Pyspark：如何从 pyspark.sql.dataframe.DataFrame 中选择唯一的 ID 数据？

Question

I am quite new to spark language and pyspark . I have a pyspark.sql.dataframe.DataFrame that looks like the following:

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

I would like to extract the information of each unique user in a loop a transform it as a pandas dataframe.

For the first user, this what I am trying to:

id0 = df.first().ID
tmpDF = df.filter((fs.col('ID')==id0))

that it works, but it takes forever to transform it into a pandas dataframe

tmpDF = tmpDF.toPandas()

Answer 1

you could convert spark df to pandas by using toPandas()

unique_df = df.select('ID').distinct()

unique_pandas_df = unique_df.toPandas()

Answer 2

The following is what you are looking, df.select("ID").distinct().rdd.flatMap(lambda x: x).collect() gives you a list of unique ID using which you can filter your spark dataframe and toPandas() can be used to convert spark dataframe to pandas dataframe.

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()

Update: As the question has been edited

toPandas() results in the collection of all records in the DataFrame to the driver program and should be done on a small subset of the data. If you are trying to convert huge DataFrame into pandas it will take a considerate amount of time to do it.