是否有一种方法可以对pandas中数据帧上的组中的重复项进行编号？

Question

我有一个带有区域、客户端和一些交付的数据框架。 此列用作采购类型，第一次和最后一次采购标记为“第一次”和“最后一次”，但任何中间交付标记为“交付” 是否有一种方法可以转换交付并获得“delivery1”、“delivery2”等标签

import pandas as pd  
data = [['NY', 'A','FIRST', 10], ['NY', 'A','DELIVERY', 20], ['NY', 'A','DELIVERY', 30], ['NY', 'A','LAST', 25],
       ['NY', 'B','FIRST', 15], ['NY', 'B','DELIVERY', 10], ['NY', 'B','LAST', 20],
       ['FL', 'A','FIRST', 15], ['FL', 'A','DELIVERY', 10], ['NY', 'A','DELIVERY', 12], ['NY', 'A','DELIVERY', 25], ['NY', 'A','LAST', 20]
       ] 
  
# Create the pandas DataFrame 
df = pd.DataFrame(data, columns = ['Region', 'Client', 'purchaseType', 'price']) 
  
# print dataframe. 
df 

期望输出：

data2 = [['NY', 'A','FIRST', 10], ['NY', 'A','DELIVERY1', 20], ['NY', 'A','DELIVERY2', 30], ['NY', 'A','LAST', 25],
       ['NY', 'B','FIRST', 15], ['NY', 'B','DELIVERY1', 10], ['NY', 'B','LAST', 20],
       ['FL', 'A','FIRST', 15], ['FL', 'A','DELIVERY1', 10], ['NY', 'A','DELIVERY2', 12], ['NY', 'A','DELIVERY3', 25], ['NY', 'A','LAST', 20]
       ] 

df2 = pd.DataFrame(data2, columns = ['Region', 'Client', 'purchaseType', 'price']) 
print(df2)

提前谢谢

Answer 1

我们可以尝试使用^{}和^{}

blocks = df['purchaseType'].eq('FIRST').cumsum()
fill_values = df['purchaseType'].str.cat(df.groupby(blocks)
                                           .cumcount().astype(str), 
                                         sep='')
df.loc[df['purchaseType'].eq('DELIVERY'), 'purchaseType'] = fill_values
print(df)

#    Region Client purchaseType  price
# 0      NY      A        FIRST     10
# 1      NY      A    DELIVERY1     20
# 2      NY      A    DELIVERY2     30
# 3      NY      A         LAST     25
# 4      NY      B        FIRST     15
# 5      NY      B    DELIVERY1     10
# 6      NY      B         LAST     20
# 7      FL      A        FIRST     15
# 8      FL      A    DELIVERY1     10
# 9      NY      A    DELIVERY2     12
# 10     NY      A    DELIVERY3     25
# 11     NY      A         LAST     20

Answer 2

您可以使用np.where来决定在何处添加数字后缀：

df['purchaseType'] = df.groupby((df['purchaseType']=='FIRST').cumsum())['purchaseType'].transform(
    lambda x: np.where(x=='DELIVERY', x+np.arange(len(x)).astype(str), x)
)

print(df)

印刷品：

   Region Client purchaseType  price
0      NY      A        FIRST     10
1      NY      A    DELIVERY1     20
2      NY      A    DELIVERY2     30
3      NY      A         LAST     25
4      NY      B        FIRST     15
5      NY      B    DELIVERY1     10
6      NY      B         LAST     20
7      FL      A        FIRST     15
8      FL      A    DELIVERY1     10
9      NY      A    DELIVERY2     12
10     NY      A    DELIVERY3     25
11     NY      A         LAST     20