GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.
If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. That is: hickle is a neat little way of dumping python variables to HDF5 files that can be read in most programming languages, not just Python. While hickle is designed to be a drop-in replacement for pickle or something like jsonit works very differently. So, if you want your data in HDF5, or if your pickling is taking too long, give hickle a try.
Hickle is particularly good at storing large numpy arrays, thanks to h5py running under the hood. Documentation for hickle can be found at telegraphic. Hickle is nice and easy to use, and should look very familiar to those of you who have pickled before.
In short, hickle provides two methods: a hickle. Here's a complete example:. A major benefit of hickle over pickle is that it allows fancy HDF5 features to be applied, by passing on keyword arguments on to h5py. So, you can do things like:. In HDF5, datasets are stored as B-trees, a tree data structure that has speed benefits over contiguous blocks of data.
In the B-tree, data are split into chunkswhich is leveraged to allow dataset resizing and compression via filter pipelines. Filters such as shuffle and scaleoffset move your data around to improve compression ratios, and fletcher32 computes a checksum.
These file-level options are abstracted away from the data model. For storing python dictionaries of lists, hickle beats the python json encoder, but is slower than uJson. For a dictionary with 64 entries, each containing a length list of random numbers, the times are:.Flying Pickle Alert! Pickle files can be hacked. If you receive a raw pickle file over the network, don't trust it!
It could have malicious code in it, that would run arbitrary python when you try to de-pickle it. However, if you are doing your own pickle writing and reading, you're safe.
Provided no one else has access to the pickle file, of course. What can you Pickle? Generally you can pickle any object if you can pickle every attribute of that object. Classes, functions, and methods cannot be pickled -- if you pickle an object, the object's class is not pickled, just a string that identifies what class it belongs to. This works fine for most pickles but note the discussion about long-term storage of pickles.
With pickle protocol v1, you cannot pickle open file objects, network connections, or database connections. When you think about it, it makes sense -- pickle cannot will the connection for file object to exist when you unpickle your object, and the process of creating that connection goes beyond what pickle can automatically do for you.
With pickle protocol v2, you are able to pickle open file objects. This will change in a future version of Python.
See this bug report for more information. See the pickle documentation for more recent protocols up to v5 as of Python 3. Contributors LionKimbroIanBickinglwickjr Discussion Pickles can cause problems if you save a pickle, then update your code and read the pickle in.
I have developed an ad-hock procedure that works for me: 1 Edit the source code to create the object under the new name AND store a copy under the old name. A more robust approach would be to perform step one above, and just leave it at that, in case you missed a pickle or two. If desired, you can then perform step 3 after you judge normal processing to have performed step 2 for you, say, a couple years later.
Awkward, but it works. Anyone have any ideas for a better way to do this? Unable to edit the page? See the FrontPage for instructions.
User Login.More recently, I showed how to profile the memory usage of Python code. Fortunately, there is an open standard called HDF, which defines a binary file format that is designed to efficiently store large scientific data sets.
I will demonstrate both approaches, and profile them to see how much memory is required. I first ran the program with both the pickle and the HDF code commented out, and profiled RAM usage with Valgrind and Massif see my post about profiling memory usage of Python code. I then uncommented the Pickle code, and profiled the program again. Look at how the memory usage almost triples!
I then commented out the Pickle code and uncommented the HDF code, and ran the profile again. Notice how efficient the HDF library is:. Why does Pickle consume so much more memory? The reason is that HDF is a binary data pipe, while Pickle is an object serialization protocol. Pickle actually consists of a simple virtual machine VM that translates an object into a series of opcodes and writes them to disk.
To unpickle something, the VM reads and interprets the opcodes and reconstructs an object. The downside of this approach is that the VM has to construct a complete copy of the object in memory before it writes it to disk. Fortunately, HDF exists to efficiently handle large binary data sets, and the Pytables package makes it easy to access in a very Pythonic way. Hey — just stumbled upon your blog googling HDF and Pickle comparisons. Hi, I have been using mpi4py to do a calculation in parallel.
One option for sending data between different processes is pickle. I ran into errors using it, and I wonder if it could be because of the large amount of memory the actual pickling process consumes. The problem was resolved when I switched to the other option to send data between processes, which is as a numpy array via some C method I believe.
I've got a Numpy array that I would like to savex 3 that I would like to save using Pickle, with the following code. This is my first time using Pickle, any ideas? You should use numpy. Like I can load this pickle with np.
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But that shouldn't be surprising - you can't read a freshly opened write file. It will be empty. But pickle uses save to serialize arrays, and save uses pickle to serialize non-array objects in the array. Resulting file sizes are similar.
Curiously in timings the pickle version is faster. On the other hand, by default numpy compress took my 5.
Learn more. Asked 1 year, 6 months ago. Active 9 months ago. Viewed 30k times. Anant Anant 1 1 gold badge 2 2 silver badges 12 12 bronze badges.
Active Oldest Votes. I can't test it now, but I thought save was the numpy pickling method. Conversely save uses pickle to write non array elements. It's been a bit but if you're finding this, Pickle completes in a fraction of the time. Joey 1 1 gold badge 12 12 silver badges 23 23 bronze badges. TomF TomF 31 1 1 bronze badge. Lucky Suman Lucky Suman 84 6 6 bronze badges. Sign up or log in Sign up using Google.
Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.N umpy, short for Numerical Pythonis the fundamental package required for high performance scientific computing and data analysis in Python ecosystem. It is the foundation on which nearly all of the higher-level tools such as Pandas and scikit-learn are built. Many articles have been written demonstrating the advantage of Numpy array over plain vanilla Python lists.
You will often come across this assertion in the data science, machine learning, and Python community that Numpy is much faster due to its vectorized implementation and due to the fact that many of its core routines are written in C based on CPython framework.
And it is indeed true this article is a beautiful demonstration of various options that one can work with Numpy, even writing bare-bone C routines with Numpy APIs. Numpy arrays are densely packed arrays of homogeneous type.
Python lists, by contrast, are arrays of pointers to objects, even when all of them are of the same type. You get the benefits of locality of reference. In one of my highly cited articles on Towards Data Science platformI demonstrated the advantage of using Numpy vectorized operations over traditional programming constructs like for-loop. However, what is less appreciated is the fact, when it comes to repeated reading of the same data from a local or networked disk storage, Numpy offers another gem called.
This file format makes incredibly fast reading speed enhancement over reading from plain text or CSV files. The catch is — of course you have to read the data in traditional manner for the first time and create a in-memory NumPy ndarray object.
But if you use the same CSV file for repeated reading of the same numerical data set, it makes perfect sense to store the ndarray in a npy file instead of reading it over and over from the original CSV.
It is a standard binary file format for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and data type information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I've got a Numpy array that I would like to savex 3 that I would like to save using Pickle, with the following code.
This is my first time using Pickle, any ideas? You should use numpy. Like I can load this pickle with np. But that shouldn't be surprising - you can't read a freshly opened write file. It will be empty. But pickle uses save to serialize arrays, and save uses pickle to serialize non-array objects in the array. Resulting file sizes are similar. Curiously in timings the pickle version is faster. On the other hand, by default numpy compress took my 5.
Learn more. Asked 1 year, 6 months ago. Active 9 months ago. Viewed 31k times. Anant Anant 1 1 gold badge 2 2 silver badges 12 12 bronze badges. Active Oldest Votes. I can't test it now, but I thought save was the numpy pickling method.
Conversely save uses pickle to write non array elements. It's been a bit but if you're finding this, Pickle completes in a fraction of the time. Joey 1 1 gold badge 12 12 silver badges 23 23 bronze badges.
TomF TomF 31 1 1 bronze badge. Lucky Suman Lucky Suman 84 6 6 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog.As a data scientist, you will use sets of data in the form of dictionaries, DataFrames, or any other data type. When working with those, you might want to save them to a file, so you can use them later on or send them to someone else. This is what Python's pickle module is for: it serializes objects so they can be saved to a file, and loaded in a program again later on.
Storing large Numpy arrays on disk: Python Pickle vs. HDF5
If you want to know more about how to import data in Python, be sure to take a look at our Importing Data In Python course and its corresponding cheat sheet. Pickle is used for serializing and de-serializing Python object structures, also called marshalling or flattening.
Serialization refers to the process of converting an object in memory to a byte stream that can be stored on disk or sent over a network. Later on, this character stream can then be retrieved and de-serialized back to a Python object. Pickling is not to be confused with compression! The former is the conversion of an object from one representation data in Random Access Memory RAM to another text on diskwhile the latter is the process of encoding data with fewer bits, in order to save disk space.
Pickling is useful for applications where you need some degree of persistency in your data. Your program's state data can be saved to disk, so you can continue working on it later on. It can also be used to send data over a Transmission Control Protocol TCP or socket connection, or to store python objects in a database. Pickle is very useful for when you're working with machine learning algorithms, where you want to save them to be able to make new predictions at a later time, without having to rewrite everything or train the model all over again.
If you want to use data across different programming languages, pickle is not recommended. Its protocol is specific to Python, thus, cross-language compatibility is not guaranteed. The same holds for different versions of Python itself. Unpickling a file that was pickled in a different version of Python may not always work properly, so you have to make sure that you're using the same version and perform an update if necessary.
You should also try not to unpickle data from an untrusted source. Malicious code inside the file might be executed upon unpickling. All the above can be pickled, but you can also do the same for classes and functions, for example, if they are defined at the top level of a module. Not everything can be pickled easilythough: examples of this are generators, inner classes, lambda functions and defaultdicts.
In the case of lambda functions, you need to use an additional package named dill. With defaultdicts, you need to create them with a module-level function. It's a lightweight format for data-interchange, that is easily readable by humans.
This is a serious advantage over pickle. It's also more secure and much faster than pickle. However, if you only need to use Python, then the pickle module is still a good choice for its ease of use and ability to reconstruct complete Python objects. An alternative is cPickle. It is nearly identical to picklebut written in C, which makes it up to times faster.
For small files, however, you won't notice the difference in speed. Both produce the same data streams, which means that Pickle and cPickle can use the same files. For this tutorial, you will be pickling a simple dictionary. A dictionary is a list of key : value elements. You will save it to a file and then load again. Declare the dictionary as such:. To pickle this dictionary, you first need to specify the name of the file you will write it to, which is dogs in this case.