How do I insert a column at a specific column index in pandas?

Slice n Dice: Inserting and Handling Columns

Dodge the Duplicates

Realize your column already exists? Don't let pandas bare its teeth with a ValueError. Instead, use the allow_duplicates=True option:

df.insert(2, 'ExistingCol', [7, 8], allow_duplicates=True) # pandas bears no grudge.
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Agile Reindexing

Prefer to handle the order of columns with more control— especially effective when adding multiple columns? Give reindexing a shot:

new_order = ['NewCol'] + df.columns[:-1].tolist() # reshuffle the DataFrame's deck 
df = df.reindex(columns=new_order)
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To List and Check Twice

Use the tolist() method to fetch column names of your DataFrame. It's your tool to reorder columns right in your hands.

Orientation Moves: Adding Columns at Start or End

The Leader of the Pack

Want your column to steal the limelight at the start? Do a simple prepend:

df.insert(0, 'StartCol', your_data) # dataframe.insert(0, 'My Rule', your_data)
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The Showstopper

Sneak in your column at the very end, just like your favorite band's encore:

df['EndCol'] = your_data # Last but never the least!
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Things that might trip you up

Exceeding the Limits

Ensure your loc remains within the valid range of your DataFrame's columns— overshooting will treat you to an IndexError.

Missmatching Lengths

Keep the value length consistent with the DataFrame's rows— unequal lengths = ValueError.

Type Discrepancies

Your inserted data should align with the column's data type requirements— divergent types can cause unexpected problems.

Practical Guide: Use Cases and Tips

When should you use insert()?

The insert() method is your friend when you know the precise index for your new column.

How fast are these insertions?

Inserting with insert() is generally speedy, but in case of multiple insertions, reposition the columns after insertion for better efficiency.

Dynamic Column Insertion

Flex your dynamic programming muscles— use insert() in loops or conditionals where the index or column name can vary with logic.