在 pandas 数据框中高效地对多列应用多个条件 [英] Applying multiple conditions for multiple columns in pandas dataframe efficiently

问题描述

我有一个包含数十列的DataFrame。

Therapy area    Procedures1 Procedures2 Procedures3
Oncology        450         450         2345
Oncology        367         367         415
Oncology        152         152         4945
Oncology        876         876         345
Oncology        1098        1098        12
Oncology        1348        1348        234
Nononcology     225         225         345
Nononcology     300         300         44
Nononcology     267         267         45
Nononcology     90          90          4567

我要将所有Procedure列中的数值更改为存储桶。

对于一列，它将类似于

def hello(x):
    if x['Therapy area'] == 'Oncology' and x['Procedures1'] < 200: return int(1)
    if x['Therapy area'] == 'Oncology' and x['Procedures1'] in range (200, 500): return 2
    if x['Therapy area'] == 'Oncology' and x['Procedures1'] in range (500, 1000): return 3
    if x['Therapy area'] == 'Oncology' and x['Procedures1'] > 1000: return 4
    if x['Therapy area'] != 'Oncology' and x['Procedures1'] < 200: return 11
    if x['Therapy area'] != 'Oncology' and x['Procedures1'] in range (200, 500): return 22
    if x['Therapy area'] != 'Oncology' and x['Procedures1'] in range (500, 1000): return 33
    if x['Therapy area'] != 'Oncology' and x['Procedures1'] > 1000: return 44  
test['Procedures1'] = test.apply(hello, axis=1)

对于具有不同列名(不是Procedures1、Procedures2、‘Procedures3`’等)的数十个列，最有效的方法是什么？

更新

我添加了第三列，但代码无法工作，并出现错误。

ValueError: bins must increase monotonically.

Bins没有直接回答我的问题。我可以有不同的价值观。我更喜欢具有逻辑运算的解决方案，而不是箱子。

对于非肿瘤性疾病，垃圾桶也可以不同，如11、22、33、44

推荐答案

您可以applypd.cut到相关列：

cols = ['Procedures1', 'Procedures2']
df[cols] = df[cols].apply(lambda col: pd.cut(col, [0,200,500,1000, col.max()], labels=[1,2,3,4]))

输出：

  Therapy_area Procedures1 Procedures2
0     Oncology           2           2
1     Oncology           2           2
2     Oncology           1           1
3     Oncology           3           3
4     Oncology           4           4
5     Oncology           4           4
6  Nononcology           2           2
7  Nononcology           2           2
8  Nononcology           2           2
9  Nononcology           1           1

您还可以使用np.select：

def encoding(col, labels):
    return np.select([col<200, col.between(200,500), col.between(500,1000), col>1000], labels, 0)

onc_labels = [1,2,3,4]
nonc_labels = [11,22,33,44]
msk = df['Therapy_area'] == 'Oncology'

df[cols] = pd.concat((df.loc[msk, cols].apply(encoding, args=(onc_labels,)), df.loc[msk, cols].apply(encoding, args=(nonc_labels,)))).reset_index(drop=True)

输出：

  Therapy_area  Procedures1  Procedures2  Procedures3
0     Oncology            2            2            4
1     Oncology            2            2            2
2     Oncology            1            1            4
3     Oncology            3            3            2
4     Oncology            4            4            1
5     Oncology            4            4            2
6  Nononcology           22           22           44
7  Nononcology           22           22           22
8  Nononcology           11           11           44
9  Nononcology           33           33           22

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