Implement the first batch of Serialization / IO / Conversion functions

See https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#serialization-io-conversion

Looks like we can easily implement almost all of them by calling toPandas().func_name().

One thing is that some of the functions support max_rows. When that argument is specified, we should add a limit call in Spark to avoid moving all the data to the driver.

The list to add in the first batch are:

• to_dict (see #169)
• to_excel (#288)
• to_html (we already have this, but let’s add a limit when max_rows is set), done in #206
• to_latex (#297)
• to_records (#298)
• to_string (done in #211 and #213)
• to_clipboard (#257)

Skipping the following because I don’t know how popular they are:

• to_pickle
• to_hdf
• to_stata
• to_msgpack
• to_records
• to_sparse
• to_dense

The following might require parallelization with Pandas UDFs, rather than collecting all the data to the driver, so leaving them for the future:

• to_sql
• to_gbq

I’m also not adding json and csv here. We need to design those properly because both Spark and Pandas have those.

[HyukjinKwon]

Thanks, @shril. This issue is nicely finished.

[rxin]

Thank you all!