Pandas中的功能功能

Question

我用Python编写了自己的函数。该函数非常简单，下面可以看到数据和函数：

  data_1 = {'id':['1','2','3','4','5'],
            'name': ['Company1', 'Company1', 'Company3', 'Company4', 'Company5'], 
            'employee': [10, 3, 5, 1, 0], 
            'sales': [100, 30, 50, 200, 0], 
           }
    df = pd.DataFrame(data_1, columns = ['id','name', 'employee','sales'])
    
    threshold_1=40
    threshold_2=50

该函数如下所示：

  def my_function(employee,sales):
        conditions = [
        (sales == 0 ),
        (sales < threshold_1), 
        (sales >= threshold_1 & employee <= threshold_2)]
        values = [0, sales*2, sales*4]
        sales_estimation = np.select(conditions, values)    
        return (sales_estimation)

df['new_column'] = df.apply(lambda x: my_function(x.employee,x.sales), axis=1)
df

因此，该函数工作良好，给出了预期的结果。

现在，我想做同样的功能，但与矢量化操作跨潘达斯系列。我需要这个函数，因为矢量化操作减少了执行时间。由于这个原因，我编写了这个函数，但是这个函数不起作用。

  def my_function1(
        pandas_series:pd.Series
        )-> pd.Series:
        """
        Vectorized operation across Pandas Series
        """
        conditions = [
        (sales == 0 ),
        (sales < threshold_1), 
        (sales >= threshold_1 & employee <= threshold_2)]
        values = [0, sales*2, sales*4]
        sales_estimation = np.select(conditions, values)    
        return sales_estimation
    
    df['new_column_1']=my_function1(data['employee','sales'])

可能我的错误与此函数的输入参数有关。那么，有谁能帮我解决这个问题，使my_function1的功能吗？

Answer 1

你需要稍微改变一个条件才能通过系列赛：

(sales >= threshold_1 & employee <= threshold_2)
# equivalent to
# sales >= (threshold_1 & employee) <= threshold_2

转入：

(sales >= threshold_1) & (employee <= threshold_2)

因为运算符优先是错误的。

def my_function(employee,sales):
        conditions = [
        (sales == 0 ),
        (sales < threshold_1), 
        (sales >= threshold_1) & (employee <= threshold_2)]
        values = [0, sales*2, sales*4]
        sales_estimation = np.select(conditions, values)    
        return (sales_estimation)

df['new_column'] = my_function(df['employee'], df['sales'])

产出：

  id      name  employee  sales  new_column
0  1  Company1        10    100         400
1  2  Company1         3     30          60
2  3  Company3         5     50         200
3  4  Company4         1    200         800
4  5  Company5         0      0           0

您还可以在那里传递整个dataframe和子集列：

def my_function(df):
    employee = df['employee']
    sales = df['sales']
    conditions = [
    (sales == 0 ),
    (sales < threshold_1), 
    (sales >= threshold_1) & (employee <= threshold_2)]
    values = [0, sales*2, sales*4]
    sales_estimation = np.select(conditions, values)    
    return (sales_estimation)

df['new_column'] = my_function(df)

Answer 2

Pass功能类似，还可以添加()以避免ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().，因为操作符的优先级：

def my_function1(employee, sales):
      conditions = [
      (sales == 0 ),
      (sales < threshold_1), 
      (sales >= threshold_1) & (employee <= threshold_2)] #<- here
      values = [0, sales*2, sales*4]
      sales_estimation = np.select(conditions, values)    
      return sales_estimation
    
df['new_column_1']= my_function1(df['employee'],df['sales'])
print (df)
  id      name  employee  sales  new_column_1
0  1  Company1        10    100           400
1  2  Company1         3     30            60
2  3  Company3         5     50           200
3  4  Company4         1    200           800
4  5  Company5         0      0             0