diff --git a/.ipynb_checkpoints/Lab Numpy-checkpoint.ipynb b/.ipynb_checkpoints/Lab Numpy-checkpoint.ipynb new file mode 100644 index 0000000..6a64603 --- /dev/null +++ b/.ipynb_checkpoints/Lab Numpy-checkpoint.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.16.4\n" + ] + } + ], + "source": [ + "print(np.version.version)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "a=np.random.randint(low=0,high=10,size=(2,3,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[5 3 7 7 7]\n", + " [1 8 8 2 6]\n", + " [9 1 3 6 9]]\n", + "\n", + " [[6 1 6 9 7]\n", + " [0 0 4 3 9]\n", + " [8 4 7 5 1]]]\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.ones((5,2,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]]\n" + ] + } + ], + "source": [ + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "30\n", + "True\n" + ] + } + ], + "source": [ + "print(a.size)\n", + "print(b.size)\n", + "\n", + "if a.size == b.size:\n", + " print(True)\n", + "else:\n", + " print(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 3, 5)\n", + "(5, 2, 3)\n", + "Not the same size\n" + ] + } + ], + "source": [ + "print(a.shape)\n", + "print(b.shape)\n", + "\n", + "if a.shape == b.shape:\n", + " print(a+b)\n", + "else:\n", + " print(\"Not the same size\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]]\n", + "\n", + " [[1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]]]\n" + ] + } + ], + "source": [ + "c= np.reshape(b,(2,3,5))\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 6. 4. 8. 8. 8.]\n", + " [ 2. 9. 9. 3. 7.]\n", + " [10. 2. 4. 7. 10.]]\n", + "\n", + " [[ 7. 2. 7. 10. 8.]\n", + " [ 1. 1. 5. 4. 10.]\n", + " [ 9. 5. 8. 6. 2.]]]\n" + ] + } + ], + "source": [ + "d = np.add(a,c)\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[5. 3. 7. 7. 7.]\n", + " [1. 8. 8. 2. 6.]\n", + " [9. 1. 3. 6. 9.]]\n", + "\n", + " [[6. 1. 6. 9. 7.]\n", + " [0. 0. 4. 3. 9.]\n", + " [8. 4. 7. 5. 1.]]]\n" + ] + } + ], + "source": [ + "e=np.multiply(a,c)\n", + "print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0\n", + "1.0\n", + "6.066666666666666\n" + ] + } + ], + "source": [ + "d_max=np.max(d)\n", + "print(d_max)\n", + "\n", + "d_min=np.min(d)\n", + "print(d_min)\n", + "\n", + "d_mean=np.mean(d)\n", + "print(d_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]]]\n" + ] + } + ], + "source": [ + "f=np.zeros((2,3,5))\n", + "print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 25. 25. 75. 75. 75.]\n", + " [ 25. 75. 75. 25. 75.]\n", + " [100. 25. 25. 75. 100.]]\n", + "\n", + " [[ 75. 25. 75. 100. 75.]\n", + " [ 0. 0. 25. 25. 100.]\n", + " [ 75. 25. 75. 25. 25.]]]\n" + ] + } + ], + "source": [ + "for j in range(len(d)):\n", + " for k in range(len(d[0])):\n", + " for l in range(len(d[0,0])):\n", + " if d[j,k,l] > d_min and d[j,k,l] < d_mean:\n", + " f[j,k,l] = 25\n", + " elif d[j,k,l]>d_mean and d[j,k,l] < d_max:\n", + " f[j,k,l]=75\n", + " elif d[j,k,l] == d_mean:\n", + " f[j,k,l]=50\n", + " elif d[j,k,l] == d_min:\n", + " f[j,k,l]=0\n", + " else:\n", + " f[j,k,l]=100\n", + "print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'numpy.dtype' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0md_min\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0md_mean\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mf1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"l\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[0md_mean\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0md_max\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mf1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"b\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: 'numpy.dtype' object does not support item assignment" + ] + } + ], + "source": [ + "f1=np.dtype(object)\n", + "for j in range(len(d)):\n", + " for k in range(len(d[0])):\n", + " for l in range(len(d[0,0])):\n", + " if d[j,k,l] > d_min and d[j,k,l] < d_mean:\n", + " f1[j,k,l] = \"l\"\n", + " elif d[j,k,l]>d_mean and d[j,k,l] < d_max:\n", + " f1[j,k,l]=\"b\"\n", + " elif d[j,k,l] == d_mean:\n", + " f1[j,k,l]=\"c\"\n", + " elif d[j,k,l] == d_min:\n", + " f1[j,k,l]=\"d\"\n", + " else:\n", + " f1[j,k,l]=\"e\"\n", + "\n", + "print(f1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": 2 +} diff --git a/Lab Numpy.ipynb b/Lab Numpy.ipynb new file mode 100644 index 0000000..1cc7803 --- /dev/null +++ b/Lab Numpy.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.16.4\n" + ] + } + ], + "source": [ + "print(np.version.version)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "a=np.random.randint(low=0,high=10,size=(2,3,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[5 3 7 7 7]\n", + " [1 8 8 2 6]\n", + " [9 1 3 6 9]]\n", + "\n", + " [[6 1 6 9 7]\n", + " [0 0 4 3 9]\n", + " [8 4 7 5 1]]]\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.ones((5,2,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "\n", + " [[1. 1. 1.]\n", + " [1. 1. 1.]]]\n" + ] + } + ], + "source": [ + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "30\n", + "True\n" + ] + } + ], + "source": [ + "print(a.size)\n", + "print(b.size)\n", + "\n", + "if a.size == b.size:\n", + " print(True)\n", + "else:\n", + " print(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 3, 5)\n", + "(5, 2, 3)\n", + "Not the same size\n" + ] + } + ], + "source": [ + "print(a.shape)\n", + "print(b.shape)\n", + "\n", + "if a.shape == b.shape:\n", + " print(a+b)\n", + "else:\n", + " print(\"Not the same size\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]]\n", + "\n", + " [[1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]]]\n" + ] + } + ], + "source": [ + "c= np.reshape(b,(2,3,5))\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 6. 4. 8. 8. 8.]\n", + " [ 2. 9. 9. 3. 7.]\n", + " [10. 2. 4. 7. 10.]]\n", + "\n", + " [[ 7. 2. 7. 10. 8.]\n", + " [ 1. 1. 5. 4. 10.]\n", + " [ 9. 5. 8. 6. 2.]]]\n" + ] + } + ], + "source": [ + "d = np.add(a,c)\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[5. 3. 7. 7. 7.]\n", + " [1. 8. 8. 2. 6.]\n", + " [9. 1. 3. 6. 9.]]\n", + "\n", + " [[6. 1. 6. 9. 7.]\n", + " [0. 0. 4. 3. 9.]\n", + " [8. 4. 7. 5. 1.]]]\n" + ] + } + ], + "source": [ + "e=np.multiply(a,c)\n", + "print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0\n", + "1.0\n", + "6.066666666666666\n" + ] + } + ], + "source": [ + "d_max=np.max(d)\n", + "print(d_max)\n", + "\n", + "d_min=np.min(d)\n", + "print(d_min)\n", + "\n", + "d_mean=np.mean(d)\n", + "print(d_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]]]\n" + ] + } + ], + "source": [ + "f=np.zeros((2,3,5))\n", + "print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 25. 25. 75. 75. 75.]\n", + " [ 25. 75. 75. 25. 75.]\n", + " [100. 25. 25. 75. 100.]]\n", + "\n", + " [[ 75. 25. 75. 100. 75.]\n", + " [ 0. 0. 25. 25. 100.]\n", + " [ 75. 25. 75. 25. 25.]]]\n" + ] + } + ], + "source": [ + "for j in range(len(d)):\n", + " for k in range(len(d[0])):\n", + " for l in range(len(d[0,0])):\n", + " if d[j,k,l] > d_min and d[j,k,l] < d_mean:\n", + " f[j,k,l] = 25\n", + " elif d[j,k,l]>d_mean and d[j,k,l] < d_max:\n", + " f[j,k,l]=75\n", + " elif d[j,k,l] == d_mean:\n", + " f[j,k,l]=50\n", + " elif d[j,k,l] == d_min:\n", + " f[j,k,l]=0\n", + " else:\n", + " f[j,k,l]=100\n", + "print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[['l' 'l' 'b' 'b' 'b']\n", + " ['l' 'b' 'b' 'l' 'b']\n", + " ['e' 'l' 'l' 'b' 'e']]\n", + "\n", + " [['b' 'l' 'b' 'e' 'b']\n", + " ['d' 'd' 'l' 'l' 'e']\n", + " ['b' 'l' 'b' 'l' 'l']]]\n" + ] + } + ], + "source": [ + "ff=np.empty((2,3,5), dtype=\"object\")\n", + " \n", + "for j in range(len(d)):\n", + " for k in range(len(d[0])):\n", + " for l in range(len(d[0,0])):\n", + " if d[j,k,l] > d_min and d[j,k,l] < d_mean:\n", + " ff[j,k,l] = \"l\"\n", + " elif d[j,k,l]>d_mean and d[j,k,l] < d_max:\n", + " ff[j,k,l]=\"b\"\n", + " elif d[j,k,l] == d_mean:\n", + " ff[j,k,l]=\"c\"\n", + " elif d[j,k,l] == d_min:\n", + " ff[j,k,l]=\"d\"\n", + " else:\n", + " ff[j,k,l]=\"e\"\n", + "\n", + "print(ff)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": 2 +} diff --git a/your-code/main.py b/your-code/main.py index 78c792b..48d7e68 100644 --- a/your-code/main.py +++ b/your-code/main.py @@ -1,72 +1,78 @@ #1. Import the NUMPY package under the name np. - - - +import numpy as np #2. Print the NUMPY version and the configuration. - - +print(np.version.version) #3. Generate a 2x3x5 3-dimensional array with random values. Assign the array to variable "a" # Challenge: there are at least three easy ways that use numpy to generate random arrays. How many ways can you find? - - +a=np.random.randint(low=0,high=10,size=(2,3,5)) #4. Print a. - - +print(a) #5. Create a 5x2x3 3-dimensional array with all values equaling 1. #Assign the array to variable "b" - - +b = np.ones((5,2,3)) #6. Print b. - - +print(b) #7. Do a and b have the same size? How do you prove that in Python code? +print(a.size) +print(b.size) - - +if a.size == b.size: + print(True) +else: + print(False) #8. Are you able to add a and b? Why or why not? +#No, they are not the same shape. +print(a.shape) +print(b.shape) - +if a.shape == b.shape: + print(a+b) +else: + print("Not the same size") #9. Transpose b so that it has the same structure of a (i.e. become a 2x3x5 array). Assign the transposed array to varialbe "c". - - +c= np.reshape(b,(2,3,5)) +print(c) #10. Try to add a and c. Now it should work. Assign the sum to varialbe "d". But why does it work now? - - +d = np.add(a,c) +print(d) #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain. - - - +print(a) +print(d) +#Each value in D is 1(one) unit bigger in A #12. Multiply a and c. Assign the result to e. - - +e=np.multiply(a,c) +print(e) #13. Does e equal to a? Why or why not? - - - +#Yes. C is a matrix of all 1s(ones) #14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean" +d_max=np.max(d) +print(d_max) +d_min=np.min(d) +print(d_min) - +d_mean=np.mean(d) +print(d_mean) #15. Now we want to label the values in d. First create an empty array "f" with the same shape (i.e. 2x3x5) as d using `np.empty`. - - - +f=np.zeros((2,3,5)) +print(f) """ -#16. Populate the values in f. For each value in d, if it's larger than d_min but smaller than d_mean, assign 25 to the corresponding value in f. +#16. Populate the values in f. For each value in d: +- if it's larger than d_min but smaller than d_mean, assign 25 to the corresponding value in f. If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f. If a value equals to d_mean, assign 50 to the corresponding value in f. Assign 0 to the corresponding value(s) in f for d_min in d. @@ -75,6 +81,19 @@ Note: you don't have to use Numpy in this question. """ +for j in range(len(d)): + for k in range(len(d[0])): + for l in range(len(d[0,0])): + if d[j,k,l] > d_min and d[j,k,l] < d_mean: + f[j,k,l] = 25 + elif d[j,k,l]>d_mean and d[j,k,l] < d_max: + f[j,k,l]=75 + elif d[j,k,l] == d_mean: + f[j,k,l]=50 + elif d[j,k,l] == d_min: + f[j,k,l]=0 + else: + f[j,k,l]=100 @@ -98,7 +117,7 @@ [ 75., 75., 75., 75., 75.], [ 25., 75., 0., 75., 75.]]]) """ - +print(f) """ #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values @@ -111,4 +130,21 @@ [ 'D', 'D', 'D', 'D', 'D'], [ 'B', 'D', 'A', 'D', 'D']]]) Again, you don't need Numpy in this question. -""" \ No newline at end of file +""" +ff=np.empty((2,3,5), dtype="object") + +for j in range(len(d)): + for k in range(len(d[0])): + for l in range(len(d[0,0])): + if d[j,k,l] > d_min and d[j,k,l] < d_mean: + ff[j,k,l] = "l" + elif d[j,k,l]>d_mean and d[j,k,l] < d_max: + ff[j,k,l]="b" + elif d[j,k,l] == d_mean: + ff[j,k,l]="c" + elif d[j,k,l] == d_min: + ff[j,k,l]="d" + else: + ff[j,k,l]="e" + +print(ff) \ No newline at end of file