What's the most efficient way to drop columns (from beginning and end) in pandas from a large dataframe?

Question

I am trying to drop a number of columns from the beginning and end of the pandas dataframe.

My dataframe has 397 rows and 291 columns. I currently have this solution to remove the first 8 columns, but I also want to remove some at the end:

SMPS_Data = SMPS_Data.drop(SMPS_Data.columns[0:8], axis=1)

I know I could just repeat this step and remove the last few columns, but I was hoping there is a more direct way to approach this problem.

I tried using

SMPS_Data = SMPS_Data.drop(SMPS_Data.columns[0:8,278:291], axis=1)

but it doesn't work.

Also, it seems that the .drop method somehow slows down the console responsiveness, so maybe there's a cleaner way to do it?

Answer 1

You could use .drop() , if you want to remove your columns by their column names

drop_these = ['column_name1', 'column_name2', 'last_columns']
df = df.drop(columns=drop_these)

---

If you know you want to remove them by their location, you could use .iloc() :

df.iloc[:, 8:15]  # For columns 8-15
df.iloc[:, :-5]   # For all columns, except the last five
df.iloc[:. 2:-5]  # For all columns, except the first column, and the last five

See this documentation on indexing and slicing data with pandas, for more information.