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- What does [:, :] mean on NumPy arrays - Stack Overflow
- How do I read CSV data into a record array in NumPy?
- python - Pretty-print a NumPy array without scientific notation and ...
- Multiple conditions using 'or' in numpy array - Stack Overflow
- python - NumPy array is not JSON serializable - Stack Overflow
- Comparing two NumPy arrays for equality, element-wise
- Extracting specific columns in numpy array - Stack Overflow
- python - Numpy array dimensions - Stack Overflow
- Most efficient way to reverse a numpy array - Stack Overflow
- Use numpy array in shared memory for multiprocessing
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Python NumPy Array Create NumPy Ndarray (multidimensional, 55% OFF
Python NumPy array tutorial
Convert Numpy Ndarray To Numpy Float64 - Printable Online
NumPy Array Tutorial - Python NumPy Array Operations and Methods ...
NumPy Array Tutorial
How To Create a NumPy Array
Python NumPy Array | Learn NumPy Arrays with Examples | Learntek
Python NumPy Array | Learn NumPy Arrays with Examples | Learntek
Python NumPy Array | Learn NumPy Arrays with Examples | Learntek
Python NumPy Array | Learn NumPy Arrays with Examples | Learntek
Python NumPy Array | Learn NumPy Arrays with Examples | Learntek
Python NumPy Array | Learn NumPy Arrays with Examples | Learntek
numpy array
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What does [:, :] mean on NumPy arrays - Stack Overflow
numpy uses tuples as indexes. In this case, this is a detailed slice assignment. [0] #means line 0 of your matrix [(0,0)] #means cell at 0,0 of your matrix [0:1] #means lines 0 to 1 excluded of your matrix [:1] #excluding the first value means all lines until line 1 excluded [1:] #excluding the last param mean all lines starting form line 1 included [:] #excluding both means all lines [::2] # ...
How do I read CSV data into a record array in NumPy?
Aug 19, 2010 · @JoséL.Patiño The second part of the question deals with the request for a Numpy record array. The first part of the answer shows df.values which gives a Numpy representation of the DataFrame; a convenient method imho. –
python - Pretty-print a NumPy array without scientific notation and ...
How do I print formatted NumPy arrays in a way similar to this: x = 1.23456 print('%.3f' % x) If I want to print the numpy.ndarray of floats, it prints several decimals, often in 'scientific' form...
Multiple conditions using 'or' in numpy array - Stack Overflow
numpy logical_and and logical_or are the ufuncs that you want (I think) Note that & is not logical and , it is bitwise and . This still works for you because (a>10) returns a logical array (e.g. 1's and 0's) as does your second condition.
python - NumPy array is not JSON serializable - Stack Overflow
Apr 18, 2018 · use NumpyEncoder it will process json dump successfully.without throwing - NumPy array is not JSON serializable. import numpy as np import json from numpyencoder import NumpyEncoder arr = array([ 0, 239, 479, 717, 952, 1192, 1432, 1667], dtype=int64) json.dumps(arr,cls=NumpyEncoder)
Comparing two NumPy arrays for equality, element-wise
May 14, 2012 · According to the results above, the numpy methods seem to be faster than the combination of the == operator and the all() method and by comparing the numpy methods the fastest one seems to be the numpy.array_equal method.
Extracting specific columns in numpy array - Stack Overflow
Dec 5, 2011 · This is an easy question but say I have an MxN matrix. All I want to do is extract specific columns and store them in another numpy array but I get invalid syntax errors. Here is the code: extractedData = data[[:,1],[:,9]]. It seems like the above line should suffice but I guess not.
python - Numpy array dimensions - Stack Overflow
Jun 17, 2010 · But in Numpy, according to the numpy doc, it's the same as axis/axes: In Numpy dimensions are called axes. The number of axes is rank. In [3]: a.ndim # num of dimensions/axes, *Mathematics definition of dimension* Out[3]: 2 axis/axes. the nth coordinate to index an array in Numpy. And multidimensional arrays can have one index per axis.
Most efficient way to reverse a numpy array - Stack Overflow
Jul 21, 2011 · Believe it or not, after profiling my current code, the repetitive operation of numpy array reversion ate a giant chunk of the running time. What I have right now is the common view-based method:
Use numpy array in shared memory for multiprocessing
Oct 26, 2011 · The array can be accessed in a ctypes manner, e.g. arr[i] makes sense. However, it is not a numpy array, and I cannot perform operations such as -1*arr, or arr.sum(). I suppose a solution would be to convert the ctypes array into a numpy array. However (besides not being able to make this work), I don't believe it would be shared anymore.