Adding a Column to a Pandas DataFrame Without Altering Index

General Discussions
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1

How to add a column to a pandas DataFrame?

I have an indexed DataFrame with named columns and non-continuous row numbers:

          a         b         c         d
2  0.671399  0.101208 -0.181532  0.241273
3  0.446172 -0.243316  0.051767  1.577318
5  0.614758  0.075793 -0.451460 -0.012493

I need to add a new column, 'e', to this DataFrame without altering its existing structure. The new column has the same length as the DataFrame:

0   -0.335485  
1   -1.166658  
2   -0.385571  
dtype: float64

I’ve tried using join, append, and merge, but none gave the expected result. How can I correctly add column 'e' to this DataFrame?

2

The quickest and cleanest way to add a new column in pandas is by direct assignment.

df['e'] = [-0.335485, -1.166658, -0.385571]

:white_check_mark: Why this?

  • It’s the most straightforward and efficient way to use pandas add column to dataframe.
  • It modifies df in place, meaning you don’t need to create a new copy.

But, what if you want to control where the new column appears? Let’s go a step further. :arrow_down:

3

Okay, direct assignment works, but what if I want to place the new column at a specific position in my DataFrame?:thinking:

You can use insert() to control where it appears:

df.insert(4, 'e', [-0.335485, -1.166658, -0.385571])

:white_check_mark: Why this?

  • The second argument (4) specifies that column 'e' will be added as the fifth column in df.
  • Unlike direct assignment, this method lets you decide the exact position while using pandas add column to dataframe.

But wait, what if you want to keep the original DataFrame unchanged while adding the new column? Keep reading. :arrow_down:

4

Sometimes, I don’t want to modify df in place, especially when I’m working with multiple transformations. How do I handle that?

The assign() method is a great choice:

df = df.assign(e=[-0.335485, -1.166658, -0.385571])

:white_check_mark: Why this?

  • It returns a new DataFrame instead of modifying df directly.
  • Useful when you’re working in pipelines or want to preserve the original DataFrame while using pandas add column to dataframe.