squeeze() Method - Teradata Python Package

Teradata® Python Package User Guide

Product
Teradata Python Package
Release Number
16.20
Published
February 2020
Language
English (United States)
Last Update
2020-02-29
dita:mapPath
rkb1531260709148.ditamap
dita:ditavalPath
Generic_no_ie_no_tempfilter.ditaval
dita:id
B700-4006
lifecycle
previous
Product Category
Teradata Vantage

Use the squeeze() method to squeeze one-dimensional axis objects into a scalar for teradataml DataFrames with a single element, or a Series object for a teradataml DataFrame with a single column.

The teradataml DataFrame is returned unchanged when both dimensions are greater than one.

The axis parameter specifies the axis along which the squeeze operation is to be attempted. The possible values are:
  • 1 or 'columns': Return Series object if number of columns equals one.
  • 0 or 'index' : Return the unchanged teradataml DataFrame object.
When axis is not specified and both dimensions equal one, a scalar object is returned which is the only element in the DataFrame.

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
22     yes  3.46    Novice    Beginner        0
36      no  3.00  Advanced      Novice        0
15     yes  4.00  Advanced    Advanced        1
38     yes  2.65  Advanced    Beginner        1
5       no  3.44    Novice      Novice        0
17      no  3.83  Advanced    Advanced        1
34     yes  3.85  Advanced    Beginner        0
13      no  4.00  Advanced      Novice        1
26     yes  3.57  Advanced    Advanced        1
19     yes  1.98  Advanced    Advanced        0

Example: Squeeze a teradataml DataFrame with both dimension greater than one.

>>> df.squeeze()
   masters   gpa     stats programming admitted
id
22     yes  3.46    Novice    Beginner        0
36      no  3.00  Advanced      Novice        0
15     yes  4.00  Advanced    Advanced        1
38     yes  2.65  Advanced    Beginner        1
5       no  3.44    Novice      Novice        0
17      no  3.83  Advanced    Advanced        1
34     yes  3.85  Advanced    Beginner        0
13      no  4.00  Advanced      Novice        1
26     yes  3.57  Advanced    Advanced        1
19     yes  1.98  Advanced    Advanced        0

Example: Squeeze a single-column teradataml DataFrame.

>>> gpa = df.select(["gpa"])
>>> gpa.squeeze()
0    4.00
1    2.33
2    3.46
3    3.83
4    4.00
5    2.65
6    3.57
7    3.44
8    3.85
9    3.95
Name: gpa, dtype: float64
>>> gpa.squeeze(axis = 1)
0    3.46
1    3.00
2    4.00
3    2.65
4    3.44
5    3.83
6    3.85
7    4.00
8    3.57
9    1.98
Name: gpa, dtype: float64

>>> gpa.squeeze(axis = 0)
    gpa
0  3.46
1  3.00
2  4.00
3  2.65
4  3.44
5  3.83
6  3.85
7  4.00
8  3.57
9  1.98

Example: Squeeze a teradataml DataFrame with multiple columns and a single row.

>>> df = DataFrame.from_query('select gpa, stats from admissions_train where gpa=2.33')

>>> df
    gpa   stats
0  2.33  Novice
>>> s = df.squeeze()
>>> s
    gpa   stats
0  2.33  Novice

Example: Squeeze a teradataml DataFrame with a single element.

>>> single_gpa = DataFrame.from_query('select gpa from admissions_train where gpa=2.33')
>>> single_gpa
    gpa
0  2.33
>>> single_gpa.squeeze()
2.33
>>> single_gpa.squeeze(axis = 1)
0    2.33
Name: gpa, dtype: float64
>>> single_gpa.squeeze(axis = 0)
    gpa
0  2.33