[Python Tutorial] create a beautiful bar chart

Matplotlib is an open-source project based on the Python language. it aims to provide a data drawing package for Python. This article briefly introduces how to use this package to draw beautiful bar charts. Matplotlib is an open-source project based on the Python language. it aims to provide a data drawing package for Python. This article briefly introduces how to use this package to draw beautiful bar charts.

Import command

1) set the working environment % cd "F: \ Dropbox \ python" 2) import the package 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 pd3) read 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 = F Alse) 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. arr Ay (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-sel F. 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, filte Rnorm = 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 (0.8) box_colors = [(0.2, 0.2, 0.2), (0.8, 0.2, 0.2, 0.2), (0.8, 0.7, 0.5 ), (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 cart') 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 the image fig. savefig ('The Price of Car.png ') plt. show ()

The output image is as follows:

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