pyspark dataframe coverting to koalas dataframe have different elements about koalas HOT 5 OPEN

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}")