Grouper For Not 1-dimensional Pivot

Paul Gonzalez
• Tuesday, 08 December, 2020
• 8 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)


Posted in Uncategorized Tagged group by, pandas, python Making statements based on opinion; back them up with references or personal experience.

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.

pandas pivot tutorial
(Source: www.youtube.com)

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.

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.

pivot science data grouper pandas series
(Source: www.youtube.com)

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.

I have the head of a data frame like this and I want to make a pivot _table. I want to make a pivot _table like this which I do it with Excel. The values in the table should be the value_count for every user_id with every cate_id.

However, for one parameter I get an error: “ValueError: Grouper for '' not1-dimensional for the line code: 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.

(Source: manuals.deere.com)

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.

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2 : Photo by Markus Winkle on Unsplash Exploratory data analysis is an important phase of machine learning projects.

This article will help you achieve more by optimal usage of the default parameters. Pivot _table.html Let’s look into the basic level usage of this method by applying it on a dataset.

The index is the column, grouper, array, or list we’d like to group our data by. By default, it will average all the numerical columns' data when the value and aground parameters are not specified.

pivot kim software
(Source: kim.bio.upenn.edu)

The function returns a pivot table with Customer_Segment as index and averages the numerical columns. We can see that the pivot table is smart enough to start aggregating the data and summarizing it by grouping based on Customer_Segment.

This increases the level of granularity in the resultant table, and we can get more specific with our findings: It is an optional field and if we don’t specify this value, then the function will aggregate on all the numerical features of the dataset.

Since the value parameter was not specified, pivot _table, by default considered all numerical columns. The pivot table aggregates data of both the features Order_Quantity and Sales and groups it with Customer_Segment.

As mentioned before, pivot _table uses mean function (bumpy.mean) for aggregating or summarizing data by default. Aggfunc is an aggregate function that pivot _table applies to our grouped data.

For example, if we are interested in both the sum and count of Sales, we can specify the functions as a list to the argument aground. Just provide a dictionary mapping as an input to the aground parameter with the feature name as the key and the corresponding aggregate function as the value.

(Source: manuals.deere.com)

Also, notice that I’ve omitted the values' keyword; when specifying a mapping for aground, this is determined automatically. When we give multiple aggregating functions, we would get a multi-indexed data frame as output.

The column parameter displays the values horizontally on the top of the resultant table. Bear in mind, columns are optional, they provide a supplementary way to segment the actual values we care about.

Here we are comparing the total profit across product categories and customer segments. Both columns and the index parameters are optional, but using them effectively will help us to intuitively understand the relationship between the features.

Margins is type boolean that adds all rows and columns (e.g. for subtotal / grand totals) and defaults to ‘ False’. Margins_name is of type string and accepts the name of the row/column that will contain the totals when margins equal True.

Dropna is type boolean, and allows us to drop the null values in the grouped table whose entries are all Nan. We don’t have any columns where all entries are Nan, but it’s worth knowing that if we did pivot _table would drop them by default according to drop definition.

pivot indicator gann levels square technique chart dynamic calculation based gbpusd h4 period
(Source: www.tradingsystemforex.com)

Thus, we can see that pivot _table() is a handy tool, and we can do some interesting analysis with this single line of code. As you build up the pivot table, I think it’s easiest to take it one step at a time.

Add items and check each step to verify if you are getting the results you expect and see what presentation makes the most sense for your needs. As soon as you start playing with the data and slowly add the items, you can get a feel for how it works.

We’ve covered the powerful parameters of pivot _table, so you can get a lot out of it if you go experiment using these methods on your project.

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