[英]Python - Pandas combine two dataframes that provide different values
I have two diffent dataframes with two columns and i want to merge them + get them sum of column B. The problem is dataframe 1 have some data, that i want to keep.我有两个不同的数据框,有两列,我想合并它们 + 得到它们 B 列的总和。问题是 dataframe 1 有一些数据,我想保留。 I'll write an example so it make sense我会写一个例子,所以它是有意义的
Dataframe 1 Dataframe 1
Columns A Column B
House walls,doors,rooms
Animal Legs,nose,eyes
car tires,engine
Dataframe 2 Dataframe 2
Column A Column B
House windows,kitchen
Bike wheels,bicycle chain
Desired result期望的结果
Column A Column B
House walls,doors,rooms,windows,kitchen
Animal Legs,nose,eyes
Car tires,engine
Bike wheels,bicycle chain
The merge function doesnt help and i tried to use pd.concat and then somehow aggregate data but didnt help either.合并 function 没有帮助,我尝试使用 pd.concat 然后以某种方式聚合数据,但也没有帮助。 Someone got an idea of how to solve it?有人知道如何解决它吗?
pd.concat([df1, df2]).groupby("Column A")["Column B"].apply(', '.join).reset_index()
After concating your dataframes, group your values by Column A, then use apply
to concat the grouped strings in column B, and finally restore Column A with reset_index()
.连接数据框后,按 A 列对值进行分组,然后使用apply
将 B 列中的分组字符串连接起来,最后使用reset_index()
恢复 A 列。
Edit: expansion on comments编辑:评论扩展
To remove duplicates, you can use the set
data structure, which only keeps a single version of each element you put into it.要删除重复项,您可以使用set
数据结构,它只保留您放入其中的每个元素的单个版本。 For each row x, split the words, then convert the list of words into a set:对于每一行 x,拆分单词,然后将单词列表转换为一个集合:
df4 = df3["Column B"].apply(lambda x: set(x.split(", "))).reset_index()
Note that after this, your column B will contain sets.请注意,在此之后,您的 B 列将包含集合。 I'll let you figure out how to reconvert from a set to a string using a similar pattern.我将让您弄清楚如何使用类似的模式从集合重新转换为字符串。
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