pandas: The two cultures

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Leo Breiman was a distinguished statistician at UC Berkeley, known among other things for his major contributions to CART (decision trees), and ensemble techniques, mainly bootstrap aggregation. Combining both, he was able to define one of the most popular machine learning models even today (18 years after the publication of the paper), Random forests.

In 2001, Breiman published the paper Statistical Modeling: The Two Cultures. In it, Breiman identified that there were two somehow conflicting cultures in the discipline of statistical modeling. One that was focusing on modeling (and trying to understand) the stochastic process generating some random data. While the other followed an algorithmic approach focused on obtaining results (minimizing the error between the model results and the data), and considered the stochastic process a black box. Today we would probably call them statistics and machine learning, and the division between them is clear. And in a way, machine learning is not even considered part of statistics. While this division among the two fields may or may not be a good thing, identifying in 2001 that both communities existed, were different and had different needs, surely helped overcome the frustration of both communities at that time, and sped up their development. One example that illustrate the differences can be seen on how in the area of neural networks, publishing research papers is mostly driven by the obtained results, more than by the theory behind the results. Ali Rahimi gave his view on this when receiving the test-of-time award at NeurIPS 2017.

But this post is not about machine learning, but about pandas. And about the two cultures in the pandas community, that I personally don't think are often well identified, causing frustration to some users, and making more complex taking decisions regarding the API of the project.

Dr Jekyll and Mr Hyde

To describe the two cultures, let me talk about my own professional experience. For the last years I've been mainly working as a data scientist. Since the developers of scikit-learn are doing all the fun work in machine learning, and implementing all the complex algorithms for the rest of us, I'll argue that my job (and the job of many other data scientists, some will probably disagree) is to work on data analysis to find out what needs to be done, and data engineering to make it work in production.

What I call data analysis is performed in a Jupyter notebook, where I analyze and visualize the data. I found out what is wrong with it, and I quickly grow the cells of my Untitled23.ipynb hoping I'll never have to look back at my dirty code. What I value the most is being able to write code fast, and focus in the problem I'm solving, and not in the code. To the extend I alias every Python library I import with incomprehensible names like np, pd, plt,... to make sure I save few microseconds compared to typing the actual names. And I really appreciate the software making as many decisions as needed to save me from having to spend the time on being explicit on what I want. Ok, this may be a bit exaggerated, I don't really let my notebook names be untitled whatever, or use aliases, but I think you get the idea.

On the other hand, when working in data engineering I use vim, and I write all my code in Python files in a clear directory structure. Every file and directory are carefully named so I can easily find them later. Every function is well documented, and the best coding standards are applied. All my code is version controlled with git, and code reviewed by my colleagues. I write every single line of code knowing that I will have to revisit it many times, and I optimize for its simplicity and its clarity. The thing I'm more adverse to is magic happening, and any software making decisions for me. I want to be in control, I want everything in my code to be deterministic, and I want everything in my code to be explicit. Everything that Tim Peters wrote in PEP-20, the Zen of Python, applies:

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Readability counts.
  • Errors should never pass silently.
  • ...

One pandas to rule them all

What I find the most interesting part about the two cultures I just described, is that I use pandas for both. I think pandas is the best tool for both use cases, and I won't admit I'm biased here, since I'm a pandas maintainer because I use the software, and not the other way round.

But how is that possible? Both use cases are radically different. Is pandas designed in a way that is able offer both kind of users the API and features they need? Is that always possible?

The next of this post will try to find an answer by analyzing some examples.

Show me the code

Creating data from a Python dict

Let's start with a single example, by manually creating some data:

>>> import pandas

>>> num_legs = pandas.Series({'unicorn': 4, 'spider': 8, 'penguin': 2})
>>> num_legs
unicorn    4
spider     8
penguin    2
dtype: int64

I think we can agree that pandas is letting us create our data in the simplest possible way. There could be other ways (and there are other ways that pandas supports), but creating a Series looks to me as simple as it can be. That's what I want as a data analyst.

But as a data engineer, there are more things to consider. Imagine that my data, instead of having 3 samples, had 3 million. How much memory is pandas consuming to store in memory my data? And why? For simplicity, let's consider only the values (and not the name of the animals):

>>> num_legs.memory_usage(index=False)

The values in our Series are consuming 24 bytes. If we see again the representation of our Series, we can see how the data type (aka dtype) is int64. Meaning that every value will consume 64 bits (8 bytes). 8 bytes per value, multiplied by 3 values (the number of legs for unicorn, spider and penguin) totals 24 bytes. But why 64 bits? pandas decided for us that representation, which can store numbers from around -9e18 to 9e18. But do we really expect animals to have a number of legs with 18 digits? Or do we expect negative numbers of legs at all? Probably not. We know it, but pandas doesn't. Because pandas doesn't know anything about our domain, or what is reasonable, it's deciding for us a conservative representation for our data that won't cause us problems (as opposed as one that saves some memory).

This is working well for us as data analysts, but not as data engineers writing production code. In this case, the Series constructor has a parameter dtype that we can use to tell pandas to not decide for us how to internally represent the data, but to tell it explicitly. This is the result:

>>> import pandas

>>> num_legs = pandas.Series({'unicorn': 4, 'spider': 8, 'penguin': 2},
...                          dtype='uint8')
>>> num_legs
unicorn    4
spider     8
penguin    2
dtype: uint8

>>> num_legs.memory_usage(index=False)

In this example, pandas provides a reasonable API for both kind of users. It doesn't force us to specify the data type when we don't care. But we're able to when we do care. Whether we want to optimize for our system resources (mainly memory) or our own time is up to us.

How many legs do unicorns have?

An important question we face is, how many legs do unicorns have? In the previous example, we specified they have 4, but do unicorns really have 4 legs? Did anybody have ever seen a unicorn? Let's try to be prudent and say that we don't know how many legs they have. By convention, when we have an unknown or missing value, we represent it as NaN (Not a Number). Every number in a computer is represented using binary numbers (e.g. 01001011). NaN is represented internally as one specific sequence of bits, reserved to have the meaning of NaN. There is a convention that translates how every binary sequence corresponds to the number they represent. And this translation has some exceptions, including one value that represents the floating point number NaN. If that sounds too complex, think that in binary, 0000 can represent the number 0, 0001 the 1, 0010: 2, 0011: 3... and 1111: 15. And what microprocessors manufacturers decided is something like letting represent only from 0 to 14 (instead of from 0 to 15, that we could encode with 4 bits), and reserve the 1111 to mean NaN. Things are in reality more complex, since NaN representations only exists for floating points numbers (aka float), which are decimals. But that explanation should give an intuition.

So, back to the example, if we want to represent that we don't know how many legs unicorns have, we can simply do:

>>> num_legs.loc['unicorn'] = float('NaN')
>>> num_legs
unicorn    NaN
spider     8.0
penguin    2.0
dtype: float64

Many things happened here. We can see, how besides the expected change of having NaN unicorn legs, now we are back to consuming 64 bits. And not only that, but also the rest of values in the column now are decimal (float) values. As I just explained, and can also be seen in the example on how NaN is created, NaN is a float value. Modern computers don't have an integer representation for NaN, so for pandas to do what we asked it to do, converting the column to float was the only option (not really the only, but let's pretend for a second).

It feels a bit weird to see in the Series representation that a penguin has 2.0 legs. It's conceptually wrong, and also misleading making us believe that animals can have a decimal number of legs. There are also technical implications too, we are consuming 4 times more memory now. And also operations among integers don't take the same time as operations among floats at the CPU level (note that while floats are a more complex representation, modern CPU's are highly optimized for them, and operations can even be faster for floats than for integers).

But there is something else, see this example:

>>> 0.1 + 0.2 == 0.3

Floating point numbers are approximations. They are mapping an infinite set of numbers (let's say all real numbers) to the finite set of possible representations with 64 bits (2 ** 64). In many cases using this approximate values won't make a difference (the height of a person keeps being the same if we change the 20th decimal). But, if for example a column contains an integer id that we use to join two data sets, converting it to floating point can mean data loss or bugs. Since floating points are just approximations, we may try to join by 20.0000000001 == 19.9999999999, which won't match. So, converting an integer column to its floating point representation can be dangerous, and probably more for the data engineering use cases described before.

In pandas 0.24 we introduced a new data type to mix integer values with missing values. This is done by instead of using the float NaN to represent the missing values, we internally keep a separate Boolean array that identifies where the missing values are. This adds an extra layer of complexity inside pandas, but avoids problems like the one just described. By default, pandas still uses the original types, but we can write the previous code as follows:

>>> num_legs = pandas.Series({'unicorn': 4, 'spider': 8, 'penguin': 2},
...                          dtype='UInt8')
>>> num_legs.loc['unicorn'] = float('NaN')
>>> num_legs
unicorn    NaN
spider       8
penguin      2
dtype: UInt8

Note that UInt8 represents the pandas type with the mask, and uint8 (lowercase) represents the original type based on numpy. Also note that the new type may not be as stable as the old, and may not implement all the operations.

While the new data type fixes this specific problem, the fact that pandas silently casts a data type when needed is very convenient for the use cases of data analysts, but in my opinion does a poor job to the interests of precision and reliability of data engineers. And while the .loc[] syntax is very convenient, doesn't allow us to solve the problem with a simple parameter. A new pandas option could be an option to control whether we want pandas to automatically cast columns when needed, or raise an exception instead. But as far as I know, there has not been discussion about it.

The most popular pandas function

CSV is in general a poor format to store data. It has a clear advantage, that is being able to open CSV files in a text editor. Other than that, I think all are disadvantages:

  • Inefficient storage (space that file uses in disk)
  • Inefficient I/O (because the volume of data, and also the required casting)
  • Lack of types (everything is a string in a CSV, so original types are lost)
  • Lack of a standard (different quoting, delimiters,...)

Despite of those, CSV happens to be one of the most popular formats out there, being the page pandas.read_csv the one with most visits in the pandas documentation.

To manage all the trickiness of the format, pandas.read_csv provides as much as 50 arguments, to customize for your file format, and for your needs. StaticFrame a project (somehow) aiming to compete with pandas, contains the next sentence in its README file:

The Pandas CSV reader far out-performs the NumPy-based reader in StaticFrame: thus, for now, using Frame.from_pandas(pd.read_csv(fp)) is recommended for loading CSV files.

This gives an idea of all the complexity in the CSV parser, not only in terms of the parameters, but also in terms of how optimized it is for performance.

Despite being one of the most powerful and optimized CSV parsers out there, James Powell gave a lightning talk at PyData London 2019 on how the parser could be easily improved in several ways for a use case he's got.

Those include:

  • Assume string columns are properly encoded and load them directly into memory
  • Optimize date casting by assuming a specific format, and a limited set of values

Again, no matter the great job done in implementing pandas, the software is being unable to fully satisfy all user cases. pandas.read_csv does again a good job at making life easy to data analysts (as defined at the beginning of this post). And it also does an impressive job at adding parameters to empower users that know what they are doing and have production-ready code need (data engineers). But even with an insane number of parameters like 50, looks like loading a CSV file into memory may be too complex for a single generic function.

What is the solution here? Personally, I think that having one pandas to rule them all is still possible and the best option. But not a pandas.read_csv to rule them all. My view is that pandas shouldn't include I/O modules that are able to load data from every possible format, and in every possible way. That's just impossible. But pandas could do a better job at allowing and encouraging an ecosystem of I/O pandas plugins. I proposed in this issue a first refactoring that would make this possible. It is still under discussion, since the proposed changes are big. I'll write in a different article more details about this proposal.

Lazy pandas

To conclude this article, I will talk about what in my opinion is one of the biggest differences between the needs of data analysts using pandas in a Jupyter notebook, compared to data engineers using it to write production pipelines.

See this example:

>>> num_legs = pandas.Series({'unicorn': 4, 'spider': 8, 'penguin': 2})
>>> num_legs.median()

>>> num_legs = num_legs.to_frame()
>>> num_legs
unicorn  4
spider   8
penguin  2

>>> num_legs = num_legs.rename(columns={0: 'legs'})
>>> num_legs
unicorn     4
spider      8
penguin     2

>>> num_legs['kind'] = num_legs['legs'].replace({2: 'biped',
...                                              4: 'quadruped',
...                                              8: 'octoped'})
>>> num_legs
         legs       kind
unicorn     4  quadruped
spider      8    octoped
penguin     2      biped

>>> num_legs = num_legs[num_legs.legs <= 4]
         legs       kind
unicorn     4  quadruped
penguin     2      biped

>>> num_legs.to_parquet('num_legs.parquet')

And compare it with this other code:

>>> (pandas.Series({'unicorn': 4, 'spider': 8, 'penguin': 2})
...        .to_frame()
...        .rename(columns={0: 'legs'})
...        .assign(kind=lambda df: df['legs'].replace({2: 'biped',
...                                                    4: 'quadruped',
...                                                    8: 'octoped'}))
...        .query('legs <= 4')
...        .to_parquet('num_legs.parquet'))

Before you are tempted to think on which one is better, let's discuss which problem solves each of them.

The first version is part of an iterative process where at every step we need to visualize how our data looks like. We also may need not only to visualize the data, but understand or verify it, for example by checking which is the median of one column. It is likely that at the end of writing that code, we don't care about it anymore, since we already verified what was in the data, and extracted the insights we care about.

In the second case, while doing almost the same, the code is written to be read and to be maintained. If there is a bug in the code, it should be easy to understand what it does, and fix it. The goal is not to discover anything while writing the code. But just to add a functionality to a system, and to be able to run it in a reliable and performant way.

For more information about the style in the second approach, you can check the must-read Method Chaining by the pandas maintainer Tom Augspurger. Also, I discussed about method chaining in my talk Towards pandas 1.0.

Back to the example, pandas let us write code in a way that suits both data analysts and data engineers. But there is something else that is worth considering. In the first version, the operations must be executed one at a time, since they are independent. But in the example using method chaining, there is no need to execute anything until to_parquet is run. The reason is that the result is not made available to the user or anywhere else.

This may sound irrelevant at first, since we are going to execute it anyway. But being able to postpone the actual execution until a later stage, can be extremely useful in some situations. In the example, if pandas postpones the execution until it knows all what the user wants to do with all the data, it could optimize the execution. For example, if the row of the spider is going to be discarded, why load it to memory and why compute which is its kind? Some memory and some computation power and time can be saved. In this toy example it doesn't make a difference, but imagine you want to operate with 1Tb of data in a file, apply some transformations, and save the result in another file in disk. With the data analyst approach this is not feasible when running the code in a normal laptop. And while pandas is not able to work in an out-of-core way, or optimize the execution even when using method chaining, that could be implemented.

There are related tools where this lazy execution approach already exists, mainly Dask. Dask implements a pandas-like API, where operations are evaluated in a lazy way, and the final task graph is not only optimized, but distributed over a cluster. Vaex is another example of pandas-like API implemented with lazy evaluation. This talk has a demo showing how Vaex uses lazy evaluation to deal with data sets with more than one billion rows.

Lazy evaluation may be out of scope for pandas, and there are many things that should be changed even before being considered. But in my opinion is another example on the different needs of the different pandas users.

I guess a dual pandas would be possible, and for the user, may be a simple pandas option pandas.options.lazy_execution = True would be enough. Together with few methods to allow users to trigger the execution of a task graph (e.g. a .collect()).

There are also other approaches that could be considered. With the recent addition of pandas extension arrays, custom data types can be implemented. And having types for memory maps, or calculated columns could be an option that could allow some sort of laziness. In the example, we could have a normal DataFrame, that could have a kind column that does not actually save the strings biped, quadruped,... but instead stores the function applied, and to which column. The actual lookup could then happen after the data is filtered.

Whatever could be the approach, it would require major changes to pandas internals, and it's not something that could be implemented easily. Custom data types can be implemented, but currently some operations will convert the data to numpy arrays, and would not allow having a proper lazy data type.


I think the number of pandas users, and the different kinds of work that are being done are evidence of many good design decisions and implementation. But conflicting interests among groups of users do exist. In some cases is doable to find a good solution for most use cases. In others is not obvious and serving better our users would require a huge amount of work.

Personally, I think a more modular pandas architecture would make it easier to adjust to every kind of user. By having more than one version of pandas.read_csv different users could implement solutions that better suit their needs. Same could apply to other areas.

But probably the most important challenge to get those implemented is not what is the technical solution, but it's in how pandas is developed. The project is mostly developed by volunteers, including the maintainers (the people who review the contributions, discuss in the issues that users open...). Our roadmap is not determined by the needs of your company or your industry. In my personal case, my roadmap is determined by my personal interests on what I want to work on, and on the kind of things I need or I want to see in pandas myself. If your company would be more productive with certain pandas features or developments, you should consider hiring someone to improve pandas based in your interests. You can contact NumFOCUS who manages the pandas funding, can assist with any question, and is in direct contact with the pandas maintainers. Besides hiring someone in your own team, you could also provide funds to develop pandas that are managed by NumFOCUS. Also feel free to contact me directly if you want more advice, and are interested in this.

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