Change column names in python pandas using list comprehension

Question

I am currently trying to change the following column names:

Unnamed: 1 | Unnamed: 2 <----- Column names Alfa Beta <----- Data

Into:

 2016 | 2017 Alfa Beta

I have used:

df.rename(columns=lambda x: re.sub(r"Unnamed\:\s\d+"," ",x))

To replace the column names with a blank space, but I want to create a loop so automatically can be replaced into 2016 and 2017

[Additionally] I made this to create the list of values

for i in range(len(df.columns[:])): i+=2016 str(i)

Result gives:

2016 2017

Is there any method in which I can substitute those values into the list comprehension?

i assume u get these headers when u read in the data. you could set the column names directly when doing so — sammywemmy
– sammywemmy, Commented May 4, 2020 at 4:54

Allen Qin · Accepted Answer · 2020-05-04 05:15:41Z

5

df.rename(columns=lambda x: int(x.strip('Unnamed: '))+2015)

answered May 4, 2020 at 5:15

Allen Qin

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Comments

jezrael · Accepted Answer · 2020-05-04 05:31:05Z

I think list comprehension here is not necessary, only assign range with start from variable:

start = 2016 df.columns = range(start, start + len(df.columns)) print (df) 2016 2017 0 Alfa Beta

jaydrill · Accepted Answer · 2021-01-18 18:45:11Z

Stumped into this, to do what OP asked with list comprehension:

df.columns = [re.sub(r'Unnamed: [12]', lambda x: str(int(x.group(0).strip('Unnamed: '))+2015), item) for item in df.columns]

I know this is a a really long one-liner but OP asked for it, but agree with others, do it manually is the way to go unless the df is huge with a lot of columns. The lambda function portion should be a stand alone function which will allow much more flexibility for more advanced manipulation.

this one line can change below header:

 col1 Unnamed: 1 col3 Unnamed: 2 0 1 Alfa 3 Beta

to:

 col1 2016 col3 2017 0 1 Alfa 3 Beta

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