Remove duplicates in DF and convert to JSON obj in python

王林
Release: 2024-02-22 13:20:03
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删除 DF 中的重复项并在 python 中转换为 JSON obj

Question content

I have a df similar to the one below

name         series
=============================
a             a1
b             b1
a             a2
a             a1
b             b2
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I need to convert the series into a list which should be assigned to each name like dictionary or json obj like below

{
   "a": ["a1", "a2"],
   "b": ["b1", "b2"]
}
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So far I have tried using groupby but it just groups everything into a single dictionary

test = df.groupby("series")[["name"]].apply(lambda x: x)
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The above code gives a df-like output

Series
Name
A     0   A1
      2   A2
      3   A1
B     1   B1
      4   B2
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Any help is greatly appreciated

Thank you


Correct answer


Firstdrop_duplicates Make sure there is, thengroupby. agg as a list:

out = df.drop_duplicates().groupby('name')['series'].agg(list).to_dict()
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Or dial unique:

out = df.groupby('name')['series'].agg(lambda x: x.unique().tolist()).to_dict()
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Output: {'a': ['a1', 'a2'], 'b': ['b1', 'b2']}

If you have additional columns, make sure to keep only the columns of interest:

out = (df[['name', 'series']].drop_duplicates()
       .groupby('name')['series'].agg(list).to_dict()
      )
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Sort the list:

out = (df.groupby('name')['series']
         .agg(lambda x: sorted(x.unique().tolist())).to_dict()
      )
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Example:

# input
  Name Series
0    A     Z1
1    B     B1
2    A     A2
3    A     Z1
4    B     B2

# output
{'A': ['A2', 'Z1'], 'B': ['B1', 'B2']}
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source:stackoverflow.com
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