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We had to buckle down and build real software to make the vision real and useful. Now that I've been working on the project for almost 2 years, we've made huge progress in realizing the things that we set out to accomplish. I strongly feel that Arrow is a key technology for the next generation of data science tools. I laid out my vision for this recently in my JupyterCon keynote. Also in late , I wrote a long set of design documents to start discussions about building a faster, cleaner core pandas implementation, which we may call pandas2. I wanted to see if the rest of the core developers agreed with my assessment of what is wrong with pandas's internals.

It's been 2 years since then, and by and large there has been general agreement on the problems, but how to solve them all without disrupting the existing pandas user community is an open question. Over this time I have focused on building computational infrastructure that will largely go unseen by pandas users. I'll write more about the roadmap for building an analytics engine for Arrow memory that we can use in projects like pandas in a follow up post. In the rest of this post, I'm going to go deeper into the "10 Things" and how they're addressed by the Arrow project. All memory in Arrow on a per column basis, whether strings, numbers, or nested types, is arranged in contiguous memory buffers optimized for random access single values and scan multiple values next to each other performance.

The idea is that you want to minimize CPU or GPU cache misses when looping over the data in a table column, even with strings or other non-numeric types. In pandas, an array of strings is an array of PyObject pointers, and the actual string data lives inside PyBytes or PyUnicode structs that live all over the process heap. As developers, we are hamstrung by the bloated, memory-bound nature of processing these objects. In Python, the simple string 'wes' occupies 52 bytes of memory. For a great discussion of issues around this, see Jake Vanderplas's epic exposé on Why Python is Slow.

In Arrow, each string is right next to the previous one in memory, so you can scan all of the data in a column of strings without any cache misses. Processing contiguous bytes right against the metal, guaranteed. Perhaps the single biggest memory management problem with pandas is the requirement that data must be loaded completely into RAM to be processed. Arrow serialization design provides a "data header" which describes the exact locations and sizes of all the memory buffers for all the columns in a table. This means you can memory map huge, bigger-than-RAM datasets and evaluate pandas-style algorithms on them in-place without loading them into memory like you have to with pandas now.

You could read 1 megabyte from the middle of a 1 terabyte table, and you only pay the cost of performing those random reads totalling 1 megabyte. With modern solid state drives, this is generally a good strategy. Arrow's memory-mapping capability also allows multiple processes to work with the same large dataset without moving it or copying it in any way. Arrow's efficient memory layout and rich type metadata make it an ideal container for inbound data from databases and columnar storage formats like Apache Parquet.

One of Arrow's primitive constructs is the concept of a "record batch stream" , a sequence of atomic tables together comprising a large dataset. This stream processing data model is an idea for databases which serve streams of records from a database cursor. We have been developing a high-speed connector with Parquet format. We've also seen the optimized turbodbc project for ODBC-based database connections.

All missing data in Arrow is represented as a packed bit array, separate from the rest of the data. This makes missing data handling simple and consistent across all data types. You can also do analytics on the null bits AND-ing bitmaps, or counting set bits using fast bit-wise built-in hardware operators and SIMD. The null count in an array is also explicitly stored in its metadata, so if data does not have nulls, we can choose faster code paths that skip null checking. With pandas, we cannot assume that arrays do not have null sentinel values and so most analytics has extra null checking which hurts performance. If you have no nulls, you don't even need to allocate the bit array. Because missing data is not natively supported in NumPy, over time we have had to implement our own null-friendly versions of most key performance-critical algorithms.

It would be better to have null-handling built into all algorithms and memory management from the ground up. In pandas, all memory is owned either by NumPy or the Python interpreter, and it can be difficult to measure exactly how much memory is used by a given pandas. It's not unusual for a line of code to double or triple the memory footprint of a process due to temporary allocations, sometimes causing a MemoryError. By using "subpools" with parent-child relationships, you can precisely measure the "high water mark" in algorithms to understand the peak memory usage of analytical operations. This technique is common in databases to monitor or limit memory usage in operator evaluation.

If you know that you are going to exceed available RAM, you can apply mitigation strategies like spilling to disk where the ability to memory-map on-disk datasets is of course key. In Arrow memory is either immutable or copy-on-write. At any given time, you know if another array references a buffer that you can see. This enables us to avoid defensive copying. When I gave my talk in , pandas did not have the pandas. Categorical type; that was implemented afterwards. But pandas's workarounds for data types not in NumPy has always been a bit warty.

If you step outside pandas, you can't work with pandas Categoricals. The way that extension dtypes are implemented works, but is a bit bolted-on due to pandas's tight coupling to NumPy. In Arrow, categorical data is a first-class citizen, and we have prioritized having an efficient and consistent representation both in-memory and on the wire or in shared memory.

We support sharing categories called dictionaries in Arrow between multiple arrays. We intend to be able to support logical data types having a particular physical memory representation in Arrow gracefully so that a particular system can faithfully transport its data using Arrow without having to make changes to the Arrow format documents. The way that Arrow helps is by enabling easier parallelization of groupby operations; due to other problems listed here, it is difficult or impossible to fully parallelize a df. In pandas, all of the data in a column in a DataFrame must reside in the same NumPy array. This is a restrictive requirement, and frequently results in memory-doubling and additional computation to concatenate Series and DataFrame objects.

By designing up front for streaming, chunked tables, appending to existing in-memory tabler is computationally inexpensive relative to pandas now. Designing for chunked or streaming data is also essential for implementing out-of-core algorithms, so we are also laying the foundation for processing larger-than-memory datasets. For example, a "currency" type could have a currently type a string, with the data physically represented as a float64 or decimal. So you could treat the currency computationally like its numeric representation, but then carry through the currency metadata in numeric operations. In Arrow we have decoupled the metadata representation from the details of computation and metadata nannying.

When you write df[df. If df contains a lot of columns, this is ridiculously wasteful. Of course you can write df.

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