pyspark.sql.DataFrame.describe¶

DataFrame.describe(*cols: Union[str, List[str]]) → pyspark.sql.dataframe.DataFrame[source]¶

Computes basic statistics for numeric and string columns.

New in version 1.3.1.

Changed in version 3.4.0: Supports Spark Connect.

This includes count, mean, stddev, min, and max. If no columns are given, this function computes statistics for all numerical or string columns.

Parameters

colsstr, list, optional: Column name or list of column names to describe by (default All columns).

Returns

DataFrame: A new DataFrame that describes (provides statistics) given DataFrame.

See also

DataFrame.summary

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.

Use summary for expanded statistics and control over which statistics to compute.

Examples

>>> df = spark.createDataFrame(
...     [("Bob", 13, 40.3, 150.5), ("Alice", 12, 37.8, 142.3), ("Tom", 11, 44.1, 142.2)],
...     ["name", "age", "weight", "height"],
... )
>>> df.describe(['age']).show()
+-------+----+
|summary| age|
+-------+----+
|  count|   3|
|   mean|12.0|
| stddev| 1.0|
|    min|  11|
|    max|  13|
+-------+----+

>>> df.describe(['age', 'weight', 'height']).show()
+-------+----+------------------+-----------------+
|summary| age|            weight|           height|
+-------+----+------------------+-----------------+
|  count|   3|                 3|                3|
|   mean|12.0| 40.73333333333333|            145.0|
| stddev| 1.0|3.1722757341273704|4.763402145525822|
|    min|  11|              37.8|            142.2|
|    max|  13|              44.1|            150.5|
+-------+----+------------------+-----------------+

pyspark.sql.DataFrame.cube pyspark.sql.DataFrame.distinct