Add column to dataframe with constant value

Question

I have an existing dataframe which I need to add an additional column to which will contain the same value for every row.

Existing df:

Date, Open, High, Low, Close
01-01-2015, 565, 600, 400, 450

New df:

Name, Date, Open, High, Low, Close
abc, 01-01-2015, 565, 600, 400, 450

I know how to append an existing series / dataframe column. But this is a different situation, because all I need is to add the 'Name' column and set every row to the same value, in this case 'abc'.

score 469 · Accepted Answer · answered Apr 08 '15 at 14:09

df['Name']='abc' will add the new column and set all rows to that value:

In [79]:

df
Out[79]:
         Date, Open, High,  Low,  Close
0  01-01-2015,  565,  600,  400,    450
In [80]:

df['Name'] = 'abc'
df
Out[80]:
         Date, Open, High,  Low,  Close Name
0  01-01-2015,  565,  600,  400,    450  abc

score 101 · Answer 2 · answered Jun 20 '17 at 15:42

101

You can use insert to specify where you want to new column to be. In this case, I use 0 to place the new column at the left.

df.insert(0, 'Name', 'abc')

  Name        Date  Open  High  Low  Close
0  abc  01-01-2015   565   600  400    450

answered Jun 20 '17 at 15:42

piRSquared

265,629
48
427
571

score 76 · Answer 3 · edited Jan 28 '21 at 09:00

Summing up what the others have suggested, and adding a third way

You can:

assign(**kwargs):
```
 df.assign(Name='abc')
```
access the new column series (it will be created) and set it:
```
 df['Name'] = 'abc'
```
insert(loc, column, value, allow_duplicates=False)
```
 df.insert(0, 'Name', 'abc')
```

where the argument loc ( 0 <= loc <= len(columns) ) allows you to insert the column where you want.

'loc' gives you the index that your column will be at after the insertion. For example, the code above inserts the column Name as the 0-th column, i.e. it will be inserted before the first column, becoming the new first column. (Indexing starts from 0).

All these methods allow you to add a new column from a Series as well (just substitute the 'abc' default argument above with the series).

score 57 · Answer 4 · answered Apr 08 '15 at 14:10

57

Single liner works

df['Name'] = 'abc'

Creates a Name column and sets all rows to abc value

answered Apr 08 '15 at 14:10

Zero

66,763
15
141
151

score 10 · Answer 5 · answered Jul 15 '21 at 11:49

10

One Line did the job for me.

df['New Column'] = 'Constant Value'
df['New Column'] = 123

answered Jul 15 '21 at 11:49

Zachary

109
1
6

score 7 · Answer 6 · answered Sep 08 '21 at 14:16

7

You can Simply do the following:

df['New Col'] = pd.Series(["abc" for x in range(len(df.index))])

answered Sep 08 '21 at 14:16

Imam_AI

101
1
6

1

Thanks, that was specially good to avoid the damn chained indexing warning. – Roni Antonio Sep 16 '21 at 01:13
1

Thank you, this worked perfectly for assigning a dataframe to a column, aka `df['date'] = pd.Series([pd.date_range('2020-01-01', '2023-12-31') for x in range(len(df.index))])` – BeRT2me Apr 14 '22 at 04:24

Add column to dataframe with constant value

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