Grouper Is Not 1-dimensional

Brent Mccoy
• Tuesday, 24 November, 2020
• 7 min read

Groupby() doesn't need to care about of or 'fruit' or 'color' or Nemo, group by() only cares about one thing, a lookup table that tells it which of.index is mapped to which label (i.e. In this case, for example, the dictionary passed to the group by() is instructing the group by() to: if you see index 11, then it is a “mine”, put the row with that index in the group named “mine”.

(Source: python-scripts.com)


I've tried to search the internet and Stack Overflow for this error, but got no results. Just like a lot of cryptic pandas errors, this one too stems from having two columns with the same name.

Jorisvandenbossche added a commit to jorisvandenbossche/pandas that referenced this issue Nov 2, 2016 This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object.

Convention {‘start’, ‘end’, ‘e’, ‘s’} If grouper is PeriodIndex and freq parameter is passed. Base int, default 0 Only when freq parameter is passed.

For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. Loffset STR, Dateset, time delta object Only when freq parameter is passed.

Dropna built, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

grouper encapsulation versus combination mesh clicking inside double its go
(Source: archimatix.com)

Every once in a while it is useful to take a step back and look at pandas’ functions and see if there is a new or better way to do things. I was recently working on a problem and noticed that pandas had a Grouper function that I had never used before.

I looked into how it can be used and it turns out it is useful for the type of summary analysis I tend to do on a frequent basis. In addition to functions that have been around a while, pandas continues to provide new and improved capabilities with every release.

The updated AGG function is another very useful and intuitive tool for summarizing data. This article will walk through how and why you may want to use the Grouper and AGG functions on your own data.

Pandas’ origins are in the financial industry so it should not be a surprise that it has robust capabilities to manipulate and summarize time series data. Just look at the extensive time series documentation to get a feel for all the options.

These strings are used to represent various common time frequencies like days vs. weeks vs. years. For example, if you were interested in summarizing all the sales by month, you could use the resample function.

grouper encapsulation versus combination mesh
(Source: archimatix.com)

Instead of having to play around with reindexing, we can use our normal group by syntax but provide a little more info on how to group the data in the date column: Since group by is one of my standard functions, this approach seems simpler to me and it is more likely to stick in my brain.

The nice benefit of this capability is that if you are interested in looking at data summarized in a different time frame, just change the freq parameter to one of the valid offset aliases. If your annual sales were on a non-calendar basis, then the data can be easily changed by modifying the freq parameter.

When dealing with summarizing time series data, this is incredibly handy. It is certainly possible (using pivot tables and custom grouping) but I do not think it is nearly as intuitive as the pandas approach.

In pandas 0.20.1, there was a new AGG function added that makes it a lot simpler to summarize data in a manner similar to the group by API. Fortunately we can pass a dictionary to AGG and specify what operations to apply to each column.

I find this approach really handy when I want to summarize several columns of data. In the past, I would run the individual calculations and build up the resulting data frame a row at a time.

(Source: www.slideshare.net)

The aggregate function using a dictionary is useful but one challenge is that it does not preserve order. Dense fell OnStar, self on datarammen Ike heir mere end en dimension.

Leg heir first at sage PA interested OG Stack Overflow after dense fell, men fit Inge resulted. Lies mange kryptiske pandafejl stammer GSA dense Frey at have to longer med same Navy.

Series DataFrame Any function in we{1,p}$, up>No, has a continuous representative by the Mobile embedding theorem so there is no issue here.

Thus, the answer to the question the way it is stated is no, the function can be essentially discontinuous everywhere. There is however, a different point of view which shows that, in fact, a Mobile function behaves nicely when restricted to an $(N- 1)$- dimensional manifold and I will present two different approaches to it.

According to Theorem 2 p. 164 in (I am referring to the first edition) any function of\in We{1,p}$ has a representative that is absolutely continuous on almost all lines. Here by a representative I mean a Bore function defined everywhere and equal to of almost everywhere.

dimensional grouper enameling glazed brooch fish
(Source: www.rubylane.com)

Any Function in we{1,p}$, up>No, has a continuous representative by the Mobile embedding theorem so there is no issue here. If of\in We{1, N}$, then of\in We{1,p}_{\rm LOC}$ for ant $1\led p$0, there is an open set UV\subset\Omega with $\operator name{Cap}_p(V)<\epsilon such that of|_{\Omega\set minus V}$ is continuous.

While I will not recall its definition I will explain how it is related to the Hausdorff measure. Therefore, is we consider an $(N- 1)$- dimensional manifold me in $\Omega this exceptional set has measure zero.

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