"Python tutorial" to draw a nice bar chart

Matplotlib is an open source project based on the Python language, which is designed to provide Python with a data-drawing package, this article briefly describes how to use the package to draw a nice bar chart.

Import command

1) Set the working environment%CD "F:\\dropbox\\python" 2) Import package imports 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) read Data data=pd.read_csv ("Car.csv") 4) define and draw the image 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 ']f MT = 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) Save Image Fig.savefig (' The Price of Car.png ') plt.show () 

The output image is as follows

The above is the "Python tutorial" to draw a beautiful histogram of the content, more relevant content please pay attention to topic.alibabacloud.com (www.php.cn)!

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