Python Pandas add data in to column if it same key

Question

I need some help about Pandas

   model        timestamp
0  Punto  20200124_083155
1  Punto  20200124_163540
2  Doblo  20200124_122052
3  Doblo  20200124_150801
4  Panda  20200124_134350
5   Tipo  20200124_195955

I want it become to

   model        timestamp
0  Punto  20200124_083155;20200124_163540
1  Doblo  20200124_122052;20200124_150801  
2  Panda  20200124_134350
3  Tipo   20200124_195955

Please, everybody help me, can give me some example?, thank so much.

My teacher asked for such data analysis, if you know can u give me some example? please — Hadilen
– Hadilen, Commented Apr 21, 2022 at 3:30

Rabinzel · Accepted Answer · 2022-04-21 03:51:43Z

1

You can use agg for that and concatenate the grouped data.

new_df = df.groupby('model')['timestamp'].agg(timestamp= lambda x: ';'.join(x))

print(new_df)
                             timestamp
model                                 
Doblo  20200124_122052;20200124_150801
Panda                  20200124_134350
Punto  20200124_083155;20200124_163540
Tipo                   20200124_195955

new_df.reset_index(inplace=True)
print(new_df)

   model                        timestamp
0  Doblo  20200124_122052;20200124_150801
1  Panda                  20200124_134350
2  Punto  20200124_083155;20200124_163540
3   Tipo                  20200124_195955

edited Apr 21, 2022 at 3:51

answered Apr 21, 2022 at 3:47

Rabinzel

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2 Comments

BeRT2me Over a year ago

.reset_index() at the end to get exactly the output requested~

Rabinzel Over a year ago

I was just about to edit it. noticed after posting the answer:)

Salamat · Accepted Answer · 2022-04-21 06:40:06Z

0

Suppose you have:

df_new=pd.DataFrame()
df_new['name']=['Punto','Doblo','Doblo','Punto','Punto','Tipo']
df_new['date']=['20200124_083155','20200124_163540','20200124_122052','20200124_150801',
'20200124_134350','20200124_195955']

output:

name    date
0   Punto   20200124_083155
1   Doblo   20200124_163540
2   Doblo   20200124_122052
3   Punto   20200124_150801
4   Punto   20200124_134350
5   Tipo    20200124_195955

Use shift with groupby:

df_new['date2']=df_new.groupby('name').shift(-1)
df_new['date2']=';'+df_new['date2']
df_new['date2']=df_new['date2'].fillna('')

output:

name    date    date2
0   Punto   20200124_083155 ;20200124_150801
1   Doblo   20200124_163540 ;20200124_122052
2   Doblo   20200124_122052 
3   Punto   20200124_150801 ;20200124_134350
4   Punto   20200124_134350 
5   Tipo    20200124_195955

if you want to see it in the same column:

df_new['date3']=df_new['date']+df_new['date2']

output:

  name  date    date2   date3
0   Punto   20200124_083155 ;20200124_150801    20200124_083155;20200124_150801
1   Doblo   20200124_163540 ;20200124_122052    20200124_163540;20200124_122052
2   Doblo   20200124_122052     20200124_122052
3   Punto   20200124_150801 ;20200124_134350    20200124_150801;20200124_134350
4   Punto   20200124_134350     20200124_134350
5   Tipo    20200124_195955     20200124_195955

It is not concise. But this approach will ensure that you only have two dates in the column. Unlike with groupby and transform method

edited Apr 21, 2022 at 6:40

answered Apr 21, 2022 at 3:35

Salamat

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2 Comments

Hadilen Over a year ago

THanks you, but I need more about if it not 2 which is 3 or 4 or ... Can u give me some example?

Salamat Over a year ago

I have modified my answer. It is applicable if you want to have two dates only in the column ('date3').

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