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PP-MX-PT-Mariana y Diego #15

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380 changes: 380 additions & 0 deletions .ipynb_checkpoints/Lab Numpy-checkpoint.ipynb
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Original file line number Diff line number Diff line change
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{
"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<ipython-input-36-a82d87f52ab7>\u001b[0m in \u001b[0;36m<module>\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
}
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