How to add columns to an empty pandas dataframe?

Question

I have an empty dataframe.

df=pd.DataFrame(columns=['a'])

for some reason I want to generate df2, another empty dataframe, with two columns 'a' and 'b'.

If I do

df.columns=df.columns+'b'

it does not work (I get the columns renamed to 'ab') and neither does the following

df.columns=df.columns.tolist()+['b']

How to add a separate column 'b' to df, and df.emtpy keep on being True?

Using .loc is also not possible

   df.loc[:,'b']=None

as it returns

  Cannot set dataframe with no defined index and a scalar

actually it does. but why is '' not adding one element to the index then? and empty string is still a string — 00__00__00
– 00__00__00, Commented May 16, 2018 at 13:33
This is something I have been wondering myself...sorry but I don't know the answer! — famargar
– famargar, Commented May 16, 2018 at 13:34

Sumit Jha · Accepted Answer · 2018-05-16 13:57:46Z

56

Here are few ways to add an empty column to an empty dataframe:

df=pd.DataFrame(columns=['a'])
df['b'] = None
df = df.assign(c=None)
df = df.assign(d=df['a'])
df['e'] = pd.Series(index=df.index)   
df = pd.concat([df,pd.DataFrame(columns=list('f'))])
print(df)

Output:

Empty DataFrame
Columns: [a, b, c, d, e, f]
Index: []

I hope it helps.

edited May 16, 2018 at 13:57

answered May 16, 2018 at 13:49

Sumit Jha

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2 Comments

MrR Over a year ago

See also df2 = df.join(pd.DataFrame(columns=['b'])) as per answer below.

stav Over a year ago

In case you're looking to add multiple columns, inplace, in a single line - I enjoy df[['c', 'd', 'e', 'f', 'g']] = [None] * 4

Ben.T · Accepted Answer · 2018-05-16 14:02:23Z

21

If you just do df['b'] = None then df.empty is still True and df is:

Empty DataFrame
Columns: [a, b]
Index: []

EDIT: To create an empty df2 from the columns of df and adding new columns, you can do:

df2 = pd.DataFrame(columns = df.columns.tolist() + ['b', 'c', 'd'])

edited May 16, 2018 at 14:02

answered May 16, 2018 at 13:39

Ben.T

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Comments

ALollz · Accepted Answer · 2018-05-16 13:55:51Z

10

If you want to add multiple columns at the same time you can also reindex.

new_cols = ['c', 'd', 'e', 'f', 'g']
df2 = df.reindex(df.columns.union(new_cols), axis=1)

#Empty DataFrame
#Columns: [a, c, d, e, f, g]
#Index: []

edited May 16, 2018 at 13:55

answered May 16, 2018 at 13:42

ALollz

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3 Comments

ALollz Over a year ago

Yeah, I like union better. It avoids the possibility of having two similarly named columns in the df

BENY Over a year ago

@piRSquared I think maybe using concat can conbine the reindex and union

piRSquared Over a year ago

@Wen I'm sure you're right. However, that requires constructing a new dataframe simply to concat. I tend to avoid constructing new pandas objects if it isn't necessary.

jpp · Accepted Answer · 2021-05-23 11:58:43Z

6

This is one way:

df2 = df.join(pd.DataFrame(columns=['b']))

The advantage of this method is you can add an arbitrary number of columns without explicit loops.

In addition, this satisfies your requirement of df.empty evaluating to True if no data exists.

edited May 23, 2021 at 11:58

answered May 16, 2018 at 13:42

jpp

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4 Comments

MrR Over a year ago

Why do you have to copy?

jpp Over a year ago

@MrR, the question states: for some reason I want to generate df2, another empty dataframe,.

MrR Over a year ago

df2 = df.join(pd.DataFrame(columns=['b'])) is sufficient. No need for df2 = df.copy()

MrR Over a year ago

Upvoted. PS: This should be added to the first answer - it's missing from that nice compendium presented there, and it's one of the most elegant ways (if not the most elegant).

MrR · Accepted Answer · 2021-05-22 05:24:16Z

4

You can use concat:

df=pd.DataFrame(columns=['a'])
df
Out[568]: 
Empty DataFrame
Columns: [a]
Index: []

df2=pd.DataFrame(columns=['b', 'c', 'd'])
pd.concat([df,df2])
Out[571]: 
Empty DataFrame
Columns: [a, b, c, d]
Index: []

edited May 22, 2021 at 5:24

MrR

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answered May 16, 2018 at 14:05

BENY

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Comments

Pramod B R · Accepted Answer · 2025-03-03 16:36:39Z

0

You can simply use the following syntax

import pandas as pd
df = pd.DataFrame(columns=['A', 'B', 'C'])
df[['D', 'E', 'F']] = None
print(df)

This creates an empty dataframe with columns from 'A' to 'F' with below result

 >>Empty DataFrame
 >>Columns: [A, B, C, D, E, F]
 >>Index: []

answered Mar 3 at 16:36

Pramod B R

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