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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "markdown",
  6.    "source": [
  7.     "# NumPy Exercises \n",
  8.     "\n",
  9.     "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."
  10.    ]
  11.   },
  12.   {
  13.    "cell_type": "markdown",
  15.    "source": [
  16.     "#### Import NumPy as np"
  17.    ]
  18.   },
  19.   {
  20.    "cell_type": "code",
  21.    "execution_count": 2,
  23.    "outputs": [],
  24.    "source": [
  25.     "import numpy as np"
  26.    ]
  27.   },
  28.   {
  29.    "cell_type": "markdown",
  31.    "source": [
  32.     "#### Create an array of 10 zeros "
  33.    ]
  34.   },
  35.   {
  36.    "cell_type": "code",
  37.    "execution_count": 3,
  39.     "collapsed": false,
  40.     "jupyter": {
  41.      "outputs_hidden": false
  42.     }
  43.    },
  44.    "outputs": [
  45.     {
  46.      "data": {
  47.       "text/plain": [
  48.        "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
  49.       ]
  50.      },
  51.      "execution_count": 3,
  53.      "output_type": "execute_result"
  54.     }
  55.    ],
  56.    "source": [
  57.     "np.zeros(10)"
  58.    ]
  59.   },
  60.   {
  61.    "cell_type": "markdown",
  63.    "source": [
  64.     "#### Create an array of 10 ones"
  65.    ]
  66.   },
  67.   {
  68.    "cell_type": "code",
  69.    "execution_count": 4,
  71.    "outputs": [
  72.     {
  73.      "data": {
  74.       "text/plain": [
  75.        "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
  76.       ]
  77.      },
  78.      "execution_count": 4,
  80.      "output_type": "execute_result"
  81.     }
  82.    ],
  83.    "source": [
  84.     "np.ones(10)"
  85.    ]
  86.   },
  87.   {
  88.    "cell_type": "markdown",
  90.    "source": [
  91.     "#### Create an array of 10 fives"
  92.    ]
  93.   },
  94.   {
  95.    "cell_type": "code",
  96.    "execution_count": 5,
  98.    "outputs": [
  99.     {
  100.      "data": {
  101.       "text/plain": [
  102.        "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
  103.       ]
  104.      },
  105.      "execution_count": 5,
  107.      "output_type": "execute_result"
  108.     }
  109.    ],
  110.    "source": [
  111.     "np.ones(10)*5"
  112.    ]
  113.   },
  114.   {
  115.    "cell_type": "markdown",
  117.    "source": [
  118.     "#### Create an array of the integers from 10 to 50"
  119.    ]
  120.   },
  121.   {
  122.    "cell_type": "code",
  123.    "execution_count": 6,
  125.    "outputs": [
  126.     {
  127.      "data": {
  128.       "text/plain": [
  129.        "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
  130.        "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
  131.        "       44, 45, 46, 47, 48, 49, 50])"
  132.       ]
  133.      },
  134.      "execution_count": 6,
  136.      "output_type": "execute_result"
  137.     }
  138.    ],
  139.    "source": [
  140.     "np.arange(10,51)"
  141.    ]
  142.   },
  143.   {
  144.    "cell_type": "markdown",
  146.    "source": [
  147.     "#### Create an array of all the even integers from 10 to 50"
  148.    ]
  149.   },
  150.   {
  151.    "cell_type": "code",
  152.    "execution_count": 7,
  154.     "collapsed": false,
  155.     "jupyter": {
  156.      "outputs_hidden": false
  157.     }
  158.    },
  159.    "outputs": [
  160.     {
  161.      "data": {
  162.       "text/plain": [
  163.        "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
  164.        "       44, 46, 48, 50])"
  165.       ]
  166.      },
  167.      "execution_count": 7,
  169.      "output_type": "execute_result"
  170.     }
  171.    ],
  172.    "source": [
  173.     "np.arange(10,51,2)"
  174.    ]
  175.   },
  176.   {
  177.    "cell_type": "markdown",
  179.    "source": [
  180.     "#### Create a 3x3 matrix with values ranging from 0 to 8"
  181.    ]
  182.   },
  183.   {
  184.    "cell_type": "code",
  185.    "execution_count": 9,
  187.    "outputs": [],
  188.    "source": [
  189.     "a = np.arange(9)"
  190.    ]
  191.   },
  192.   {
  193.    "cell_type": "code",
  194.    "execution_count": 10,
  196.    "outputs": [
  197.     {
  198.      "data": {
  199.       "text/plain": [
  200.        "array([0, 1, 2, 3, 4, 5, 6, 7, 8])"
  201.       ]
  202.      },
  203.      "execution_count": 10,
  205.      "output_type": "execute_result"
  206.     }
  207.    ],
  208.    "source": [
  209.     "a"
  210.    ]
  211.   },
  212.   {
  213.    "cell_type": "code",
  214.    "execution_count": 11,
  216.    "outputs": [
  217.     {
  218.      "data": {
  219.       "text/plain": [
  220.        "array([[0, 1, 2],\n",
  221.        "       [3, 4, 5],\n",
  222.        "       [6, 7, 8]])"
  223.       ]
  224.      },
  225.      "execution_count": 11,
  227.      "output_type": "execute_result"
  228.     }
  229.    ],
  230.    "source": [
  231.     "a.reshape(3,3)"
  232.    ]
  233.   },
  234.   {
  235.    "cell_type": "markdown",
  237.    "source": [
  238.     "#### Create a 3x3 identity matrix"
  239.    ]
  240.   },
  241.   {
  242.    "cell_type": "code",
  243.    "execution_count": 12,
  245.    "outputs": [
  246.     {
  247.      "data": {
  248.       "text/plain": [
  249.        "array([[1., 0., 0.],\n",
  250.        "       [0., 1., 0.],\n",
  251.        "       [0., 0., 1.]])"
  252.       ]
  253.      },
  254.      "execution_count": 12,
  256.      "output_type": "execute_result"
  257.     }
  258.    ],
  259.    "source": [
  260.     "np.eye(3)"
  261.    ]
  262.   },
  263.   {
  264.    "cell_type": "markdown",
  266.    "source": [
  267.     "#### Use NumPy to generate a random number between 0 and 1"
  268.    ]
  269.   },
  270.   {
  271.    "cell_type": "code",
  272.    "execution_count": 13,
  274.    "outputs": [
  275.     {
  276.      "data": {
  277.       "text/plain": [
  278.        "array([0.61895209])"
  279.       ]
  280.      },
  281.      "execution_count": 13,
  283.      "output_type": "execute_result"
  284.     }
  285.    ],
  286.    "source": [
  287.     "np.random.rand(1)"
  288.    ]
  289.   },
  290.   {
  291.    "cell_type": "markdown",
  293.    "source": [
  294.     "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
  295.    ]
  296.   },
  297.   {
  298.    "cell_type": "code",
  299.    "execution_count": 14,
  301.    "outputs": [
  302.     {
  303.      "data": {
  304.       "text/plain": [
  305.        "array([-0.5992264 , -0.07965042,  1.72113865,  0.06624785,  0.32592046,\n",
  306.        "       -0.02850312,  0.36569141, -0.61519631,  0.79396004, -0.10803859,\n",
  307.        "       -0.61303596, -0.72655377,  1.3243589 , -0.87795404, -0.23940706,\n",
  308.        "        0.17175587,  1.15218163, -1.74887861, -1.18665146, -0.50752569,\n",
  309.        "       -2.11234357,  1.20751702,  0.01478667,  1.0441076 , -1.11223452])"
  310.       ]
  311.      },
  312.      "execution_count": 14,
  314.      "output_type": "execute_result"
  315.     }
  316.    ],
  317.    "source": [
  318.     "np.random.randn(25)"
  319.    ]
  320.   },
  321.   {
  322.    "cell_type": "markdown",
  324.    "source": [
  325.     "#### Create the following matrix:"
  326.    ]
  327.   },
  328.   {
  329.    "cell_type": "code",
  330.    "execution_count": 20,
  332.    "outputs": [
  333.     {
  334.      "data": {
  335.       "text/plain": [
  336.        "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
  337.        "       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
  338.        "       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
  339.        "       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
  340.        "       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
  341.        "       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
  342.        "       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
  343.        "       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
  344.        "       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
  345.        "       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])"
  346.       ]
  347.      },
  348.      "execution_count": 20,
  350.      "output_type": "execute_result"
  351.     }
  352.    ],
  353.    "source": [
  354.     "np.arange(1,101).reshape(10,10)/100"
  355.    ]
  356.   },
  357.   {
  358.    "cell_type": "markdown",
  360.    "source": [
  361.     "#### Create an array of 20 linearly spaced points between 0 and 1:"
  362.    ]
  363.   },
  364.   {
  365.    "cell_type": "code",
  366.    "execution_count": 24,
  368.    "outputs": [
  369.     {
  370.      "data": {
  371.       "text/plain": [
  372.        "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
  373.        "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
  374.        "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
  375.        "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
  376.       ]
  377.      },
  378.      "execution_count": 24,
  380.      "output_type": "execute_result"
  381.     }
  382.    ],
  383.    "source": [
  384.     "np.linspace(0,1,20)"
  385.    ]
  386.   },
  387.   {
  388.    "cell_type": "markdown",
  390.    "source": [
  391.     "## Numpy Indexing and Selection\n",
  392.     "\n",
  393.     "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
  394.    ]
  395.   },
  396.   {
  397.    "cell_type": "code",
  398.    "execution_count": 25,
  400.     "collapsed": false,
  401.     "jupyter": {
  402.      "outputs_hidden": false
  403.     }
  404.    },
  405.    "outputs": [
  406.     {
  407.      "data": {
  408.       "text/plain": [
  409.        "array([[ 1,  2,  3,  4,  5],\n",
  410.        "       [ 6,  7,  8,  9, 10],\n",
  411.        "       [11, 12, 13, 14, 15],\n",
  412.        "       [16, 17, 18, 19, 20],\n",
  413.        "       [21, 22, 23, 24, 25]])"
  414.       ]
  415.      },
  416.      "execution_count": 25,
  418.      "output_type": "execute_result"
  419.     }
  420.    ],
  421.    "source": [
  422.     "mat = np.arange(1,26).reshape(5,5)\n",
  423.     "mat"
  424.    ]
  425.   },
  426.   {
  427.    "cell_type": "code",
  428.    "execution_count": 26,
  430.    "outputs": [],
  431.    "source": [
  432.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  433.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  434.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  435.    ]
  436.   },
  437.   {
  438.    "cell_type": "code",
  439.    "execution_count": 27,
  441.    "outputs": [
  442.     {
  443.      "data": {
  444.       "text/plain": [
  445.        "array([[12, 13, 14, 15],\n",
  446.        "       [17, 18, 19, 20],\n",
  447.        "       [22, 23, 24, 25]])"
  448.       ]
  449.      },
  450.      "execution_count": 27,
  452.      "output_type": "execute_result"
  453.     }
  454.    ],
  455.    "source": [
  456.     "mat[2:,1:]"
  457.    ]
  458.   },
  459.   {
  460.    "cell_type": "code",
  461.    "execution_count": 28,
  463.    "outputs": [],
  464.    "source": [
  465.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  466.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  467.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  468.    ]
  469.   },
  470.   {
  471.    "cell_type": "code",
  472.    "execution_count": 29,
  474.    "outputs": [
  475.     {
  476.      "data": {
  477.       "text/plain": [
  478.        "20"
  479.       ]
  480.      },
  481.      "execution_count": 29,
  483.      "output_type": "execute_result"
  484.     }
  485.    ],
  486.    "source": [
  487.     "mat[3,4]"
  488.    ]
  489.   },
  490.   {
  491.    "cell_type": "code",
  492.    "execution_count": 30,
  494.    "outputs": [],
  495.    "source": [
  496.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  497.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  498.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  499.    ]
  500.   },
  501.   {
  502.    "cell_type": "code",
  503.    "execution_count": 35,
  505.    "outputs": [
  506.     {
  507.      "data": {
  508.       "text/plain": [
  509.        "array([[ 2],\n",
  510.        "       [ 7],\n",
  511.        "       [12]])"
  512.       ]
  513.      },
  514.      "execution_count": 35,
  516.      "output_type": "execute_result"
  517.     }
  518.    ],
  519.    "source": [
  520.     "mat[:3,1:2]"
  521.    ]
  522.   },
  523.   {
  524.    "cell_type": "code",
  525.    "execution_count": 36,
  527.    "outputs": [],
  528.    "source": [
  529.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  530.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  531.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  532.    ]
  533.   },
  534.   {
  535.    "cell_type": "code",
  536.    "execution_count": 37,
  538.    "outputs": [
  539.     {
  540.      "data": {
  541.       "text/plain": [
  542.        "array([21, 22, 23, 24, 25])"
  543.       ]
  544.      },
  545.      "execution_count": 37,
  547.      "output_type": "execute_result"
  548.     }
  549.    ],
  550.    "source": [
  551.     "mat[4]"
  552.    ]
  553.   },
  554.   {
  555.    "cell_type": "code",
  556.    "execution_count": 38,
  558.    "outputs": [],
  559.    "source": [
  560.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  561.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  562.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  563.    ]
  564.   },
  565.   {
  566.    "cell_type": "code",
  567.    "execution_count": 40,
  569.    "outputs": [
  570.     {
  571.      "data": {
  572.       "text/plain": [
  573.        "array([[16, 17, 18, 19, 20],\n",
  574.        "       [21, 22, 23, 24, 25]])"
  575.       ]
  576.      },
  577.      "execution_count": 40,
  579.      "output_type": "execute_result"
  580.     }
  581.    ],
  582.    "source": [
  583.     "mat[3:]"
  584.    ]
  585.   },
  586.   {
  587.    "cell_type": "markdown",
  589.    "source": [
  590.     "#### Get the sum of all the values in mat"
  591.    ]
  592.   },
  593.   {
  594.    "cell_type": "code",
  595.    "execution_count": 41,
  597.    "outputs": [
  598.     {
  599.      "data": {
  600.       "text/plain": [
  601.        "325"
  602.       ]
  603.      },
  604.      "execution_count": 41,
  606.      "output_type": "execute_result"
  607.     }
  608.    ],
  609.    "source": [
  610.     "mat.sum()"
  611.    ]
  612.   },
  613.   {
  614.    "cell_type": "markdown",
  616.    "source": [
  617.     "#### Get the standard deviation of the values in mat"
  618.    ]
  619.   },
  620.   {
  621.    "cell_type": "code",
  622.    "execution_count": 42,
  624.    "outputs": [
  625.     {
  626.      "data": {
  627.       "text/plain": [
  628.        "7.211102550927978"
  629.       ]
  630.      },
  631.      "execution_count": 42,
  633.      "output_type": "execute_result"
  634.     }
  635.    ],
  636.    "source": [
  637.     "mat.std()"
  638.    ]
  639.   },
  640.   {
  641.    "cell_type": "markdown",
  643.    "source": [
  644.     "#### Get the sum of all the columns in mat"
  645.    ]
  646.   },
  647.   {
  648.    "cell_type": "code",
  649.    "execution_count": 44,
  651.    "outputs": [
  652.     {
  653.      "data": {
  654.       "text/plain": [
  655.        "array([55, 60, 65, 70, 75])"
  656.       ]
  657.      },
  658.      "execution_count": 44,
  660.      "output_type": "execute_result"
  661.     }
  662.    ],
  663.    "source": [
  664.     "mat.sum(0)"
  665.    ]
  666.   }
  667.  ],
  669.   "kernelspec": {
  670.    "display_name": "Python",
  671.    "language": "python",
  672.    "name": "conda-env-python-py"
  673.   },
  674.   "language_info": {
  675.    "codemirror_mode": {
  676.     "name": "ipython",
  677.     "version": 3
  678.    },
  679.    "file_extension": ".py",
  680.    "mimetype": "text/x-python",
  681.    "name": "python",
  682.    "nbconvert_exporter": "python",
  683.    "pygments_lexer": "ipython3",
  684.    "version": "3.6.7"
  685.   }
  686.  },
  687.  "nbformat": 4,
  688.  "nbformat_minor": 4
  689. }
  RAW Paste Data
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explanation: The provided text extract is a Jupyter Notebook containing a series of NumPy exercises. Each exercise is presented as a question, followed by a code cell that provides the answer. The questions range from creating simple arrays to more complex operations like matrix manipulation and statistical calculations.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
  Output rules:
  
  1. Briefly describe the document and mention if it contains a question or a question and answer.
  2. Finalize the output with: Label: no-question,question,question-and-answer,multiple-question,multiple-question-and-answer
distilabel_metadata.role: system
distilabel_metadata.content: • API
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  # Untitled
  
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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "markdown",
  6.    "source": [
  7.     "# NumPy Exercises \n",
  8.     "\n",
  9.     "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."
  10.    ]
  11.   },
  12.   {
  13.    "cell_type": "markdown",
  15.    "source": [
  16.     "#### Import NumPy as np"
  17.    ]
  18.   },
  19.   {
  20.    "cell_type": "code",
  21.    "execution_count": 2,
  23.    "outputs": [],
  24.    "source": [
  25.     "import numpy as np"
  26.    ]
  27.   },
  28.   {
  29.    "cell_type": "markdown",
  31.    "source": [
  32.     "#### Create an array of 10 zeros "
  33.    ]
  34.   },
  35.   {
  36.    "cell_type": "code",
  37.    "execution_count": 3,
  39.     "collapsed": false,
  40.     "jupyter": {
  41.      "outputs_hidden": false
  42.     }
  43.    },
  44.    "outputs": [
  45.     {
  46.      "data": {
  47.       "text/plain": [
  48.        "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
  49.       ]
  50.      },
  51.      "execution_count": 3,
  53.      "output_type": "execute_result"
  54.     }
  55.    ],
  56.    "source": [
  57.     "np.zeros(10)"
  58.    ]
  59.   },
  60.   {
  61.    "cell_type": "markdown",
  63.    "source": [
  64.     "#### Create an array of 10 ones"
  65.    ]
  66.   },
  67.   {
  68.    "cell_type": "code",
  69.    "execution_count": 4,
  71.    "outputs": [
  72.     {
  73.      "data": {
  74.       "text/plain": [
  75.        "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
  76.       ]
  77.      },
  78.      "execution_count": 4,
  80.      "output_type": "execute_result"
  81.     }
  82.    ],
  83.    "source": [
  84.     "np.ones(10)"
  85.    ]
  86.   },
  87.   {
  88.    "cell_type": "markdown",
  90.    "source": [
  91.     "#### Create an array of 10 fives"
  92.    ]
  93.   },
  94.   {
  95.    "cell_type": "code",
  96.    "execution_count": 5,
  98.    "outputs": [
  99.     {
  100.      "data": {
  101.       "text/plain": [
  102.        "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
  103.       ]
  104.      },
  105.      "execution_count": 5,
  107.      "output_type": "execute_result"
  108.     }
  109.    ],
  110.    "source": [
  111.     "np.ones(10)*5"
  112.    ]
  113.   },
  114.   {
  115.    "cell_type": "markdown",
  117.    "source": [
  118.     "#### Create an array of the integers from 10 to 50"
  119.    ]
  120.   },
  121.   {
  122.    "cell_type": "code",
  123.    "execution_count": 6,
  125.    "outputs": [
  126.     {
  127.      "data": {
  128.       "text/plain": [
  129.        "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
  130.        "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
  131.        "       44, 45, 46, 47, 48, 49, 50])"
  132.       ]
  133.      },
  134.      "execution_count": 6,
  136.      "output_type": "execute_result"
  137.     }
  138.    ],
  139.    "source": [
  140.     "np.arange(10,51)"
  141.    ]
  142.   },
  143.   {
  144.    "cell_type": "markdown",
  146.    "source": [
  147.     "#### Create an array of all the even integers from 10 to 50"
  148.    ]
  149.   },
  150.   {
  151.    "cell_type": "code",
  152.    "execution_count": 7,
  154.     "collapsed": false,
  155.     "jupyter": {
  156.      "outputs_hidden": false
  157.     }
  158.    },
  159.    "outputs": [
  160.     {
  161.      "data": {
  162.       "text/plain": [
  163.        "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
  164.        "       44, 46, 48, 50])"
  165.       ]
  166.      },
  167.      "execution_count": 7,
  169.      "output_type": "execute_result"
  170.     }
  171.    ],
  172.    "source": [
  173.     "np.arange(10,51,2)"
  174.    ]
  175.   },
  176.   {
  177.    "cell_type": "markdown",
  179.    "source": [
  180.     "#### Create a 3x3 matrix with values ranging from 0 to 8"
  181.    ]
  182.   },
  183.   {
  184.    "cell_type": "code",
  185.    "execution_count": 9,
  187.    "outputs": [],
  188.    "source": [
  189.     "a = np.arange(9)"
  190.    ]
  191.   },
  192.   {
  193.    "cell_type": "code",
  194.    "execution_count": 10,
  196.    "outputs": [
  197.     {
  198.      "data": {
  199.       "text/plain": [
  200.        "array([0, 1, 2, 3, 4, 5, 6, 7, 8])"
  201.       ]
  202.      },
  203.      "execution_count": 10,
  205.      "output_type": "execute_result"
  206.     }
  207.    ],
  208.    "source": [
  209.     "a"
  210.    ]
  211.   },
  212.   {
  213.    "cell_type": "code",
  214.    "execution_count": 11,
  216.    "outputs": [
  217.     {
  218.      "data": {
  219.       "text/plain": [
  220.        "array([[0, 1, 2],\n",
  221.        "       [3, 4, 5],\n",
  222.        "       [6, 7, 8]])"
  223.       ]
  224.      },
  225.      "execution_count": 11,
  227.      "output_type": "execute_result"
  228.     }
  229.    ],
  230.    "source": [
  231.     "a.reshape(3,3)"
  232.    ]
  233.   },
  234.   {
  235.    "cell_type": "markdown",
  237.    "source": [
  238.     "#### Create a 3x3 identity matrix"
  239.    ]
  240.   },
  241.   {
  242.    "cell_type": "code",
  243.    "execution_count": 12,
  245.    "outputs": [
  246.     {
  247.      "data": {
  248.       "text/plain": [
  249.        "array([[1., 0., 0.],\n",
  250.        "       [0., 1., 0.],\n",
  251.        "       [0., 0., 1.]])"
  252.       ]
  253.      },
  254.      "execution_count": 12,
  256.      "output_type": "execute_result"
  257.     }
  258.    ],
  259.    "source": [
  260.     "np.eye(3)"
  261.    ]
  262.   },
  263.   {
  264.    "cell_type": "markdown",
  266.    "source": [
  267.     "#### Use NumPy to generate a random number between 0 and 1"
  268.    ]
  269.   },
  270.   {
  271.    "cell_type": "code",
  272.    "execution_count": 13,
  274.    "outputs": [
  275.     {
  276.      "data": {
  277.       "text/plain": [
  278.        "array([0.61895209])"
  279.       ]
  280.      },
  281.      "execution_count": 13,
  283.      "output_type": "execute_result"
  284.     }
  285.    ],
  286.    "source": [
  287.     "np.random.rand(1)"
  288.    ]
  289.   },
  290.   {
  291.    "cell_type": "markdown",
  293.    "source": [
  294.     "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
  295.    ]
  296.   },
  297.   {
  298.    "cell_type": "code",
  299.    "execution_count": 14,
  301.    "outputs": [
  302.     {
  303.      "data": {
  304.       "text/plain": [
  305.        "array([-0.5992264 , -0.07965042,  1.72113865,  0.06624785,  0.32592046,\n",
  306.        "       -0.02850312,  0.36569141, -0.61519631,  0.79396004, -0.10803859,\n",
  307.        "       -0.61303596, -0.72655377,  1.3243589 , -0.87795404, -0.23940706,\n",
  308.        "        0.17175587,  1.15218163, -1.74887861, -1.18665146, -0.50752569,\n",
  309.        "       -2.11234357,  1.20751702,  0.01478667,  1.0441076 , -1.11223452])"
  310.       ]
  311.      },
  312.      "execution_count": 14,
  314.      "output_type": "execute_result"
  315.     }
  316.    ],
  317.    "source": [
  318.     "np.random.randn(25)"
  319.    ]
  320.   },
  321.   {
  322.    "cell_type": "markdown",
  324.    "source": [
  325.     "#### Create the following matrix:"
  326.    ]
  327.   },
  328.   {
  329.    "cell_type": "code",
  330.    "execution_count": 20,
  332.    "outputs": [
  333.     {
  334.      "data": {
  335.       "text/plain": [
  336.        "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
  337.        "       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
  338.        "       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
  339.        "       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
  340.        "       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
  341.        "       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
  342.        "       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
  343.        "       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
  344.        "       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
  345.        "       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])"
  346.       ]
  347.      },
  348.      "execution_count": 20,
  350.      "output_type": "execute_result"
  351.     }
  352.    ],
  353.    "source": [
  354.     "np.arange(1,101).reshape(10,10)/100"
  355.    ]
  356.   },
  357.   {
  358.    "cell_type": "markdown",
  360.    "source": [
  361.     "#### Create an array of 20 linearly spaced points between 0 and 1:"
  362.    ]
  363.   },
  364.   {
  365.    "cell_type": "code",
  366.    "execution_count": 24,
  368.    "outputs": [
  369.     {
  370.      "data": {
  371.       "text/plain": [
  372.        "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
  373.        "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
  374.        "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
  375.        "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
  376.       ]
  377.      },
  378.      "execution_count": 24,
  380.      "output_type": "execute_result"
  381.     }
  382.    ],
  383.    "source": [
  384.     "np.linspace(0,1,20)"
  385.    ]
  386.   },
  387.   {
  388.    "cell_type": "markdown",
  390.    "source": [
  391.     "## Numpy Indexing and Selection\n",
  392.     "\n",
  393.     "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
  394.    ]
  395.   },
  396.   {
  397.    "cell_type": "code",
  398.    "execution_count": 25,
  400.     "collapsed": false,
  401.     "jupyter": {
  402.      "outputs_hidden": false
  403.     }
  404.    },
  405.    "outputs": [
  406.     {
  407.      "data": {
  408.       "text/plain": [
  409.        "array([[ 1,  2,  3,  4,  5],\n",
  410.        "       [ 6,  7,  8,  9, 10],\n",
  411.        "       [11, 12, 13, 14, 15],\n",
  412.        "       [16, 17, 18, 19, 20],\n",
  413.        "       [21, 22, 23, 24, 25]])"
  414.       ]
  415.      },
  416.      "execution_count": 25,
  418.      "output_type": "execute_result"
  419.     }
  420.    ],
  421.    "source": [
  422.     "mat = np.arange(1,26).reshape(5,5)\n",
  423.     "mat"
  424.    ]
  425.   },
  426.   {
  427.    "cell_type": "code",
  428.    "execution_count": 26,
  430.    "outputs": [],
  431.    "source": [
  432.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  433.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  434.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  435.    ]
  436.   },
  437.   {
  438.    "cell_type": "code",
  439.    "execution_count": 27,
  441.    "outputs": [
  442.     {
  443.      "data": {
  444.       "text/plain": [
  445.        "array([[12, 13, 14, 15],\n",
  446.        "       [17, 18, 19, 20],\n",
  447.        "       [22, 23, 24, 25]])"
  448.       ]
  449.      },
  450.      "execution_count": 27,
  452.      "output_type": "execute_result"
  453.     }
  454.    ],
  455.    "source": [
  456.     "mat[2:,1:]"
  457.    ]
  458.   },
  459.   {
  460.    "cell_type": "code",
  461.    "execution_count": 28,
  463.    "outputs": [],
  464.    "source": [
  465.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  466.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  467.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  468.    ]
  469.   },
  470.   {
  471.    "cell_type": "code",
  472.    "execution_count": 29,
  474.    "outputs": [
  475.     {
  476.      "data": {
  477.       "text/plain": [
  478.        "20"
  479.       ]
  480.      },
  481.      "execution_count": 29,
  483.      "output_type": "execute_result"
  484.     }
  485.    ],
  486.    "source": [
  487.     "mat[3,4]"
  488.    ]
  489.   },
  490.   {
  491.    "cell_type": "code",
  492.    "execution_count": 30,
  494.    "outputs": [],
  495.    "source": [
  496.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  497.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  498.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  499.    ]
  500.   },
  501.   {
  502.    "cell_type": "code",
  503.    "execution_count": 35,
  505.    "outputs": [
  506.     {
  507.      "data": {
  508.       "text/plain": [
  509.        "array([[ 2],\n",
  510.        "       [ 7],\n",
  511.        "       [12]])"
  512.       ]
  513.      },
  514.      "execution_count": 35,
  516.      "output_type": "execute_result"
  517.     }
  518.    ],
  519.    "source": [
  520.     "mat[:3,1:2]"
  521.    ]
  522.   },
  523.   {
  524.    "cell_type": "code",
  525.    "execution_count": 36,
  527.    "outputs": [],
  528.    "source": [
  529.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  530.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  531.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  532.    ]
  533.   },
  534.   {
  535.    "cell_type": "code",
  536.    "execution_count": 37,
  538.    "outputs": [
  539.     {
  540.      "data": {
  541.       "text/plain": [
  542.        "array([21, 22, 23, 24, 25])"
  543.       ]
  544.      },
  545.      "execution_count": 37,
  547.      "output_type": "execute_result"
  548.     }
  549.    ],
  550.    "source": [
  551.     "mat[4]"
  552.    ]
  553.   },
  554.   {
  555.    "cell_type": "code",
  556.    "execution_count": 38,
  558.    "outputs": [],
  559.    "source": [
  560.     "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
  561.     "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
  562.     "# BE ABLE TO SEE THE OUTPUT ANY MORE"
  563.    ]
  564.   },
  565.   {
  566.    "cell_type": "code",
  567.    "execution_count": 40,
  569.    "outputs": [
  570.     {
  571.      "data": {
  572.       "text/plain": [
  573.        "array([[16, 17, 18, 19, 20],\n",
  574.        "       [21, 22, 23, 24, 25]])"
  575.       ]
  576.      },
  577.      "execution_count": 40,
  579.      "output_type": "execute_result"
  580.     }
  581.    ],
  582.    "source": [
  583.     "mat[3:]"
  584.    ]
  585.   },
  586.   {
  587.    "cell_type": "markdown",
  589.    "source": [
  590.     "#### Get the sum of all the values in mat"
  591.    ]
  592.   },
  593.   {
  594.    "cell_type": "code",
  595.    "execution_count": 41,
  597.    "outputs": [
  598.     {
  599.      "data": {
  600.       "text/plain": [
  601.        "325"
  602.       ]
  603.      },
  604.      "execution_count": 41,
  606.      "output_type": "execute_result"
  607.     }
  608.    ],
  609.    "source": [
  610.     "mat.sum()"
  611.    ]
  612.   },
  613.   {
  614.    "cell_type": "markdown",
  616.    "source": [
  617.     "#### Get the standard deviation of the values in mat"
  618.    ]
  619.   },
  620.   {
  621.    "cell_type": "code",
  622.    "execution_count": 42,
  624.    "outputs": [
  625.     {
  626.      "data": {
  627.       "text/plain": [
  628.        "7.211102550927978"
  629.       ]
  630.      },
  631.      "execution_count": 42,
  633.      "output_type": "execute_result"
  634.     }
  635.    ],
  636.    "source": [
  637.     "mat.std()"
  638.    ]
  639.   },
  640.   {
  641.    "cell_type": "markdown",
  643.    "source": [
  644.     "#### Get the sum of all the columns in mat"
  645.    ]
  646.   },
  647.   {
  648.    "cell_type": "code",
  649.    "execution_count": 44,
  651.    "outputs": [
  652.     {
  653.      "data": {
  654.       "text/plain": [
  655.        "array([55, 60, 65, 70, 75])"
  656.       ]
  657.      },
  658.      "execution_count": 44,
  660.      "output_type": "execute_result"
  661.     }
  662.    ],
  663.    "source": [
  664.     "mat.sum(0)"
  665.    ]
  666.   }
  667.  ],
  669.   "kernelspec": {
  670.    "display_name": "Python",
  671.    "language": "python",
  672.    "name": "conda-env-python-py"
  673.   },
  674.   "language_info": {
  675.    "codemirror_mode": {
  676.     "name": "ipython",
  677.     "version": 3
  678.    },
  679.    "file_extension": ".py",
  680.    "mimetype": "text/x-python",
  681.    "name": "python",
  682.    "nbconvert_exporter": "python",
  683.    "pygments_lexer": "ipython3",
  684.    "version": "3.6.7"
  685.   }
  686.  },
  687.  "nbformat": 4,
  688.  "nbformat_minor": 4
  689. }
  RAW Paste Data
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distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The provided text extract is a Jupyter Notebook containing a series of NumPy exercises. Each exercise is presented as a question, followed by a code cell that provides the answer. The questions range from creating simple arrays to more complex operations like matrix manipulation and statistical calculations.
  
  Label: multiple-question-and-answer