drop() Method | Teradata Python Package - 17.00 - drop() Method - Teradata Package for Python

Teradata® Package for Python User Guide

Product
Teradata Package for Python
Release Number
17.00
Release Date
November 2021
Content Type
User Guide
Publication ID
B700-4006-070K
Language
English (United States)

Use the drop() method to drop specified labels from rows or columns in a DataFrame. The method takes a single label or a list of labels as an argument and returns a new DataFrame with the specified rows or columns removed.

Arguments:
  • labels

    This is an optional argument. It could be single label or list-like. It also can be Index or column labels to drop depending on the axis argument.

  • axis
    This is an optional argument. Use the axis argument to specify whether the labels refer to index labels or column labels:
    • 0 or 'index' for index labels
    • 1 or 'column' for column labels
    The default is 0.
  • columns

    This is an optional argument. Use the columns argument to specify a single column label or a list of column labels. The columns argument is an alternative to specifying axis=1 with labels. You cannot specify both labels and columns.

Examples Prerequisite

Assume the table "admissions_train" exists and its index column is "id". And a DataFrame "df" is created based on this table using the command:

>>> df = DataFrame("admissions_train")
>>> df
    masters   gpa     stats programming admitted
id
5        no  3.44    novice      novice        0
7       yes  2.33    novice      novice        1
22      yes  3.46    novice    beginner        0
17       no  3.83  advanced    advanced        1
13       no  4.00  advanced      novice        1
19      yes  1.98  advanced    advanced        0
36       no  3.00  advanced      novice        0
15      yes  4.00  advanced    advanced        1
34      yes  3.85  advanced    beginner        0
40      yes  3.95    novice    beginner        0

Example 1: Drop columns 'stats' and 'admitted' with axis=1

>>> df.drop(['stats', 'admitted'], axis=1)
    programming masters   gpa
id
5        novice      no  3.44
34     beginner     yes  3.85
13       novice      no  4.00
40     beginner     yes  3.95
22     beginner     yes  3.46
19     advanced     yes  1.98
36       novice      no  3.00
15     advanced     yes  4.00
7        novice     yes  2.33
17     advanced      no  3.83

Example 2: Drop columns 'stats' and 'admitted' with columns argument

>>> df.drop(columns=['stats', 'admitted'])
    programming masters   gpa
id
5        novice      no  3.44
34     beginner     yes  3.85
13       novice      no  4.00
19     advanced     yes  1.98
15     advanced     yes  4.00
40     beginner     yes  3.95
7        novice     yes  2.33
22     beginner     yes  3.46
36       novice      no  3.00
17     advanced      no  3.83

Example 3: Drop rows with index values 13 and 15 with axis=0

>>> df1 = df[df.gpa == 4.00]
>>> df1
   masters  gpa     stats programming admitted
id                                           
13      no  4.0  Advanced      Novice        1
29     yes  4.0    Novice    Beginner        0
15     yes  4.0  Advanced    Advanced        1
>>> df1.drop([13,15], axis=0)
masters  gpa   stats programming admitted
id                                         
29     yes  4.0  Novice    Beginner        0
>>>