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[英]Pyspark: count on pyspark.sql.dataframe.DataFrame takes long time
[英]Pyspark: how to select unique ID data from a pyspark.sql.dataframe.DataFrame?
我对spark
语言和pyspark
很pyspark
。 我有一个pyspark.sql.dataframe.DataFrame
:
df.show()
+--------------------+----+----+---------+----------+---------+----------+---------+
| ID|Code|bool| lat| lon| v1| v2| v3|
+--------------------+----+----+---------+----------+---------+----------+---------+
|5ac52674ffff34c98...|IDFA| 1|42.377167| -71.06994|17.422535|1525319638|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37747|-71.069824|17.683573|1525319639|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37757| -71.06942|22.287935|1525319640|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37761| -71.06943|19.110023|1525319641|36.853622|
|5ac52674ffff34c98...|IDFA| 1|42.377243| -71.06952|18.904774|1525319642|36.853622|
|5ac52674ffff34c98...|IDFA| 1|42.378254| -71.06948|20.772903|1525319643|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37801| -71.06983|18.084948|1525319644|36.853622|
|5ac52674ffff34c98...|IDFA| 1|42.378693| -71.07033| 15.64326|1525319645|36.853622|
|5ac52674ffff34c98...|IDFA| 1|42.378723|-71.070335|21.093477|1525319646|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37868| -71.07034|21.851894|1525319647|36.853622|
|5ac52674ffff34c98...|IDFA| 1|42.378716| -71.07029|20.583202|1525319648|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37872| -71.07067|19.738768|1525319649|36.853622|
|5ac52674ffff34c98...|IDFA| 1|42.379112| -71.07097|20.480911|1525319650|36.853622|
|5ac52674ffff34c98...|IDFA| 1| 42.37952| -71.0708|20.526752|1525319651| 44.93808|
|5ac52674ffff34c98...|IDFA| 1| 42.37902| -71.07056|20.534052|1525319652| 44.93808|
|5ac52674ffff34c98...|IDFA| 1|42.380203| -71.0709|19.921381|1525319653| 44.93808|
|5ac52674ffff34c98...|IDFA| 1| 42.37968|-71.071144| 20.12599|1525319654| 44.93808|
|5ac52674ffff34c98...|IDFA| 1|42.379696| -71.07114|18.760069|1525319655| 36.77853|
|5ac52674ffff34c98...|IDFA| 1| 42.38011| -71.07123|19.155525|1525319656| 36.77853|
|5ac52674ffff34c98...|IDFA| 1| 42.38022| -71.0712|16.978994|1525319657| 36.77853|
+--------------------+----+----+---------+----------+---------+----------+---------+
only showing top 20 rows
我想在循环中提取每个唯一用户的信息,并将其转换为熊猫数据帧。
对于第一个用户,这就是我想要的:
id0 = df.first().ID
tmpDF = df.filter((fs.col('ID')==id0))
它有效,但将其转换为熊猫数据框需要很长时间
tmpDF = tmpDF.toPandas()
您可以使用toPandas()
将 spark df 转换为 pandas
unique_df = df.select('ID').distinct()
unique_pandas_df = unique_df.toPandas()
以下是您正在查找的内容, df.select("ID").distinct().rdd.flatMap(lambda x: x).collect()
为您提供了一个唯一ID
列表,您可以使用它来filter
df.select("ID").distinct().rdd.flatMap(lambda x: x).collect()
和toPandas()
可用于将 spark 数据帧转换为toPandas()
数据帧。
for i in df.select("ID").distinct().rdd.flatMap(lambda x: x).collect():
tmp_df = df.filter(df.ID == i)
user_pd_df = tmp_df.toPandas()
更新:由于问题已被编辑
toPandas()
导致将 DataFrame 中的所有记录收集到驱动程序中,并且应该在数据的一个小子集上完成。 如果您尝试将巨大的 DataFrame 转换为 Pandas,则需要花费大量时间。
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