{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Create a vector with values ranging from 10 to 49"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n",
" 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n"
]
}
],
"source": [
"x = np.arange(10, 50)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Reverse a vector (first element becomes last)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33,\n",
" 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16,\n",
" 15, 14, 13, 12, 11, 10])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.flip(x)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33,\n",
" 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16,\n",
" 15, 14, 13, 12, 11, 10])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[::-1]\n",
"np.array(sorted(x.tolist(), reverse=True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Find indices of non-zero elements from [1,2,0,0,4,0]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0, 1, 4]),)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x3 = np.array([1,2,0,0,4,0])\n",
"np.nonzero(x3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 4])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = x3 > 0\n",
"np.arange(x3.shape[0])[y]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Create a 10x10 array with random values and find the minimum and maximum values "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.998333820021144\n",
"0.029637167775761752\n"
]
}
],
"source": [
"a = np.random.rand(10, 10)\n",
"print(a.max())\n",
"print(a.min())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10, 10)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. Create a random vector of size 30 and find the mean value"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5239838122398937"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(30).mean()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ],\n",
" [ 3.01521561, 9.14164803, 0.68791109, 2.89815842, 3.15728656,\n",
" 0.90899513, 9.86866755, 6.80097454, 0.84650805, 4.37712331],\n",
" [ 8.39586685, 9.4721158 , 11.47671301, 9.98210744, 19.57495897,\n",
" 18.98315206, 10.96557931, 19.17685266, 2.34494457, 15.07271328],\n",
" [10.63894158, 18.78178247, 2.11627782, 18.8364192 , 27.86439724,\n",
" 28.77513901, 3.00212305, 12.7432143 , 24.22477423, 5.126669 ],\n",
" [ 8.01506109, 17.38200813, 16.83001306, 38.81288373, 27.20045135,\n",
" 36.48203461, 39.25869368, 6.1629328 , 33.86671744, 12.76535218],\n",
" [12.58241805, 25.83004732, 42.08704151, 7.98036465, 30.59215561,\n",
" 47.90822083, 4.00450191, 17.97320344, 46.43838656, 37.61576568],\n",
" [ 0.30392063, 34.98596739, 2.26471787, 39.25422566, 25.28928102,\n",
" 19.45190256, 19.43995083, 13.79122425, 33.15481927, 14.5385231 ],\n",
" [39.23274693, 31.45647923, 37.37418164, 26.55868052, 20.35728236,\n",
" 1.82627947, 37.40446428, 36.32240979, 0.11695751, 43.2333394 ],\n",
" [39.46145399, 14.49899384, 18.75064899, 55.59832686, 34.71155617,\n",
" 25.22728419, 10.70105541, 55.67846468, 52.71682548, 57.88698127],\n",
" [ 2.09842409, 68.3146222 , 19.55644611, 53.8228737 , 86.47861251,\n",
" 56.04636399, 64.62943253, 17.43242805, 31.94168966, 61.49230741]])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([np.random.uniform(0, 10*i, 10) for i in range(10)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. Create a 2d array with 1 on the border and 0 inside"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bordermat = np.ones(shape=(10,10))\n",
"bordermat[1:-1, 1:-1] = 0\n",
"# bordermat[1:-1, 1:-1] = np.zeros(shape=(8, 8))\n",
"bordermat\n",
"# bordermat[0] = 1\n",
"# bordermat[-1] = 1\n",
"# bordermat[:, 0] = 1\n",
"# bordermat[:, -1] = 1"
]
},
{
"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",
"version": "3.6.5"
}
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}