Aggregation and pivot tables

Aggregation Syntax

There are a number of ways to produce aggregations in PySpark. We recommend this syntax as the most reliable.

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aggregated_df = df.groupBy('state').agg(
	F.max('city_population').alias('largest_city_in_state')
)

Examples

The .groupBy() method on a DataFrame takes an arbitrary number of columns over which to perform the aggregations. Alternatively, to aggregate across the whole DataFrame, include no columns.

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aggregated_df = df.groupBy('state', 'county').agg(
	F.max('city_population').alias('largest_city_in_state_county')
)

aggregated_df = df.groupBy().agg(
	F.max('city_population').alias('largest_city_overall')
)

The .agg() method on a grouped DataFrame takes an arbitrary number of aggregation functions.

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aggregated_df = df.groupBy('state').agg(
	F.max('city_population').alias('largest_city_in_state'),
	F.avg('city_population').alias('average_population_in_state')
)

By default aggregations produce columns of the form aggregation_name(target_column). However, column names in Foundry cannot contain parentheses or other non-alphanumeric characters. Alias each aggregation to a specific name instead.

Pivot Tables

Pivot tables in PySpark work very similarly to ordinary grouped aggregations.

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pivoted_df = df.groupBy('equipment').pivot('sensor').mean('value')

Aggregation Functions

Learn more about PySpark aggregate functions. ↗

avg(column) / mean(column)

collect_list(column)

• Combine all values into an array

collect_set(column)

• Combine all values into an array with duplicates removed

count(column)

corr(x, y)

• Pearson Correlation Coefficient for columns x and y.

covar_pop(col1, col2)

covar_samp(col1, col2)

countDistinct(column, *cols)

first(column, ignorenulls=False)

• First value of the column in the group. Useful for Pivot tables for which we expect only one value to exist but must choose an aggregation anyway.

grouping(column)

grouping_id(*cols)

kurtosis(column)

last(column, ignorenulls=False)

max(column)

min(column)

skewness(column)

stddev(column)

stddev_pop(column)

• Population standard deviation

stddev_samp(column)

• Unbiased sample standard deviation

sum(column)

sumDistinct(column)

var_pop(column)

• Population variance

var_samp(column)

• Unbiased sample variance

variance(column)