如何在python中找到某些列的总和并找到其他列的平均值？

Question

enter image description here 我已经将 10 个 excel 文件与 1 年的 NFL 传球统计数据结合在一起，并且我已经总结了某些列（游戏次数、完成次数、尝试次数等），但我需要（传球者评分和 QBR）我想要查看平均值。

df3 = df3.groupby(['Player'],as_index=False).agg({'GS':'sum','Cmp':'sum','Att':'sum','Cmp% ':'sum','Yds':'sum','TD':'sum','TD%':'sum','Int':'sum','Int%':'sum','Y /A':'sum','AY/A':'sum','Y/C':'sum','Y/G':'sum','Rate':'sum','QBR': 'sum','Sk':'sum','Yds.1':'sum','NY/A':'sum','ANY/A':'sum','Sk%':'sum' ,'4QC':'sum','GWD':'sum'})

Answer 1

快速注意：不要附上你的代码、数据集、错误等的照片。提供实际代码、实际数据集（或数据集的样本）等，以便用户重现错误、问题、等等。没有人真的会花时间从照片中制作数据集（或者我应该说很少，因为我确实这样做了......因为我喜欢处理体育数据，而且我可以很快地抓住它）。

但要获得平均值而不是总和，您可以使用'mean'。另外，在您的代码中，为什么要对百分比求和？

import pandas as pd

df = pd.DataFrame()
for season in range(2010, 2020):
    url = 'https://www.pro-football-reference.com/years/{season}/passing.htm'.format(season=season)
    df = df.append(pd.read_html(url)[0], sort=False)

df = df[df['Rk'] != 'Rk']
df = df.reset_index(drop=True)
df['Player'] = df.Player.str.replace('[^a-zA-Z .]', '')
df['Player'] = df['Player'].str.strip()


strCols = ['Player','Tm', 'Pos', 'QBrec']
numCols = [ x for x in df.columns if x not in strCols ]

df[['QB_wins','QB_loss', 'QB_ties']] = df['QBrec'].str.split('-', expand=True)

df[numCols] = df[numCols].apply(pd.to_numeric)

df3 = df.groupby(['Player'],as_index=False).agg({'GS':'sum', 'TD':'sum', 'QBR':'mean'})

输出：

print (df3)
                 Player   GS   TD         QBR
0           A.J. Feeley    3    1   27.300000
1         A.J. McCarron    4    6         NaN
2         Aaron Rodgers  142  305   68.522222
3           Ace Sanders    4    1  100.000000
4          Adam Podlesh    0    0    0.000000
5         Albert Wilson    7    1   99.700000
6         Alex Erickson    6    0         NaN
7            Alex Smith  121  156   55.122222
8           Alex Tanney    0    1   42.900000
9          Alvin Kamara    9    0         NaN
10          Andrew Beck    6    0         NaN
11          Andrew Luck   86  171   62.766667
12          Andy Dalton  133  204   53.375000
13             Andy Lee    0    0    0.000000
14        Anquan Boldin   32    0   11.600000
15       Anthony Miller    4    0   81.200000
16      Antonio Andrews   10    1  100.000000
17        Antonio Brown   55    1   29.300000
18     Antonio Morrison    4    0         NaN
19    Antwaan Randle El    0    2  100.000000
20         Arian Foster   13    1  100.000000
21      Armanti Edwards    0    0   41.466667
22         Austin Davis   10   13   38.150000
23         B.J. Daniels    0    0         NaN
24       Baker Mayfield   29   49   53.200000
25   Ben Roethlisberger  130  236   66.833333
26        Bernard Scott    1    0    5.600000
27         Bilal Powell   12    0   17.700000
28          Billy Volek    0    0   89.400000
29       Blaine Gabbert   48   48   37.687500
..                  ...  ...  ...         ...
329           Tim Boyle    0    0         NaN
330         Tim Masthay    0    1    5.700000
331           Tim Tebow   16   17   42.733333
332         Todd Bouman    1    2   57.400000
333        Todd Collins    1    0    0.800000
334           Tom Brady  156  316   72.755556
335     Tom Brandstater    0    0    0.000000
336          Tom Savage    9    5   38.733333
337           Tony Pike    0    0    2.500000
338           Tony Romo   72  141   71.185714
339      Travaris Cadet    1    0         NaN
340     Travis Benjamin    8    0    1.700000
341        Travis Kelce   15    0    1.800000
342       Trent Edwards    3    2   98.100000
343           Tress Way    0    0         NaN
344      Trevone Boykin    0    1   66.400000
345      Trevor Siemian   25   30   40.750000
346          Troy Smith    6    5   38.500000
347          Tyler Boyd   14    0    2.800000
348          Tyler Bray    0    0    0.000000
349         Tyler Palko    4    2   56.600000
350       Tyler Thigpen    1    2   30.233333
351         Tyreek Hill   13    0    0.000000
352        Tyrod Taylor   46   54   51.242857
353         Vince Young   11   14   50.850000
354          Will Grier    2    0         NaN
355        Willie Snead    4    1  100.000000
356   Zach Mettenberger   10   12   24.600000
357         Zach Pascal   13    0         NaN
358           Zay Jones   15    0    0.000000