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PySpark DataFrame | summary method

Machine Learning
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PySpark
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PySpark DataFrame
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark DataFrame's summary(~) method returns a PySpark DataFrame containing basic summary statistics of numeric columns.

Parameters

1. statistics | string | optional

The statistic to compute. The following are available:

  • count

  • mean

  • stddev

  • min

  • max

  • arbitrary percentiles (e.g. "60%")

By default, all the above as well as the 25%, 50%, and 75% percentiles are computed.

Return Value

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

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([["Alex", 20], ["Bob", 24], ["Cathy", 22], ["Doge", 30]], ["name", "age"])
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 24|
|Cathy| 22|
| Doge| 30|
+-----+---+

Getting the summary statistics of numeric columns of PySpark DataFrame

The summary statistics of our DataFrame is as follows:

df.summary().show()
+-------+----+-----------------+
|summary|name| age|
+-------+----+-----------------+
| count| 4| 4|
| mean|null| 24.0|
| stddev|null|4.320493798938574|
| min|Alex| 20|
| 25%|null| 20|
| 50%|null| 22|
| 75%|null| 24|
| max|Doge| 30|
+-------+----+-----------------+

To compute certain summary statistics only:

df.summary("max", "min").show()
+-------+----+---+
|summary|name|age|
+-------+----+---+
| max|Doge| 30|
| min|Alex| 20|
+-------+----+---+

Getting n-th percentile of numeric columns in PySpark DataFrame

To compute the 60th percentile:

df.summary("60%").show()
+-------+----+---+
|summary|name|age|
+-------+----+---+
| 60%|null| 24|
+-------+----+---+

Getting summary statistics of certain columns in PySpark DataFrame

To summarise certain columns instead, use the select(~) method first to select the columns that you want to summarize:

df.select("age").summary("max", "min").show()
+-------+---+
|summary|age|
+-------+---+
| max| 30|
| min| 20|
+-------+---+
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Published by Isshin Inada
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