Pandas 0.21+ answer

Pandas version 0.21 has changed the drop method slightly to include both the index and columns parameters to match the signature of the rename and reindex methods.

df.drop(columns=['column_a', 'column_c'])

Personally, I prefer using the axis parameter to denote columns or index because it is the predominant keyword parameter used in nearly all pandas methods. But, now you have some added choices in version 0.21.

Another way of deleting a column in a Pandas DataFrame

If you're not looking for in-place deletion then you can create a new DataFrame by specifying the columns using DataFrame(...) function as:

my_dict = { 'name' : ['a','b','c','d'], 'age' : [10,20,25,22], 'designation' : ['CEO', 'VP', 'MD', 'CEO']}

df = pd.DataFrame(my_dict)

Create a new DataFrame as

newdf = pd.DataFrame(df, columns=['name', 'age'])

You get a result as good as what you get with del / drop.

To remove columns before and after specific columns you can use the method truncate. For example:

   A   B    C     D      E
0  1  10  100  1000  10000
1  2  20  200  2000  20000

df.truncate(before='B', after='D', axis=1)

Output:

    B    C     D
0  10  100  1000
1  20  200  2000

Viewed from a general Python standpoint, del obj.column_name makes sense if the attribute column_name can be deleted. It needs to be a regular attribute - or a property with a defined deleter.

The reasons why this doesn't translate to Pandas, and does not make sense for Pandas Dataframes are:

• Consider df.column_name to be a “virtual attribute”, it is not a thing in its own right, it is not the “seat” of that column, it's just a way to access the column. Much like a property with no deleter.

Deleting a column using the iloc function of dataframe and slicing, when we have a typical column name with unwanted values:

df = df.iloc[:,1:] # Removing an unnamed index column

Here 0 is the default row and 1 is the first column, hence :,1: is our parameter for deleting the first column.

Use:

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

Or else you can go with

del df['colname']

To delete multiple columns based on column numbers

df.drop(df.iloc[:,1:3], axis = 1, inplace = True)

To delete multiple columns based on columns names

df.drop(['col1','col2',..'coln'], axis = 1, inplace = True)

We can remove or delete a specified column or specified columns by the drop() method.

Suppose df is a dataframe.

Column to be removed = column0

Code:

df = df.drop(column0, axis=1)

To remove multiple columns col1, col2, . . . , coln, we have to insert all the columns that needed to be removed in a list. Then remove them by the drop() method.

Code:

df = df.drop([col1, col2, . . . , coln], axis=1)

If your original dataframe df is not too big, you have no memory constraints, and you only need to keep a few columns, or, if you don't know beforehand the names of all the extra columns that you do not need, then you might as well create a new dataframe with only the columns you need:

new_df = df[['spam', 'sausage']]

Drop by index

Delete first, second and fourth columns:

df.drop(df.columns[[0,1,3]], axis=1, inplace=True)

Delete first column:

df.drop(df.columns[[0]], axis=1, inplace=True)

There is an optional parameter inplace so that the original data can be modified without creating a copy.

Popped

Column selection, addition, deletion

Delete column column-name:

df.pop('column-name')

Examples:

df = DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6]), ('C', [7,8, 9])], orient='index', columns=['one', 'two', 'three'])

print df:

   one  two  three
A    1    2      3
B    4    5      6
C    7    8      9

df.drop(df.columns[[0]], axis=1, inplace=True) print df:

   two  three
A    2      3
B    5      6
C    8      9

three = df.pop('three') print df:

   two
A    2
B    5
C    8

Use:

columns = ['Col1', 'Col2', ...]
df.drop(columns, inplace=True, axis=1)

This will delete one or more columns in-place. Note that inplace=True was added in pandas v0.13 and won't work on older versions. You'd have to assign the result back in that case:

df = df.drop(columns, axis=1)

It's good practice to always use the [] notation. One reason is that attribute notation (df.column_name) does not work for numbered indices:

In [1]: df = DataFrame([[1, 2, 3], [4, 5, 6]])

In [2]: df[1]
Out[2]:
0    2
1    5
Name: 1

In [3]: df.1
  File "<ipython-input-3-e4803c0d1066>", line 1
    df.1
       ^
SyntaxError: invalid syntax

As you've guessed, the right syntax is

del df['column_name']

It's difficult to make del df.column_name work simply as the result of syntactic limitations in Python. del df[name] gets translated to df.__delitem__(name) under the covers by Python.

The best way to do this in Pandas is to use drop:

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

Also working with "text" syntax for the columns:

df.drop(['column_nameA', 'column_nameB'], axis=1, inplace=True)

Note: Introduced in v0.21.0 (October 27, 2017), the drop() method accepts index/columns keywords as an alternative to specifying the axis.

So we can now just do:

df = df.drop(columns=['column_nameA', 'column_nameB'])

The dot syntax works in JavaScript, but not in Python.

• Python: del df['column_name']
• JavaScript: del df['column_name'] or del df.column_name

From version 0.16.1, you can do

df.drop(['column_name'], axis = 1, inplace = True, errors = 'ignore')

A nice addition is the ability to drop columns only if they exist. This way you can cover more use cases, and it will only drop the existing columns from the labels passed to it:

Simply add errors='ignore', for example.:

df.drop(['col_name_1', 'col_name_2', ..., 'col_name_N'], inplace=True, axis=1, errors='ignore')

• This is new from pandas 0.16.1 onward. Documentation is here.

In Pandas 0.16.1+, you can drop columns only if they exist per the solution posted by eiTan LaVi. Prior to that version, you can achieve the same result via a conditional list comprehension:

df.drop([col for col in ['col_name_1','col_name_2',...,'col_name_N'] if col in df],
        axis=1, inplace=True)