In this tutorial, we will look at how to get the summary statistics for a Pyspark dataframe with the help of some examples.

## How to get the summary statistics of a Pyspark dataframe?

You can use the Pyspark dataframe `summary()`

function to get the summary statistics for a dataframe in Pyspark. The following is the syntax –

# dataframe summary statistics df.summary().show()

The `summary()`

function is commonly used in exploratory data analysis. It shows statistics like the count, mean, standard deviation, min, max, and common percentiles (for example, 25th, 50th, and 75th) of values in each column of the dataframe.

## Examples

Let’s look at some examples of getting dataframe statistics from a Pyspark dataframe. First, we’ll create a Pyspark dataframe that we will be using throughout this tutorial.

# import the pyspark module import pyspark # import the sparksession class from pyspark.sql from pyspark.sql import SparkSession # create an app from SparkSession class spark = SparkSession.builder.appName('datascience_parichay').getOrCreate() # data of competition participants data = [["Tim", 19, 172, "M"], ["Viraj", 20, 186, "L"], ["Emma", 18, 168, "M"], ["Jack", 21, 166, "S"], ["Max", 20, 173, "M"]] # create a Pyspark dataframe using the above data df = spark.createDataFrame(data, ["Name", "Age", "Height", "Shirt Size"]) # display the dataframe df.show()

Output:

+-----+---+------+----------+ | Name|Age|Height|Shirt Size| +-----+---+------+----------+ | Tim| 19| 172| M| |Viraj| 20| 186| L| | Emma| 18| 168| M| | Jack| 21| 166| S| | Max| 20| 173| M| +-----+---+------+----------+

We now have a dataframe containing the name, age, height, and t-shirt size of some students participating in a sports contest.

### Summary stats for the entire dataframe in Pyspark

Let’s get the summary statistics for the above dataframe. For this, apply the `summary()`

function on the dataframe and then use the `show()`

function to display the results.

# display dataframe summary df.summary().show()

Output:

+-------+-----+-----------------+-----------------+----------+ |summary| Name| Age| Height|Shirt Size| +-------+-----+-----------------+-----------------+----------+ | count| 5| 5| 5| 5| | mean| null| 19.6| 173.0| null| | stddev| null|1.140175425099138|7.810249675906654| null| | min| Emma| 18| 166| L| | 25%| null| 19| 168| null| | 50%| null| 20| 172| null| | 75%| null| 20| 173| null| | max|Viraj| 21| 186| S| +-------+-----+-----------------+-----------------+----------+

You can see that we get summary statistics for all the columns in the dataframe. Note that for the non-numerical columns (“Name” and “Shirt Size”), we get `null`

for mean, standard deviation, and percentile values as these cannot be computed for string values.

Alternatively, you can also use the Pyspark dataframe `describe()`

function to get some summary statistics. Let’s apply this function to the above dataframe.

# summary stats using describe() df.describe().show()

Output:

+-------+-----+-----------------+-----------------+----------+ |summary| Name| Age| Height|Shirt Size| +-------+-----+-----------------+-----------------+----------+ | count| 5| 5| 5| 5| | mean| null| 19.6| 173.0| null| | stddev| null|1.140175425099138|7.810249675906654| null| | min| Emma| 18| 166| L| | max|Viraj| 21| 186| S| +-------+-----+-----------------+-----------------+----------+

We get a selection of statistics that we got from the `summary()`

function. Note that the `describe()`

function doesn’t give the common percentile values (25%, 50%, and 75%).

You might also be interested in –

- Print Pyspark DataFrame Schema
- Pyspark – Standard Deviation of a Column
- Aggregate Functions in PySpark

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