如何获取值以及具有该特定值的行数，该特定值源自熊猫数据帧上的多个条件？

Question

我有熊猫数据框

Id  drove   swimmed walked  winPerc
0   247.3   1050    782.4   1
1   37.65   1072    119.6   0.04
2   93.73   1404    3248    1
3   95.88   1069    21.49   0.1146
4   0       1034    640.8   0
5   128.1   1000    1016    0.9368

average 100.4433333 1104.833333 971.3816667 
Min     0           1000        21.49   
max     247.3       1404        3248`

winPerc = 1 表示该玩家以第一名获胜，类似地 winPerc = 0 表示该玩家排在最后

print("The person who ends up winning the match usually drives {:.2f} , swims {:.2f} meters, has a walked {} meters".format(df.set_index('drove')['winPerc'].idxmax(),df.set_index('swimmed')['winPerc'].idxmax(),df.set_index('walked')['winPerc'].idxmax()))

为此我得到：-

IndexError: 元组索引超出范围

我想要的是如您在上面的数据框中看到的那样，id 为 0 和 2 的行具有 winPerc = 1 我应该得到如下响应： The person who ends up winning the match usually drives 170.52 , swims 1227 meters, has a walked 2015.2 meters 如果有多个记录 winPerc =1 那么我应该得到相应的值

还有可能没有驾驶过的玩家 (drove = 0)，并且，

赢得比赛 (winPerc = 1)

print("{} number of confident Players won without driving".format(len(df['drove'].min()['winPerc'].idxmax())))

为此我收到此错误：-

IndexError: 标量变量的索引无效。

如果万一没有列值为min()或max()或mean()的行，那么我应该取那些接近的值特殊情况。

提前致谢，如果我需要解释更多，请告诉我。 :)

Answer 1

我复制了第一个打印件，没有做任何更改，它对我来说很好：

The person who ends up winning the match usually drives 247.30 , swims 1050.00 meters, has a walked 782.4 meters.

当您使用.format() 并获得IndexError: tuple out of range 时，这意味着您使用的变量太少。

---

对于第二个问题，您需要过滤您的DataFrame。这可以通过不同的方式来完成，使用布尔掩码是一种常见的方式。

>> drove_is_0 = df["drove"] == df['drove'].min()
>> is_winner =  df['winPerc'] == df['winPerc'].idxmax()

然后将您的过滤器应用到您的DataFrame:

>> filtered = df[drove_is_0 & is_winner]

最后打印：

>> print("{} number of confident Players won without driving".format(len(filtered)))
1 number of confident Players won without driving

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OP 已澄清第一个问题不是关于提出的IndexError，而是关于过滤。他们想要过滤列winPerc 上的df，其中值为1，然后计算不同列的mean 值。为了保持一致性，我将使用如上所示的布尔掩码：

>> is_winner = df["winPerc"] == 1

>> mean_driven_winner = df[is_winner]["drove"].mean()
>> mean_swimmed_winner = df[is_winner]["swimmed"].mean()
>> mean_walked_winner = df[is_winner]["walked"].mean()

>> print("The person who ends up winning the match usually drives {:.2f} , swims {:.2f} meters, has a walked {} meters".format(
    mean_driven_winner, mean_swimmed_winner, mean_walked_winner)
)

The person who ends up winning the match usually drives 170.52 , swims 1227.00 meters, has a walked 2015.2 meters