Pandas Dataframe从最高到最低排序一列double

qcbq4gxm  于 2023-09-29  发布在  其他
关注(0)|答案(3)|浏览(66)

我在网页上抓取NBA的统计数据，并希望能够排序的统计数据，如点，助攻和盖帽。
我有我的Pandas框架，它可以正确地打印出球员和统计数据，包括按年龄等整数排序，如下所示。Example of dataframe sorted by age
然而，当我尝试按点数排序时，它并没有从最高值到最低值进行正确排序，而是从最高的初始值进行排序，比如从9.9到0，尽管很明显有球员每场比赛得分超过10.0。Example of dataframe sorted by points
数据框中存储的数字是否实际上是字符串，因此字符串的比较导致了这个问题？
下面是我正在运行的代码：

year = 2021
# URL page we will scraping (see image above)
url = "https://www.basketball-reference.com/leagues/NBA_{}_per_game.html".format(year)
# this is the HTML from the given URL

html = urlopen(url)
soup = BeautifulSoup(html, features="html.parser")

table = soup.find_all(class_="full_table")

head = soup.find(class_="thead")
headers_raw = [head.text for item in head][0]

headers = headers_raw.replace("\n", ",").split(",")[2:-1]
players = []

for i in range(len(table)):
    player = []
    for td in table[i].find_all("td"):
        player.append(td.text)

    players.append(player)

stats = pd.DataFrame(players, columns = headers)
sorted_by_points = stats.sort_values('PTS', ascending=False)
pandas

3条答案

按热度按时间
wswtfjt7

wswtfjt71#

是的，听起来像是琴弦。你可以使用dtypes()来查看。

赞(0）分享  回复(0）举报  2023-09-29
41ik7eoe

41ik7eoe2#

是，将PTS列转换为浮点数：

stats["PTS"] = pd.to_numeric(stats["PTS"])
sorted_by_points = stats.sort_values(by="PTS", ascending=False)
print(sorted_by_points)

图纸：

Player    Pos Age   Tm   G  GS    MP    FG   FGA    FG%   3P   3PA    3P%    2P   2PA    2P%   eFG%    FT   FTA    FT%  ORB   DRB   TRB   AST  STL  BLK  TOV   PF   PTS
37               Bradley Beal     SG  27  WAS  47  47  35.4  10.9  22.7   .483  2.2   6.5   .338   8.7  16.2   .541   .531   7.0   7.9   .897  1.2   3.6   4.8   4.8  1.1  0.3  3.3  2.4  31.1
112             Stephen Curry     PG  32  GSW  49  49  34.0  10.2  20.8   .491  5.1  12.0   .427   5.1   8.8   .579   .614   5.5   6.0   .922  0.5   5.1   5.6   5.9  1.2  0.1  3.2  1.8  31.0
140               Joel Embiid      C  26  PHI  38  38  32.2   9.4  18.2   .516  1.2   3.1   .379   8.2  15.1   .544   .548  10.1  11.8   .855  2.2   8.9  11.1   3.0  1.0  1.4  3.1  2.4  30.0
286            Damian Lillard     PG  30  POR  52  52  35.9   8.8  20.1   .440  4.1  10.8   .379   4.8   9.3   .510   .541   6.9   7.5   .925  0.5   3.7   4.2   7.7  0.9  0.3  3.2  1.6  28.7
125               Luka Dončić     PG  21  DAL  51  51  35.1  10.2  20.9   .486  3.0   8.3   .359   7.2  12.6   .569   .557   5.3   7.3   .727  0.8   7.1   7.9   8.7  1.0  0.7  4.3  2.3  28.6
11      Giannis Antetokounmpo     PF  26  MIL  47  47  33.7  10.3  18.3   .565  1.1   3.7   .299   9.2  14.6   .632   .595   6.7   9.8   .683  1.8   9.5  11.2   6.1  1.1  1.3  3.7  2.7  28.4
276               Zach LaVine     SG  25  CHI  53  53  35.2   9.8  19.4   .506  3.4   8.2   .416   6.4  11.2   .572   .594   4.4   5.2   .848  0.6   4.5   5.1   5.1  0.8  0.5  3.6  2.3  27.5
236              Kyrie Irving     PG  28  BRK  41  41  35.1  10.4  20.4   .511  2.7   7.0   .389   7.7  13.5   .573   .577   3.7   4.0   .916  1.1   3.7   4.8   6.1  1.3  0.6  2.4  2.7  27.3
134              Kevin Durant     PF  32  BRK  24  22  32.7   9.3  17.1   .543  2.5   5.5   .462   6.8  11.6   .581   .617   6.1   7.0   .870  0.4   6.3   6.7   5.2  0.6  1.3  3.7  2.0  27.3
511           Zion Williamson     PF  20  NOP  52  52  33.1  10.3  16.8   .614  0.2   0.6   .300  10.1  16.2   .625   .619   6.0   8.6   .699  2.6   4.6   7.2   3.7  0.9  0.7  2.6  2.3  26.8
335          Donovan Mitchell     PG  24  UTA  53  53  33.4   9.0  20.6   .438  3.4   8.7   .386   5.7  11.9   .476   .520   5.0   6.0   .845  0.9   3.5   4.4   5.2  1.0  0.3  2.8  2.2  26.4
253              Nikola Jokić      C  25  DEN  56  56  35.2  10.2  18.1   .567  1.4   3.3   .422   8.8  14.8   .599   .605   4.2   4.9   .855  2.9   8.1  11.0   8.8  1.5  0.6  3.1  2.7  26.1
458              Jayson Tatum     SF  22  BOS  51  51  35.6   9.4  20.4   .462  2.9   7.5   .388   6.5  12.9   .505   .534   4.2   4.8   .873  0.7   6.5   7.1   4.2  1.2  0.4  2.5  1.8  26.0
282             Kawhi Leonard     SF  29  LAC  46  46  34.4   9.4  18.2   .516  2.0   5.0   .393   7.4  13.2   .562   .569   5.0   5.7   .878  1.1   5.5   6.7   5.1  1.7  0.4  2.0  1.7  25.7
57               Devin Booker     SG  24  PHO  52  52  33.6   9.3  19.2   .485  2.0   5.8   .349   7.3  13.3   .545   .539   4.8   5.6   .862  0.4   3.8   4.1   4.5  0.9  0.3  3.2  2.8  25.5
243              LeBron James     PG  36  LAL  41  41  33.9   9.5  18.4   .513  2.4   6.5   .368   7.1  12.0   .592   .578   4.1   5.8   .703  0.6   7.3   7.9   7.9  1.0  0.6  3.7  1.6  25.4
521                Trae Young     PG  22  ATL  52  52  34.3   7.7  17.9   .431  2.3   6.5   .357   5.4  11.4   .472   .495   7.7   8.8   .871  0.6   3.3   3.9   9.5  0.8  0.2  4.3  1.9  25.4
156              De'Aaron Fox     PG  23  SAC  56  56  35.2   9.2  19.2   .481  1.8   5.5   .323   7.4  13.6   .545   .527   5.1   7.1   .713  0.6   2.9   3.5   7.2  1.5  0.5  3.0  2.9  25.3
194              James Harden  PG-SG  31  TOT  42  42  37.1   8.0  17.2   .463  2.8   7.8   .358   5.2   9.4   .549   .544   6.5   7.5   .870  0.8   7.2   8.0  10.9  1.2  0.7  4.1  2.3  25.2
475        Karl-Anthony Towns      C  25  MIN  36  36  34.2   8.6  17.6   .491  2.4   6.1   .395   6.2  11.5   .542   .560   5.0   5.8   .874  2.6   8.2  10.8   4.6  0.7  1.4  3.2  3.6  24.7
71               Jaylen Brown     SG  24  BOS  52  52  34.1   9.3  18.8   .493  2.7   6.8   .400   6.6  12.0   .546   .566   3.3   4.3   .754  1.2   4.6   5.8   3.4  1.2  0.6  2.7  2.9  24.6
437             Collin Sexton     SG  22  CLE  47  47  35.7   8.8  18.4   .479  1.6   4.4   .367   7.2  14.0   .514   .523   4.9   6.0   .816  0.8   2.1   2.9   4.1  1.1  0.1  2.6  2.7  24.2
235            Brandon Ingram     SF  23  NOP  52  52  34.7   8.5  18.2   .466  2.4   6.3   .382   6.1  12.0   .510   .532   4.8   5.4   .886  0.6   4.4   5.0   4.8  0.7  0.7  2.5  2.0  24.2
487            Nikola Vučević      C  30  TOT  57  57  33.6   9.7  20.1   .485  2.6   6.3   .417   7.1  13.8   .516   .550   2.1   2.5   .836  2.0   9.3  11.3   3.8  1.0  0.7  1.7  1.9  24.1
170   Shai Gilgeous-Alexander     SG  22  OKC  35  35  33.7   8.2  16.1   .508  2.0   4.9   .418   6.2  11.3   .547   .571   5.3   6.5   .808  0.5   4.2   4.7   5.9  0.8  0.7  3.0  2.0  23.7
407             Julius Randle     PF  26  NYK  57  57  37.4   8.4  18.3   .461  2.1   5.2   .405   6.3  13.1   .483   .519   4.8   5.9   .802  1.3   9.2  10.5   6.1  1.0  0.3  3.5  3.2  23.7
167               Paul George     SF  30  LAC  44  44  33.6   8.4  17.5   .480  3.3   7.5   .437   5.1   9.9   .513   .574   3.5   4.0   .886  0.9   5.4   6.3   5.5  1.2  0.5  3.2  2.4  23.6
315               CJ McCollum     SG  29  POR  31  31  33.5   8.6  19.4   .444  3.8   9.6   .396   4.8   9.8   .492   .542   2.3   2.8   .818  0.6   3.3   3.9   4.7  1.1  0.5  1.3  1.9  23.4
114             Anthony Davis     PF  27  LAL  23  23  32.8   8.9  16.7   .533  0.7   2.5   .293   8.1  14.1   .575   .555   4.0   5.7   .715  2.0   6.3   8.4   3.0  1.3  1.8  2.0  1.8  22.5
178              Jerami Grant     SF  26  DET  50  50  34.3   7.4  17.3   .428  2.2   6.2   .354   5.2  11.2   .469   .491   5.4   6.3   .860  0.7   4.0   4.7   2.9  0.7  1.1  2.1  2.3  22.4
500         Russell Westbrook     PG  32  WAS  49  49  35.4   8.3  18.9   .441  1.3   4.1   .312   7.1  14.8   .477   .475   4.0   6.3   .624  1.7   9.3  10.9  10.8  1.3  0.4  4.9  2.8  21.9
67            Malcolm Brogdon     PG  28  IND  50  50  35.1   8.1  17.6   .459  2.7   6.7   .399   5.4  10.9   .496   .535   2.6   3.0   .859  1.1   3.9   5.0   6.0  0.9  0.2  2.0  2.0  21.4
80               Jimmy Butler     SF  31  MIA  41  41  33.7   7.1  14.4   .495  0.5   2.0   .232   6.7  12.4   .537   .511   6.7   7.9   .852  1.9   5.3   7.2   7.2  2.1  0.4  2.1  1.4  21.4
346              Jamal Murray     PG  23  DEN  48  48  35.5   7.9  16.5   .477  2.7   6.6   .408   5.2   9.9   .523   .559   2.8   3.2   .869  0.8   3.3   4.0   4.8  1.3  0.3  2.3  2.0  21.2
119             DeMar DeRozan     PF  31  SAS  47  47  34.0   7.4  14.9   .495  0.4   1.4   .281   7.0  13.5   .517   .508   6.1   6.9   .880  0.7   3.6   4.3   7.2  0.9  0.3  1.9  2.0  21.2
517            Christian Wood      C  25  HOU  34  34  31.9   8.2  15.4   .531  1.8   4.7   .385   6.4  10.7   .595   .590   2.9   4.6   .641  1.7   7.6   9.4   1.6  0.9  1.2  1.9  2.1  21.1
440             Pascal Siakam     PF  26  TOR  46  46  35.6   7.5  16.5   .453  1.2   4.2   .282   6.3  12.3   .512   .489   4.7   5.5   .839  1.7   5.5   7.2   4.7  1.1  0.7  2.2  3.3  20.8
427              Terry Rozier     SG  26  CHO  53  53  34.0   7.5  16.0   .468  3.4   8.3   .405   4.1   7.7   .536   .573   2.4   2.9   .824  0.6   3.7   4.2   3.8  1.3  0.4  1.9  1.8  20.7
492                 John Wall     PG  30  HOU  37  37  32.1   7.4  18.1   .405  2.1   6.3   .328   5.3  11.9   .446   .462   3.8   5.2   .734  0.5   2.8   3.2   6.8  1.1  0.8  3.5  1.2  20.6
201             Tobias Harris     PF  28  PHI  49  49  33.3   7.9  15.2   .521  1.4   3.5   .407   6.5  11.7   .555   .568   3.2   3.6   .886  1.0   6.2   7.2   3.6  0.9  0.9  1.9  2.0  20.5
399        Kristaps Porziņģis      C  25  DAL  37  37  31.4   7.7  16.4   .473  2.3   6.3   .359   5.5  10.0   .544   .542   2.8   3.2   .850  2.0   7.4   9.4   1.6  0.4  1.5  1.4  2.6  20.5
330           Khris Middleton     SF  29  MIL  54  54  33.4   7.4  15.5   .479  2.2   5.1   .433   5.2  10.4   .502   .550   3.0   3.4   .886  0.8   5.2   6.1   5.6  1.1  0.2  2.8  2.4  20.1
430          Domantas Sabonis     PF  24  IND  53  53  35.7   7.5  14.4   .520  0.8   2.6   .302   6.7  11.8   .568   .547   4.1   5.6   .731  2.5   9.1  11.6   6.0  1.1  0.5  3.4  3.4  19.9
371            Victor Oladipo     SG  28  TOT  33  33  32.7   7.1  17.5   .408  2.4   7.2   .326   4.8  10.2   .466   .476   3.2   4.2   .754  0.4   4.5   4.8   4.6  1.4  0.4  2.5  2.5  19.8
207            Gordon Hayward     SF  30  CHO  44  44  34.0   7.1  15.0   .473  1.9   4.7   .415   5.1  10.3   .499   .537   3.5   4.2   .843  0.8   5.0   5.9   4.1  1.2  0.3  2.1  1.7  19.6
38              Malik Beasley     SG  24  MIN  37  36  32.8   7.1  16.2   .440  3.5   8.7   .399   3.7   7.5   .487   .547   1.8   2.2   .850  0.8   3.6   4.4   2.4  0.8  0.2  1.6  1.7  19.6
483             Fred VanVleet     SG  26  TOR  45  45  36.1   6.4  16.4   .391  3.3   8.9   .366   3.1   7.4   .422   .491   3.4   3.9   .885  0.6   3.5   4.2   6.1  1.7  0.8  2.0  2.4  19.5
401             Norman Powell  SG-SF  27  TOT  54  43  31.1   6.5  13.4   .486  2.6   6.2   .421   3.9   7.1   .543   .584   3.4   4.0   .860  0.6   2.5   3.1   1.8  1.2  0.3  1.8  2.4  19.1
429          D'Angelo Russell     PG  24  MIN  28  19  27.7   6.7  15.6   .429  2.8   7.1   .399   3.9   8.6   .454   .519   2.8   3.4   .802  0.4   2.0   2.4   4.9  1.0  0.4  2.8  1.9  19.0
3                 Bam Adebayo      C  23  MIA  51  51  33.4   7.2  12.7   .566  0.0   0.2   .250   7.2  12.6   .570   .568   4.5   5.7   .803  2.3   7.0   9.3   5.2  1.0  1.1  2.8  2.3  19.0
339                 Ja Morant     PG  21  MEM  47  47  31.9   6.7  15.1   .444  1.0   3.6   .274   5.7  11.5   .497   .477   4.3   5.8   .739  0.9   2.7   3.6   7.3  0.8  0.2  3.1  1.4  18.7
155             Evan Fournier  SF-SG  28  TOT  30  26  30.1   6.2  13.6   .457  2.8   7.0   .398   3.4   6.5   .520   .560   3.4   4.2   .795  0.2   2.6   2.7   3.4  1.1  0.4  1.9  2.2  18.6
284              Caris LeVert     SG  26  TOT  32  24  30.7   7.0  16.2   .431  1.8   5.6   .315   5.2  10.6   .491   .485   2.6   3.2   .804  0.8   3.7   4.5   4.8  1.4  0.5  2.0  2.0  18.3
505            Andrew Wiggins     PF  25  GSW  57  57  32.7   6.9  14.4   .476  2.0   5.2   .387   4.9   9.2   .527   .546   2.4   3.4   .697  1.0   3.7   4.7   2.3  0.9  0.9  1.8  2.2  18.2
135           Anthony Edwards     SG  19  MIN  58  41  31.3   6.6  16.5   .397  2.2   6.9   .320   4.3   9.6   .453   .465   2.8   3.5   .788  0.8   3.6   4.4   2.7  1.1  0.4  2.1  1.7  18.1
101              John Collins     PF  23  ATL  48  48  30.1   7.0  12.8   .545  1.3   3.3   .377   5.8   9.5   .604   .594   2.7   3.3   .840  2.1   5.6   7.6   1.4  0.5  1.0  1.2  3.3  18.0
176               Eric Gordon     SG  32  HOU  27  13  29.2   5.9  13.6   .433  2.6   7.8   .329   3.3   5.8   .573   .527   3.5   4.2   .825  0.3   1.9   2.1   2.6  0.5  0.5  1.9  1.6  17.8
490              Kemba Walker     PG  30  BOS  37  37  31.4   6.1  15.2   .401  2.8   8.0   .345   3.4   7.2   .464   .492   2.8   3.0   .937  0.3   3.6   3.9   5.1  1.1  0.3  2.1  1.4  17.8

...
赞(0）分享  回复(0）举报  2023-09-29
qhhrdooz

qhhrdooz3#

def comparison_summarize(compare_df):
orig_col=list(compare_df.columns.levels[0])
numeric_cols=[]
results_dict_cat={}
results_dict_num={}
for ele in compare_df._get_numeric_data().columns:
    numeric_cols.append(ele[0])
categorical_cols=list(set(orig_col)-set(numeric_cols))
numeric_cols=list(set(numeric_cols))
compare_df.columns = ['@'.join(col) for col in compare_df.columns.values]
for col in categorical_cols:
    nomatch_cat=compare_df[compare_df[col+'@self']!=compare_df[col+'@other']]
    if nomatch_cat.shape[0]==0:
        results_dict_cat[col]={}
        results_dict_cat[col]['Difference']="no difference in 2 dataframes"
    else:
        results_dict_cat[col]={}
        results_dict_cat[col]['Difference']="there is difference present"
        results_dict_cat[col]['difference_summary']=nomatch_cat[[col+'@self',col+'@other']].groupby([col+'@self',col+'@other']).size().reset_index().rename(columns={0:'count'})
tempdf=compare_df.copy()
for col in numeric_cols:
    tempdf[col+'diff']=tempdf[col+'@self']-tempdf[col+'@other']
    tempdf[col+'diff']=tempdf[col+'diff'].abs()
    nomatch_num=tempdf[tempdf[col+'diff']!=0]
    if nomatch_num.shape[0]==0:
        results_dict_num[col]={}
        results_dict_num[col]['Difference']="no difference in 2 dataframes"
    else:
        results_dict_num[col]={}
        results_dict_num[col]['Difference']="there is difference present"
        results_dict_num[col]['difference_summary']=nomatch_num[[col+'@self',col+'@other',col+'diff']].describe()
return compare_df,results_dict_cat,results_dict_num

compare_df，results_dict_cat，results_dict_num=comparison_summarize（df）

赞(0）分享  回复(0）举报  2023-09-29
我来回答

相关问题

    查看更多

    热门标签

    更多
    JavaquerypythonNode开发语言requestUtil数据库Table后端算法LoggerMessageElementParser

    最新问答

    更多
    • 蜀ICP备2022004421号-2 NULL123 https://www.null123.com © All rights reserved
    • 本站内容来源互联网,如果侵犯您的权益请联系我们删除, 联系方式：448109455@qq.com