Question

使用pandas在python中工作，我试图将控制和治疗组分配给不同的客户组。

我有一个大型数据集。让我向您展示数据，而不是给出数据的示例，因为这总结了最重要的数据。

pd.pivot_table(df,index=['Test Group'],values=["Customer_ID"],aggfunc=lambda x: len(x.unique()))

我明白这些测试组Customer_ID

Innovators 4634
Early Adopters 2622
Early Majority 8653
Late Majority 7645
Laggards 7645
Lost 4354
Prospective 653

我运行以下代码：

percentages = {'Innovators':[0.0,1.0],\
     'Early Adopters':[0.2,0.8], \
     'Early Majority':[0.1,0.9],\
     'Late Majority':[0.0,1.0],\
     'Laggards':[0.2,0.8],\
     'Lost':[0.1,0.9],\
     'Prospective':[0.1,0.9]}

def assigner(gp):
     ...:     group = gp['Test Group'].iloc[0]
     ...:     cut = pd.qcut(
                  np.arange(gp.shape[0]), 
                  q=np.cumsum([0] + percentages[group]), 
                  labels=range(len(percentages[group]))
              ).get_values()
     ...:     return pd.Series(cut[np.random.permutation(gp.shape[0])], index=gp.index, name='flag')

df['flag'] = df.groupby('Test Group', group_keys=False).apply(assigner)

ValueError: Bin edges must be unique: array([   0,    0, 2621], dtype=int64).
You can drop duplicate edges by setting the 'duplicates' kwarg

...并继续收到此错误

我找到了这个答案，这可能会有所帮助How to qcut with non unique bin edges?;但排名不适用于np

def assigner(gp):
     ...:     group = gp['Campaign Test Description'].iloc[0]
     ...:     cut = pd.qcut(
                  np.arange(gp.shape[0]).rank(method='first'), 
                  q=np.cumsum([0] + percentages[group]), 
                  labels=range(len(percentages[group]))
              ).get_values()
     ...:     return pd.Series(cut[np.random.permutation(gp.shape[0])], index=gp.index, name='flag')

AttributeError: 'numpy.ndarray' object has no attribute 'rank'

我尝试删除重复项

def assigner(gp):
     ...:     group = gp['Campaign Test Description'].iloc[0]
     ...:     cut = pd.qcut(
                  np.arange(gp.shape[0]), 
                  q=np.cumsum([0] + percentages[group]), 
                  labels=range(len(percentages[group])),duplicates='drop'
              ).get_values()
     ...:     return pd.Series(cut[np.random.permutation(gp.shape[0])], index=gp.index, name='flag')

ValueError: Bin labels must be one fewer than the number of bin edges

仍然出现错误

Answer 1

您正在进行火车/测试拆分，这通常用于机器学习。这是一种方法（仔细检查我的百分比是否正确）：

df_pct = pd.DataFrame({ 'ID': ['Innovators','Early Adopters' ,'Early Majority','Late Majority','Laggards','Lost','Prospective'], 'test_cutoff':[1,0.8,0.9,0.1,0.8,0.9,0.9]}) df=df.merge(df_pct) df['is_test'] = np.random.uniform(0, 1, len(df)) >= df['test_cutoff']

此外，您的“延迟多数”百分比不等于100。

Python ValueError：Bin边必须是唯一的

1 个答案: