Pandas Python Grouper Not 1-dimensional

Maria Johnson
• Sunday, 26 September, 2021
• 12 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. 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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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.

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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.

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Amolkahat added a commit to amolkahat/ pandas that referenced this issue Nov 26, 2016 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.

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.

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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.

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.

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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.

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.

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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.

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. The reason that a DataFrameGroupBy object can be difficult to wrap your head around is that it’s lazy in nature.

It can be difficult to inspect of.group by(“state”) because it does virtually none of these things until you do something with the resulting object. It delays virtually every part of the split-apply-combine process until you invoke a method on it.

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So, how can you mentally separate the split, apply, and combine stages if you can’t see any of them happening in isolation? One useful way to inspect a Pandas Group object and see the splitting in action is to iterate over it.

If you’re working on a challenging aggregation problem, then iterating over the Pandas Group object can be a great way to visualize the split part of split-apply-combine. Each value is a sequence of the index locations for the rows belonging to that particular group.

In the output above, 4, 19, and 21 are the first indices in of at which the state equals “PA.” It’s also worth mentioning that.group by() does do some, but not all, of the splitting work by building a Grouping class instance for each key that you pass.

However, many of the methods of the BaseGrouper class that holds these groupings are called lazily rather than at __unit__(), and many also use a cached property design. You can think of this step of the process as applying the same operation (or callable) to every “sub-table” that is produced by the splitting stage.

It simply takes the results of all the applied operations on all the sub-tables and combines them back together in an intuitive way. The dataset contains members’ first and last names, birthdate, gender, type (“rep” for House of Representatives or “sen” for Senate), U.S. state, and political party.

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You can see that most columns of the dataset have the type category, which reduces the memory load on your machine. Now that you’re familiar with the dataset, you’ll start with a “Hello, World!” for the Pandas Group operation.

What is the count of Congressional members, on a state-by-state basis, over the entire history of the dataset? You call.group by() and pass the name of the column you want to group on, which is “state”.

As you’ll see next,.group by() and the comparable SQL statements are close cousins, but they’re often not functionally identical. As we developed this tutorial, we encountered a small but tricky bug in the Pandas source that doesn’t handle the observed parameter well with certain types of data.

This produces a Database with three columns and a Rangefinder, rather than a Series with a Multitude. In short, using as_index=False will make your result more closely mimic the default SQL output for a similar operation.

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