## How to save a float numpy array to a binary file with less storage (e.g.: not in scientific notation)?

Assume that I have a big float numpy array:

How to save this float numpy array to a binary file with less storage using numpy.save?

np.save(nucleosomenpyOutputFilePath,averageSignalArray)

Thanks @hpaulj. You openedmy eyes.

By playing with dtype=np.float32 or dtype=np.float16 in the following statements

averageSignalArray=np.divide(accumulatedSignalArray,accumulatedCountArray,dtype=np.float32) averageSignalArray=np.divide(accumulatedSignalArray,accumulatedCountArray,dtype=np.float16)

I got different nparrays and save them in the following step:

np.save(nucleosomenpyOutputFilePath,averageSignalArray)

**1.4.1. The NumPy array object,** How to save a float numpy array to a binary file with less storage (e.g.: not in scientific notation)?. Asked by burcak At 03 February at 20:44. Arrays are saved by default in an uncompressed raw binary format with file extension .npy. In [183]: arr = np.arange(10) In [184]: np.save('some_array', arr) If the file path does not already end in .npy, the extension will be appended. The array on disk can then be loaded using np.load:

If your goal is to just save size on the resulting files and you can install additional python packages and use compressed arrays. https://github.com/Blosc/bcolz

**Input and output,** high-level number objects: integers, floating point; containers: lists (costless designed for scientific computation (convenience); Also known as array oriented computing Create a memory-map to an array stored in a *binary* file on disk. Note that, in the example above, NumPy auto-detects the data-type from the input. If you want to save the array to a text file, you can use the savetxt() function to do this: import numpy as np x = np.arange(0.0,5.0,1.0) np.savetxt('test.out', x, delimiter=',') Remember that np.arange() creates a NumPy array of evenly-spaced values. The third value that you pass to this function is the step value.

Probably one of the fastest and most space-efficient ways of doing this is by using Bloscpack:

https://github.com/blosc/bloscpack

You can read about using the Python API here:

https://github.com/blosc/bloscpack#python-api

And lastly, here is an example:

>>> import numpy as np >>> import bloscpack as bp >>> a = np.linspace(0, 1, 3e8) >>> print a.size, a.dtype 300000000 float64 >>> bp.pack_ndarray_to_file(a, 'a.blp') >>> b = bp.unpack_ndarray_from_file('a.blp') >>> (a == b).all() True

**(Tutorial) Python NUMPY Array TUTORIAL,** Save an array to a binary file in NumPy .npy format. savez (file, *args (x[, precision, …]) Format a floating-point scalar as a decimal string in scientific notation. These commands will create a binary file called sin.dat, which contains the values stored in the array x, followed by the values for sin(x). We can't look at this data directly using the more command, because it's not stored in a format that is human-readable, but we can verify that the right amount of data is there by doing the following at

**4. NumPy Basics: Arrays and Vectorized Computation,** This NumPy tutorial will not only show you what NumPy arrays actually are Intro to Python for Data Science, which has a chapter on NumPy. That also means that the array is stored in memory as 64 bytes (as each save(), Save an array to a binary file in NumPy .npy format my_array.astype(float). It's possible to read binary data, if the data type is known. It's also possible to parse simply formatted text files. The data from the file is turned into an array. The general syntax looks like this: numpy.fromfile(file, dtype=float, count=-1, sep='')

**NumPy: Array Object - Exercises, Practice, Solution,** While NumPy by itself does not provide very much high-level data analytical and thinking is a key step along the way to becoming a scientific Python guru. The data type is stored in a special dtype object; for example, in the above two Arrays are saved by default in an uncompressed raw binary format with file numpy.save aligns data to 64 bytes instead of 16¶ Saving NumPy arrays in the npy format with numpy.save inserts padding before the array data to align it at 64 bytes. Previously this was only 16 bytes (and sometimes less due to a bug in the code for version 2).

**1.14. Decimals, Floats, and Floating Point Arithmetic,** Write a NumPy program to convert an array to a float type. Go to the editor Centigrade values are stored into a NumPy array. Go to the editor NumPy - Array From Existing Data. In this chapter, we will discuss how to create an array from existing data. numpy.asarray. This function is similar to numpy.array except for the fact that it has fewer parameters. This routine is useful for converting Python sequence into ndarray. numpy.asarray(a, dtype = None, order = None)

##### Comments

- What's the current
`dtype`

? - I got this numpy array by the following statement
`averageSignalArray=np.divide(accumulatedSignalArray,accumulatedCountArray)`

I guess its dtype=float - Do you know what I mean by
`dtype`

? - Yes, of course dtype.
- There's
`float32`

and`float64`

, 4 and 8 bytes per element.