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This article introduces the relevant knowledge of "how to draw pie charts and histograms with python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

# pie chart Often combined with structural analysis, import pandasimport numpyimport matplotlibimport matplotlib.pyplot as pltimport matplotlib.font_manager as font_manager# import data plot_pie=pandas.read_csv ('D://Python projects//reference data//6.3//data.csv') # is used to calculate the number of users of each brand and retain the sequence result=plot_pie.groupby (by= [' communication brand'] As_index=False) ['number'] .agg ({'number of users': numpy.size}) # use pop-up drawing% matplotlib qt# to set length and width resolution plt.figure (figsize= (20) Dpi=10) # method of obtaining the name of a font using an absolute path fontname=font_manager.FontProperties (fname= "C://Windows//Fonts//FZSTK.TTF") # set font font= {'family':fontname.get_name (),' size':20} matplotlib.rc ('font',**font) # set pie chart plt.axis (' equal') # draw pie chart plt.pie (result ['number of users] Labels=result ['Communications Brand'], autopct='%.2f%%') # sets the prominent part explode= (0.1 mine0.2, 0.3) plt.axis ('equal') plt.pie (result [' number of users'], labels=result ['Communications Brand], autopct='%.2f%%')

The result is:

Histogram:

# histogram import pandasimport matplotlibfrom matplotlib import pyplot as plt# sets font font= {'family':'SimHei',' size':15} matplotlib.rc ('font',**font) # Import data data_histogram=pandas.read_csv (' D://Python projects//reference data//6.5//data.csv') maincolor= (42 D://Python projects//reference data//6.5//data.csv') 256141) # draw preliminary histogram plt.hist (data_histogram. Number of users purchased, color=maincolor) plt.hist (data_histogram ['number of users purchased'], color=maincolor)

The result is:

# set the number of groups to 30plt.hist (data_histogram. Number of users purchased, bins= (30), color=maincolor)

The result is:

# drawing a waterfall diagram, that is, accumulating plt.hist (data_histogram. Number of users purchased, bins= (30), cumulative=True, color=maincolor)

The result is:

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