【Python教程】绘制漂亮的柱状图
程序员文章站
2022-04-24 08:34:54
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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 plt import numpy as np from matplotlib.image import BboxImage from matplotlib._png import read_png import matplotlib.colors from matplotlib.cbook import get_sample_data import 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 = 70 b_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.shape ny2 = int(ny*stretch_factor)stretched_image = np.empty((ny2, nx, nch), self.im.dtype) cut = self.im[self.cut_location,:,:] stretched_image[:,:,:] = cut stretched_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_image return stretched_image class 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, TransformedBbox from 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()
输出图像如下
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