Add a new row to a Pandas DataFrame with specific index name

Question

I'm trying to add a new row to the DataFrame with a specific index name 'e'.

    number   variable       values
a    NaN       bank          true   
b    3.0       shop          false  
c    0.5       market        true   
d    NaN       government    true

I have tried the following but it's creating a new column instead of a new row.

new_row = [1.0, 'hotel', 'true']
df = df.append(new_row)

Still don't understand how to insert the row with a specific index. Will be grateful for any suggestions.

Possible duplicate of [Pandas: Appending a row to a dataframe and specify its index label](https://stackoverflow.com/questions/16824607/pandas-appending-a-row-to-a-dataframe-and-specify-its-index-label) — Zero, Oct 07 '17 at 15:34
@Zero I have read the answers in the link but they're discussing adding random values there. — samba, Oct 07 '17 at 16:12

MaxU - stand with Ukraine · Accepted Answer · 2018-11-11T14:36:14.247

73

You can use df.loc[_not_yet_existing_index_label_] = new_row.

Demo:

In [3]: df.loc['e'] = [1.0, 'hotel', 'true']

In [4]: df
Out[4]:
   number    variable values
a     NaN        bank   True
b     3.0        shop  False
c     0.5      market   True
d     NaN  government   True
e     1.0       hotel   true

PS using this method you can't add a row with already existing (duplicate) index value (label) - a row with this index label will be updated in this case.

UPDATE:

This might not work in recent Pandas/Python3 if the index is a DateTimeIndex and the new row's index doesn't exist.

it'll work if we specify correct index value(s).

Demo (using pandas: 0.23.4):

In [17]: ix = pd.date_range('2018-11-10 00:00:00', periods=4, freq='30min')

In [18]: df = pd.DataFrame(np.random.randint(100, size=(4,3)), columns=list('abc'), index=ix)

In [19]: df
Out[19]:
                      a   b   c
2018-11-10 00:00:00  77  64  90
2018-11-10 00:30:00   9  39  26
2018-11-10 01:00:00  63  93  72
2018-11-10 01:30:00  59  75  37

In [20]: df.loc[pd.to_datetime('2018-11-10 02:00:00')] = [100,100,100]

In [21]: df
Out[21]:
                       a    b    c
2018-11-10 00:00:00   77   64   90
2018-11-10 00:30:00    9   39   26
2018-11-10 01:00:00   63   93   72
2018-11-10 01:30:00   59   75   37
2018-11-10 02:00:00  100  100  100

In [22]: df.index
Out[22]: DatetimeIndex(['2018-11-10 00:00:00', '2018-11-10 00:30:00', '2018-11-10 01:00:00', '2018-11-10 01:30:00', '2018-11-10 02:00:00'], dtype='da
tetime64[ns]', freq=None)

edited Nov 11 '18 at 14:36

answered Oct 07 '17 at 15:14

MaxU - stand with Ukraine

205,989
36
386
419

2

Wow super simple. Wish I had used that. Its all about timings – Bharath M Shetty Oct 07 '17 at 15:15
@Bharathshetty, yeah, i use this method if i need to add a single row, if i need to add 2+ rows - i;m using your method (`df.append(another_DF)`) – MaxU - stand with Ukraine Oct 07 '17 at 15:16
2

I added that in my answer. :) – Bharath M Shetty Oct 07 '17 at 15:17
3

`df.append(pd.Series(new_row, index=df.columns, name='e')` -- series should do for single row. – Zero Oct 07 '17 at 15:39
@Zero, thank you! I didn't know about that trick with named Series... - don't you want to add it as an asnwer? – MaxU - stand with Ukraine Oct 07 '17 at 15:41
I think, an update to Bharath's answer would be deserving? It started from there.. – Zero Oct 07 '17 at 15:42
@Zero, right, it wasn't there when i wrote my comment ;-) – MaxU - stand with Ukraine Oct 07 '17 at 15:45
This might not work in recent Pandas/Python3 if the index is a DateTimeIndex and the new row's index doesn't exist. In such cases @Dark's append solution works. – yeliabsalohcin Jul 31 '18 at 16:36
1

@yeliabsalohcin, it'll work - please see the updated answer – MaxU - stand with Ukraine Nov 11 '18 at 14:36
In the UPDATE for DateTimeIndex, it's not quite clear to me what you mean by "it'll work if we specify correct index value(s).". Also, the data-frame I need to add to is initially empty - does this change the solution? Thank you – Confounded Jul 10 '20 at 13:23

score 14 · Answer 2 · edited Jul 31 '19 at 12:09

14

Use append by converting list a dataframe in case you want to add multiple rows at once i.e

df = df.append(pd.DataFrame([new_row],index=['e'],columns=df.columns))

Or for single row (Thanks @Zero)

df = df.append(pd.Series(new_row, index=df.columns, name='e'))

Output:

  number    variable values
a     NaN        bank   True
b     3.0        shop  False
c     0.5      market   True
d     NaN  government   True
e     1.0       hotel   true

edited Jul 31 '19 at 12:09

Markus Dutschke

9,341
4
63
58

answered Oct 07 '17 at 15:14

Bharath M Shetty

30,075
6
57
108

1

`df.append(pd.Series(new_row, index=df.columns, name='e')` series should do. – Zero Oct 07 '17 at 15:38
@Zero Series was my very first thought confused a bit with name and index. So went to DataFrame approach. I updated my answer. I wanted to be first to answer so. – Bharath M Shetty Oct 07 '17 at 15:43
1

This works in the case of a pandas dataframe with a DateTimeIndex when trying to add a row with a new datetime which doesn't exist in the index. – yeliabsalohcin Jul 31 '18 at 16:37

score 4 · Answer 3 · answered Oct 18 '18 at 18:14

4

If it's the first row you need:

df = Dataframe(columns=[number, variable, values])
df.loc['e', [number, variable, values]] = [1.0, 'hotel', 'true']

answered Oct 18 '18 at 18:14

Kim Miller

886
8
11

score 1 · Answer 4 · answered Jan 06 '21 at 14:30

1

df.loc['e', :] = [1.0, 'hotel', 'true']

should be the correct implementation in case of conflicting index and column names.

answered Jan 06 '21 at 14:30

gunesevitan

882
10
25

andrewliam.dev · Answer 5 · 2022-08-24T20:55:01.460

In future versions of Pandas, DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False) will be deprecated.

Source: Pandas Documentation

The documentation recommends using .concat().

It would look like this (if you wanted an empty row with only the added index name:

df = pd.concat([df, pd.Series(index=['New index label'], dtype=str)])

If you wanted to add data use this:

df = pd.concat([df, pd.Series(data, index=['New index label'], dtype=str)])

Hope that helps!

Add a new row to a Pandas DataFrame with specific index name

5 Answers5

Linked