pyspark.sql.DataFrame.summary¶
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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 (e.g., 75%)
If no statistics are given, this function computes count, mean, stddev, min, approximate quartiles (percentiles at 25%, 50%, and 75%), and max.
New in version 2.3.0.
See also
DataFrame.display
Notes
This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting
DataFrame.Examples
>>> 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| +-------+---+----+