from pyspark.sql.functions import var_pop, stddev_pop
from pyspark.sql.functions import var_samp, stddev_samp
df.select(var_pop("Quantity"), var_samp("Quantity"), stddev_pop("Quantity"),
stddev_samp("Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import skewness, kurtosis
df.select(skewness("Quantity"), kurtosis("Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import corr, covar_pop, covar_samp
df.select(corr("InvoiceNo", "Quantity"), covar_samp("InvoiceNo", "Quantity"),
covar_pop("InvoiceNo", "Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import collect_set, collect_list
df.agg(collect_set("Country"), collect_list("Country")).show()
# COMMAND ----------
from pyspark.sql.functions import count
df.groupBy("InvoiceNo").agg(
count("Quantity").alias("quan"), expr("count(Quantity)")).show()
# COMMAND ----------
dailyActivitiesDF.select(var_pop("CaloriesBurned"), var_samp("CaloriesBurned"),
stddev_pop("CaloriesBurned"),
stddev_samp("CaloriesBurned")).show()
# COMMAND ----------
# Any extreme points in our data?
dailyActivitiesDF.select(skewness("CaloriesBurned"),
kurtosis("CaloriesBurned")).show()
# COMMAND ----------
# Covariance and Correlation
dailyActivitiesDF.select(corr("CaloriesBurned", "Steps"),
covar_samp("CaloriesBurned", "Steps"),
covar_pop("CaloriesBurned", "Steps")).show()
# COMMAND ----------
# MAGIC %md
# MAGIC
# MAGIC ## Multiple languages in one notebook
# MAGIC
# MAGIC - One cool thing about Databricks is that we can combine languages within a notebook
# MAGIC - So one example of this could be that our Data Scientists are comfortable writing Python, then our Data Engineers optimise that using Scala
# COMMAND ----------
# MAGIC %sql
# MAGIC
# MAGIC SELECT Count(Date) as NumberOfDaysCaloriesBurnedOver4000
from pyspark.sql.functions import var_samp, stddev_samp
df.select(var_pop("Quantity"), var_samp("Quantity"),
stddev_pop("Quantity"), stddev_samp("Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import skewness, kurtosis
df.select(skewness("Quantity"), kurtosis("Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import corr, covar_pop, covar_samp
df.select(corr("InvoiceNo", "Quantity"), covar_samp("InvoiceNo", "Quantity"),
covar_pop("InvoiceNo", "Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import collect_set, collect_list
df.agg(collect_set("Country"), collect_list("Country")).show()
# COMMAND ----------
from pyspark.sql.functions import count
df.groupBy("InvoiceNo").agg(
count("Quantity").alias("quan"),
expr("count(Quantity)")).show()
rides.agg(min("distance"), max("distance")).show()
# Use the `first` and `last` functions to compute the first and last values, respectively:
from pyspark.sql.functions import first, last
rides \
.orderBy("distance") \
.agg(first("distance", ignorenulls=False), last("distance", ignorenulls=False)) \
.show()
# **Note:** Null values sort before valid numerical values.
# Use the `corr`, `covar_samp`, or `covar_pop` functions to measure the linear
# association between two columns:
from pyspark.sql.functions import corr, covar_samp, covar_pop
rides \
.agg(corr("distance", "duration"), covar_samp("distance", "duration"), covar_pop("distance", "duration")) \
.show()
# The `collect_list` and `collect_set` functions return a column of array type:
from pyspark.sql.functions import collect_list, collect_set
rides.agg(collect_set("service")).show(truncate=False)
# **Note:** `collect_list` does not remove duplicates and will return a very
# long array in this case.
# ## Grouping data
# Use the `agg` method with the `groupBy` (or `groupby`) method to refine your
# analysis:
rides \
.groupBy("rider_student") \
def get_builtin_aggregator_column(agg, ctx):
try:
aggregator = ctx.aggregators[agg["aggregator"]]
try:
input = ctx.populate_values(agg["input"],
aggregator["input"],
preserve_column_refs=False)
except CortexException as e:
e.wrap("input")
raise
if aggregator["name"] == "approx_count_distinct":
return F.approxCountDistinct(input["col"],
input.get("rsd")).alias(agg["name"])
if aggregator["name"] == "avg":
return F.avg(input).alias(agg["name"])
if aggregator["name"] in {
"collect_set_int", "collect_set_float", "collect_set_string"
}:
return F.collect_set(input).alias(agg["name"])
if aggregator["name"] == "count":
return F.count(input).alias(agg["name"])
if aggregator["name"] == "count_distinct":
return F.countDistinct(*input).alias(agg["name"])
if aggregator["name"] == "covar_pop":
return F.covar_pop(input["col1"], input["col2"]).alias(agg["name"])
if aggregator["name"] == "covar_samp":
return F.covar_samp(input["col1"],
input["col2"]).alias(agg["name"])
if aggregator["name"] == "kurtosis":
return F.kurtosis(input).alias(agg["name"])
if aggregator["name"] in {"max_int", "max_float", "max_string"}:
return F.max(input).alias(agg["name"])
if aggregator["name"] == "mean":
return F.mean(input).alias(agg["name"])
if aggregator["name"] in {"min_int", "min_float", "min_string"}:
return F.min(input).alias(agg["name"])
if aggregator["name"] == "skewness":
return F.skewness(input).alias(agg["name"])
if aggregator["name"] == "stddev":
return F.stddev(input).alias(agg["name"])
if aggregator["name"] == "stddev_pop":
return F.stddev_pop(input).alias(agg["name"])
if aggregator["name"] == "stddev_samp":
return F.stddev_samp(input).alias(agg["name"])
if aggregator["name"] in {"sum_int", "sum_float"}:
return F.sum(input).alias(agg["name"])
if aggregator["name"] in {"sum_distinct_int", "sum_distinct_float"}:
return F.sumDistinct(input).alias(agg["name"])
if aggregator["name"] == "var_pop":
return F.var_pop(input).alias(agg["name"])
if aggregator["name"] == "var_samp":
return F.var_samp(input).alias(agg["name"])
if aggregator["name"] == "variance":
return F.variance(input).alias(agg["name"])
raise ValueError("missing builtin aggregator") # unexpected
except CortexException as e:
e.wrap("aggregate " + agg["name"])
raise
min("StockCode"), max("StockCode")).show(2)
#sum,sumDistinct, avg
from pyspark.sql.functions import sum, sumDistinct, avg
df.select(sum("Quantity"), sumDistinct("Quantity"), avg("Quantity")).show(2)
#표본분산 , 표본표준편차
from pyspark.sql.functions import var_samp, stddev_samp
df.select(var_samp("Quantity"), stddev_samp("Quantity")).show(2)
#모분산, 모표본편차
from pyspark.sql.functions import var_pop, stddev_pop
df.select(var_pop("Quantity"), stddev_pop("Quantity")).show(2)
#비대칭도, 척도
from pyspark.sql.functions import skewness, kurtosis
df.select(skewness("Quantity"), kurtosis("Quantity")).show(2)
#공분산과 상관관계
from pyspark.sql.functions import corr, covar_pop, covar_samp
df.select(corr("InvoiceNo", "Quantity"), covar_pop("InvoiceNo", "Quantity"),
covar_samp("InvoiceNo", "Quantity")).show(2)
#복합데이터 타입의 집계
from pyspark.sql.functions import collect_set, collect_list
df.agg(collect_set("Country"), collect_list("Country")).show(2)
# COMMAND ----------
# COMMAND ----------