# Grouper For Not 1-dimensional Python

### 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)

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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.

(Source: rstudio.github.io)

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.

(Source: towardsdatascience.com)

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.

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.

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2 : Hello programmers, in today’s article, we will be discussing different ways to flatten a list in python.

(Source: stackoverrun.com)

We will be learning about 8 distinct ways to flatten multidimensional or nested lists into single-dimensional ones. The list is created by placing all its elements inside square brackets, separated by a comma.

There is a built-in function called deep flatten() in the iteration_utilities library, enabling you to implement this method to flatten the list. However, to implement this deep flatten() function in your program, you need to install the iteration_utilities package from pip as it is not available in python by default.

In this example, we call the function recursively inside itself to run till the end. Only if it’s true, it calls the function to flatten the list or else stores it as an ordinary number.

This function runs the while loop until all the elements are popped out of the nested list of variable depth. Once it gets into the loop, it checks the type of item popped out of the list.

This is a very simple way of flattening lists by using a function named reduce(). To use these built-in flattening functions of this package, you will have to install it from pip.

All three functions perform the flattening of lists and return the same output. Consequently, it takes a longer time when dealt with a larger set of values.

The best way to flatten a list depends on your program’s needs and the libraries you are using. Multidimensional arrays and nested lists having varying sizes and depths are also flattened.

Hjpotter92 8,50811 gold badge1919 silver badges4646 bronze badges Due to your insertions and deletions, it's rather O(MN), where n is the length of the entire list.

Your function offers to sort only a part of the list, but you don't test that. I'd use shorter ones, especially i and j for the main running indices.

Deleting (or popping) before inserting reduces that risk and thus increases the chance that you only take O(log n) extra space. Yours isn't, as in case of a tie, your merge prefers the right half's next value.

(Source: ajaytech.co)

\$\begin group\$ Python has a style guide to help developers write clean, maintainable and readable code. Avoid extraneous whitespace in the following situations: Use 4 spaces per indentation level.

Yet another PEP (PEP-484) for putting in type hints for your variables and function parameters. The variable names: length_sublist, lst1_start/end and similarly lst2_start/end are more readable (and sensible) as subsist_length, start1/end1, start2/end2.

This feels wrong, and should be handled by the test driver itself. Also, python provides an excellent testing module, unit test.

If you defined it outside the definition (perhaps with a leading underscore in the identifier to warn clients it was not meant to be used directly), you would get three advantages: The start and end identifiers wouldn't hide other identifiers of the same name, and risk confusing a reader about which they referred to.

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