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()

A Tensorflow based function to calculate Corr for series and columns, similar to Pandas Corr function.

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;

Problem description

When I use the function of concatenating multiple columns, I find that it does not handle null values as expected.

This is the current output

df.concatenate_columns(["cat_1","cat_2","cat_3"],"cat",sep=",")

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!

Need implementation of Variable Index Dynamic Average (VIDYA). I have tried to calculate based on this indicator on tradingview Variable Index Dynamic Average (VIDYA) but it results in wrong calculation

    p = 10
    alpha = 2 / (p + 1)
    df['cmo'] = ta.cmo(df.close, length=p, talib=False).abs()