# How to Calculate Quartiles in PySpark (With Example)

In statistics, quartiles are values that split up a dataset into four equal parts.

When analyzing a distribution, we’re typically interested in the following quartiles:

• First Quartile (Q1): The value located at the 25th percentile
• Second Quartile (Q2): The value located at the 50th percentile
• Third Quartile (Q3): The value located at the 75th percentile

You can use the following syntax to calculate the quartiles for a column in a PySpark DataFrame:

```#calculate quartiles of 'points' column
df.approxQuantile('points', [0.25, 0.5, 0.75], 0)
```

The following example shows how to use this syntax in practice.

## Example: How to Calculate Quartiles in PySpark

Suppose we have the following PySpark DataFrame that contains information about points scored by various basketball players:

```from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

#define data
data = [['Mavs', 18],
['Nets', 33],
['Lakers', 12],
['Kings', 15],
['Hawks', 19],
['Wizards', 24],
['Magic', 28],
['Jazz', 40],
['Thunder', 24],
['Spurs', 13]]

#define column names
columns = ['team', 'points']

#create dataframe using data and column names
df = spark.createDataFrame(data, columns)

#view dataframe
df.show()

+-------+------+
|   team|points|
+-------+------+
|   Mavs|    18|
|   Nets|    33|
| Lakers|    12|
|  Kings|    15|
|  Hawks|    19|
|Wizards|    24|
|  Magic|    28|
|   Jazz|    40|
|Thunder|    24|
|  Spurs|    13|
+-------+------+
```

We can use the following syntax to calculate the quartiles for the points column:

```#calculate quartiles of 'points' column
df.approxQuantile('points', [0.25, 0.5, 0.75], 0)

[15.0, 19.0, 28.0]
```

From the output we can see:

• The first quartile is located at 15.
• The second quartile is located at 19.
• The third quartile is located at 28.

By knowing only these three values, we can have a good understanding of the distribution of values in the points column.

Note: You can find the complete documentation for the PySpark approxQuantile function here.