英文:
How to check in which bin an entry of a pandas data frame group belongs?
问题
我明白你想要的是如何创建一个quantile_bin列，该列包含整数值从1到10，用于指示特定Player的wins列中的数字属于哪个分位数。其中1代表在0到0.1分位数之间，2代表在0.1到0.2分位数之间，以此类推，10代表在0.9到1.0分位数之间。
你可以通过以下方式定义magic_quantile_function来实现这一目标：
import pandas as pdimport numpy as npdef magic_quantile_function(x):quantiles = np.arange(0.1, 1.1, 0.1) # 定义分位数的分割点bins = pd.qcut(x, quantiles, labels=False, duplicates='drop') + 1return binsexample = {'Player': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A','B', 'B', 'B', 'B', 'B', 'B', 'B', 'B','C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],'wins': [2, 4, 4, 5, 6, 9, 13, 13, 15, 15, 14, 13,11, 9, 9, 8, 8, 16, 19, 21, 14, 20, 19, 18]}df = pd.DataFrame(example)df['quantile_bin'] = df.groupby("Player")["wins"].transform(magic_quantile_function)print(df)
这段代码首先定义了magic_quantile_function，它使用pd.qcut函数来计算每个Player的wins列所属的分位数，并将结果存储在quantile_bin列中。然后，使用df.groupby("Player")["wins"].transform将这个函数应用到每个分组中，最终得到你想要的结果。
输出的结果应该类似于你所期望的那样。
英文:
I have a following problem.
I have a pandas data frame, eg something like this:
example = {'Player': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A','B', 'B', 'B', 'B', 'B', 'B', 'B', 'B','C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],'wins': [2, 4, 4, 5, 6, 9, 13, 13, 15, 15, 14, 13,11, 9, 9, 8, 8, 16, 19, 21, 14, 20, 19, 18]}df = pd.DataFrame(example)
What I want to build, is a column quantile_bin, which has integer values from 1 to 10, which tells me, to which quantile the specific number in wins for a specific Player belongs, where 1 is between 0 and 0.1 quantile, 2 is between 0.1 and 0.2 quantile, ..., and 10 is between 0.9 and 1.0 quantile.
In other words
df.groupby("Player")["wins"].apply(magic_quantile_function)
The result should be something like
>>> dfPlayer wins quantile_bin0 A 2 11 A 4 22 A 4 23 A 5 54 A 6 65 A 9 86 A 13 107 A 13 108 B 15 109 B 15 1010 B 14 911 B 13 612 B 11 513 B 9 214 B 9 215 B 8 116 C 8 117 C 16 318 C 19 719 C 21 1020 C 14 221 C 20 922 C 19 723 C 18 5
I would be grateful if one could help me defining this magic_quantile_function, or at least giving me a hint on how to pass a grouped wins distribution to a user defined function which is applied to a groupby object.
Thank you in advance!
答案1
得分: 1
看起来你可以使用qcut
df['quantile'] = df.groupby('Player')['wins'].transform(lambda s: pd.qcut(s, q=10, duplicates='drop', labels=range(2, 11)))
Player wins quantile0 A 2 21 A 4 32 A 4 33 A 5 64 A 6 75 A 9 96 A 13 107 A 13 108 B 15 109 B 15 1010 B 14 911 B 13 712 B 11 613 B 9 314 B 9 315 B 8 216 C 8 217 C 16 418 C 19 719 C 21 1020 C 14 321 C 20 922 C 19 723 C 18 6
英文:
Seems like you could use qcut
df['quantile'] = df.groupby('Player')['wins'].transform(lambda s: pd.qcut(s,q=10,duplicates='drop',labels=range(2, 11)))
Player wins quantile0 A 2 21 A 4 32 A 4 33 A 5 64 A 6 75 A 9 96 A 13 107 A 13 108 B 15 109 B 15 1010 B 14 911 B 13 712 B 11 613 B 9 314 B 9 315 B 8 216 C 8 217 C 16 418 C 19 719 C 21 1020 C 14 321 C 20 922 C 19 723 C 18 6
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