{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Week 5 - Numpy & Matplotlib\n", "\n", "## Today's Agenda\n", "* Numpy\n", "* Matplotlib" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## [Numpy](http://www.numpy.org/) - Numerical Python\n", "\n", "From their website ([http://www.numpy.org/](http://www.numpy.org/)):\n", "\n", "> NumPy is the fundamental package for scientific computing with Python. \n", "> * a powerful N-dimensional array object\n", "> * sophisticated (broadcasting) functions\n", "> * tools for integrating C/C++ and Fortran code\n", "> * useful linear algebra, Fourier transform, and random number capabilities" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "You can import __\"`numpy`\"__ as" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy arrays\n", "\n", "In standard Python, data is stored as lists, and multidimensional data as **lists of lists**. In `numpy`, however, we can now work with arrays. To get these arrays, we can use `np.asarray` to convert a list into an array. Below we take a quick look at how a list behaves differently from an array." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 2 3 4 5 6 7 8 9 10]\n" ] } ], "source": [ "# We first create an array `x`\n", "start = 1\n", "stop = 11\n", "step = 1\n", "\n", "x = np.arange(start, stop, step)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also manipulate the array. For example, we can:\n", "\n", "- Multiply by two:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x * 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Take the square of all the values in the array:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x ** 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Or even do some math on it:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6.33333333, 14.66666667, 25. , 37.33333333,\n", " 51.66666667, 68. , 86.33333333, 106.66666667,\n", " 129. , 153.33333333])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(x**2) + (5*x) + (x / 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to set up an array in numpy, we can use range to make a list and then convert it to an array, but we can also just create an array directly in numpy. `np.arange` will do this with integers, and `np.linspace` will do this with floats, and allows for non-integer steps." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n", "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n" ] } ], "source": [ "print(np.arange(10))\n", "\n", "print(np.linspace(1,10,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last week we had to use a function or a loop to carry out math on a list. However with numpy we can do this a lot simpler by making sure we're working with an array, and carrying out the mathematical operations on that array." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n", "[ 0 1 4 9 16 25 36 49 64 81]\n" ] } ], "source": [ "x=np.arange(10)\n", "print(x)\n", "\n", "print(x**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In numpy, we also have more options for quickly (and without much code) examining the contents of an array. One of the most helpful tools for this is `np.where`. `np.where` uses a conditional statement on the array and returns an array that contains indices of all the values that were true for the conditional statement. We can then call the original array and use the new array to get all the **values** that were true for the conditional statement.\n", "\n", "There are also functions like `max` and `min` that will give the maximum and minimum, respectively." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5\n", " 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5\n", " 12. 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5\n", " 18. 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5\n", " 24. 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5\n", " 30. 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5\n", " 36. 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5\n", " 42. 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5\n", " 48. 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5\n", " 54. 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5\n", " 60. 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5\n", " 66. 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5\n", " 72. 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5\n", " 78. 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5\n", " 84. 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5\n", " 90. 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5\n", " 96. 96.5 97. 97.5 98. 98.5 99. 99.5 100. ]\n" ] } ], "source": [ "# Defining starting and ending values of the array, as well as the number of elements in the array.\n", "start = 0\n", "stop = 100\n", "n_elements = 201\n", "\n", "x = np.linspace(start, stop, n_elements)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can select only those values that are divisible by *5*:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120,\n", " 130, 140, 150, 160, 170, 180, 190, 200]),)\n", "[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55. 60. 65.\n", " 70. 75. 80. 85. 90. 95. 100.]\n" ] } ], "source": [ "# This function returns the indices that match the criteria of `x % 5 == 0`:\n", "x_5 = np.where(x%5 == 0)\n", "\n", "print(x_5)\n", "\n", "# And one can use those indices to *only* select those values:\n", "print(x[x_5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or similarly:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.,\n", " 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[x%5 == 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can find the `max` and ``min`` values of the array:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The minimum of `x` is `0.0`\n", "The maximum of `x` is `100.0`\n" ] } ], "source": [ "print('The minimum of `x` is `{0}`'.format(x.min()))\n", "\n", "print('The maximum of `x` is `{0}`'.format(x.max()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy also provides some tools for loading and saving data, loadtxt and savetxt. Here I'm using a function called transpose so that instead of each array being a row, they each get treated as a column.\n", "\n", "When we load the information again, it's now a 2D array. We can select parts of those arrays just as we could for 1D arrays." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2D-array from file `myfile.txt`:\n", "\n", " [[ 0.000000e+00 3.000000e+00]\n", " [ 2.000000e-01 2.000400e+00]\n", " [ 4.000000e-01 1.001600e+00]\n", " ...\n", " [ 9.960000e+01 -3.957984e+02]\n", " [ 9.980000e+01 -3.963996e+02]\n", " [ 1.000000e+02 -3.970000e+02]] \n", "\n", "Selecting certain elements from `data`:\n", "\n", " [[0. 3. ]\n", " [0.2 2.0004]\n", " [0.4 1.0016]] \n", "\n" ] } ], "source": [ "start = 0\n", "stop = 100\n", "n_elem = 501\n", "\n", "x = np.linspace(start, stop, n_elem)\n", "\n", "# We can now create another array from `x`:\n", "y = (.1*x)**2 - (5*x) + 3\n", "\n", "# And finally, we can dump `x` and `y` to a file:\n", "np.savetxt('myfile.txt', np.transpose([x,y]))\n", "\n", "# We can also load the data from `myfile.txt` and display it:\n", "data = np.loadtxt('myfile.txt')\n", "print('2D-array from file `myfile.txt`:\\n\\n', data, '\\n')\n", "\n", "# You can also select certain elements of the 2D-array\n", "print('Selecting certain elements from `data`:\\n\\n', data[:3,:], '\\n')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Resources\n", "* Scientific Lectures on Python - __Numpy__: [iPython Notebook](https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-2-Numpy.ipynb)\n", "* Data Science iPython Notebooks - __Numpy__: [iPython Notebook](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## [Matplotlib](http://matplotlib.org/)\n", "\n", "Matplotlib is a Python 2D plotting library which\n", "* produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms\n", "* Quick way to visualize data from Python\n", "* Main plotting utility in Python\n", "\n", "From their website ([http://matplotlib.org/](http://matplotlib.org/)):\n", "\n", "> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n", "\n", "A great starting point to figuring out how to make a particular figure is to start from the [Matplotlib gallery](http://matplotlib.org/gallery.html) and look for what you want to make." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "## Importing modules\n", "%matplotlib inline\n", "\n", "# Importing LaTeX\n", "from matplotlib import rc\n", "rc('text', usetex=True)\n", "\n", "# Importing matplotlib and other modules\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now load in the data from `myfile.txt`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data = np.loadtxt('myfile.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest figure is to simply make a plot. We can have multiple figures, but for now, just one. The plt.plot function will connect the points, but if we want a scatter plot, then plt.scatter will work." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(8,8))\n", "plt.plot(data[:,0],data[:,1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also pass the `*data.T` value instead:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(8,8))\n", "plt.plot(*data.T)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can take that same figure and add on the needed labels and titles." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Creating figure\n", "plt.figure(figsize=(8,8))\n", "plt.plot(*data.T)\n", "plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)\n", "plt.xlabel('x value', fontsize=20)\n", "plt.ylabel('y value', fontsize=20)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a large number of options available for plotting, so try using the initial code below, combined with the information [here](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) to try out a few of the following things: changing the line width, changing the line color" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.plot(data[:,0],data[:,1])\n", "plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)\n", "plt.xlabel('x value', fontsize=20)\n", "plt.ylabel('y value', fontsize=20)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }