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This article mainly shows you "how to climb the salary of NBA Lakers through Python and analyze it". The content is simple and clear. I hope it can help you solve your doubts. Let the editor lead you to study and learn this article "how to climb and analyze the salary of NBA Lakers through Python".

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The following NBA Lakers players' salary is crawled through Python and analyzed. The code is as follows:

1import pandas as pd 2import matplotlib.pyplot as plt 3df = pd.DataFrame () 4url_list = ['http://www.espn.com/nba/salaries/_/seasontype/4'] 5for i in range (2) 13): 6 url = 'http://www.espn.com/nba/salaries/_/page/%s/seasontype/4'% I 7 url_list.append (url) traverse the table in the web page to read the web page table data 9for url in url_list:10 df = df.append (pd.read_html (url), ignore_index=True) 1 list parsing: traversing the third column of dataframe Start with the substring $12df = df [[x.startswith ('$') for x in df [3] 1 filter "Lakers" 14df1=df [df [2] = = 'Los Angeles Lakers'] 15df2=df1.copy () # make a copy 16df2 [3] = df2 [3] .map (lambda a: a.replace (' $',')) # remove the "$" from the salary 17df2 [3] = df2 [3] .apply (lambda x: float (x.replace (",") (")) 18print (df2) adds a list of 20df2 ['Chinese name'] = ['LeBron James', 'Anthony Davis', 'Danny Green', 'Kentavius Caldwell Pop', 'Avery Bradley', 'Javier McKee', 'Devente Carcock', 'Quinn Cook', 'Alex Caruso', 'Rajan Rondo' 'Kyle Kuzma', 'Makif Morris', 'Jared Dudley', 'Dwight Howard', 'David Stockton'] 21df2.to_excel ('NBA1.xlsx',header= [' RK','NAME','TEAM','SALARY',' Chinese name'], index=False) 22df3=df2.sort_values (by=3) Ascending=True) # descending sort 23fig=plt.figure (figsize= (8Jing 4)) # canvas size 24plt.subplots_adjust (left=0.3) # adjust the blank space of the chart 25plt.rcParams ['font.sans-serif'] = [' SimHei'] # solve Chinese garbled 26plt.ticklabel_format (useOffset=False, style='plain') # prohibit scientific counting method 27plt.title ('NBA Lakers salary analysis' Fontsize='18') # Chart title 28plt.xlim ['Chinese name'] 30y=df3 [3] 31median=df3 [3] .salary () 32plt.barh () 34plt.axvline (median,color='blue',linestyle='--',) 35plt.show () 3 median salary reference line 34plt.axvline (median,color='blue',linestyle='--',) 35plt.show () above is all the content of the article "how to climb and analyze the salary of NBA Lakers players through Python" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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