Pandas - 根据其他列对列进行分组并将其标记为新列

Question

I have a data frame which I want to group based on the value of another column in the same data frame.

For example:

The Parent_ID and Child ID are linked and defines who is related to who in a hierarchical tree.

The dataframe looks like (input from a csv file)

No  Name    ID  Parent_Id
1   Tom     211 111
2   Galie   209 111
3   Remo    200 101
4   Carmen  212 121
5   Alfred  111 191
6   Marvela 101 111
7   Armin   234 101
8   Boris   454 109
9   Katya   109 323

I would like to group this data frame based on the ID and Parent_ID in the below grouping, and generate CSV files out of this based on the top level parent. Ie, Alfred.csv, Carmen.csv (will have only its own entry, ice line #4) , Katya.csv using the to_csv() function.

Alfred
  |_ Galie
   _ Tom
   _ Marvela
       |_ Remo
        _ Armin
Carmen
Katya
  |_ Boris

And, I want to create a new column in the same data frame, that will have a tag indicating the hierarchy. Like:

No  Name    ID  Parent_Id   Tag
1   Tom     211 111     Alfred
2   Galie   209 111     Alfred
3   Remo    200 101     Marvela, Alfred
4   Carmen  212 121 
5   Alfred  111 191 
6   Marvela 101 111     Alfred
7   Armin   234 101     Marvela, Alfred
8   Boris   454 109     Katya
9   Katya   109 323

Note that the names can repeat, but the ID will be unique.

Kindly let me know how to achieve this using pandas. I tried out groupby() but seems a little complicated and not getting what I intend. There should be one file for each parent, and the child records in the parent file. If a child has other child (like marvel), it qualifies to have its own csv file.

And the final output would be

Alfred.csv - All records matching Galie, Tom, Marvela
Marvela.csv - All records matching Remo, Armin
Carmen.csv - Only record matching carmen (row)
Katya.csv - all records matching katya, boris

Answer 1

I am assuming your dataframe as a dictionary:

mydf = ({"No":[1,2,3,4,5,6,7,8,9],"Name":["Tom","Galie","Remo","Carmen","Alfred","Marvela","Armin","Boris","Katya"],
        "ID":[211,209,200,212,111,101,234,454,109],"Parent_Id":[111,111,101,121,191,111,101,109,323]})
df = pd.DataFrame(mydf)

Then, I identify the Parent_Id from each row. Finally stored them into new column:

tag = []
for z in df['Parent_Id']:
    try:
        tag.append(df.query('ID==%s'%z)['Name'].item())
    except:
        tag.append('')
df['Tag'] = tag

To filter the dataframe based on a value in column Tag , eg Alfred :

df[df['Tag'].str.match('Alfred')]

Then save it in a csv file. Repeat for other values. Alternatively, if you have a large number of names in column Tag , then use for loop.