Hello,
Considering your amazing efficiency on pandas, numpy, and more, it would seem to make sense for your module to work with even bigger data, such as Audio (for example .mp3 and .wav). This is something that would help a lot considering the nature audio (ie. where one of the lowest and most common sampling rates is still 44,100 samples/sec). For a use case, I would consider vaex.open('Hu

1. "Conclusion" section of "Getting started with Tablesaw" page contains broken link to "Java Docs".
   https://jtablesaw.github.io/tablesaw/gettingstarted#conclusion
2. "Exploring tables" section of "Getting started with Tablesaw" page contains broken link to "plotting".
   https://jtablesaw.github.io/tablesaw/gettingstarted.html#exploring-tables

Support Series.median()

Excel is a popular format for storing data, so we intend to support it, we need to be able to read .xls files in danfo data structures. This should be in the reader.py module and can look something like:

// /**
//  * Reads a Excel file from local or remote address
//  * 
//  * @param {source} URL 

Update the TPCH example to support query 6:

select
	sum(l_extendedprice * l_discount) as revenue
from
	lineitem
where
	l_shipdate >= date ':1'
	and l_shipdate < date ':1' + interval '1' year
	and l_discount between :2 - 0.01 and :2 + 0.01
	and l_quantity < :3;

Brief Description of Fix

Currently, the docstrings for some functions are lacking a return description and (where applicable) a raises description.

I would like to propo

Hi, would it be possible to make the user warnings display only when using pipes that actually depend on these imports? Or at least display them in a way that allows filtering out (with logging package perhaps)?

It's just a minor flaw on otherwise great package. Awesome work!

hi thanks a lot of the great project, been helping me a lot. there is only one thing that has been bothering me here:

• for indicators like rsi(length = 14), seems the program still carry out the calculation even when total non NA datapoints are less than 14, so the result would be misleading. Is there anyway the program can return NaN when there is less than 14 valid datapoints passed.

Th