Python Pandas : Create new rows from values of a List Column

Question

I have a Pandas Dataframe that look like this :

              tags   value
[tag1, tag2, tag3]       0
[tag2, tag3]            10
[tag1, tag3]            50
                       ...

On this Dataframe, I want to apply a function that, for each tags of each rows, will create a new row with a column 'tag', and a column 'related_tags'. Here is an example of what I am expecting :

 tag   value    related_tags
tag1       0    [tag2, tag3] 
tag2       0    [tag1, tag3] 
tag3       0    [tag1, tag2] 
tag2      10    [tag3]     
tag3      10    [tag2]    
tag1      50    [tag3]   
tag3      50    [tag1]

I am familiar with Spark DataFrames but not with Pandas, is there a simple way to achieve this ?

Answer 1

This is unnesting problem firstly , after explode the list columns tags, questions is more clear

newdf=unnesting(df,['tags']).reset_index()

newdf['related_tags']=newdf['index'].map(df.tags)

newdf['related_tags']=[list(set(y)-{x})for x , y in zip(newdf.tags,newdf.related_tags)]
newdf
Out[48]: 
   index  tags  value  related_tags
0      0  tag1      0  [tag2, tag3]
1      0  tag2      0  [tag3, tag1]
2      0  tag3      0  [tag2, tag1]
3      1  tag2     10        [tag3]
4      1  tag3     10        [tag2]

---

Data input

df=pd.DataFrame({'tags':[['tag1','tag2','tag3'],['tag2','tag3']],'value':[0,10]})

---

self-define function

def unnesting(df, explode):
    idx=df.index.repeat(df[explode[0]].str.len())
    df1=pd.concat([pd.DataFrame({x:np.concatenate(df[x].values)} )for x in explode],axis=1)
    df1.index=idx
    return df1.join(df.drop(explode,1),how='left')