From b6d0d5b3133b62e1b489072a15b389636d825cac Mon Sep 17 00:00:00 2001
From: ferRm <fernando.rojas.mtz@gmail.com>
Date: Wed, 23 Oct 2019 15:48:01 -0500
Subject: [PATCH] DE-DA-PT-Fernando

---
 .DS_Store                                     | Bin 0 -> 6148 bytes
 your-code/.DS_Store                           | Bin 0 -> 6148 bytes
 .../.ipynb_checkpoints/main-checkpoint.ipynb  |   6 +
 your-code/main.ipynb                          | 525 ++++++++++++++++++
 4 files changed, 531 insertions(+)
 create mode 100644 .DS_Store
 create mode 100644 your-code/.DS_Store
 create mode 100644 your-code/.ipynb_checkpoints/main-checkpoint.ipynb
 create mode 100644 your-code/main.ipynb

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..3dcd9092d222cf11a39611e0626a9bc24356afde
GIT binary patch
literal 6148
zcmeHKJ5EC}5S)b+L1|J_`T{AqffXeu-~xy|x=2q7N<sH3&c)G~{S-tGy3j<k(t7Om
zjxA5|_ALP0o`)x34PZ%k#HSB)^L_W3-BrYhbe^%n0V4*y4ezt;-viElzz11poPP2T
z$JcQ@`nF68NC7Dz1*Cu!xS|48T$igWo~fgxfE2h51^oNa=#IT`N{mkjhiCza3x>lu
zk6wb<JV5M)Qz9cYODZv`RxO4lo$*$Ay>LoQI;@%xtDCJl6pPz=ev5QiPt+&{q`;*D
z_qkkq{om97=>IQCT1f#Za8nA{V)wk;@<~-&XOHt*+vqQJ&-tReaUK*7QI3gGj=Au1
e{1{1@*L=?XUN|KNo$;U(^)ukQ$fUr3EAS16%oQL2

literal 0
HcmV?d00001

diff --git a/your-code/.DS_Store b/your-code/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
GIT binary patch
literal 6148
zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3
zem<@ulZcFPQ@L2!n>{z**<q8>++&mCkOWA81W14cNZ<zv;LbK1Poaz?KmsK2CSc!(
z0ynLxE!0092;Krf2c+FF_Fe*7ECH>lEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ
zLs35+`xjp>T0<F0fCPF1$Cyrb|F7^5{eNG?83~ZUUlGt@xh*qZDeu<Z%US-OSsOPv
j)R!Z4KLME7ReXlK;d!wEw5GODWMKRea10D2@KpjYNUI8I

literal 0
HcmV?d00001

diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
new file mode 100644
index 0000000..e586ee0
--- /dev/null
+++ b/your-code/main.ipynb
@@ -0,0 +1,525 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#1. Import the NUMPY package under the name np.\n",
+    "\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.16.4'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#2. Print the NUMPY version and the configuration.\n",
+    "\n",
+    "np.version.version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.21147871 0.75804237 0.9115814  0.40667329 0.53182288]\n",
+      "  [0.98374763 0.54271426 0.88799577 0.13082761 0.55748123]\n",
+      "  [0.30306828 0.86861946 0.7067565  0.21372597 0.89081739]]\n",
+      "\n",
+      " [[0.8598921  0.02632583 0.90106924 0.07250084 0.03533543]\n",
+      "  [0.48241424 0.76747678 0.5778101  0.06510067 0.91968937]\n",
+      "  [0.63679283 0.01218623 0.79229505 0.21035119 0.66692815]]]\n",
+      "[[[258 535   2 264 689]\n",
+      "  [812 825 978 573 726]\n",
+      "  [474 378 528 917 619]]\n",
+      "\n",
+      " [[843 949 484 498 974]\n",
+      "  [853 905  57 592 185]\n",
+      "  [250 997 465 546 776]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#3. Generate a 2x3x5 3-dimensional array with random values. Assign the array to variable \"a\"\n",
+    "#4. Print a.\n",
+    "\n",
+    "a = np.random.random((2,3,5))\n",
+    "print(a)\n",
+    "\n",
+    "a2 = np.random.randint(low=0, high=1000, size=(2,3,5))\n",
+    "print(a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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",
+      "[[[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": [
+    "#5. Create a 5x2x3 3-dimensional array with all values equaling 1.\n",
+    "#Assign the array to variable \"b\"\n",
+    "#6. Print b.\n",
+    "\n",
+    "b = np.full((5,2,3),1)\n",
+    "print(b)\n",
+    "\n",
+    "b2 = np.ones((5,2,3))\n",
+    "print(b2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "#7. Do a and b have the same size? How do you prove that in Python code?\n",
+    "\n",
+    "if a.size == b.size:\n",
+    "    print(\"True\")\n",
+    "else:\n",
+    "    print(\"False\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#8. Are you able to add a and b? Why or why not?\n",
+    "\n",
+    "Nel no son las mismas dimensiones (python no reescribre la matemática)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": [
+    "#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\".\n",
+    "\n",
+    "c = np.reshape(b,(2,3,5))\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.21147871 1.75804237 1.9115814  1.40667329 1.53182288]\n",
+      "  [1.98374763 1.54271426 1.88799577 1.13082761 1.55748123]\n",
+      "  [1.30306828 1.86861946 1.7067565  1.21372597 1.89081739]]\n",
+      "\n",
+      " [[1.8598921  1.02632583 1.90106924 1.07250084 1.03533543]\n",
+      "  [1.48241424 1.76747678 1.5778101  1.06510067 1.91968937]\n",
+      "  [1.63679283 1.01218623 1.79229505 1.21035119 1.66692815]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#10. Try to add a and c. Now it should work. Assign the sum to varialbe \"d\". But why does it work now?\n",
+    "\n",
+    "d = a+c\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ahora si, las dimenciones son las mismas "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.21147871 0.75804237 0.9115814  0.40667329 0.53182288]\n",
+      "  [0.98374763 0.54271426 0.88799577 0.13082761 0.55748123]\n",
+      "  [0.30306828 0.86861946 0.7067565  0.21372597 0.89081739]]\n",
+      "\n",
+      " [[0.8598921  0.02632583 0.90106924 0.07250084 0.03533543]\n",
+      "  [0.48241424 0.76747678 0.5778101  0.06510067 0.91968937]\n",
+      "  [0.63679283 0.01218623 0.79229505 0.21035119 0.66692815]]]\n",
+      "[[[1.21147871 1.75804237 1.9115814  1.40667329 1.53182288]\n",
+      "  [1.98374763 1.54271426 1.88799577 1.13082761 1.55748123]\n",
+      "  [1.30306828 1.86861946 1.7067565  1.21372597 1.89081739]]\n",
+      "\n",
+      " [[1.8598921  1.02632583 1.90106924 1.07250084 1.03533543]\n",
+      "  [1.48241424 1.76747678 1.5778101  1.06510067 1.91968937]\n",
+      "  [1.63679283 1.01218623 1.79229505 1.21035119 1.66692815]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.\n",
+    "\n",
+    "print(a)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "La matriz \"d\" tiene una unidad más que la \"a\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.21147871 0.75804237 0.9115814  0.40667329 0.53182288]\n",
+      "  [0.98374763 0.54271426 0.88799577 0.13082761 0.55748123]\n",
+      "  [0.30306828 0.86861946 0.7067565  0.21372597 0.89081739]]\n",
+      "\n",
+      " [[0.8598921  0.02632583 0.90106924 0.07250084 0.03533543]\n",
+      "  [0.48241424 0.76747678 0.5778101  0.06510067 0.91968937]\n",
+      "  [0.63679283 0.01218623 0.79229505 0.21035119 0.66692815]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#12. Multiply a and c. Assign the result to e.\n",
+    "\n",
+    "e = a*c\n",
+    "print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#13. Does e equal to a? Why or why not?\n",
+    "\n",
+    "Ps si, se multiplicó por 1 no manches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.983747626836083\n",
+      "1.0121862260860133\n",
+      "1.531050692419854\n"
+     ]
+    }
+   ],
+   "source": [
+    "#14. Identify the max, min, and mean values in d. Assign those values to variables \"d_max\", \"d_min\", and \"d_mean\".\n",
+    "\n",
+    "d_max = np.amax(d)\n",
+    "print(d_max)\n",
+    "d_min = np.amin(d)\n",
+    "print(d_min)\n",
+    "d_mean = np.mean(d)\n",
+    "print(d_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.21147871 0.75804237 0.9115814  0.40667329 0.53182288]\n",
+      "  [0.98374763 0.54271426 0.88799577 0.13082761 0.55748123]\n",
+      "  [0.30306828 0.86861946 0.7067565  0.21372597 0.89081739]]\n",
+      "\n",
+      " [[0.8598921  0.02632583 0.90106924 0.07250084 0.03533543]\n",
+      "  [0.48241424 0.76747678 0.5778101  0.06510067 0.91968937]\n",
+      "  [0.63679283 0.01218623 0.79229505 0.21035119 0.66692815]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#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`.\n",
+    "\n",
+    "f = np.empty([2,3,5])\n",
+    "print(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1.211478712467478\n",
+      "1 1.7580423654782424\n",
+      "2 1.9115814014272692\n",
+      "3 1.4066732947736849\n",
+      "4 1.531822879894447\n",
+      "0 1.983747626836083\n",
+      "1 1.542714256073828\n",
+      "2 1.887995767307331\n",
+      "3 1.1308276055223487\n",
+      "4 1.557481232506913\n",
+      "0 1.3030682774695825\n",
+      "1 1.8686194614604958\n",
+      "2 1.7067565011962236\n",
+      "3 1.2137259695321427\n",
+      "4 1.8908173850031273\n",
+      "0 1.8598921042930185\n",
+      "1 1.0263258309293926\n",
+      "2 1.9010692394796234\n",
+      "3 1.0725008444600972\n",
+      "4 1.0353354251282092\n",
+      "0 1.4824142400485254\n",
+      "1 1.7674767785449834\n",
+      "2 1.577810097145484\n",
+      "3 1.0651006683450883\n",
+      "4 1.9196893735647433\n",
+      "0 1.6367928278426498\n",
+      "1 1.0121862260860133\n",
+      "2 1.7922950482196591\n",
+      "3 1.2103511851073276\n",
+      "4 1.6669281464516108\n"
+     ]
+    }
+   ],
+   "source": [
+    "#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.\n",
+    "#If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f.\n",
+    "#If a value equals to d_mean, assign 50 to the corresponding value in f.\n",
+    "#Assign 0 to the corresponding value(s) in f for d_min in d.\n",
+    "#Assign 100 to the corresponding value(s) in f for d_max in d.\n",
+    "#In the end, f should have only the following values: 0, 25, 50, 75, and 100.\n",
+    "#Note: you don't have to use Numpy in this question.\n",
+    "    \n",
+    "for i,a in enumerate(d):\n",
+    "    #print(i,a)\n",
+    "    for x,b in enumerate(a):\n",
+    "        #print(x,b)\n",
+    "        for y,c in enumerate(b):\n",
+    "            print(y,c)\n",
+    "            if c > d_min and c < d_mean:\n",
+    "                f[i][x][y] = 25\n",
+    "            elif c > d_mean and c < d_max:\n",
+    "                f[i][x][y] = 75\n",
+    "            elif c == d_mean:\n",
+    "                f[i][x][y] = 50\n",
+    "            elif c == d_min:\n",
+    "                f[i][x][y] = 0\n",
+    "            elif c == d_max:\n",
+    "                f[i][x][y] = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.21147871 1.75804237 1.9115814  1.40667329 1.53182288]\n",
+      "  [1.98374763 1.54271426 1.88799577 1.13082761 1.55748123]\n",
+      "  [1.30306828 1.86861946 1.7067565  1.21372597 1.89081739]]\n",
+      "\n",
+      " [[1.8598921  1.02632583 1.90106924 1.07250084 1.03533543]\n",
+      "  [1.48241424 1.76747678 1.5778101  1.06510067 1.91968937]\n",
+      "  [1.63679283 1.01218623 1.79229505 1.21035119 1.66692815]]]\n",
+      "[[[ 25.  75.  75.  25.  75.]\n",
+      "  [100.  75.  75.  25.  75.]\n",
+      "  [ 25.  75.  75.  25.  75.]]\n",
+      "\n",
+      " [[ 75.  25.  75.  25.  25.]\n",
+      "  [ 25.  75.  75.  25.  75.]\n",
+      "  [ 75.   0.  75.  25.  75.]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#17. Print d and f. Do you have your expected f?\n",
+    "\n",
+    "print(d)\n",
+    "print(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[['B' 'D' 'D' 'B' 'D']\n",
+      "  ['E' 'D' 'D' 'B' 'D']\n",
+      "  ['B' 'D' 'D' 'B' 'D']]\n",
+      "\n",
+      " [['D' 'B' 'D' 'B' 'B']\n",
+      "  ['B' 'D' 'D' 'B' 'D']\n",
+      "  ['D' 'A' 'D' 'B' 'D']]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100).\n",
+    "#How to use string values. (\"A\", \"B\", \"C\", \"D\", and \"E\") to label the array elements?\n",
+    "\n",
+    "ff = np.empty([2,3,5],dtype=\"object\")\n",
+    "\n",
+    "for i,a in enumerate(d):\n",
+    "    #print(i,a)\n",
+    "    for x,b in enumerate(a):\n",
+    "        #print(x,b)\n",
+    "        for y,c in enumerate(b):\n",
+    "            #print(y,c)\n",
+    "            if c > d_min and c < d_mean:\n",
+    "                ff[i][x][y] = \"B\"\n",
+    "            elif c > d_mean and c < d_max:\n",
+    "                ff[i][x][y] = \"D\"\n",
+    "            elif c == d_mean:\n",
+    "                ff[i][x][y] = \"C\"\n",
+    "            elif c == d_min:\n",
+    "                ff[i][x][y] = \"A\"\n",
+    "            elif c == d_max:\n",
+    "                ff[i][x][y] = \"E\"\n",
+    "print(ff)"
+   ]
+  },
+  {
+   "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
+}