The sample() function samples rows from a DataFrame, directly or based on conditions. The function creates a new column 'sampleid' which has a unique ID for each sample, helping identify each sample.
If more than one sample() operations are performed on teradataml DataFrame, then 'sampleid' from the latest call is projected. Previous 'sampleid' columns are ignored.
In this example, 'sampleid' column shown is from the latest sample() operation (sample(frac = [0.5, 0.1]).
>>> from teradataml import *
>>> load_example_data("dataframe", "admissions_train")
>>> df = DataFrame("admissions_train")
>>> df.sample(n = 20).sample(frac = [0.5, 0.1])
masters gpa stats programming admitted sampleid
id
15 yes 4.00 Advanced Advanced 1 1
37 no 3.52 Novice Novice 1 1
35 no 3.68 Novice Beginner 1 2
17 no 3.83 Advanced Advanced 1 1
9 no 3.82 Advanced Advanced 1 1
3 no 3.70 Novice Beginner 1 1
34 yes 3.85 Advanced Beginner 0 1
39 yes 3.75 Advanced Beginner 0 1
36 no 3.00 Advanced Novice 0 2
19 yes 1.98 Advanced Advanced 0 1
In the following example, two sample() operations are performed on a DataFrame, but not consecutively. The 'sampleid' column in the result is from the latest sample operation (sample(n = [5, 4])). 'sampleid' column from the previous call is ignored.
>>> from teradataml import*
>>> load_example_data("dataframe", "admissions_train")
>>> df = DataFrame.from_table('admissions_train')
>>> df.sample(frac = 0.8).filter(items = ["masters"]).sample(n = [5, 4])
masters sampleid
0 yes 1
1 no 1
2 yes 1
3 no 2
4 no 1
5 no 2
6 yes 2
7 yes 1
8 yes 2
Example Prerequisite
>>> from teradataml import *
>>> load_example_data("dataframe", "admissions_train")
>>> df = DataFrame("admissions_train")
>>> df
masters gpa stats programming admitted
id
13 no 4.00 Advanced Novice 1
26 yes 3.57 Advanced Advanced 1
5 no 3.44 Novice Novice 0
19 yes 1.98 Advanced Advanced 0
15 yes 4.00 Advanced Advanced 1
40 yes 3.95 Novice Beginner 0
7 yes 2.33 Novice Novice 1
22 yes 3.46 Novice Beginner 0
36 no 3.00 Advanced Novice 0
38 yes 2.65 Advanced Beginner 1
Example 1: Sample one specific number of rows
This example randomly samples 2 rows from the teradataml DataFrame. As there is only one sample, the 'sampleid' is 1.
>>> df.sample(n = 2)
masters gpa stats programming admitted SampleId
id
18 yes 3.81 Advanced Advanced 1 1
19 yes 1.98 Advanced Advanced 0 1
Example 2: Sample multiple values for the number of rows to be sampled
This example creates two samples, one with 2 rows and one with 1 row. There are two values (1,2) for 'sampleid', each indicates one sample.
>> df.sample(n = [2, 1])
masters gpa stats programming admitted SampleId
id
1 yes 3.95 Beginner Beginner 0 1
10 no 3.71 Advanced Advanced 1 1
11 no 3.13 Advanced Advanced 1 2
Example 3: Sample one specific percentage of rows
This example randomly samples 20% of the total rows in the input teradataml DataFrame.
>>> df.sample(frac = 0.2)
masters gpa stats programming admitted SampleId
id
18 yes 3.81 Advanced Advanced 1 1
15 yes 4.00 Advanced Advanced 1 1
14 yes 3.45 Advanced Advanced 0 1
35 no 3.68 Novice Beginner 1 1
27 yes 3.96 Advanced Advanced 0 1
25 no 3.96 Advanced Advanced 1 1
10 no 3.71 Advanced Advanced 1 1
9 no 3.82 Advanced Advanced 1 1
Example 4: Sample multiple values for the percentage of rows to be sampled
This example creates two samples, one with 4% of total rows and one with 2% of total rows.
>>> df.sample(frac = [0.04, 0.02])
masters gpa stats programming admitted SampleId
id
29 yes 4.00 Novice Beginner 0 1
19 yes 1.98 Advanced Advanced 0 2
11 no 3.13 Advanced Advanced 1 1
Example 5: Sample specific number of rows, replace and randomization
This example creates two samples, one with 2 rows and one with 1 row, with possible redundant sampling as replace is True and also selects rows from different AMPs as randomize is True.
>>> df.sample(n = [2, 1], replace = True, randomize = True)
masters gpa stats programming admitted SampleId
id
12 no 3.65 Novice Novice 1 1
39 yes 3.75 Advanced Beginner 0 1
20 yes 3.90 Advanced Advanced 1 2
Example 6: Sample specific percentage of rows, replace and randomization
This example creates two samples, one with 4% of total rows and one with 2% of total rows in teradataml DataFrame, with possible redundant sampling and also selects rows from different AMPs.
>>> df.sample(frac = [0.04, 0.02], replace = True, randomize = True)
masters gpa stats programming admitted SampleId
id
7 yes 2.33 Novice Novice 1 2
30 yes 3.79 Advanced Novice 0 1
33 no 3.55 Novice Novice 1 1
Example 7: Sample with condition and number of samples to be sampled
This example creates two samples, with 1, 2 rows respectively from rows which satisfy df.gpa < 2 and 2.5% of rows from rows which satisfy df.stats == 'Advanced'.
>>> df.sample(case_when_then={df.gpa < 2 : [1, 2], df.stats == 'Advanced' : 0.025})
masters gpa stats programming admitted SampleId
id
19 yes 1.98 Advanced Advanced 0 1
24 no 1.87 Advanced Novice 1 1
11 no 3.13 Advanced Advanced 1 3
Example 8: Sample with condition and number of samples to be sampled, replace and randomization
This example creates two samples with 1 and 2 rows respectively from rows which satisfy df.gpa < 2 and 2.5% of rows from rows which satisfy df.stats == 'Advanced' and selects rows from different AMPs with replacement.
>>> df.sample(replace = True, randomize = True, case_when_then={df.gpa < 2 : [1, 2], df.stats == 'Advanced' : 0.025})
masters gpa stats programming admitted SampleId
id
24 no 1.87 Advanced Novice 1 1
24 no 1.87 Advanced Novice 1 2
24 no 1.87 Advanced Novice 1 2
24 no 1.87 Advanced Novice 1 2
24 no 1.87 Advanced Novice 1 2
24 no 1.87 Advanced Novice 1 1
31 yes 3.50 Advanced Beginner 1 3
Example 9: Sample with different conditions and numbers of samples to be sampled
This example creates creates seven samples:
- Two with 1, 3 rows from rows which satisfy df.gpa > 2
- One with 5 rows from rows which satisfy df.programming == 'Novice'
- One with 5 rows from rows which satisfy df.masters == 'no'
- One with 1 row from rows which does not meet all the previous conditions
>>> df.sample(case_when_then = {df.gpa > 2 : [1, 3], df.stats == 'Novice' : [1, 2], df.programming == 'Novice' : 5, df.masters == 'no': 5}, case_else = 1)
masters gpa stats programming admitted SampleId
id
24 no 1.87 Advanced Novice 1 5
2 yes 3.76 Beginner Beginner 0 1
12 no 3.65 Novice Novice 1 2
38 yes 2.65 Advanced Beginner 1 2
36 no 3.00 Advanced Novice 0 2
19 yes 1.98 Advanced Advanced 0 7
Example 10: Sample with different conditions and numbers of samples to be sampled, replace and randomization
This example creates Four samples:
- Two with 1, 3 rows from rows which satisfy df.gpa > 2
- Two with 2.5%, 5% of rows from rows which does not meet the previous condition with possible redundant replacement and also select rows from different AMPs
>>> df.sample(case_when_then = {df.gpa < 2 : [1, 3]}, replace = True, randomize = True, case_else = [0.025, 0.05])
masters gpa stats programming admitted SampleId
id
19 yes 1.98 Advanced Advanced 0 1
19 yes 1.98 Advanced Advanced 0 2
19 yes 1.98 Advanced Advanced 0 2
19 yes 1.98 Advanced Advanced 0 2
19 yes 1.98 Advanced Advanced 0 2
40 yes 3.95 Novice Beginner 0 3
3 no 3.70 Novice Beginner 1 4
19 yes 1.98 Advanced Advanced 0 2
19 yes 1.98 Advanced Advanced 0 2
19 yes 1.98 Advanced Advanced 0 1