pyspark.sql.DataFrame.summary¶

DataFrame.summary(*statistics)[source]¶

Computes specified statistics for numeric and string columns. Available statistics are: - count - mean - stddev - min - max - arbitrary approximate percentiles specified as a percentage (eg, 75%)

If no statistics are given, this function computes count, mean, stddev, min, approximate quartiles (percentiles at 25%, 50%, and 75%), and max.

Note

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame.

>>> df.summary().show()
+-------+------------------+-----+
|summary|               age| name|
+-------+------------------+-----+
|  count|                 2|    2|
|   mean|               3.5| null|
| stddev|2.1213203435596424| null|
|    min|                 2|Alice|
|    25%|                 2| null|
|    50%|                 2| null|
|    75%|                 5| null|
|    max|                 5|  Bob|
+-------+------------------+-----+

>>> df.summary("count", "min", "25%", "75%", "max").show()
+-------+---+-----+
|summary|age| name|
+-------+---+-----+
|  count|  2|    2|
|    min|  2|Alice|
|    25%|  2| null|
|    75%|  5| null|
|    max|  5|  Bob|
+-------+---+-----+

To do a summary for specific columns first select them:

>>> df.select("age", "name").summary("count").show()
+-------+---+----+
|summary|age|name|
+-------+---+----+
|  count|  2|   2|
+-------+---+----+