Pandas Grouper For Not 1-dimensional

James Smith
• Friday, 17 September, 2021
• 10 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”.

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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.

Figure out which one you want to use, rename or drop the other column and redo the operation. 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.

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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.

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.

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In the past, I would run the individual calculations and build up the resulting data frame a row at a time. For instance, I frequently find myself needing to aggregate data and use a mode function that works on text.

Locdf.columns.duplicated() 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.

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Pivot() will error with a ValueError:Indexcontainsduplicateentries, cannotreshape if the index/column pair is not unique. In this case, consider using pivot_table() which is a generalization of pivot that can handle duplicate values for one index/column pair.

These methods are designed to work together with Multitude objects (see the section on hierarchical indexing). Stack : “pivot” a level of the (possibly hierarchical) column labels, returning a Database with an index with a new inner-most level of row labels.

These functions are intelligent about handling missing data and do not expect each subgroup within the hierarchical index to have the same set of labels. They also can handle the index being unsorted (but you can make it sorted by calling sort_index, of course).

Unsticking can result in missing values if subgroups do not have the same set of labels. By default, missing values will be replaced with the default fill value for that data type, Nan for float, Nat for datetimelike, etc.

For integer types, by default data will converted to float and missing values will be set to Nan. Alternatively, unstuck takes an optional fill_value argument, for specifying the value of missing data.

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Unsticking when the columns are a Multitude is also careful about doing the right thing: The top-level melt() function and the corresponding Database.melt() are useful to massage a Database into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are “pivoted” to the row axis, leaving just two non-identifier columns, “variable” and “value”.

The original index values can be kept around by setting the ignore_index parameter to False (default is True). Another way to transform is to use the wide_to_long() panel data convenience function.

It should be no shock that combining pivot / stack / unstuck with Group and the basic Series and Database statistical functions can produce some very expressive and fast data manipulations. ), pandas also provides pivot_table() for pivoting with aggregation of numeric data.

Index : a column, Grouper, array which has the same length as data, or list of them. If an array is passed, it is being used as the same manner as column values.

Note that pivot_table is also available as an instance method on Database, If you pass margins=True to pivot_table, special All columns and rows will be added with partial group aggregates across the categories on the rows and columns: By default, cross tab computes a frequency table of the factors unless an array of values and an aggregation function are passed.

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Aggfunc : function, optional, If no values array is passed, computes a frequency table. Rownames : sequence, default None, must match number of row arrays passed.

Normalize : boolean, {‘all’, ‘index’, ‘columns’}, or {0,1}, default False. If cross tab receives only two Series, it will provide a frequency table.

Notice that the B column is still included in the output, it just hasn’t been encoded. You can drop B before calling get_dummies if you don’t want to include it in the output.

The following bumpy.unique will fail under Python 3 with a Terror because of an ordering bug. In this section, we will review frequently asked questions and examples.

Another aggregation we can do is calculate the frequency in which the columns and rows occur together a.k.a. For example, to perform both a sum and mean, we can pass in a list to the aground argument.

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Series.explode() will replace empty lists with NP.Nan and preserve scalar entries. You have comma separated strings in a column and want to expand this.

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03: Juvenile Gag Grouper
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05: Juvenile Grouper Identification
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07: Asc Grouper Listing
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1 www.fisheries.noaa.gov - https://www.fisheries.noaa.gov/species/black-grouper
2 fishchoice.com - https://fishchoice.com/buying-guide/black-grouper
3 en.wikipedia.org - https://en.wikipedia.org/wiki/Black_grouper
4 www.fishanywhere.com - https://www.fishanywhere.com/blog/grouper-season-2020-in-florida/