**The Application For A Row Is** – Applying a function to every row in a Pandas DataFrame is one of the most common operations during data wrangling. The Pandas DataFrame apply (df.apply) function is the most obvious choice for this. Takes a function as an argument and applies it along the DataFrame ‘s axes. However, this is not always the best choice.

This article measures the performance of 12 alternatives. The Code Lab companion lets you try it all in your browser. No need to attach anything to your car.

## The Application For A Row Is

We recently analyzed user behavior data for an e-commerce app. Assign each user to one of her four groups based on the number of times the user has performed text and voice searches.

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This is a huge dataset ranging from his 100,000 to his 1,000,000 users depending on the time period you choose. Calculations using Panda’s deployment functions are very slow, so we are looking into alternatives. This article is a lesson learned from that.

This dataset cannot be shared. So I chose another similar problem, the Eisenhower method, to demonstrate the solution.

In the Eisenhower method, he assigns tasks to one of four bins based on their importance and urgency. Each container has an associated action.

Use the logic matrix shown in the adjacent figure. Boolean values of importance and urgency generate binary integer values for each action: DO(3), SCHEDULE(2), DELEGATE(1), DELETE(0).

#### Solved ! Required Information A 02 Series Single Row

Create a performance profile that maps a task to one of your activities. Measure which of the 12 choices takes the least amount of time. It then outlines the execution of up to 1 million tasks.

Now is a good time to open a companion notebook on Google Colab or Kaggle. If you want to see the code in action, you can run the code lab cells as you read. Go through all the cells in the Settings section.

Faker is a convenient library for generating data. In Code Lab it is used to create a DataFrame with 1 million tasks. Each assignment is a row in the DataFrame. It consists of task_name (str), due_date (datetime.date), and priority (str). Priority can be one of three values: LOW, MEDIUM, HIGH.

Minimize storage size to eliminate impact on alternatives. A DataFrame with about 2 million rows occupies 48 MB.

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For this exercise, we consider high priority tasks to be important. If the due date is within two days, the task is urgent.

The Eisenhower actions for tasks (that is, rows in the DataFrame) are calculated using the Due_date and priority fields.

The rest of this article evaluates 12 alternatives for applying the eisenhower_action function to row dataframes. First, measure the sampling time for 100,000 rows. Then measure and plot the time for 1 million rows.

The easiest way to process each row is with a good old Python loop. This is the worst possible method and no sane person would do it.

## Solved What Is Wrong With The Following

Set a worst-case performance cap. Since the cost is linear, i.e. O(n), this is a good baseline for comparing alternatives.

Let’s find out what is taking so long in line_profiler. But for samples with less than 100 rows.

Retrieving the row (row #6) from the DataFrame takes 90% of the time. This is understandable as Pandas DataFrame storage is column based. Consecutive elements in a column are stored sequentially in memory. Therefore, concatenating elements into one row is expensive.

Even ignoring 56.6 seconds for 100k rows at 90% cost, it still takes 5.66 seconds. There are still many.

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Data storage layout by rows and columns. Pandas Dataframe uses columnar-based storage, so row fetching is an expensive operation.

The Pandas DataFrame apply (df.apply) function is very versatile and popular. To make it work with strings, you need to pass the axis=1 argument.

A DataFrame’s columns are series that can be used as a list in a list comprehension.

Panda’s real strength lies in vectorization. But that requires parsing the function as a vector expression.

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Depending on the complexity of the function, vectorization may require considerable effort. Sometimes it’s not even worth it.

NumPy provides an alternative to move from Python to Numpy using vectorization. For example, there is the vectorize() function that vectorizes a scalar function to take and return a NumPy array.

So far only Pandas and NumPy packages have been used. However, if you need additional package dependencies, there is yet another way.

Numba is often used to speed up the execution of math functions. Contains various decorators for JIT compilation and vectorization.

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Its vectorizing decorator is similar to NumPy’s vectorizing functions, but offers better performance (18.9 ms) (similar to Panda vectorization). However, it also shows a warning about caching.

It took 2.27 seconds on a 2 CPU machine. The overhead of partitioning and bookkeeping seems to pay off at 100,000 records and 2 CPUs.

Dask is a parallel computing library that supports enhancements to NumPy, Pandas, Scikit-learn, and many other Python libraries. It provides an efficient infrastructure for processing large amounts of data on multi-node clusters.

It took 2.13 seconds on a 2 CPU machine. As with parallelism, rewards only matter when processing large amounts of data on multiple machines.

## Selecting A Range With A Variable Row (or Column) Number

Faster automatically decides whether it is faster to use Dask’s parallelism or a simple Panda implementation. It’s very easy to use. A short description of how to use the Pandas Apply function is df.swifter.apply.

Plotting the graph helps understand the relative performance of the alternatives compared to the input rate. Perfplot is a useful tool for this. This requires some setup to create a list of implementations to compare against inputs of a certain size.

Performance comparison of Pandas DataFrame when implementing alternatives. Left: Time required to apply the function to 100,000 rows of Pandas DataFrame. Right: Logarithmic scale plot of up to 1 million rows in a Pandas DataFrame.

Understanding the costs of different options is essential to making the right choice. Measure the performance of this alternative using timeit, line_profiler, and perfplot. Choose the best option for your use case, balancing performance and ease of use.

## Apache Ranger Row Level Filtering & Column Masking

A simpler and faster alternative to Pandas DataFrame’s apply() function for iterating over 5 rows. No bulky libraries needed.

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