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Aktualizacja materiałów do laboratoriów 1

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Paweł Skórzewski 2023-03-02 11:09:27 +01:00
parent d81fefa352
commit e481384b66
1 changed files with 99 additions and 62 deletions
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161
lab/01_Podstawowe_narzędzia.ipynb
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@ -35,7 +35,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -78,8 +78,7 @@
], ],
"source": [ "source": [
"zdanie = \"tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł\"\n", "zdanie = \"tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł\"\n",
"wyrazy = zdanie.split()\n", "dlugosci_wyrazow = [len(wyraz) for wyraz in zdanie.split()]\n",
"dlugosci_wyrazow = [len(wyraz) for wyraz in wyrazy]\n",
"\n", "\n",
"print(dlugosci_wyrazow)\n" "print(dlugosci_wyrazow)\n"
] ]
@ -134,6 +133,25 @@
"Wszystkie listy i krotki w Pythonie, w tym łańcuchy (które trakowane są jak krotki znaków), są indeksowane od 0:" "Wszystkie listy i krotki w Pythonie, w tym łańcuchy (które trakowane są jak krotki znaków), są indeksowane od 0:"
] ]
}, },
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4, 16, 36, 64, 100]\n",
"[4, 16, 36, 64, 100]\n"
]
}
],
"source": [
"print(lista)\n",
"print(lista[:])"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 11,
@ -255,7 +273,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -264,14 +282,15 @@
"text": [ "text": [
"[[1 2 3]\n", "[[1 2 3]\n",
" [4 5 6]\n", " [4 5 6]\n",
" [7 8 9]]\n" " [7 8 9]\n",
" [2 5 8]]\n"
] ]
} }
], ],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"\n", "\n",
"x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", "x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [2, 5, 8]])\n",
"print(x)\n" "print(x)\n"
] ]
}, },
@ -284,62 +303,55 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
"(3, 3)" "text": [
] "(4, 3)\n"
}, ]
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
} }
], ],
"source": [ "source": [
"x.shape\n" "print(x.shape) # wymiary macierzy\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
"array([12, 15, 18])" "text": [
] "[14 20 26]\n",
}, "[ 6 15 24 15]\n"
"execution_count": 16, ]
"metadata": {},
"output_type": "execute_result"
} }
], ],
"source": [ "source": [
"x.sum(axis=0)\n" "print(x.sum(axis=0)) # suma liczb w każdej kolumnie\n",
"print(x.sum(axis=1)) # suma liczb w każdym wierszu"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
"array([2., 5., 8.])" "text": [
] "[2. 5. 8. 5.]\n"
}, ]
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
} }
], ],
"source": [ "source": [
"x.mean(axis=1)\n" "print(x.mean(axis=1)) # średnia liczb w każdym wierszu\n"
] ]
}, },
{ {
@ -450,19 +462,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[0. 1.25 2.5 3.75 5. ]\n" "[0. 0.625 1.25 1.875 2.5 3.125 3.75 4.375 5. ]\n"
] ]
} }
], ],
"source": [ "source": [
"x = np.linspace(0, 5, 5)\n", "x = np.linspace(0, 5, 9)\n",
"print(x)\n" "print(x)\n"
] ]
}, },
@ -623,14 +635,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 53,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[2. 3. 4.]\n" "[3 4 5]\n",
"[1. 1. 1.]\n",
"[3. 4. 5.]\n"
] ]
} }
], ],
@ -638,8 +652,10 @@
"import numpy as np\n", "import numpy as np\n",
"\n", "\n",
"a = np.array([3, 4, 5])\n", "a = np.array([3, 4, 5])\n",
"print(a)\n",
"b = np.ones(3)\n", "b = np.ones(3)\n",
"print(a - b)\n" "print(b)\n",
"print(a / b)\n"
] ]
}, },
{ {
@ -651,7 +667,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 39,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -670,7 +686,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 40,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -689,17 +705,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 41,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[ 1, 4],\n", "array([[ 7, 10],\n",
" [ 9, 16]])" " [15, 22]])"
] ]
}, },
"execution_count": 30, "execution_count": 41,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -708,6 +724,27 @@
"a * b # mnożenie element po elemencie\n" "a * b # mnożenie element po elemencie\n"
] ]
}, },
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 7, 10],\n",
" [15, 22]])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a @ b # mnożenie macierzowe"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 31,
@ -907,7 +944,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -916,7 +953,7 @@
"16" "16"
] ]
}, },
"execution_count": 39, "execution_count": 44,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1128,7 +1165,7 @@
} }
], ],
"source": [ "source": [
"x[:, 1]\n" "x[:, 1] # kolumna nr 1\n"
] ]
}, },
{ {
@ -1148,7 +1185,7 @@
} }
], ],
"source": [ "source": [
"x[1, :]\n" "x[1, :] # wiersz nr 1\n"
] ]
}, },
{ {
@ -1352,16 +1389,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 50, "execution_count": 48,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([2, 0, 7, 3, 5])" "array([1, 3, 3, 1, 2])"
] ]
}, },
"execution_count": 50, "execution_count": 48,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1372,16 +1409,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 52,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([-0.7907838 , -0.65971486, 0.0375355 , 2.00045956, 0.32631216])" "array([ 2.25701199, -0.62666283, -0.58260693, 0.91053811, -0.12398967])"
] ]
}, },
"execution_count": 51, "execution_count": 52,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1392,16 +1429,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 52, "execution_count": 50,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([1.50130054, 1.20710594, 0.45451505, 0.70098876, 0.90371663])" "array([0.64188687, 1.98379682, 0.4690363 , 1.26967692, 0.84376779])"
] ]
}, },
"execution_count": 52, "execution_count": 50,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -2220,7 +2257,7 @@
"metadata": { "metadata": {
"celltoolbar": "Slideshow", "celltoolbar": "Slideshow",
"kernelspec": { "kernelspec": {
"display_name": "Python 3.10.6 64-bit", "display_name": "Python 3 (ipykernel)",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },