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Jupyter notebook ATMS-305-Content/Week-6/Week 6 Exercise 2 introduction_to_numpy.ipynb

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Kernel: Anaconda (Python 3)

Introduction to numpy

In your homework, you performed your first bit of data analysis (congratulations!). You learned how to use lists and dictionaries, and processed through data using for loops and if statements.

  • How can we make this process more efficient?

  • Also, what do we do if we need to analyze 100 clouds? 10000 clouds? 1,000,000,000,000,000 clouds?

What are numpy and numpy arrays?

Numpy provides

  • extension package to Python for multi-dimensional arrays

  • closer to hardware (efficiency)

  • designed for scientific computation (convenience)

  • Also known as array oriented computing

import numpy as np
a = np.array([0, 1, 2, 3])
a
array([0, 1, 2, 3])

For example, a numpy array may contain:

  • values of an experiment/simulation at discrete time steps

  • signal recorded by a measurement device, e.g. weather station data

  • pixels of an image, grey-level or color, e.g. a satellite image

  • 3-D data measured at different X-Y-Z positions, e.g. climate model data

  • ...

Why it is useful: Memory-efficient container that provides fast numerical operations. The numpy code base is written in C, which is highly optimized for the cpu.

L = range(10000)

%timeit [i**2 for i in L]
100 loops, best of 3: 2.87 ms per loop 
a = np.arange(10000)

%timeit a**2
The slowest run took 18.82 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 3: 6.62 µs per loop

Reference documentation

# Get some help on np.array without going to the web
np.array?

  • Looking for something:

np.lookfor('create array')
Search results for 'create array' --------------------------------- numpy.array Create an array. numpy.memmap Create a memory-map to an array stored in a *binary* file on disk. numpy.diagflat Create a two-dimensional array with the flattened input as a diagonal. numpy.fromiter Create a new 1-dimensional array from an iterable object. numpy.partition Return a partitioned copy of an array. numpy.ctypeslib.as_array Create a numpy array from a ctypes array or a ctypes POINTER. numpy.ma.diagflat Create a two-dimensional array with the flattened input as a diagonal. numpy.ma.make_mask Create a boolean mask from an array. numpy.ctypeslib.as_ctypes Create and return a ctypes object from a numpy array. Actually numpy.ma.mrecords.fromarrays Creates a mrecarray from a (flat) list of masked arrays. numpy.lib.format.open_memmap Open a .npy file as a memory-mapped array. numpy.ma.MaskedArray.__new__ Create a new masked array from scratch. numpy.lib.arrayterator.Arrayterator Buffered iterator for big arrays. numpy.ma.mrecords.fromtextfile Creates a mrecarray from data stored in the file `filename`. numpy.asarray Convert the input to an array. numpy.ndarray ndarray(shape, dtype=float, buffer=None, offset=0, numpy.recarray Construct an ndarray that allows field access using attributes. numpy.chararray chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0, numpy.pad Pads an array. numpy.asanyarray Convert the input to an ndarray, but pass ndarray subclasses through. numpy.copy Return an array copy of the given object. numpy.diag Extract a diagonal or construct a diagonal array. numpy.load Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files. numpy.sort Return a sorted copy of an array. numpy.array_equiv Returns True if input arrays are shape consistent and all elements equal. numpy.dtype Create a data type object. numpy.choose Construct an array from an index array and a set of arrays to choose from. numpy.nditer Efficient multi-dimensional iterator object to iterate over arrays. numpy.swapaxes Interchange two axes of an array. numpy.full_like Return a full array with the same shape and type as a given array. numpy.ones_like Return an array of ones with the same shape and type as a given array. numpy.empty_like Return a new array with the same shape and type as a given array. numpy.zeros_like Return an array of zeros with the same shape and type as a given array. numpy.asarray_chkfinite Convert the input to an array, checking for NaNs or Infs. numpy.diag_indices Return the indices to access the main diagonal of an array. numpy.chararray.tolist a.tolist() numpy.ma.choose Use an index array to construct a new array from a set of choices. numpy.savez_compressed Save several arrays into a single file in compressed ``.npz`` format. numpy.matlib.rand Return a matrix of random values with given shape. numpy.ma.empty_like Return a new array with the same shape and type as a given array. numpy.ma.make_mask_none Return a boolean mask of the given shape, filled with False. numpy.ma.mrecords.fromrecords Creates a MaskedRecords from a list of records. numpy.around Evenly round to the given number of decimals. numpy.source Print or write to a file the source code for a NumPy object. numpy.diagonal Return specified diagonals. numpy.einsum_path Evaluates the lowest cost contraction order for an einsum expression by numpy.histogram2d Compute the bi-dimensional histogram of two data samples. numpy.fft.ifft Compute the one-dimensional inverse discrete Fourier Transform. numpy.fft.ifftn Compute the N-dimensional inverse discrete Fourier Transform. numpy.busdaycalendar A business day calendar object that efficiently stores information
np.con*?

np.a*?

np.l*?

import numpy as np

values = np.array([12,321,5,236,57,2345,6345,7])
print(values)
[ 12 321 5 236 57 2345 6345 7] 
values**0.5
array([ 3.46410162, 17.91647287, 2.23606798, 15.3622915 , 7.54983444, 48.42520005, 79.65550828, 2.64575131])
np.sqrt(values)
array([ 3.46410162, 17.91647287, 2.23606798, 15.3622915 , 7.54983444, 48.42520005, 79.65550828, 2.64575131])
np.cos(values)
array([ 0.84385396, 0.84855433, 0.28366219, -0.92846012, 0.89986683, 0.19760673, 0.52578361, 0.75390225])
values**values
array([ 8916100448256, -1434141394955693759, 3125, 0, 4951460147845608313, 8129662023945473129, -3670746008300892663, 823543])
values**values/values+values**0.5/values
array([ 7.43008371e+11, -4.46773020e+15, 6.25447214e+02, 6.50944555e-02, 8.68677219e+16, 3.46680683e+15, -5.78525770e+14, 1.17649378e+05])