Issues while trying to export pyspark pandas dataframe to csv in pyspark

Question

df=df_full[df_fill.part_col.isin(['part_a','part_b'])]
df=df[df.some_other_col =='some_value']
#df has shape of roughly 240k,200
#df_full has shape of roughly 30m, 200

df.to_pandas().reset_index().to_csv('testyyy.csv',index=False)

If I do any groupby operation it is amazingly fast. However the issue lies when I try to export small subset of this large dataset to csv. While I am eventually able to export the dataframe to csv but it is taking too much time.

Warnings:

2022-05-08 13:01:15,948 WARN window.WindowExec: No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation.

PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df[column_name] = series

Note: part_a and part_b are stored as two separate parquet partitioned files. Also I am using pyspark.pandas in spark3+

So question is what is happening? And what is most efficient wat to export the filtered dataframe to csv?