If True, then sample with replacement, that is, allow for duplicate rows.
If False, then sample without replacement, that is, do not allow for duplicate rows.

By default, withReplacement=False.

WARNING

If withReplacement=False, then Bernoulli sampling is performed, which is a technique where we iterate over each element and we include the element into sample with a probability of fraction. On the other hand, withReplacemnt=True will use Poisson sampling. I actually don't quite understand this, and if you have any idea as to what this is, please let me know!

2. fraction | float

A number between 0 and 1, which represents the probability that a value will be included in the sample. For instance, if fraction=0.5, then each element will be included in the sample with a probability of 0.5.

WARNING

The sample size of the subset will be random since the sampling is performed using Bernoulli sampling (if withReplacement=True). This means that even setting fraction=0.5 may result in a sample without any rows! On average though, the supplied fraction value will reflect the number of rows returned.

3. seed | int | optional

The seed for reproducibility. By default, no seed will be set which means that the derived samples will be random each time.

Return Value

A PySpark DataFrame (pyspark.sql.dataframe.DataFrame).

Examples

Consider the following PySpark DataFrame:


        
        
            
                
                
                    df = spark.createDataFrame([["Alex", 20],\
                            ["Bob", 24],\
                            ["Cathy", 22],\
                            ["Doge", 22]],\
                            ["name", "age"])
df.show()
                
            
            +-----+---+
| name|age|
+-----+---+
| Alex| 20|
|  Bob| 24|
|Cathy| 22|
| Doge| 22|
+-----+---+

Sampling random rows from a PySpark DataFrame (Bernoulli sampling)

To get a random sample in which the probability that an element is included in the sample is 0.5:


        
        
            
                
                
                    df.sample(fraction=0.5).show()
                
            
            +----+---+
|name|age|
+----+---+
|Doge| 22|
+----+---+

Running the code once again may yield a sample of different size:


        
        
            
                
                
                    df.sample(fraction=0.5).show()
                
            
            +-----+---+
| name|age|
+-----+---+
| Alex| 20|
|Cathy| 22|
+-----+---+

This is because the sampling is based on Bernoulli sampling as explained in the beginning.

Sampling with replacement (Poisson Sampling)

Once again, consider the following PySpark DataFrame:


        
        
            
                
                
                    df = spark.createDataFrame([["Alex", 20],\
                            ["Bob", 24],\
                            ["Cathy", 22],\
                            ["Doge", 22]],\
                            ["name", "age"])
df.show()
                
            
            +-----+---+
| name|age|
+-----+---+
| Alex| 20|
|  Bob| 24|
|Cathy| 22|
| Doge| 22|
+-----+---+

To sample with replacement (using Poisson sampling), use withReplacement=True:


        
        
            
                
                
                    df.sample(fraction=0.5, withReplacement=True).show()
                
            
            +-----+---+
| name|age|
+-----+---+
| Alex| 20|
|  Bob| 24|
|  Bob| 24|
|  Bob| 24|
|Cathy| 22|
+-----+---+

Notice how the sample size can exceed the original dataset size.

Published by Isshin Inada

Edited by 0 others

Did you find this page useful?

thumb_up

thumb_down

Comment

Citation

Ask a question or leave a feedback...

Official PySpark Documentation

https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.sample.html

thumb_up

thumb_down

chat_bubble_outline

settings

Enjoy our search

Hit / to insta-search docs and recipes!