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353 changes: 353 additions & 0 deletions your-code/Lab_Numpy.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.17.2\n"
]
}
],
"source": [
"print(np.version.version)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#3. Generate a 2x3x5 3-dimensional array with random values. Assign the array to variable \"a\"\n",
"# Challenge: there are at least three easy ways that use numpy to generate random arrays. How many ways can you find?\n",
"a = np.random.random((2,3,5))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.92598104 0.40197817 0.42482632 0.66371613 0.93045929]\n",
" [0.13760188 0.03166089 0.0704651 0.57791757 0.4598114 ]\n",
" [0.1690768 0.13553113 0.90339829 0.38074646 0.19877052]]\n",
"\n",
" [[0.49544784 0.60575526 0.809597 0.38395514 0.55734081]\n",
" [0.34173021 0.80337932 0.15776063 0.02389911 0.4974305 ]\n",
" [0.26252848 0.69824184 0.87914224 0.63275067 0.58913698]]]\n"
]
}
],
"source": [
"#4. Print a.\n",
"\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#5. Create a 5x2x3 3-dimensional array with all values equaling 1.\n",
"#Assign the array to variable \"b\"\n",
"\n",
"b = np.ones((5,2,3))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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": [
"#6. Print b.\n",
"\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30\n",
"30\n"
]
}
],
"source": [
"#7. Do a and b have the same size? How do you prove that in Python code?\n",
"\n",
"print(a.size)\n",
"print(b.size)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"#8. Are you able to add a and b? Why or why not?\n",
"\n",
"#No se pueden agregar porque no tienen la misma forma \n"
]
},
{
"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": [
"#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": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[1.92598104 1.40197817 1.42482632 1.66371613 1.93045929]\n",
" [1.13760188 1.03166089 1.0704651 1.57791757 1.4598114 ]\n",
" [1.1690768 1.13553113 1.90339829 1.38074646 1.19877052]]\n",
"\n",
" [[1.49544784 1.60575526 1.809597 1.38395514 1.55734081]\n",
" [1.34173021 1.80337932 1.15776063 1.02389911 1.4974305 ]\n",
" [1.26252848 1.69824184 1.87914224 1.63275067 1.58913698]]]\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 = np.add(a,c)\n",
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.92598104 0.40197817 0.42482632 0.66371613 0.93045929]\n",
" [0.13760188 0.03166089 0.0704651 0.57791757 0.4598114 ]\n",
" [0.1690768 0.13553113 0.90339829 0.38074646 0.19877052]]\n",
"\n",
" [[0.49544784 0.60575526 0.809597 0.38395514 0.55734081]\n",
" [0.34173021 0.80337932 0.15776063 0.02389911 0.4974305 ]\n",
" [0.26252848 0.69824184 0.87914224 0.63275067 0.58913698]]]\n",
"[[[1.92598104 1.40197817 1.42482632 1.66371613 1.93045929]\n",
" [1.13760188 1.03166089 1.0704651 1.57791757 1.4598114 ]\n",
" [1.1690768 1.13553113 1.90339829 1.38074646 1.19877052]]\n",
"\n",
" [[1.49544784 1.60575526 1.809597 1.38395514 1.55734081]\n",
" [1.34173021 1.80337932 1.15776063 1.02389911 1.4974305 ]\n",
" [1.26252848 1.69824184 1.87914224 1.63275067 1.58913698]]]\n"
]
}
],
"source": [
"#11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.\n",
"\n",
"#Los dos arreglos son iguales porque tienen la misma forma es un arreglo de 3 dimensiones con 30 elementos. \n",
"print(a)\n",
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.92598104 0.40197817 0.42482632 0.66371613 0.93045929]\n",
" [0.13760188 0.03166089 0.0704651 0.57791757 0.4598114 ]\n",
" [0.1690768 0.13553113 0.90339829 0.38074646 0.19877052]]\n",
"\n",
" [[0.49544784 0.60575526 0.809597 0.38395514 0.55734081]\n",
" [0.34173021 0.80337932 0.15776063 0.02389911 0.4974305 ]\n",
" [0.26252848 0.69824184 0.87914224 0.63275067 0.58913698]]]\n"
]
}
],
"source": [
"#12. Multiply a and c. Assign the result to e.\n",
"\n",
"e = np.multiply(a, c) \n",
"print(e)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.92598104 0.40197817 0.42482632 0.66371613 0.93045929]\n",
" [0.13760188 0.03166089 0.0704651 0.57791757 0.4598114 ]\n",
" [0.1690768 0.13553113 0.90339829 0.38074646 0.19877052]]\n",
"\n",
" [[0.49544784 0.60575526 0.809597 0.38395514 0.55734081]\n",
" [0.34173021 0.80337932 0.15776063 0.02389911 0.4974305 ]\n",
" [0.26252848 0.69824184 0.87914224 0.63275067 0.58913698]]]\n",
"[[[0.92598104 0.40197817 0.42482632 0.66371613 0.93045929]\n",
" [0.13760188 0.03166089 0.0704651 0.57791757 0.4598114 ]\n",
" [0.1690768 0.13553113 0.90339829 0.38074646 0.19877052]]\n",
"\n",
" [[0.49544784 0.60575526 0.809597 0.38395514 0.55734081]\n",
" [0.34173021 0.80337932 0.15776063 0.02389911 0.4974305 ]\n",
" [0.26252848 0.69824184 0.87914224 0.63275067 0.58913698]]]\n"
]
}
],
"source": [
"#13. Does e equal to a? Why or why not?\n",
"print(a)\n",
"print(e)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.9304592859190688\n",
"1.0238991148469805\n",
"1.4716678997998724\n"
]
}
],
"source": [
"d_max = d.max()\n",
"d_min = d.min()\n",
"d_mean = d.mean()\n",
"\n",
"print(d_max)\n",
"\n",
"print(d_min)\n",
"\n",
"print(d_mean)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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",
"\n"
]
},
{
"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
}
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