.4, 0.6, 0.3),(0.5, 0.5, 0.1),]heights = data['price']fmt = ScalarFormatter(useOffset=False)ax.xaxis.set_major_formatter(fmt)for year, h, bc in zip(years, heights, box_colors):bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)bbox = TransformedBbox(bbox0, ax.transData)rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")ax.add_artist(rb_patch)ax.annotate(h,(year, h), va="bottom", ha="center")ax.set_title('The Price of Car')patch_gradient = BboxImage(ax.bbox,interpolation="bicubic",zorder=0.1,)gradient = np.zeros((2, 2, 4), dtype=np.float)gradient[:,:,:3] = [1, 1, 0.]gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]]patch_gradient.set_array(gradient)ax.add_artist(patch_gradient)ax.set_xlim(years[0]-0.5, years[-1]+0.5)ax.set_ylim(0, 15000)5）保存图像fig.savefig('The Price of Car.png')plt.show()
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输出图像如下
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