Pandas Grouper Is Not Defined

Danielle Fletcher
• Wednesday, 22 September, 2021
• 9 min read

After some investigation with the aid of Jezebel it looks like the issue is in the plot method. I don't know if I did something wrong or if there is an error in matplotlib but this is the position I find myself stuck on.

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(Source: greenfuture.io)


The previous line ax shows counts and times as expected 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.

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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. For instance, I frequently find myself needing to aggregate data and use a mode function that works on text.

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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.

SeriesGroupBy.transform (fun, *arms, engine=None, engine_quarks=None, **quarks) Call function producing a like-indexed Series on each group and return a Series having the same indexes as the original object filled with the transformed values Each group’s index will be passed to the user defined function and optionally available for use.

Engine STR, default None 'Python' : Runs the function through C-extensions from Python. 'Numba' : Runs the function through JIT compiled code from numb.

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(Source: www.dreamstime.com)

ReturnsSeries Series.group by.apply to Apply function fun group-wise and combine the results together. Series.transform Transforms the Series on each group based on the given function.

For example, if f returns a scalar it will be broadcast to have the same shape as the input subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk.

The group data and group index will be passed as bumpy arrays to the Cited user defined function, and no alternative execution attempts will be tried. In some cases, this level of analysis may be sufficient to answer business questions.

In other instances, this activity might be the first step in a more complex data science analysis. However, they might be surprised at how useful complex aggregation functions can be for supporting sophisticated analysis.

In the context of this article, an aggregation function is one which takes multiple individual values and returns a summary. The most common aggregation functions are a simple average or summation of values.

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Here’s a quick example of calculating the total and average fare using the Titanic dataset (loaded from seaborne): One area that needs to be discussed is that there are multiple ways to call an aggregation function.

As shown above, you may pass a list of functions to apply to one or more columns of data. The tuple approach is limited by only being able to apply one aggregation at a time to a specific column.

The most common built-in aggregation functions are basic math functions including sum, mean, median, minimum, maximum, standard deviation, variance, mean absolute deviation and product. As an aside, I have not found a good usage for the prod function which computes the product of all the values in a group.

After basic math, counting is the next most common aggregation I perform on grouped data. The major distinction to keep in mind is that count will not include Nan values whereas size will.

In addition, the unique function will exclude Nan values in the unique counts. Keep reading for an example of how to include Nan in the unique value counts.

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(Source: www.kickassfacts.com)

In this example, we can select the highest and lowest fare by embarked town. One important point to remember is that you must sort the data first if you want first and last to pick the max and min values.

In the example above, I would recommend using max and min, but I am including first and last for the sake of completeness. The city.stats mode function returns the most frequent value as well as the count of occurrences.

This summary of the class and deck shows how this approach can be useful for some data sets. This is an area of programmer preference but I encourage you to be familiar with the options since you will encounter most of these in online solutions.

Like many other areas of programming, this is an element of style and preference but I encourage you to pick one or two approaches and stick with them for consistency. As shown above, there are multiple approaches to developing custom aggregation functions.

Using apply with group gives maximum flexibility over all aspects of the results. For the first example, we can figure out what percentage of the total fares sold can be attributed to each embark_town and class combination.

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(Source: www.upi.com)

One important thing to keep in mind is that you can actually do this more simply using a pd.cross tab as described in my previous article : While we are talking about cross tab, a useful concept to keep in mind is that AGG functions can be combined with pivot tables too.

To understand this, you need to look at the quarter boundary (end of March through start of April) to get a good sense of what is going on. If you want to just get a cumulative quarterly total, you can chain multiple group by functions.

In this example, I included the named aggregation approach to rename the variable to clarify that it is now daily sales. By default, pandas creates a hierarchical column index on the summary Database.

At some point in the analysis process you will likely want to “flatten” the columns so that there is a single row of names. Just keep in mind that it will be easier for your subsequent analysis if the resulting column names do not have spaces.

Refer to the package documentation for more examples of how side table can summarize your data. There is a lot of detail here but that is due to how many uses there are for grouping and aggregating data with pandas.

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(Source: www.change.org)

My hope is that this post becomes a useful resource that you can bookmark and come back to when you get stuck with a challenging problem of your own.

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