How to delete a column from a data frame with pandas?

Question

I read my data

import pandas as pd
df = pd.read_csv(\'/path/file.tsv\', header=0, delimiter=\'\\t\')
print df

and get:

Answer 1

The best way to delete a column in pandas is to use drop:

df = df.drop('column_name', axis=1)

where 1 is the axis number (0 for rows and 1 for columns.)

To delete the column without having to reassign df you can do:

df.drop('column_name', axis=1, inplace=True)

Finally, to drop by column number instead of by column label, try this. To delete, e.g. the 1st, 2nd and 4th columns:

df.drop(df.columns[[0, 1, 3]], axis=1)  # df.columns is zero-based pd.Index 

Exceptions:

If a wrong column number or label is requested an error will be thrown. To check the number of columns use df.shape[1] or len(df.columns.values) and to check the column labels use df.columns.values.

An exception would be raised answer was based on @LondonRob's answer and left here to help future visitors of this page.

Answer 2

To actually delete the column

del df['id'] or df.drop('id', 1) should have worked if the passed column matches exactly

However, if you don't need to delete the column then you can just select the column of interest like so:

In [54]:

df['text']
Out[54]:
0    text1
1    text2
2    textn
Name: text, dtype: object

If you never wanted it in the first place then you pass a list of cols to read_csv as a param usecols:

In [53]:
import io
temp="""id    text
363.327    text1
366.356    text2
37782    textn"""
df = pd.read_csv(io.StringIO(temp), delimiter='\s+', usecols=['text'])
df
Out[53]:
    text
0  text1
1  text2
2  textn

Regarding your error it's because 'id' is not in your columns or that it's spelt differently or has whitespace. To check this look at the output from print(df.columns.tolist()) this will output a list of the columns and will show if you have any leading/trailing whitespace.

Answer 3

df.drop(colname, axis=1) (or del df[colname]) is the correct method to use to delete a column.

If a ValueError is raised, it means the column name is not exactly what you think it is.

Check df.columns to see what Pandas thinks are the names of the columns.