如何使用 python 中另一个数据框中的列的重复值为唯一行的数据框子集?

How can I subset a data frame for unique rows using repeating values from a column in another data frame in python?

我有 2 个数据框。我想根据 df_2 对 df_1 进行子集化,以便生成的数据框中的行对应于 df_2 中的行。以下是两个示例数据框:

df_1 = pd.DataFrame({
    "ID": ["Lemon","Banana","Apple","Cherry","Tomato","Blueberry","Avocado","Lime"], 
    "Color": ["Yellow","Yellow","Red","Red","Red","Blue","Green","Green"]})

df_2 = pd.DataFrame({"Color": ["Red","Blue","Yellow","Green","Red","Yellow"]})

我想要的输出是 df_3,其中“颜色”列与 df_2 中的相同:

df_3 = pd.DataFrame({
    "ID": ["Apple","Blueberry","Lemon","Avocado","Cherry","Banana"], 
    "Color": ["Red","Blue","Yellow","Green","Red","Yellow"]})

当我合并 df_1 和 df_2 时,我得到了重复的行,因为 df_2 中的大多数行在 df_1.

中有多个匹配项
merged = df_2.merge(df_1, how="left", on="Color")

删除重复项对“黄色”颜色正常工作,因为它在 df_2 中的值和 df_1 中的选项有 2:2 比率,但它不适用于“红色”或“绿色”,因为它们分别具有 2:3 比率和 1:2 比率,导致额外的行。

no_duplicates = merged.drop_duplicates(subset = "ID")

有没有办法对 df_1 进行子集化,其中 df_2 中第一次出现的“Red”拉出 df_1 中第一次出现的“Red”,第二次出现df_2 中的“Red”拉出 df_1 中第二次出现的“Red”,等等?除非别无选择,否则我宁愿不使用循环。谢谢。

尝试向 df_1df_2 添加一个指标列,同时使用 groupby cumcount 来获得位置:

df_1['i'] = df_1.groupby('Color').cumcount()
df_2['i'] = df_2.groupby('Color').cumcount()

df_1:

          ID   Color  i
0      Lemon  Yellow  0
1     Banana  Yellow  1
2      Apple     Red  0
3     Cherry     Red  1
4     Tomato     Red  2
5  Blueberry    Blue  0
6    Avocado   Green  0
7       Lime   Green  1

df_2:

    Color  i
0     Red  0
1    Blue  0
2  Yellow  0
3   Green  0
4     Red  1
5  Yellow  1

然后merge on both the indicator and the Color then drop指标栏:

merged_df = df_1.merge(df_2, how='right', on=['Color', 'i']).drop('i', axis=1)

merged_df:

          ID   Color
0      Apple     Red
1  Blueberry    Blue
2      Lemon  Yellow
3    Avocado   Green
4     Cherry     Red
5     Banana  Yellow

或者创建将系列直接传递给 merge(这使得 df_1df_2 不受影响):

merged_df = df_1.merge(
    df_2, how='right',
    left_on=['Color', df_1.groupby('Color').cumcount()],
    right_on=['Color', df_2.groupby('Color').cumcount()]
).drop('key_1', axis=1)

merged_df:

          ID   Color
0      Apple     Red
1  Blueberry    Blue
2      Lemon  Yellow
3    Avocado   Green
4     Cherry     Red
5     Banana  Yellow