pyspark dataframe coverting to koalas dataframe have different elements

[Alxe1]

Code:

import databricks.koalas as ks
from pyspark import SparkConf
from pyspark.sql import SparkSession

if __name__ == '__main__':
    conf = SparkConf().setAppName("test")

    spark = SparkSession.builder.config(conf=conf).enableHiveSupport().getOrCreate()
    sdf = spark.sql("select uid,vr_id, gender, follow_count, byfollow_count, is_click "
                    "from database.table where data_date=20220726 "
                    "and uid=249462081764458496 limit 5")
    sdf.show(n=20)

    print("=======================to_koalas===============================")
    df = sdf.to_koalas()

    category_features_df = df[["uid", "vr_id", "gender"]].fillna(0)
    dense_features_df = df[["follow_count", "byfollow_count"]].fillna(0)

    y = df["is_click"].values

    print("category_features_df: {}".format(category_features_df))
    print("dense_features_df: {}".format(dense_features_df))

    total_uids = category_features_df["uid"].unique().tolist()
    total_vids = category_features_df["vr_id"].unique().tolist()
    uid_id2index = {uid: i for i, uid in enumerate(total_uids)}
    uid_index2id = {i: uid for uid, i in uid_id2index.items()}
    vid_id2index = {vid: i for i, vid in enumerate(total_vids)}
    vid_index2id = {i: vid for vid, i in vid_id2index.items()}
    print(f"uid_id2index: {uid_id2index}")
    print(f"vid_id2index: {vid_id2index}")

The result:

+------------------+------------------+------+------------+--------------+--------+
|               uid|             vr_id|gender|follow_count|byfollow_count|is_click|
+------------------+------------------+------+------------+--------------+--------+
|249462081764458496|234389742446182400|     0|           4|             2|       0|
|249462081764458496|247965851351777280|     0|           4|             2|       0|
|249462081764458496|303938736226304000|     0|           4|             2|       0|
|249462081764458496|305220054218178560|     0|           4|             2|       0|
|249462081764458496|150357127037190144|     0|           4|             2|       0|
+------------------+------------------+------+------------+--------------+--------+

=======================to_koalas===============================
/mnt/softwares/my_env/lib/python3.6/site-packages/databricks/koalas/generic.py:603: UserWarning: We recommend using `Series.to_numpy()` instead.
  warnings.warn("We recommend using `{}.to_numpy()` instead.".format(type(self).__name__))
category_features_df:                   uid               vr_id  gender
0  249462081764458496  239951849459810304       0
1  249462081764458496  218479966654824448       0
2  249462081764458496  269598027864342528       0
3  249462081764458496  306587488548290560       0
4  249462081764458496  270454206781980672       0
dense_features_df:    follow_count  byfollow_count
0             4               2
1             4               2
2             4               2
3             4               2
4             4               2
uid_id2index: {249462081764458496: 0}
vid_id2index: {298760687402876928: 0, 306851269564170240: 1, 306601561927188480: 2, 269902057735979008: 3, 286263993075499008: 4}

why sdf is different with df?

[ljluestc]

import databricks.koalas as ks
from pyspark import SparkConf
from pyspark.sql import SparkSession

if __name__ == '__main__':
    conf = SparkConf().setAppName("test")

    spark = SparkSession.builder.config(conf=conf).enableHiveSupport().getOrCreate()
    sdf = spark.sql("select uid,vr_id, gender, follow_count, byfollow_count, is_click "
                    "from database.table where data_date=20220726 "
                    "and uid=249462081764458496 limit 5")
    sdf.show(n=20)

    print("=======================to_koalas===============================")
    df = sdf.to_koalas()

    # Explicitly cast columns to the desired data types
    df["uid"] = df["uid"].astype("int64")
    df["vr_id"] = df["vr_id"].astype("int64")
    df["gender"] = df["gender"].astype("int32")
    df["follow_count"] = df["follow_count"].astype("int32")
    df["byfollow_count"] = df["byfollow_count"].astype("int32")
    df["is_click"] = df["is_click"].astype("int32")

    category_features_df = df[["uid", "vr_id", "gender"]].fillna(0)
    dense_features_df = df[["follow_count", "byfollow_count"]].fillna(0)

    y = df["is_click"].values

    print("category_features_df: {}".format(category_features_df))
    print("dense_features_df: {}".format(dense_features_df))

    total_uids = category_features_df["uid"].unique().tolist()
    total_vids = category_features_df["vr_id"].unique().tolist()
    uid_id2index = {uid: i for i, uid in enumerate(total_uids)}
    uid_index2id = {i: uid for uid, i in uid_id2index.items()}
    vid_id2index = {vid: i for i, vid in enumerate(total_vids)}
    vid_index2id = {i: vid for vid, i in vid_id2index.items()}
    print(f"uid_id2index: {uid_id2index}")
    print(f"vid_id2index: {vid_id2index}")

[tsafacjo]

can I take it ?

[itholic]

@tsafacjo could you open a ticket on Spark JIRA and made a fix for pyspark.pandas instead of Koalas? This repository is no longer maintained since Koalas has been migrated into Apache Spark.

[tsafacjo]

ok, thanks @itholic

[itholic]

np! please feel free to ping me if you want to any help for contributing Apache Spark.