python - Pandas groupby:获得每个组的counts()的最佳z得分

我有一个pandas groupby对象，它返回每种基因类型的计数，大致如下所示（为清晰起见，手动设置了列标题）：

counts = df.groupby(["ID", "Gene"]).size()

counts
ID      Gene      Count
1_1_1   SMARCB1     1
        smad       12
1_1_10  SMARCB1     2
        smad       17
1_1_100 SMARCB1     3

我需要获取zscore组内的内容，然后返回具有最高zscore的基因。

我尝试了以下操作，但似乎正在整个数据集中计算zscores，但未返回正确的zscore：

zscore = lambda x: (x - x.mean()) / x.std()
counts = df.groupby(["ID", "Match"]).size().pipe(zscore)

我已经尝试过转换并获得相同的结果。

我试过了：

counts = match_df.groupby(["ID", "Match"]).size().apply(zscore)

这给了我以下错误：

'int' object has no attribute 'mean'

无论我尝试什么，它都不会给出正确的输出。前两行的zscores应该为[-1,1]，在这种情况下，我将返回1_1_1 SMARCB1的行。等等，谢谢！

更新资料

感谢@ZaxR的帮助并切换到numpy均值和标准差，如下所示，我能够解决此问题。此解决方案还提供每个基因的原始计数和zscores的摘要数据框：

# group by id and gene match and sum hits to each molecule
counts = df.groupby(["ID", "Match"]).size()

# calculate zscore by feature for molecule counts
# features that only align to one molecule are given a score of 1
zscore = lambda x: (x - np.mean(x)) / np.std(x)
zscores = counts.groupby('ID').apply(zscore).fillna('1').to_frame('Zscore')

# group results back together with counts and output to
# merge with positions and save to file
zscore_df = zscores.reset_index()
zscore_df.columns = ["ID", "Match", "Zscore"]
count_df = counts.reset_index()
count_df.columns = ["ID", "Match", "Counts"]
zscore_df["Counts"] = count_df["Counts"]

# select gene with best zscore meeting threshold
max_df = zscore_df[zscore_df.groupby('ID')['Zscore'].transform(max) \
                       == zscore_df['Zscore']]

最佳答案

df.groupby(["ID", "Gene"]).size().transform(zscore)不起作用的原因是因为最后一组是只有一个项目的系列，因此当您尝试将lambda函数zscore应用于单个[integer]时，会出现'int' object has no attribute 'mean'错误。请注意，x.mean（）的行为不同于熊猫的“ mean”。

更新资料

我认为应该这样做：

# Setup code
df = pd.DataFrame({"ID": ["1_1_1", "1_1_1", "1_1_10", "1_1_10", "1_1_100"],
                   "Gene": ["SMARCB1", "smad", "SMARCB1", "smad", "SMARCB1"],
                   "Count": [1, 12, 2, 17, 3]})
df = df.set_index(['ID', 'Gene'])

# Add standard deviation for every row
# Note: .transform(zscore) would also work
df['std_dev'] = df.groupby('ID')['Count'].apply(zscore)

# Find the max standard deviation for each group and
# use that as a mask for the original df
df[df.groupby('ID')['std_dev'].transform(max) == df['std_dev']]

Out:
                  Count   std_dev
ID       Gene
1_1_1    smad     12      0.707107
1_1_10   smad     17      0.707107