\n"
]
}
],
"source": [
"tpl=(1,2,3)\n",
"tpl2=1,2,3\n",
"print(type(tpl2))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gyq28VgrNP1S"
},
"source": [
"## Comprehension"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SLQwqaX-NP1S"
},
"source": [
"tüm veri yapılarıyla uygulanabilir. uzun döngü yazmaktan kurtarır. c#'taki LINQ işlemlerinin benzer hatta daha güzel alternatifi"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"id": "NqPd0hjyNP1S",
"outputId": "c91b871e-6f35-4792-f37c-118843c0fa38",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198]\n"
]
}
],
"source": [
"rangelistinikikatı=[x*2 for x in rangelist]\n",
"print(rangelistinikikatı)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pxwrKmaWNP1S"
},
"source": [
"### koşullu comprehension"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1WQWgwHqNP1S"
},
"source": [
"[x for x in datastruct if x ...]
\n",
"[x if ... else y for x in datastruct]"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"id": "HRX1WVkpNP1S",
"outputId": "d6f82004-2737-45b7-b734-8643d70c7f3f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['üzüm', 'muz', 'muz', 'elma']"
]
},
"metadata": {},
"execution_count": 116
}
],
"source": [
"kısaisimlimeyveler=[x for x in meyveler if len(x)<5]\n",
"kısaisimlimeyveler"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"scrolled": true,
"id": "JVjanVF3NP1T",
"outputId": "9d2fa365-40ef-415a-d66e-56d12bf2578f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[1, 3, 5, 7, 9]\n",
"[1, '', 3, '', 5, '', 7, '', 9]\n"
]
}
],
"source": [
"liste=range(1,10)\n",
"sadecetekler=[sayı for sayı in liste if sayı % 2 !=0] #tek if\n",
"tekler=[sayı if sayı%2!=0 else \"\" for sayı in liste] #if-else\n",
"print(sadecetekler)\n",
"print(tekler)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IheriaOWNP1T"
},
"source": [
"### içiçe(nested) list comprehension"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EmbFdo9XNP1T"
},
"source": [
"****syntax:[x for iç in dış for x in iç]****"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0TagRdcuNP1T"
},
"source": [
"2 boyutlu bir matrisi düzleştirmek istiyorum
\n",
"matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
\n",
"Beklediğimiz çıktı: flatten_matrix = [1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"id": "jPpkAqNKNP1T",
"outputId": "921ba5ce-8183-4552-85b8-29a770fdc6d6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
]
}
],
"source": [
"# 2-D List\n",
"matrix = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]\n",
"\n",
"flatten_matrix = []\n",
"\n",
"for sublist in matrix:\n",
"\tfor val in sublist:\n",
"\t\tflatten_matrix.append(val)\n",
"\n",
"print(flatten_matrix)\n"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"id": "7z41QOV3NP1U",
"outputId": "320e7517-697b-4ffc-bdf5-f5c0dc3293b2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
]
}
],
"source": [
"# 2-D List\n",
"matrix = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]\n",
"\n",
"# Nested List Comprehension to flatten a given 2-D matrix\n",
"flatten_matrix = [val for sublist in matrix for val in sublist]\n",
"\n",
"print(flatten_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"id": "-hA1EVDlNP1U",
"outputId": "7ee51822-27c3-4c44-8ac0-1b1ab3a05da4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['Venus', 'Earth', 'Mars', 'Pluto']\n"
]
}
],
"source": [
"# 2-D List of planets\n",
"planets = [['Mercury', 'Venus', 'Earth'], ['Mars', 'Jupiter', 'Saturn'], ['Uranus', 'Neptune', 'Pluto']]\n",
"\n",
"flatten_planets = []\n",
"\n",
"for sublist in planets:\n",
"\tfor planet in sublist:\n",
"\n",
"\t\tif len(planet) < 6:\n",
"\t\t\tflatten_planets.append(planet)\n",
"\n",
"print(flatten_planets)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"scrolled": true,
"id": "3sF4mxwgNP1U",
"outputId": "d19e9fec-d249-47b5-c1e3-dcbf37908ad0",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['Venus', 'Earth', 'Mars', 'Pluto']\n"
]
}
],
"source": [
"flatten_planets = [planet for sublist in planets for planet in sublist if len(planet) < 6]\n",
"print(flatten_planets)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"id": "Gm5-pZTzNP1U",
"outputId": "be895b8d-96f0-432b-adaf-9ce6107b7515",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Venus', 'Earth', 'Mars', 'Pluto']"
]
},
"metadata": {},
"execution_count": 122
}
],
"source": [
"kısalar=[p for iç in planets for p in iç if len(p)<6]\n",
"kısalar"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0JOWbgJqNP1V"
},
"source": [
"Daha genel bir gösterim için https://stackoverflow.com/questions/18072759/list-comprehension-on-a-nested-list sayfasındaki gif animasyonlu açıklamaya bakınız"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8gG0zt1xNP1V"
},
"source": [
"### Matrisler ve matrislerde comprehension"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"id": "Mne2ty4qNP1V",
"outputId": "b953d00f-fa5f-409b-ba93-f2348694a4d6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"3\n",
"(7, ['printy([satır for satır in matris]) #satır satır\\n']) \n",
"----------\n",
"[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n",
" \n",
"(8, ['printy([satır[0] for satır in matris]) #ilk sütun\\n']) \n",
"----------\n",
"[1, 4, 7]\n",
" \n",
"(9, ['printy([[satır[i] for satır in matris] for i in range(3)]) #sütun sütun, transpozesi\\n']) \n",
"----------\n",
"[[1, 4, 7], [2, 5, 8], [3, 6, 9]]\n",
" \n",
"(10, ['printy([x for iç in matris for x in iç]) #nested\\n']) \n",
"----------\n",
"[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
" \n"
]
}
],
"source": [
"matris=[\n",
" [1,2,3],\n",
" [4,5,6],\n",
" [7,8,9]\n",
"]\n",
"print(len(matris))\n",
"printy([satır for satır in matris]) #satır satır\n",
"printy([satır[0] for satır in matris]) #ilk sütun\n",
"printy([[satır[i] for satır in matris] for i in range(3)]) #sütun sütun, transpozesi\n",
"printy([x for iç in matris for x in iç]) #nested"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_LEp7Ue3NP1V"
},
"source": [
"amaç aşağıdakini elde etmek olsun\n",
"\n",
"matrix = [[0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4]]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"id": "OFfwnWT5NP1V",
"outputId": "6d14e9aa-858d-4ba9-8a3a-495f7e807a1b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]\n"
]
}
],
"source": [
"matrix = []\n",
"\n",
"for i in range(5):\n",
"\n",
"\t# Append an empty sublist inside the list\n",
"\tmatrix.append([])\n",
"\n",
"\tfor j in range(5):\n",
"\t\tmatrix[i].append(j)\n",
"\n",
"print(matrix)\n"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"id": "pTZObgruNP1V",
"outputId": "87fc9d2b-96d8-4a04-824d-c914309e2a6d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]\n"
]
}
],
"source": [
"# Nested list comprehension\n",
"matrix = [[j for j in range(5)] for i in range(5)]\n",
"\n",
"print(matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bOPaavfoNP1W"
},
"source": [
"### Generators"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JVGurksxNP1W"
},
"source": [
"Bu konu biraz daha advanced bi konu olup ben sadece toplam alınması gereken durumlar için bir öenride bulunucam. Bi list comprehension sonunda(özellikle çok büyük bi list sözkonusuya) toplam alınacaksa [] kullanamya gerek yok, böylece memory tasarrufu yapmış olursunuz. Detaylar için şu sayfaya bakabilirsiniz: https://www.johndcook.com/blog/2020/01/15/generator-expression/"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"id": "5wo-U8QoNP1X",
"outputId": "a535516b-294b-47ab-d45b-ae7ebfc5bcb7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"285"
]
},
"metadata": {},
"execution_count": 126
}
],
"source": [
"#list comprehension: tüm liste elemanları bellekte tutuluyor\n",
"sum([x**2 for x in range(10)])"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"id": "tVNCqHRENP1X",
"outputId": "93732567-cba1-476c-a131-381e128270c6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"285"
]
},
"metadata": {},
"execution_count": 127
}
],
"source": [
"#generator expression: liste elemanları bellekte tutulmuyor\n",
"sum(x**2 for x in range(10))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w_aH2KmvNP1X"
},
"source": [
"## Stack"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "koT7RrSyNP1X"
},
"source": [
"Normalde böyle bi sınıf yok. list'i stack gibi kullanırız. append ve pop sayesinde. ilk giren ilk çıkar"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"id": "AOYlOLN2NP1X",
"outputId": "b546321f-c5e3-41a5-914f-cf564248245d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"4"
]
},
"metadata": {},
"execution_count": 128
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"metadata": {},
"execution_count": 128
}
],
"source": [
"stack=[1,2,3]\n",
"stack.append(4)\n",
"stack.pop()\n",
"stack"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m9mI5senNP1Y"
},
"source": [
"## Queue"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fhGjV581NP1Y"
},
"source": [
"Bunu da istersek listten yaparız, ilk giren son çıkar. ama bunun için collections modülünde bi sınıf var"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"id": "yWhScMN7NP1Y",
"outputId": "76ffb7b1-8748-426f-bf3d-33c5331c3868",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1\n",
"deque([2, 3, 4])\n"
]
}
],
"source": [
"from collections import deque\n",
"kuyruk=deque([1,2,3])\n",
"kuyruk.append(4)\n",
"sıradaki=kuyruk.popleft()\n",
"print(sıradaki)\n",
"print(kuyruk)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cdl9pcc0NP1Y"
},
"source": [
"## Dictionary"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SUa2Se8GNP1Y"
},
"source": [
"Key-value ikililerini tutarlar. Sırasızdırlar(EDIT:Python 3.7den itibaren girdilen sırayı korur), indeksle ulaşamayız. key'lerle valuelara ulaşırız veya döngü içinde dolanarak ikisine birden tek seferde de ulaşabiliriz."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kwb7sDe9NP1Y"
},
"source": [
"### Yaratım"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nJUUXyxBNP1Z"
},
"source": [
"#### Klasik"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"id": "o_7JO3-LNP1Z",
"outputId": "ab3f9c0c-f6c8-4e24-a158-9a6a4a603349",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(5, ['printy(dict_.keys())\\n']) \n",
"----------\n",
"dict_keys(['one', 'two'])\n",
" \n",
"(6, ['printy(dict_.values())\\n']) \n",
"----------\n",
"dict_values(['bir', 'zwei'])\n",
" \n",
"(7, ['printy(dict_.items())\\n']) \n",
"----------\n",
"dict_items([('one', 'bir'), ('two', 'zwei')])\n",
" \n",
"bir\n",
"N/A\n"
]
}
],
"source": [
"dict_={}\n",
"dict_[\"one\"]=\"bir\" #add,append, insert gibi bir metodu yok, direkt atanıyor\n",
"dict_[\"two\"]=\"iki\"\n",
"dict_[\"two\"]=\"zwei\"\n",
"printy(dict_.keys())\n",
"printy(dict_.values())\n",
"printy(dict_.items())\n",
"print(dict_[\"one\"])\n",
"#print(dict_[\"three\"]) # hata alır, almaması için get kullan\n",
"print(dict_.get(\"three\",\"N/A\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uDsCwp-BNP1Z"
},
"source": [
"#### dict metodu ile ikili elemanlardan oluşan bir yapıdan"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "knXEsR2TNP1Z"
},
"source": [
"bu ikili yapılar genelde zip veya enumerate olacaktır. bakınız ilgili fonksiyonar."
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"id": "I4q3Wwo7NP1Z",
"outputId": "1388f47f-3570-4675-dfba-2ce248167b92",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'bir'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 131
}
],
"source": [
"tpl=[(\"one\",\"bir\"),(\"two\",\"iki\"),(\"three\",\"üç\")]\n",
"dict_=dict(tpl)\n",
"print(type(dict_))\n",
"dict_[\"one\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l4Z0Cw0PNP1Z"
},
"source": [
"#### comprehension ile"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"id": "2cwYcIVFNP1Z",
"outputId": "17ba06de-e090-4352-d7ed-90b2a88419e4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"dict_items([(0, 0), (2, 4), (4, 16), (6, 36), (8, 64)])\n"
]
}
],
"source": [
"sayılar=list(range(10))\n",
"ciftlerinkaresi={x: x**2 for x in sayılar if x%2==0}\n",
"print(ciftlerinkaresi.items())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qncIYxruNP1a"
},
"source": [
"### elemanlarda dolaşma"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"id": "TSXUJrk_NP1a",
"outputId": "e427ec17-1015-41d3-bc73-de3470135ec6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0 0\n",
"2 4\n",
"4 16\n",
"6 36\n",
"8 64\n"
]
}
],
"source": [
"for k,v in ciftlerinkaresi.items():\n",
" print(k,v)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nadIvmlzNP1a"
},
"source": [
"### çeşitli metodlar"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"id": "V9iUJKtpNP1a",
"outputId": "b1a454ed-a461-4dc4-d4bd-834c0242ff72",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_items([])"
]
},
"metadata": {},
"execution_count": 134
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"name 'ciftlerinkaresi' is not defined\n"
]
}
],
"source": [
"try:\n",
" ciftlerinkaresi.clear()\n",
" ciftlerinkaresi.items()\n",
" del ciftlerinkaresi\n",
" ciftlerinkaresi #hata verir, artık bellekten uçtu\n",
"except Exception as err:\n",
" print(err)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6M08JSXFNP1a"
},
"source": [
"## Set"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H8O819D9NP1a"
},
"source": [
"Bunlar da dict gibi sırasızdır. dict gibi {} içinde tanımlanırlar. uniqe değerleri tutarlar. bir listteki duplikeleri ayırmak ve membership kontrolü için çok kullanılırlar"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"id": "tYuMoLaTNP1a",
"outputId": "c5ed3a78-a497-4df3-b7b9-0a392b43b42b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{1, 2, 3, 4, 5}"
]
},
"metadata": {},
"execution_count": 135
}
],
"source": [
"liste=[1,1,2,3,4,4,5]\n",
"set_=set(liste)\n",
"set_"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"scrolled": true,
"id": "PLLzA6DqNP1b",
"outputId": "638755e6-20ea-4fef-dcc9-30f9bd21df7b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(5, ['printy(set1,set2,set3)\\n']) \n",
"----------\n",
"{1, 2, 3, 4, 5} {2, 3, 4} {2, 3, 4, 5, 6}\n",
" \n",
"(6, ['printy(\"----diff\")\\n']) \n",
"----------\n",
"----diff\n",
" \n",
"(7, ['printy(set1.difference(set2))\\n']) \n",
"----------\n",
"{1, 5}\n",
" \n",
"(8, ['printy(set1.difference(set3))\\n']) \n",
"----------\n",
"{1}\n",
" \n",
"(9, ['printy(set2.difference(set1))\\n']) \n",
"----------\n",
"set()\n",
" \n",
"(10, ['printy(set2.difference(set3))\\n']) \n",
"----------\n",
"set()\n",
" \n",
"(11, ['printy(set3.difference(set1))\\n']) \n",
"----------\n",
"{6}\n",
" \n",
"(12, ['printy(set3.difference(set2))\\n']) \n",
"----------\n",
"{5, 6}\n",
" \n",
"(13, ['printy(\"- intersection----\")\\n']) \n",
"----------\n",
"- intersection----\n",
" \n",
"(14, ['printy(set1.intersection(set2))\\n']) \n",
"----------\n",
"{2, 3, 4}\n",
" \n",
"(15, ['printy(set1.intersection(set3))\\n']) \n",
"----------\n",
"{2, 3, 4, 5}\n",
" \n",
"(16, ['printy(set2.intersection(set1))\\n']) \n",
"----------\n",
"{2, 3, 4}\n",
" \n",
"(17, ['printy(set2.intersection(set3))\\n']) \n",
"----------\n",
"{2, 3, 4}\n",
" \n",
"(18, ['printy(set3.intersection(set1))\\n']) \n",
"----------\n",
"{2, 3, 4, 5}\n",
" \n",
"(19, ['printy(set3.intersection(set2))\\n']) \n",
"----------\n",
"{2, 3, 4}\n",
" \n",
"(20, ['printy(\"----union---\")\\n']) \n",
"----------\n",
"----union---\n",
" \n",
"(21, ['printy(set1.union(set2))\\n']) \n",
"----------\n",
"{1, 2, 3, 4, 5}\n",
" \n",
"(22, ['printy(set1.union(set3))\\n']) \n",
"----------\n",
"{1, 2, 3, 4, 5, 6}\n",
" \n",
"(23, ['printy(set2.union(set1))\\n']) \n",
"----------\n",
"{1, 2, 3, 4, 5}\n",
" \n",
"(24, ['printy(set2.union(set3))\\n']) \n",
"----------\n",
"{2, 3, 4, 5, 6}\n",
" \n",
"(25, ['printy(set3.union(set1))\\n']) \n",
"----------\n",
"{1, 2, 3, 4, 5, 6}\n",
" \n",
"(26, ['printy(set3.union(set2))\\n']) \n",
"----------\n",
"{2, 3, 4, 5, 6}\n",
" \n"
]
}
],
"source": [
"set1={1,2,3,4,5}\n",
"set2={2,3,4}\n",
"set3={2,3,4,5,6}\n",
"\n",
"printy(set1,set2,set3)\n",
"printy(\"----diff\")\n",
"printy(set1.difference(set2))\n",
"printy(set1.difference(set3))\n",
"printy(set2.difference(set1))\n",
"printy(set2.difference(set3))\n",
"printy(set3.difference(set1))\n",
"printy(set3.difference(set2))\n",
"printy(\"- intersection----\")\n",
"printy(set1.intersection(set2))\n",
"printy(set1.intersection(set3))\n",
"printy(set2.intersection(set1))\n",
"printy(set2.intersection(set3))\n",
"printy(set3.intersection(set1))\n",
"printy(set3.intersection(set2))\n",
"printy(\"----union---\")\n",
"printy(set1.union(set2))\n",
"printy(set1.union(set3))\n",
"printy(set2.union(set1))\n",
"printy(set2.union(set3))\n",
"printy(set3.union(set1))\n",
"printy(set3.union(set2))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g2M7EbFbNP1b"
},
"source": [
"Not: Yukarıdaki altalta aynı hizada olan tüm printy ifadesini tek seferde yapmanın yolu var. ben mesela bunların hepsi print iken printy'yi tek seferde yaptım. Alt tuşuna basarak seçmek. aşağıdaki gibi seçip t tuşuna basarsam tüm ty'ler t olur."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SgE1e1v8NP1b"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BNbPUbLBNP1b"
},
"source": [
"## Zip"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"id": "PEvEjCdmNP1c"
},
"outputs": [],
"source": [
"x=[1,2,3]\n",
"y=[10,20,30]\n",
"onkatlar=zip(x,y)\n",
"#print(list(onkatlar)) #yazdırmak için liste çevir. bi kez liste çevirlince artık zip özelliği kalmaz,\n",
"#o yüzden alttaki blok çalışmaz,o yüzden geçici olarak commentledim. deneyin ve görün"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"id": "O7BG__WUNP1c",
"outputId": "4ccf793d-465e-4ebe-d047-99ef439b8b40",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1, 2, 3)"
]
},
"metadata": {},
"execution_count": 138
}
],
"source": [
"#tekrar ayırmak için\n",
"x2, y2 = zip(*onkatlar)\n",
"x2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JGOPan-RNP1c"
},
"source": [
"### Zip vs Dict"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"id": "rzotDh-vNP1c",
"outputId": "5627cbb1-ca84-4325-f235-61737dc2d5f5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1 10\n",
"2 20\n",
"3 30\n"
]
}
],
"source": [
"a=[1,2,3]\n",
"b=[10,20,30]\n",
"c=zip(a,b)\n",
"for i,j in c:\n",
" print(i,j)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"id": "yaVPVoMiNP1d",
"outputId": "0910ea05-e1fe-462a-d98e-65f5548e1f13",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n"
]
}
],
"source": [
"a=[1,2,3]\n",
"b=[10,20,30]\n",
"c=zip(a,b)\n",
"print(type(list(c)[0]))\n",
"dict_=dict(c) #zipten dict üretimi\n",
"for k,v in dict_.items():\n",
" print(k,v)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DAIKLenQNP1d"
},
"source": [
"## Listlerle kullanılan önemli fonksiyonlar"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7Ik8QJWvNP1d"
},
"source": [
"### Map ve Reduce"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "feqvx2VsNP1d"
},
"source": [
"Map: bir veri yapısındaki elemanları sırayla bir fonksiyona gönderir ve sonuç yine bir veri yapısıdır
\n",
"Reduce: elemanları sırayla gönderir, bir eritme mantığı var, her bir önceki elamnını sonucyla bir sonraki eleman işleme girer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aTuNdPsxNP1d"
},
"source": [
"#### Map"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"id": "A5_yqtr9NP1e",
"outputId": "92a6faa2-d3f2-400d-bf19-1da66ae9744a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[1, 4, 9, 16, 25]"
]
},
"metadata": {},
"execution_count": 141
}
],
"source": [
"items=[1,2,3,4,5]\n",
"def kareal(sayı):\n",
" return sayı**2\n",
"\n",
"kareler=map(kareal,items) #lambdalı da olur. map(lambda x: x**2, items)\n",
"list(kareler) #yazdırmak için liste çevir\n"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"id": "aAd1opFtNP1e",
"outputId": "ab89bb40-c032-4755-efbc-337228e912ac",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1\n",
"6\n",
"15\n",
"28\n",
"45\n",
"66\n",
"91\n",
"120\n",
"153\n"
]
}
],
"source": [
"#birden fazla veri yapısı da girebilir işleme\n",
"range1=range(1,10)\n",
"range2=range(1,20,2)\n",
"\n",
"mymap=map(lambda x,y:x*y,range1,range2)\n",
"for i in mymap:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"id": "XCiepbUMNP1e",
"outputId": "aebaa2d4-038b-435f-9d61-8f8decdfc920",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[1, 6, 15, 28, 45, 66, 91, 120, 153]"
]
},
"metadata": {},
"execution_count": 143
}
],
"source": [
"#comprehensionla da yapılabilir.\n",
"çarpım=[x*y for x,y in zip(range1,range2)]\n",
"çarpım"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"scrolled": true,
"id": "8a4dlVGBNP1e",
"outputId": "33d10e22-9ea5-4e5a-f610-5fcf372d1830",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o']"
]
},
"metadata": {},
"execution_count": 144
}
],
"source": [
"harfler=map(chr,range(97,112))\n",
"list(harfler)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"id": "GQa0tF0xNP1e",
"outputId": "c8eef550-d3b7-40c0-fd43-7d7d2c7ca4e4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o']"
]
},
"metadata": {},
"execution_count": 145
}
],
"source": [
"harfler2=[chr(x) for x in range(97,112)]\n",
"harfler2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hsRSWXPYNP1f"
},
"source": [
"#### Reduce"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"id": "k-33ZD62NP1f",
"outputId": "bbf6ec4f-8b8d-4f9a-bb64-a0e294142b28",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"362880"
]
},
"metadata": {},
"execution_count": 146
}
],
"source": [
"from functools import reduce\n",
"def faktoriyel(sayı1,sayı2):\n",
" return sayı1*sayı2\n",
"\n",
"sayılar=range(1,10)\n",
"fakt=reduce(faktoriyel,sayılar)\n",
"fakt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wx8xOTGGNP1f"
},
"source": [
"#### Filter"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-04T17:50:33.788733Z",
"start_time": "2020-12-04T17:50:33.783746Z"
},
"id": "tQB2akYENP1f",
"outputId": "b90a7a49-d3c6-4c28-a9f8-dcd583b16bef",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"18\n",
"24\n",
"32\n"
]
}
],
"source": [
"ages = [5, 12, 17, 18, 24, 32]\n",
"\n",
"def myFunc(x):\n",
" if x < 18:\n",
" return False\n",
" else:\n",
" return True\n",
"\n",
"adults = filter(myFunc, ages)\n",
"\n",
"for x in adults:\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-04T17:51:27.064547Z",
"start_time": "2020-12-04T17:51:27.060518Z"
},
"id": "4F6I7k98NP1f",
"outputId": "0af51789-c6f1-4b02-8a53-dbd2bd39af36",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[18, 24, 32]"
]
},
"metadata": {},
"execution_count": 148
}
],
"source": [
"#veya lambda ile\n",
"list(filter(lambda x:x>=18,ages))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FmA92zRANP1f"
},
"source": [
"### Enumerate"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"scrolled": true,
"id": "WMSPL5qrNP1g",
"outputId": "1bc437fa-9a6f-402a-869e-cfa3f97e40d6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[(1, 'Ocak'), (2, 'Şubat'), (3, 'Mart')]\n",
"1 Ocak\n",
"2 Şubat\n",
"3 Mart\n"
]
}
],
"source": [
"aylar=[\"Ocak\",\"Şubat\",\"Mart\"]\n",
"print(list(enumerate(aylar,start=1)))\n",
"dict_=dict(enumerate(aylar,start=1))\n",
"for k,v in dict_.items():\n",
" print(k,v)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y41iO0qPNP1g"
},
"source": [
"### All ve Any"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"id": "vjuEJ-uPNP1g",
"outputId": "0c136fee-c30e-4df5-c75d-d635a15afd6f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"False\n",
"True\n",
"True\n"
]
}
],
"source": [
"liste1=[True,True,False]\n",
"liste2=[True,True,True]\n",
"print(all(liste1))\n",
"print(any(liste1))\n",
"print(all(liste2))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GOOpbi13NP1g"
},
"source": [
"# Date Time işlemleri"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0nLGJSCiNP1g"
},
"source": [
"## Modüller ve üyeleri"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"id": "u9LRhmkENP1g"
},
"outputs": [],
"source": [
"import datetime\n",
"import time\n",
"import timeit\n",
"import dateutil\n",
"import calendar\n",
"#import timer as tr #bu threadlerle ilgili kullanma, settimer ve killtimer var"
]
},
{
"cell_type": "markdown",
"source": [
"bunlardan en çok kullanılan datetime olup, bu modül içinde bir de datetime tipi vardır. HAngisini import edeceğinize karar vermek önemli. ernizde olsam sadece modül olanı import eder, sonraki alt tip ve diğer üyeleri bunun üzeirnden çağırırım"
],
"metadata": {
"id": "ABTWlGHd8PiA"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "ogHsS-WWNP1h"
},
"source": [
"### datetime"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"id": "KuI7TPyGNP1h",
"outputId": "01d3bd66-b733-4d74-a905-c7bb1d6bf53f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['MAXYEAR', 'MINYEAR', 'date', 'datetime', 'datetime_CAPI', 'sys', 'time', 'timedelta', 'timezone', 'tzinfo']\n"
]
}
],
"source": [
"print([i for i in dir(datetime) if not \"__\" in i])"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"id": "XMeABvTLNP1i",
"outputId": "52fe2452-770a-4d64-9be8-2c5f4f7056b5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.datetime(2024, 9, 22, 15, 25, 25, 30003)"
]
},
"metadata": {},
"execution_count": 153
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"15"
]
},
"metadata": {},
"execution_count": 153
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.date(2024, 9, 22)"
]
},
"metadata": {},
"execution_count": 153
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2024"
]
},
"metadata": {},
"execution_count": 153
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.date(2019, 4, 3)"
]
},
"metadata": {},
"execution_count": 153
}
],
"source": [
"datetime.datetime.now()\n",
"datetime.datetime.now().hour #saliseden yıla kadar hepsi elde edilebili\n",
"datetime.date.today()\n",
"datetime.date.today().year\n",
"datetime.date(2019,4,3)"
]
},
{
"cell_type": "code",
"source": [
"datetime.datetime.now().day # yada dt.date.today().day"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OLx1qWvFUANF",
"outputId": "32a42b07-955e-42a7-ff89-b1358fe450db"
},
"execution_count": 154,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"22"
]
},
"metadata": {},
"execution_count": 154
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zx00B2efNP1i"
},
"source": [
"### time (süre ölçümlerinde bunu kullancaz, bundaki time metodunu)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"id": "o4y2YQ-4NP1i",
"outputId": "7c121e27-cf33-4adb-97e2-64b755f4d742",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['CLOCK_BOOTTIME', 'CLOCK_MONOTONIC', 'CLOCK_MONOTONIC_RAW', 'CLOCK_PROCESS_CPUTIME_ID', 'CLOCK_REALTIME', 'CLOCK_TAI', 'CLOCK_THREAD_CPUTIME_ID', '_STRUCT_TM_ITEMS', 'altzone', 'asctime', 'clock_getres', 'clock_gettime', 'clock_gettime_ns', 'clock_settime', 'clock_settime_ns', 'ctime', 'daylight', 'get_clock_info', 'gmtime', 'localtime', 'mktime', 'monotonic', 'monotonic_ns', 'perf_counter', 'perf_counter_ns', 'process_time', 'process_time_ns', 'pthread_getcpuclockid', 'sleep', 'strftime', 'strptime', 'struct_time', 'thread_time', 'thread_time_ns', 'time', 'time_ns', 'timezone', 'tzname', 'tzset']\n"
]
}
],
"source": [
"print([i for i in dir(time) if not \"__\" in i])"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"id": "qCo8RMt8NP1i",
"outputId": "1136be26-57ad-4342-d58f-11359319431d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['Timer', '_globals', 'default_number', 'default_repeat', 'default_timer', 'dummy_src_name', 'gc', 'itertools', 'main', 'reindent', 'repeat', 'sys', 'template', 'time', 'timeit']\n"
]
}
],
"source": [
"print([i for i in dir(timeit) if not \"__\" in i])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pS9EitMsNP1i"
},
"source": [
"### dateutil"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"id": "flMnC1jgNP1i",
"outputId": "6a2d948d-df98-4cac-e53c-1633d214e6f5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['_common', '_version', 'parser', 'relativedelta', 'tz']\n"
]
}
],
"source": [
"print([i for i in dir(dateutil) if not \"__\" in i])"
]
},
{
"cell_type": "markdown",
"source": [
"### Calendar"
],
"metadata": {
"id": "LgyH2682UQhd"
}
},
{
"cell_type": "code",
"source": [
"calendar.mdays"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q7L5Sj7nUQAa",
"outputId": "005203f0-d728-42ed-b805-37916fea1b3e"
},
"execution_count": 158,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]"
]
},
"metadata": {},
"execution_count": 158
}
]
},
{
"cell_type": "code",
"source": [
"calendar.Calendar().monthdayscalendar(datetime.date.today().year,datetime.date.today().month)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tIvUKaF4UaX8",
"outputId": "17fcb4fa-827b-4e7e-f56b-29e28fff1dcf"
},
"execution_count": 159,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[[0, 0, 0, 0, 0, 0, 1],\n",
" [2, 3, 4, 5, 6, 7, 8],\n",
" [9, 10, 11, 12, 13, 14, 15],\n",
" [16, 17, 18, 19, 20, 21, 22],\n",
" [23, 24, 25, 26, 27, 28, 29],\n",
" [30, 0, 0, 0, 0, 0, 0]]"
]
},
"metadata": {},
"execution_count": 159
}
]
},
{
"cell_type": "code",
"source": [
"#bu aydaki gün sayısı\n",
"calendar.mdays[datetime.date.today().month]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NfRlfsy55_W8",
"outputId": "02f4c58c-1d6d-4227-ee8c-e72652b8e3db"
},
"execution_count": 160,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"30"
]
},
"metadata": {},
"execution_count": 160
}
]
},
{
"cell_type": "markdown",
"source": [
"### Tarih farkları"
],
"metadata": {
"id": "iCldvgMoTMx0"
}
},
{
"cell_type": "code",
"source": [
"datetime.date.today()-datetime.date(2024,4,3)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QCoSkQIKTMV4",
"outputId": "ba10e902-b1fd-4bda-ace6-8b27a375941c"
},
"execution_count": 161,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.timedelta(days=172)"
]
},
"metadata": {},
"execution_count": 161
}
]
},
{
"cell_type": "code",
"source": [
"#gün farkını alalım\n",
"(datetime.date.today()-datetime.date(2024,4,3)).days"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cHH11kgV6nn6",
"outputId": "89f9132a-2171-4a68-fa02-70d131b0c10a"
},
"execution_count": 162,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"172"
]
},
"metadata": {},
"execution_count": 162
}
]
},
{
"cell_type": "code",
"source": [
"#30 gün sonrası\n",
"datetime.date.today()+datetime.timedelta(days=30)\n",
"#1 ay sonra(bazen 30, bazen 31, hatta 28/29 olabilir)\n",
"datetime.date.today()+datetime.timedelta(days=calendar.mdays[datetime.date.today().month])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G3c0_TWmTUhk",
"outputId": "fdba2571-5c11-43e3-a4f4-52e8e231f1b3"
},
"execution_count": 163,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.date(2024, 10, 22)"
]
},
"metadata": {},
"execution_count": 163
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.date(2024, 10, 22)"
]
},
"metadata": {},
"execution_count": 163
}
]
},
{
"cell_type": "markdown",
"source": [
"### Dönüşümler"
],
"metadata": {
"id": "FgQkOvgZ7TKO"
}
},
{
"cell_type": "code",
"source": [
"# strftime: datetime nesnesini string'e dönüştürme\n",
"now = datetime.datetime.now()\n",
"date_string = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
"print(date_string)\n",
"\n",
"# strptime: string'i datetime nesnesine dönüştürme\n",
"date_string = \"2023-10-26 15:30:00\"\n",
"datetime_object = datetime.datetime.strptime(date_string, \"%Y-%m-%d %H:%M:%S\")\n",
"print(datetime_object, type(datetime_object))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4ob8gIMu7XP4",
"outputId": "d650b3ac-2d5b-434e-99fd-394927d8a177"
},
"execution_count": 164,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-09-22 15:25:25\n",
"2023-10-26 15:30:00 \n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**datetime.strptime** fonksiyonu, belirli bir formattaki tarih ve saat bilgilerini ayrıştırmak için kullanılırken, yukarıda gördüğümüz **dateutil.parser** fonksiyonu daha genel bir ayrıştırıcıdır ve birçok farklı formatı tanıyabilir.\n",
"\n",
"**dateutil.parser** fonksiyonu, **datetime.strptime** fonksiyonundan daha esnektir ve daha fazla tarih ve saat formatını destekler. Ayrıca, dateutil.parser fonksiyonu, bilinmeyen formattaki tarih ve saat bilgilerini ayrıştırmak için de kullanılabilir.\n"
],
"metadata": {
"id": "x8FxG4UrA5ka"
}
},
{
"cell_type": "code",
"source": [
"from_util = dateutil.parser.parse(date_string)\n",
"print(from_util, type(from_util))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P4IavL9iAAFe",
"outputId": "d2a3bcfc-ca74-4b10-c9dd-8a5d6fb5761e"
},
"execution_count": 165,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-10-26 15:30:00 \n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UK2fWkAVNP1j"
},
"source": [
"## Süre ölçümü"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bhG0hYSwNP1j"
},
"source": [
"### Performans amaçlı süre ölçümü(sonuçtan bağımsız)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MMxYbw0eNP1j"
},
"source": [
"- `%'li` olanı bir fonksiyon takip eder, `%%'li` kullanımda ise alt satırdan yazarsın.\n",
"- `Time` olan tek seferlik run'ın süresini verirken `timeit` onlarca kez çalıştırıp ortalama süre verir"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"id": "dBc-f8JzNP1j",
"outputId": "565ae13b-c342-404f-a191-f82fbf2b3d80",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"37.7 ms ± 2.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"x=sum(range(1000000))"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"ExecuteTime": {
"end_time": "2020-09-02T18:59:00.443453Z",
"start_time": "2020-09-02T18:59:00.277897Z"
},
"id": "QBiH7YAkNP1j",
"outputId": "b5a5a8d6-4608-4660-be9d-04603599e209",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 35.5 ms, sys: 0 ns, total: 35.5 ms\n",
"Wall time: 35.6 ms\n"
]
}
],
"source": [
"%%time\n",
"x=sum(range(1000000))"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"id": "2PXDCsVTNP1k",
"outputId": "4293a549-6a2f-490e-ad2d-c9feff7fd4fa",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"25.7 ms ± 7.89 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"def hesapla():\n",
" y=sum(range(1000000))\n",
"\n",
"%timeit hesapla()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ubb3G85qNP1k"
},
"source": [
"### Süreyle birlikte sonuç görme"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"id": "4h98INXtNP1k",
"outputId": "27237b24-a405-4a5e-ccb7-cbeead847909",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"hey\n",
"süre:0.019402027130126953\n"
]
}
],
"source": [
"bas=time.time()\n",
"print(\"hey\")\n",
"hesapla()\n",
"bit=time.time()\n",
"print(\"süre:{}\".format(bit-bas))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VSthGf9ZNP1k"
},
"source": [
"### jupyter nbextensions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hmbvwVIcNP1k"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DJqzb_e3NP1l"
},
"source": [
"nbextensionsı hala kurmadıysanız alın size bir sebep daha. bu süre ölçümü kodlarına gerek yok. yukarıdaki eklentiyle her hücrenin çalışma süresini veriyor. Bir örnek:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ku4FWmrvNP1l"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5jd7IUY-NP1l"
},
"source": [
"# Önemli bazı modüller"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9flgVhIsNP1l"
},
"source": [
"Aşağıda önemli olduğunu düşündüğüm bazı modülleri bulacaksınız. Tabiki bunların dışında da başak kütüphaneler/modüller var, bunları da zamanla öğreneceksiniz."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zIBmaEyINP1l"
},
"source": [
"## os"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ETNToPtcNP1m"
},
"source": [
"İşletim sisteminden bağımsız çalışacak bir koda(dosyalama sistemi v.s ile ilgili) ihtiyacımız olduğunda os modülünü kullanırız."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hvw5ofEoNP1m"
},
"source": [
"### Klasör ve dosya işlemleri"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"id": "idhsE3pENP1m",
"outputId": "72ec3d22-1dc3-46c1-b56e-ac3f98e17786",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 170
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'.'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 170
}
],
"source": [
"import os\n",
"os.getcwd() #o anki aktif folder\n",
"os.curdir #mevcut dizini temsil eden karakter, genelde bu . oluyor. Bunu daha çok prefix path olarak kullanırız"
]
},
{
"cell_type": "code",
"source": [
"os.listdir() #o anki aktif folderdaki dosyaları listeler"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YRnxg8nGITn8",
"outputId": "978b5141-6a0d-45a9-b24f-9c2df3dbbe1e"
},
"execution_count": 171,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['.config', 'drive', 'sample_data']"
]
},
"metadata": {},
"execution_count": 171
}
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"id": "0oHJDb3zNP1m",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2a8c7a49-1c9f-4bcd-ae15-61eee8b06a4e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Othercomputers',\n",
" '.file-revisions-by-id',\n",
" 'MyDrive',\n",
" '.shortcut-targets-by-id',\n",
" '.Trash-0']"
]
},
"metadata": {},
"execution_count": 172
}
],
"source": [
"os.chdir(\"drive\") #aktif klasörü değiştiriyoruz\n",
"os.listdir()"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"id": "MJ9SFSM1NP1m",
"outputId": "274ebbb8-ea9d-4cd2-9290-b1ebd7033088",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'..'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 173
}
],
"source": [
"os.pardir #parent klasörü temsil eden karakter"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"id": "Ey3N5KpINP1n",
"outputId": "83ec6a2f-9800-417d-dad9-266ce94edb9c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['.config', 'drive', 'sample_data']"
]
},
"metadata": {},
"execution_count": 174
}
],
"source": [
"os.listdir(os.pardir)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"id": "n3LZWSB3NP1n",
"outputId": "afccb4c8-66e6-40a5-81b3-34f2d3a74a7c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {},
"execution_count": 175
}
],
"source": [
"os.path.exists('Python Genel.ipynb') #bir dosya/klasör mevcut mu"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"id": "ZnWb90SkNP1n",
"outputId": "2fdc4f0c-407c-465a-c648-f689bc660556",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {},
"execution_count": 176
}
],
"source": [
"os.path.isfile('olmayandosya.txt') #parametredeki bir dosya mı? veya bir dosya mevcut mu anlamında da kullanılabilir."
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"id": "XhSxCvnqNP1n",
"outputId": "fd06880b-bbd7-4773-aa31-482526f71ca4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {},
"execution_count": 177
}
],
"source": [
"os.path.isdir(\"klasor\")"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"id": "9TEIONcbNP1n",
"outputId": "7e1d8090-d275-48ff-c63a-6908af1cac11",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[]\n"
]
}
],
"source": [
"# list all files in a directory in Python.\n",
"from os import listdir\n",
"from os.path import isfile, join\n",
"p=os.getcwd()\n",
"files_list = [f for f in listdir(p) if isfile(join(p, f))]\n",
"print(files_list);"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"scrolled": true,
"id": "pjdzqreQNP1o",
"outputId": "87141785-3f64-4fe3-cd05-8acf35fb12ab",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/MyDrive/Colab Notebooks\n",
"/content/drive/MyDrive/Colab Notebooks/.config\n",
"/content/drive/MyDrive/Colab Notebooks/.config/startup\n"
]
}
],
"source": [
"dizin= \"/content/drive/MyDrive/Colab Notebooks\"\n",
"for kökdizin, altdizinler, dosyalar in os.walk(dizin):\n",
" print(kökdizin)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"id": "uBHDharYNP1o",
"outputId": "390d5cbc-b628-4c14-e6ae-a18c11c88a57",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/MyDrive/Colab Notebooks/Snippets: Drive adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/Snippets: Accessing files adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/Data Table Display adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/Forms adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/gemini.ipynb adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/Harici veriler: Yerel Dosyalar, Drive, E-Tablolar ve Cloud Storage adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/_My Custom Snippets.ipynb\n",
"/content/drive/MyDrive/Colab Notebooks/_Colab Aboneliğinizden En İyi Şekilde Yararlanma\n",
"/content/drive/MyDrive/Colab Notebooks/_pathler, drive, github.ipynb\n",
"/content/drive/MyDrive/Colab Notebooks/pandas.ipynb adlı not defterinin kopyası\n",
"/content/drive/MyDrive/Colab Notebooks/.config/startup/starters.py\n"
]
}
],
"source": [
"for kökdizin, altdizinler, dosyalar in os.walk(dizin):\n",
" for dosya in dosyalar:\n",
" print(os.sep.join([kökdizin, dosya]))"
]
},
{
"cell_type": "code",
"source": [
"%cd \"MyDrive\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_UCgZfRxJX2f",
"outputId": "81d5ee3e-bdb6-45ef-bcf1-84b5e89f6dec"
},
"execution_count": 181,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/MyDrive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!touch abc.txt #dosya yaratalım"
],
"metadata": {
"id": "5_X3DvwpIrwo"
},
"execution_count": 182,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"scrolled": true,
"id": "qluw2XfeNP1o",
"outputId": "d35987c6-867c-462e-b4c1-4be768a54ea2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Last modified: 1727018731.0\n",
"Created: Sun Sep 22 15:25:31 2024\n"
]
}
],
"source": [
"import os.path, time, datetime\n",
"print(\"Last modified: %s\" % os.path.getmtime(\"abc.txt\")) #formatlanmamış\n",
"print(\"Created: %s\" % time.ctime(os.path.getctime(\"abc.txt\")))"
]
},
{
"cell_type": "code",
"source": [
"!rm abc.txt #tekrar silelim"
],
"metadata": {
"id": "cBXWgS90JqmK"
},
"execution_count": 184,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"id": "WyqHfL5gNP1o"
},
"outputs": [],
"source": [
"#Sadece windowsta. Linux için-->https://stackoverflow.com/questions/17317219/is-there-an-platform-independent-equivalent-of-os-startfile\n",
"# os.startfile('calc.exe') #dosya açar, program başaltır, internet sitesine gider v.s"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"id": "YM8SBcjENP1o"
},
"outputs": [],
"source": [
"# os.startfile(\"/falanca/klasördeki/filanca/dosya\")"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"id": "NgmG0Uw_NP1p"
},
"outputs": [],
"source": [
"# os.startfile(\"www.volkanyurtseven.com\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qwCvraQ9NP1p"
},
"source": [
"### Sistem ve Environment bilgileri"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"scrolled": true,
"id": "kLkITj7VNP1p",
"outputId": "e2edc182-1e94-4361-8007-a142c568a7b6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"posix\n",
"Linux\n",
"6.1.85+\n"
]
}
],
"source": [
"import platform, os\n",
"print(os.name)\n",
"print(platform.system())\n",
"print(platform.release())"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"id": "B7-oLbonNP1p",
"outputId": "7cb1bc7e-8f7a-461d-86a8-d493052061c4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'SHELL': '/bin/bash',\n",
" 'NV_LIBCUBLAS_VERSION': '12.2.5.6-1',\n",
" 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
" 'COLAB_JUPYTER_TRANSPORT': 'ipc',\n",
" 'NV_NVML_DEV_VERSION': '12.2.140-1',\n",
" 'NV_CUDNN_PACKAGE_NAME': 'libcudnn8',\n",
" 'CGROUP_MEMORY_EVENTS': '/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events',\n",
" 'NV_LIBNCCL_DEV_PACKAGE': 'libnccl-dev=2.19.3-1+cuda12.2',\n",
" 'NV_LIBNCCL_DEV_PACKAGE_VERSION': '2.19.3-1',\n",
" 'VM_GCE_METADATA_HOST': '169.254.169.253',\n",
" 'HOSTNAME': '51c8695c6b32',\n",
" 'LANGUAGE': 'en_US',\n",
" 'TBE_RUNTIME_ADDR': '172.28.0.1:8011',\n",
" 'COLAB_TPU_1VM': '',\n",
" 'GCE_METADATA_TIMEOUT': '3',\n",
" 'NVIDIA_REQUIRE_CUDA': 'cuda>=12.2 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526',\n",
" 'NV_LIBCUBLAS_DEV_PACKAGE': 'libcublas-dev-12-2=12.2.5.6-1',\n",
" 'NV_NVTX_VERSION': '12.2.140-1',\n",
" 'COLAB_JUPYTER_IP': '172.28.0.12',\n",
" 'NV_CUDA_CUDART_DEV_VERSION': '12.2.140-1',\n",
" 'NV_LIBCUSPARSE_VERSION': '12.1.2.141-1',\n",
" 'COLAB_LANGUAGE_SERVER_PROXY_ROOT_URL': 'http://172.28.0.1:8013/',\n",
" 'NV_LIBNPP_VERSION': '12.2.1.4-1',\n",
" 'NCCL_VERSION': '2.19.3-1',\n",
" 'KMP_LISTEN_PORT': '6000',\n",
" 'TF_FORCE_GPU_ALLOW_GROWTH': 'true',\n",
" 'ENV': '/root/.bashrc',\n",
" 'PWD': '/',\n",
" 'TBE_EPHEM_CREDS_ADDR': '172.28.0.1:8009',\n",
" 'COLAB_LANGUAGE_SERVER_PROXY_REQUEST_TIMEOUT': '30s',\n",
" 'TBE_CREDS_ADDR': '172.28.0.1:8008',\n",
" 'NV_CUDNN_PACKAGE': 'libcudnn8=8.9.6.50-1+cuda12.2',\n",
" 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',\n",
" 'COLAB_JUPYTER_TOKEN': '',\n",
" 'LAST_FORCED_REBUILD': '20240627',\n",
" 'NV_NVPROF_DEV_PACKAGE': 'cuda-nvprof-12-2=12.2.142-1',\n",
" 'NV_LIBNPP_PACKAGE': 'libnpp-12-2=12.2.1.4-1',\n",
" 'NV_LIBNCCL_DEV_PACKAGE_NAME': 'libnccl-dev',\n",
" 'TCLLIBPATH': '/usr/share/tcltk/tcllib1.20',\n",
" 'NV_LIBCUBLAS_DEV_VERSION': '12.2.5.6-1',\n",
" 'NVIDIA_PRODUCT_NAME': 'CUDA',\n",
" 'COLAB_KERNEL_MANAGER_PROXY_HOST': '172.28.0.12',\n",
" 'NV_LIBCUBLAS_DEV_PACKAGE_NAME': 'libcublas-dev-12-2',\n",
" 'NV_CUDA_CUDART_VERSION': '12.2.140-1',\n",
" 'COLAB_WARMUP_DEFAULTS': '1',\n",
" 'HOME': '/root',\n",
" 'LANG': 'en_US.UTF-8',\n",
" 'COLUMNS': '100',\n",
" 'CUDA_VERSION': '12.2.2',\n",
" 'CLOUDSDK_CONFIG': '/content/.config',\n",
" 'NV_LIBCUBLAS_PACKAGE': 'libcublas-12-2=12.2.5.6-1',\n",
" 'NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE': 'cuda-nsight-compute-12-2=12.2.2-1',\n",
" 'COLAB_RELEASE_TAG': 'release-colab_20240919-060125_RC00',\n",
" 'KMP_TARGET_PORT': '9000',\n",
" 'KMP_EXTRA_ARGS': '--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https://colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/m-s-2x16lz196cw9 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true --gorilla_ws_opt_in --log_code_content',\n",
" 'NV_LIBNPP_DEV_PACKAGE': 'libnpp-dev-12-2=12.2.1.4-1',\n",
" 'COLAB_LANGUAGE_SERVER_PROXY_LSP_DIRS': '/datalab/web/pyright/typeshed-fallback/stdlib,/usr/local/lib/python3.10/dist-packages',\n",
" 'NV_LIBCUBLAS_PACKAGE_NAME': 'libcublas-12-2',\n",
" 'COLAB_KERNEL_MANAGER_PROXY_PORT': '6000',\n",
" 'CLOUDSDK_PYTHON': 'python3',\n",
" 'NV_LIBNPP_DEV_VERSION': '12.2.1.4-1',\n",
" 'NO_GCE_CHECK': 'False',\n",
" 'PYTHONPATH': '/env/python',\n",
" 'NV_LIBCUSPARSE_DEV_VERSION': '12.1.2.141-1',\n",
" 'LIBRARY_PATH': '/usr/local/cuda/lib64/stubs',\n",
" 'NV_CUDNN_VERSION': '8.9.6.50',\n",
" 'SHLVL': '0',\n",
" 'NV_CUDA_LIB_VERSION': '12.2.2-1',\n",
" 'COLAB_LANGUAGE_SERVER_PROXY': '/usr/colab/bin/language_service',\n",
" 'NVARCH': 'x86_64',\n",
" 'NV_CUDNN_PACKAGE_DEV': 'libcudnn8-dev=8.9.6.50-1+cuda12.2',\n",
" 'NV_CUDA_COMPAT_PACKAGE': 'cuda-compat-12-2',\n",
" 'NV_LIBNCCL_PACKAGE': 'libnccl2=2.19.3-1+cuda12.2',\n",
" 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib:/usr/local/nvidia/lib64',\n",
" 'COLAB_GPU': '',\n",
" 'NV_CUDA_NSIGHT_COMPUTE_VERSION': '12.2.2-1',\n",
" 'GCS_READ_CACHE_BLOCK_SIZE_MB': '16',\n",
" 'NV_NVPROF_VERSION': '12.2.142-1',\n",
" 'LC_ALL': 'en_US.UTF-8',\n",
" 'COLAB_FILE_HANDLER_ADDR': 'localhost:3453',\n",
" 'PATH': '/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin',\n",
" 'NV_LIBNCCL_PACKAGE_NAME': 'libnccl2',\n",
" 'COLAB_DEBUG_ADAPTER_MUX_PATH': '/usr/local/bin/dap_multiplexer',\n",
" 'NV_LIBNCCL_PACKAGE_VERSION': '2.19.3-1',\n",
" 'PYTHONWARNINGS': 'ignore:::pip._internal.cli.base_command',\n",
" 'DEBIAN_FRONTEND': 'noninteractive',\n",
" 'COLAB_BACKEND_VERSION': 'next',\n",
" 'OLDPWD': '/',\n",
" 'JPY_PARENT_PID': '92',\n",
" 'TERM': 'xterm-color',\n",
" 'CLICOLOR': '1',\n",
" 'PAGER': 'cat',\n",
" 'GIT_PAGER': 'cat',\n",
" 'MPLBACKEND': 'module://ipykernel.pylab.backend_inline',\n",
" 'ENABLE_DIRECTORYPREFETCHER': '1',\n",
" 'USE_AUTH_EPHEM': '1',\n",
" 'PYDEVD_USE_FRAME_EVAL': 'NO'}"
]
},
"metadata": {},
"execution_count": 189
}
],
"source": [
"dict(os.environ)"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"id": "PrSwwZeBNP1p"
},
"outputs": [],
"source": [
"# print(dict(os.environ)[\"HOMEPATH\"])\n",
"# print(dict(os.environ)[\"USERNAME\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QkXjdGPuNP1p"
},
"source": [
"## random"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T15:44:49.592635Z",
"start_time": "2020-11-22T15:44:49.579668Z"
},
"id": "3rj9nepQNP1q",
"outputId": "3fff8a56-1bb1-45bf-9dce-6e95b2f5eee2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"10\n",
"13\n",
"8\n",
"[3, 1, 3]\n",
"0.37961522332372777\n",
"2.6298644191144316\n",
"[3, 3]\n"
]
}
],
"source": [
"import random\n",
"dizi=[3,5,8,3,2,1,9]\n",
"random.seed(1)\n",
"print(random.randint(2,48)) #2 ile 48 arasında\n",
"print(random.randrange(1,100,3))\n",
"print(random.choice(dizi))\n",
"print(random.choices(dizi,k=3)) #çektiğini geri koyar, sampledan farkı bu, o yüzden aynısı çıkabilir\n",
"print(random.random()) #0-1 arası\n",
"print(random.uniform(2,5)) #2-5 arası küsurlu sayı\n",
"print(random.sample(dizi,2)) #çektiğini geri koymaz, choicestan farkı bu"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"id": "Chjq0qoINP1r",
"outputId": "45695dcb-97e3-44a1-df0d-64479541b198",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[2, 5, 8, 3, 1, 3, 9]"
]
},
"metadata": {},
"execution_count": 192
}
],
"source": [
"#liste karıştırmak\n",
"random.shuffle(dizi)\n",
"dizi"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zIVd55b_NP1r"
},
"source": [
"## array"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yEQlL9_LNP1r"
},
"source": [
"listin hemen hemen aynısı olup önemli bir farkı, içine sadece belli tipte eleman almasıdır. Bu yüzden memory performansı açısından liste göre daha iyidir.ancak list'ler daha hızlıdır. Ayrıca sayısal bir dizi kullanacaksanız numpy kullanmanızı tavsiye ederim. Bunun dışıdna çok büyük metinsel diziler oluşturacaksanız array kullanabilirsinz."
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"id": "Aehg6NoUNP1r",
"outputId": "67cb0480-235a-4ccc-9e7f-99631a36be54",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"92 88\n"
]
}
],
"source": [
"from array import array\n",
"import sys\n",
"arr=array('i',[1,2,3])\n",
"lst=[1,2,3]\n",
"print(sys.getsizeof(arr),sys.getsizeof(lst)) #arr'ın memory kullanımı daha düşüktür"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wwf1DhqXNP1r"
},
"source": [
"## collections"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T12:42:13.976560Z",
"start_time": "2020-11-22T12:42:13.970578Z"
},
"id": "OigSvt6TNP1r",
"outputId": "03e468fd-4d7e-4764-ca7a-2dad1d4a37fb",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2"
]
},
"metadata": {},
"execution_count": 194
}
],
"source": [
"my_list = [\"a\",\"b\",\"c\",\"c\",\"e\",\"c\",\"b\",\"b\",\"a\"]\n",
"my_list.count(\"a\")"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T12:42:42.671252Z",
"start_time": "2020-11-22T12:42:42.668256Z"
},
"id": "MKziwSJ3NP1r",
"outputId": "1aa014a1-2768-4f65-e9fd-fdea33bf3fb8",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Counter({'b': 3, 'c': 3, 'a': 2, 'e': 1})\n",
"2\n"
]
}
],
"source": [
"from collections import Counter\n",
"\n",
"cn = Counter(my_list) #dictionary benzeri bir yapı. tüm dictionary metodları kullanılabilir\n",
"print(cn)\n",
"print(cn[\"a\"]) #yukarda list ile yaptığımızın aynısı"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"id": "29BWc8LiNP1r",
"outputId": "66290e3b-9233-4adb-9629-606a4b561366",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Counter({'sen': 3, 'seni': 2, 'bil': 1, 'bilmezsen': 1, 'kendini': 1})"
]
},
"metadata": {},
"execution_count": 196
}
],
"source": [
"str_ = \"sen seni bil sen seni sen bilmezsen kendini\"\n",
"cn = Counter(str_.split(' '))\n",
"cn"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"id": "y0tSvEiiNP1s",
"outputId": "533830e2-2567-4395-a2a7-a9890728fc31",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[('sen', 3), ('seni', 2)]\n",
"['sen', 'seni', 'bil', 'bilmezsen', 'kendini']\n",
"{'sen': 3, 'seni': 2, 'bil': 1, 'bilmezsen': 1, 'kendini': 1}\n"
]
}
],
"source": [
"print(cn.most_common(2)) #en çok kullanılan 2 kelime\n",
"print(list(cn)) #key değerlerinin listeye çevrilmiş hali\n",
"print(dict(cn)) #key-value değerlerinin sözlüğe çevrilmiş hali"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"id": "0yot3vKfNP1s",
"outputId": "c4d448b4-05e1-46c3-cc0a-23b8600d4c62",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"a 4\n",
"c 3\n",
"b 2\n",
"----\n",
"a 4\n",
"c 3\n",
"b 2\n"
]
}
],
"source": [
"#OrderDict'e artık ihtiyaç yoktur. Python 3.7den itibaren de dictioanryye girilen sırayı korumaktadır\n",
"#aşağıdaki örnekten de görebiliyoruz\n",
"from collections import OrderedDict\n",
"liste = [\"a\",\"c\",\"c\",\"a\",\"b\",\"a\",\"a\",\"b\",\"c\"]\n",
"cnt = Counter(liste)\n",
"od = OrderedDict(cnt.most_common())\n",
"d=dict(cnt.most_common())\n",
"for key, value in od.items():\n",
" print(key, value)\n",
"print(\"----\")\n",
"for key, value in d.items():\n",
" print(key, value)"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"id": "stMs_GdcNP1s",
"outputId": "620cbbeb-3e0c-49cf-8f92-8d9a2fe84fad",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"John\n"
]
}
],
"source": [
"from collections import namedtuple\n",
"\n",
"Student = namedtuple('Student', 'fname, lname, age') #class yapısına benzer bir kullanım. indeks ezberlemeye son!\n",
"s1 = Student('John', 'Clarke', '13')\n",
"print(s1.fname)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "by8Up3HxNP1s"
},
"source": [
"## itertools"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XjRcEeAlNP1s"
},
"source": [
"### Permütasyon"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"id": "NCwAzv4bNP1t",
"outputId": "728eec2b-54e6-4dc2-d4bf-8df05907000e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[('A', 'B', 'C'), ('A', 'C', 'B'), ('B', 'A', 'C'), ('B', 'C', 'A'), ('C', 'A', 'B'), ('C', 'B', 'A')]\n",
"[('A', 'B'), ('A', 'C'), ('B', 'A'), ('B', 'C'), ('C', 'A'), ('C', 'B')]\n"
]
}
],
"source": [
"from itertools import permutations #sadece tekrarsızlar yapılabiliyor. n!/(n-r)!\n",
"liste=[\"A\",\"B\",\"C\"]\n",
"per1=list(permutations(liste))\n",
"per2=list(permutations(liste,2))\n",
"print(per1)\n",
"print(per2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oYYgBnk7NP1t"
},
"source": [
"### Kombinasyon"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"id": "jpiwGFjSNP1t",
"outputId": "105dd14f-1704-4385-f7a9-6b2482333805",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('A', 'B'), ('A', 'C'), ('B', 'C')]"
]
},
"metadata": {},
"execution_count": 201
}
],
"source": [
"from itertools import combinations #n!/(r!*(n-r)!)\n",
"com=list(combinations(liste,2))\n",
"com"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oySaIniiNP1t"
},
"source": [
"### Kartezyen çarpım"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"id": "imyEWmAzNP1t",
"outputId": "fa1d5c39-5245-4174-a288-040494180a60",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('A', 2000),\n",
" ('A', 2001),\n",
" ('A', 2002),\n",
" ('A', 2003),\n",
" ('B', 2000),\n",
" ('B', 2001),\n",
" ('B', 2002),\n",
" ('B', 2003),\n",
" ('C', 2000),\n",
" ('C', 2001),\n",
" ('C', 2002),\n",
" ('C', 2003)]"
]
},
"metadata": {},
"execution_count": 202
}
],
"source": [
"from itertools import product\n",
"list1=[\"A\",\"B\",\"C\"]\n",
"list2=[2000,2001,2002,2003]\n",
"krt=list(product(list1,list2))\n",
"krt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8c-v2GPbNP1u"
},
"source": [
"## Regex (Düzenli ifadeler)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T7ffL3MvNP1u"
},
"source": [
"Pythona özgü değildir, hemen her dilde implementasyonu vardır. Başlı başına büyük bi konudur. Burada özet vermeye çalışıcam. İleride Text mining, NLP v.s çalışacaksanız iyi öğrenmenizde fayda var"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yYTdlQvRNP1u"
},
"source": [
"### Isınma"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T14:55:50.942122Z",
"start_time": "2020-11-22T14:55:50.939128Z"
},
"id": "LstCA0g5NP1u"
},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"id": "cp0dScCoNP1u"
},
"outputs": [],
"source": [
"a=\"benim adım volkan\""
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"id": "bc2oiGJMNP1u"
},
"outputs": [],
"source": [
"#match: başında var mı diye kontrol eder\n",
"re.match(\"volkan\",a) #eşleşme yok, sonuç dönmez"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"id": "A2r-VT66NP1v",
"outputId": "3e901d97-7e7e-481d-b3bb-fcf0084d58ac",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 206
}
],
"source": [
"re.match(\"ben\",a) #var"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"id": "vGUREnelNP1v",
"outputId": "c09044fe-487a-403d-b212-e1ebabf6fb01",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 207
}
],
"source": [
"a.startswith(\"ben\") #bu daha kullanışlıdır."
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"id": "_43rk05kNP1v",
"outputId": "91555923-4984-46f5-de5c-a83218bde2e9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 208
}
],
"source": [
"#search, herhangi bi yerde var mı, matche göre daha yavaş çalışır\n",
"re.search(\"volkan\",a)"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"id": "eINL0Yl7NP1v",
"outputId": "aefcdf3c-561f-43e3-9e87-c3df2d851fb3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 209
}
],
"source": [
"\"volkan\" in a #bu daha pratik"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"id": "hHIC0gVqNP1v",
"outputId": "b85a86b4-f2bb-4088-cf10-fd8f2d4b52b8",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['sen', 'sen', 'sen', 'sen']\n",
"4\n",
"4\n"
]
}
],
"source": [
"b=\"sen seni bil sen seni\"\n",
"bul=re.findall(\"sen\", b)\n",
"\n",
"print(bul)\n",
"print(len(bul))\n",
"print(b.count(\"sen\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hiAVtFvyNP1w"
},
"source": [
"### Metakarakter kullanımı"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T14:56:00.364786Z",
"start_time": "2020-11-22T14:56:00.361819Z"
},
"id": "YIAHevecNP1w"
},
"outputs": [],
"source": [
"isimler=[\"123a\",\"ali\",\"veli\",\"hakan\",\"volkan\",\"osman\",\"kandemir\",\"VOLkan\"]"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T14:56:03.790850Z",
"start_time": "2020-11-22T14:56:03.785864Z"
},
"id": "5IurCv6oNP1w",
"outputId": "bf8c26f5-ccfc-47e0-dae7-805166919943",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['hakan', 'volkan', 'VOLkan']"
]
},
"metadata": {},
"execution_count": 212
}
],
"source": [
"#kan ile bitenler, regex olmadan\n",
"kan=[x for x in isimler if x[-3:]==\"kan\"]\n",
"kan"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wPMAW5iXNP1w"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BkbwbRCTNP1w"
},
"source": [
"#### [ ] Köşeli parantez"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fw-efq3_NP1x"
},
"source": [
"[] işareti, \"içine giren karakterleri içeren\" filtresi uygular. Burada önemli olan [] içinde gördügümüz tüm karaktereleri tek tek uyguluyor olmasıdır. Ör: [abc]: a veya b veya c içeren demek. [a-z]: A ile Z arasındakiler demek"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T14:56:09.039799Z",
"start_time": "2020-11-22T14:56:09.035811Z"
},
"id": "ov68JgukNP1x",
"outputId": "7dee106c-f324-4279-ae9b-4e0437b0824f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"hakan\n",
"volkan\n"
]
}
],
"source": [
"#regex ile\n",
"for i in isimler:\n",
" if re.search(\"[a-z]kan\",i):\n",
" print(i)\n",
"#başta veya ortada bir yerde a-z arasında bir karekter olsun, sonu kan olsun demiş olduk. yani \"kan\"ın önünde bir harf\n",
"#olsun da nerede olursa olsun, bşta mı ortada mı önemli değil, önemli olan kan'ın öncesinde olmaslı"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {
"id": "Fsvv35iGNP1y",
"outputId": "384c7687-3e20-4112-ea72-3f63886637d1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['veli', 'osman', 'kandemir']"
]
},
"metadata": {},
"execution_count": 214
}
],
"source": [
"#içinde e veya m geçenler, bu sefer list comprehension ile yapalım\n",
"[i for i in isimler if re.search(\"[em]\",i)]"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"id": "WCYR4kscNP1y",
"outputId": "02899ce3-f02f-4869-dfe1-c67cc809ef0b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['123a', 'b123', '1234']"
]
},
"metadata": {},
"execution_count": 215
}
],
"source": [
"#rakam içerenler\n",
"liste = [\"123a\",\"b123\",\"1234\",\"volkan\"]\n",
"sayıiçerenler=[x for x in liste if re.search(\"[0-9]\",x)]\n",
"sayıiçerenler"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {
"id": "SnmWLT3YNP1y",
"outputId": "111b849b-581f-4867-f9a0-e00aae075f6d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['123a', '1234']"
]
},
"metadata": {},
"execution_count": 216
}
],
"source": [
"#rakam ile başlayanları bulma\n",
"rakamlabaşlayanlar=[x for x in liste if re.match(\"[0-9]\",x)] #yukardakinden farklı olarak match kullandık, daha hızlı çalışır\n",
"rakamlabaşlayanlar"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {
"id": "GcqMsXEENP1y",
"outputId": "aba87780-4f1b-47be-8dc8-c20fe2c4ed94",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['ABC', 'Abc', 'abc', '12a', 'A12', 'Ab1', 'Ab23']\n",
"['Abc', 'Ab1', 'Ab23']\n",
"['ABC', 'Abc', 'abc', '12a', 'A12', '123', 'Ab1', 'Ab23']\n",
"['Ab1', 'Ab23']\n"
]
}
],
"source": [
"liste=[\"ABC\",\"Abc\",\"abc\",\"12a\",\"A12\",\"123\",\"Ab1\",\"Ab23\"]\n",
"print([x for x in liste if re.search(\"[A-Za-z]\",x)]) #büyük veya küçük harf içerenler\n",
"print([x for x in liste if re.search(\"[A-Z][a-z]\",x)]) #ilki büyük ikincisi küçük harf içerenler\n",
"print([x for x in liste if re.search(\"[A-Za-z0-9]\",x)]) #büyük veya küçük harf veya sayı içerenler\n",
"print([x for x in liste if re.search(\"[A-Z][a-z][0-9]\",x)]) #ilki büyük ikincisi küçük üçüncüsü sayı olanlar"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9giRPBb3NP1y"
},
"source": [
"#### . Nokta"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bo5mfg5cNP1y"
},
"source": [
"\".\" tek karekteri için joker anlamındadır"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"id": "ZsSkaV5qNP1z"
},
"outputs": [],
"source": [
"isimler=[\"arhan\",\"volkan\",\"osman\",\"hakan\",\"demirhan\",\"1ozan\"]"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {
"id": "ApljdIy-NP1z",
"outputId": "9f03dac5-a52a-4cb2-9197-d070c67cffa3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['arhan', 'osman', 'hakan']"
]
},
"metadata": {},
"execution_count": 219
}
],
"source": [
"#5 karekterli olup an ile biten gerçek isimler(gerçek isim: sayı ile başlayan birşey isim olamaz)\n",
"liste=[x for x in isimler if re.match(\"[a-z]..an\",x)]\n",
"liste"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lfCcup0ANP1z"
},
"source": [
"#### * Yıldız"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yb-V7AdfNP1z"
},
"source": [
"Kendinden önce gelen 1 ifadeyi en az 0(evet 0, 1 değil) sayıda eşleştirir. Özellikle bir ifadenin yazılamayabildiği durumları da kapsamak için kullanılır. Aşağıdkai örnek gayet açıklayıcıdır."
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"id": "nw9onGxYNP1z",
"outputId": "6e1b138e-f400-4caa-8b2b-30adc79e354a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['kıral', 'kral', 'kıro', 'kro', 'kritik', 'kıritik', 'kııral']"
]
},
"metadata": {},
"execution_count": 220
}
],
"source": [
"liste = [\"kıral\", \"kral\", \"kıro\", \"kro\", \"kırmızı\",\"kırçıllı\",\"kritik\",\"kıritik\",\"kııral\"]\n",
"[x for x in liste if re.match(\"kı*r[aeıioöuü]\",x)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wEXQD-HtNP10"
},
"source": [
"Bu örnekte, yabancı dilden geçen kelimelerden \"kr\" ile başlayanları inceledik. Bunlar bazen aralarına ı harfi girilerek yazılabiliyor. Bu kelimelerde genelde r'den sonra sesli bi harf gelir. Biz de bunları yakalamaya çalıştık."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LC-ketd1NP10"
},
"source": [
"#### + Artı"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WU2zW6m5NP10"
},
"source": [
"Yıldıza benzer, ancak bu sefer en az 1 sayıda eşleşme yapar."
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"id": "YHDg_aX3NP11",
"outputId": "13cd2b57-c1eb-456e-cdd8-75010ab5629e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['kıral', 'kıro', 'kıritik', 'kııral']"
]
},
"metadata": {},
"execution_count": 221
}
],
"source": [
"liste = [\"kıral\", \"kral\", \"kıro\", \"kro\", \"kırmızı\",\"kırçıllı\",\"kritik\",\"kıritik\",\"kııral\"]\n",
"[x for x in liste if re.match(\"kı+r[aeıioöuü]\",x)]"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T14:57:05.136600Z",
"start_time": "2020-11-22T14:57:05.132611Z"
},
"id": "KObxbEcsNP11",
"outputId": "4ef84528-d5fe-4f7c-ad91-6697ca046e15",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"volkan\n",
"hakan\n"
]
}
],
"source": [
"#yukardaki kan'la biten isimler örneğin. bu sefer VOLkan yazdırılır\n",
"for i in isimler:\n",
" if re.search(\".+kan\",i):\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rV6ooe6aNP11"
},
"source": [
"#### ? Soru işareti"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ekAtIxZnNP11"
},
"source": [
"Kendinden önce gelen karakterin 0 veya 1 kez geçtiği durumları eşleştirir."
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"id": "yu6V0LzTNP11",
"outputId": "aca00f6a-c436-42a9-e8b7-097197983682",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['kıral', 'kral', 'kıro', 'kro', 'kritik', 'kıritik']"
]
},
"metadata": {},
"execution_count": 223
}
],
"source": [
"liste = [\"kıral\", \"kral\", \"kıro\", \"kro\", \"kırmızı\",\"kırçıllı\",\"kritik\",\"kıritik\",\"kııral\"]\n",
"[x for x in liste if re.match(\"kı?r[aeıioöuü]\",x)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E4bdPfQoNP12"
},
"source": [
"#### {} süslü parantez"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gf5Uj6JPNP12"
},
"source": [
"bir karekterin n adet geçtiği durumlar eşleştirilir."
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {
"id": "ujabWlTENP12",
"outputId": "7521267c-e0b1-49f2-ad64-353a46a5b2a9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['kııral']"
]
},
"metadata": {},
"execution_count": 224
}
],
"source": [
"liste = [\"kıral\", \"kral\", \"kıro\", \"kro\", \"kırmızı\",\"kırçıllı\",\"kritik\",\"kıritik\",\"kııral\"]\n",
"[x for x in liste if re.match(\"kı{2}r[aeıioöuü]\",x)]"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"id": "DXKgN-erNP12",
"outputId": "cda37a34-912f-408e-d0f9-23b61883985c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['kııral']"
]
},
"metadata": {},
"execution_count": 225
}
],
"source": [
"#böyle de yapılabilirdi ama n sayısı yükseldikçe {} ile yapmak daha mantıklı\n",
"[x for x in liste if re.match(\"kıır[aeıioöuü]\",x)]"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {
"id": "lJ2-PlDENP12",
"outputId": "5327b5f8-4a10-4055-d192-1af27c6ad563",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['gool', 'gooool']"
]
},
"metadata": {},
"execution_count": 226
}
],
"source": [
"#{min,max}\n",
"liste=[\"gol\",\"gool\",\"gooool\",\"gööl\",\"gooooooool\"]\n",
"[x for x in liste if re.match(\"[a-z]o{2,5}l\",x)] #en az 2 en çok 5 oo içersin"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KNreV4PoNP12"
},
"source": [
"##### Çeşitli linkler"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MGQ1_VJ_NP12"
},
"source": [
"Regex dünyası çok büyük bi dünya, burada hepsini anlatmak yerine kısa bir girizgah yapmış olduk. Öncelikle genel oalrak regexi kavramanız sonrasında da python implementasonunu kavramanız gerekiyor. Aşağıdaki linkleri incelemenizi tavisye ederim.\n",
"\n",
"**Genel bilgi**\n",
"- https://en.wikipedia.org/wiki/Regular_expression\n",
"- https://regexr.com/ (pratik amaçlı)\n",
"\n",
"**Python**\n",
"- https://docs.python.org/3/howto/regex.html\n",
"- https://docs.python.org/3/library/re.html"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GwqZZyGrNP12"
},
"source": [
"## json"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MH6E3UfyNP13"
},
"source": [
"### jsondan pythona"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZN4qHLaRNP13"
},
"source": [
"json yapısı python dictionary'lerine çok benzer. elde edeceğimiz nesne de bir dictionary olacaktır"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:50:09.106861Z",
"start_time": "2021-04-11T10:50:09.100878Z"
},
"id": "oJVvrFqFNP13"
},
"outputs": [],
"source": [
"import json"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:50:09.603020Z",
"start_time": "2021-04-11T10:50:09.595037Z"
},
"id": "wP1nOj35NP13",
"outputId": "5edbf058-7746-4e12-fe4a-33643d07acf3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"str"
]
},
"metadata": {},
"execution_count": 228
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict"
]
},
"metadata": {},
"execution_count": 228
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'John'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 228
}
],
"source": [
"#örnek bir json stringden\n",
"x = '{ \"name\":\"John\", \"age\":30, \"city\":\"New York\"}'\n",
"y=json.loads(x) #dcitionary olarak yükler\n",
"type(x)\n",
"type(y)\n",
"y[\"name\"]"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:51:12.111273Z",
"start_time": "2021-04-11T10:51:12.102295Z"
},
"id": "9TkJmx4gNP13",
"outputId": "26a2d4ba-7607-418e-b62d-8f903cec694c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict"
]
},
"metadata": {},
"execution_count": 231
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Bolge': {'0': 'Akdeniz', '1': 'Marmara', '2': 'Akdeniz', '3': 'Marmara'},\n",
" 'Yil': {'0': 2020, '1': 2020, '2': 2021, '3': 2021},\n",
" 'Satis': {'0': 10, '1': 15, '2': 42, '3': 56}}"
]
},
"metadata": {},
"execution_count": 231
}
],
"source": [
"#veya json dosyadan. ama bu indenti dikkate almıyor, çünkü elde edilen nesne bi string değil, dictionary\n",
"import io\n",
"with io.open(\"/content/drive/MyDrive/Programming/PythonRocks/dataset/json/indentli_bolgesatis.json\", 'r') as f:\n",
" data = json.load(f)\n",
"type(data)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:52:53.530875Z",
"start_time": "2021-04-11T10:52:53.525888Z"
},
"scrolled": true,
"id": "hRNmBbs7NP13",
"outputId": "114afbfe-5d59-4643-ba51-3020d03313f5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"Bolge\":{\n",
" \"0\":\"Akdeniz\",\n",
" \"1\":\"Marmara\",\n",
" \"2\":\"Akdeniz\",\n",
" \"3\":\"Marmara\"\n",
" },\n",
" \"Yil\":{\n",
" \"0\":2020,\n",
" \"1\":2020,\n",
" \"2\":2021,\n",
" \"3\":2021\n",
" },\n",
" \"Satis\":{\n",
" \"0\":10,\n",
" \"1\":15,\n",
" \"2\":42,\n",
" \"3\":56\n",
" }\n",
"}\n"
]
}
],
"source": [
"#indentli formatı yazdırmak istersek dosya okuması yaparak bi string içine okuruz\n",
"with io.open(\"/content/drive/MyDrive/Programming/PythonRocks/dataset/json/indentli_bolgesatis.json\", mode='r') as f:\n",
" content=f.read() # bu stringdir, ve indentli yapı korunmuştur\n",
"print(content)"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {
"scrolled": true,
"id": "1KVAmiwdNP14",
"outputId": "4b240885-5cbc-4d9a-d41c-91efb1b36097",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"columns\":[\n",
" \"Bolge\",\n",
" \"Yil\",\n",
" \"Satis\"\n",
" ],\n",
" \"data\":[\n",
" [\n",
" \"Akdeniz\",\n",
" 2020,\n",
" 10\n",
" ],\n",
" [\n",
" \"Marmara\",\n",
" 2020,\n",
" 15\n",
" ],\n",
" [\n",
" \"Akdeniz\",\n",
" 2021,\n",
" 42\n",
" ],\n",
" [\n",
" \"Marmara\",\n",
" 2021,\n",
" 56\n",
" ]\n",
" ]\n",
"}\n"
]
}
],
"source": [
"#split oriented kaydedilmiş dosyadan\n",
"with io.open(\"/content/drive/MyDrive/Programming/PythonRocks/dataset/json/indentli_bolgesatis_split.json\", mode='r') as f:\n",
" content=f.read()\n",
"print(content)"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:54:03.948942Z",
"start_time": "2021-04-11T10:54:03.598989Z"
},
"scrolled": true,
"id": "5G-iiEBINP14",
"outputId": "17d65dbf-4a64-417c-f105-e290fedf1853",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"schema\":{\n",
" \"fields\":[\n",
" {\n",
" \"name\":\"Bolge\",\n",
" \"type\":\"string\"\n",
" },\n",
" {\n",
" \"name\":\"Yil\",\n",
" \"type\":\"integer\"\n",
" },\n",
" {\n",
" \"name\":\"Satis\",\n",
" \"type\":\"integer\"\n",
" }\n",
" ],\n",
" \"pandas_version\":\"0.20.0\"\n",
" },\n",
" \"data\":[\n",
" {\n",
" \"Bolge\":\"Akdeniz\",\n",
" \"Yil\":2020,\n",
" \"Satis\":10\n",
" },\n",
" {\n",
" \"Bolge\":\"Marmara\",\n",
" \"Yil\":2020,\n",
" \"Satis\":15\n",
" },\n",
" {\n",
" \"Bolge\":\"Akdeniz\",\n",
" \"Yil\":2021,\n",
" \"Satis\":42\n",
" },\n",
" {\n",
" \"Bolge\":\"Marmara\",\n",
" \"Yil\":2021,\n",
" \"Satis\":56\n",
" }\n",
" ]\n",
"}\n"
]
}
],
"source": [
"#table oriented kaydedilmiş dosyadan\n",
"with io.open(\"/content/drive/MyDrive/Programming/PythonRocks/dataset/json/indentli_bolgesatis_table.json\", mode='r') as f:\n",
" content=f.read()\n",
"print(content)"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:54:05.350155Z",
"start_time": "2021-04-11T10:54:05.343174Z"
},
"id": "Q_41Obj_NP14",
"outputId": "3c9d6fb0-5467-4f71-951c-71aec5aa7a26",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict"
]
},
"metadata": {},
"execution_count": 235
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'schema': {'fields': [{'name': 'Bolge', 'type': 'string'},\n",
" {'name': 'Yil', 'type': 'integer'},\n",
" {'name': 'Satis', 'type': 'integer'}],\n",
" 'pandas_version': '0.20.0'},\n",
" 'data': [{'Bolge': 'Akdeniz', 'Yil': 2020, 'Satis': 10},\n",
" {'Bolge': 'Marmara', 'Yil': 2020, 'Satis': 15},\n",
" {'Bolge': 'Akdeniz', 'Yil': 2021, 'Satis': 42},\n",
" {'Bolge': 'Marmara', 'Yil': 2021, 'Satis': 56}]}"
]
},
"metadata": {},
"execution_count": 235
}
],
"source": [
"c=json.loads(content)\n",
"type(c)\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:54:13.377699Z",
"start_time": "2021-04-11T10:54:13.369758Z"
},
"id": "k2jY8dmbNP14",
"outputId": "5cc913d8-e0b6-400f-99c4-4a5de5ac1f09",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Akdeniz'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 236
}
],
"source": [
"#içteki tek bir bilgiye ulaşma\n",
"c[\"data\"][0][\"Bolge\"] #dict of list of dict"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T10:54:14.362891Z",
"start_time": "2021-04-11T10:54:14.358940Z"
},
"id": "9THsxWPVNP15",
"outputId": "d7b24dfc-50a4-4fda-945e-9d2cfd4525ca",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Akdeniz\n",
"Marmara\n",
"Akdeniz\n",
"Marmara\n"
]
}
],
"source": [
"#tüm bölgeleri alma\n",
"for l in c[\"data\"]:\n",
" print(l[\"Bolge\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ycJ0g1LtNP15"
},
"source": [
"### pythondan jsona"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {
"id": "6XY4uNXINP15",
"outputId": "67beeaef-c239-480b-9da9-7efb469075be",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict"
]
},
"metadata": {},
"execution_count": 238
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"str"
]
},
"metadata": {},
"execution_count": 238
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'{\"name\": \"John\", \"age\": 30, \"city\": \"New York\"}'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 238
}
],
"source": [
"x = {\n",
" \"name\": \"John\",\n",
" \"age\": 30,\n",
" \"city\": \"New York\"\n",
"}\n",
"\n",
"j = json.dumps(x)\n",
"type(x)\n",
"type(j)\n",
"j"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {
"id": "ZtP7EQwpNP16",
"outputId": "79ad0169-ff34-4ebb-dde9-e20dd2fb54be",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\"name\": \"John\", \"age\": 30, \"married\": true, \"divorced\": false, \"children\": [\"Ann\", \"Billy\"], \"pets\": null, \"cars\": [{\"model\": \"BMW 230\", \"mpg\": 27.5}, {\"model\": \"Ford Edge\", \"mpg\": 24.1}]}\n"
]
}
],
"source": [
"#komplike(nested) json\n",
"x = {\n",
" \"name\": \"John\",\n",
" \"age\": 30,\n",
" \"married\": True,\n",
" \"divorced\": False,\n",
" \"children\": (\"Ann\",\"Billy\"),\n",
" \"pets\": None,\n",
" \"cars\": [\n",
" {\"model\": \"BMW 230\", \"mpg\": 27.5},\n",
" {\"model\": \"Ford Edge\", \"mpg\": 24.1}\n",
" ]\n",
"}\n",
"\n",
"print(json.dumps(x))"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {
"id": "Z9HyTevzNP16",
"outputId": "349dbdf7-2774-4bce-eda3-3ed06f8e6525",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"name\": \"John\",\n",
" \"age\": 30,\n",
" \"married\": true,\n",
" \"divorced\": false,\n",
" \"children\": [\n",
" \"Ann\",\n",
" \"Billy\"\n",
" ],\n",
" \"pets\": null,\n",
" \"cars\": [\n",
" {\n",
" \"model\": \"BMW 230\",\n",
" \"mpg\": 27.5\n",
" },\n",
" {\n",
" \"model\": \"Ford Edge\",\n",
" \"mpg\": 24.1\n",
" }\n",
" ]\n",
"}\n"
]
}
],
"source": [
"#bu da şık hali\n",
"print(json.dumps(x,indent=4))"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {
"id": "KdCqAtjUNP16",
"outputId": "33cb15d7-09c7-4631-ee98-87696ab386b5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"age\": 30,\n",
" \"cars\": [\n",
" {\n",
" \"model\": \"BMW 230\",\n",
" \"mpg\": 27.5\n",
" },\n",
" {\n",
" \"model\": \"Ford Edge\",\n",
" \"mpg\": 24.1\n",
" }\n",
" ],\n",
" \"children\": [\n",
" \"Ann\",\n",
" \"Billy\"\n",
" ],\n",
" \"divorced\": false,\n",
" \"married\": true,\n",
" \"name\": \"John\",\n",
" \"pets\": null\n",
"}\n"
]
}
],
"source": [
"print(json.dumps(x,indent=4,sort_keys=True))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t0GIaoxuNP16"
},
"source": [
"Bunları aşağıdaki I/O işlemleriyle bir dosyaya da yazdırabilirsiniz."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W_Azx01eNP16"
},
"source": [
"## Request"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c9pQ-nD9NP17"
},
"source": [
"https://realpython.com/python-requests/ sitesinden faydalandım"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sLIO_t5oNP17"
},
"source": [
"### Basics"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:34:22.862704Z",
"start_time": "2021-05-15T15:34:22.641341Z"
},
"id": "p9pEubABNP17"
},
"outputs": [],
"source": [
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:34:24.362239Z",
"start_time": "2021-05-15T15:34:24.358213Z"
},
"id": "kFF-hydsNP17"
},
"outputs": [],
"source": [
"httpget0=r\"https://www.excelinefendisi.com/httpapiservice/ResponseveRequestTarget.aspx\" #buna parametre verebiliyoruz, Anakonu=VBAMakro, Altkonu=Temeller\n",
"httpget0j=r\"https://www.excelinefendisi.com/httpapiservice/ReturnJson.aspx\" #tüm duyurlar as json\n",
"httpget3=r\"https://httpbin.org/get\" #as json\n",
"httpget4img=r\"https://httpbin.org/image\"\n",
"httpget5=r\"https://www.google.com/search?q=excel&oq=excel&aqs=chrome..69i57j35i39l2j0i433l4j46i433l2j0.839j0j15&sourceid=chrome&ie=UTF-8\"\n",
"httpget6githubjson=r\"https://raw.githubusercontent.com/VolkiTheDreamer/dataset/master/json/bolgesatis.json\"\n",
"\n",
"httppost3=r\"https://httpbin.org/post\""
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:34:29.194045Z",
"start_time": "2021-05-15T15:34:28.511013Z"
},
"id": "suK8G-UYNP17"
},
"outputs": [],
"source": [
"r=requests.get(httpget3)"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:34:32.053841Z",
"start_time": "2021-05-15T15:34:32.048854Z"
},
"id": "NFHER8uYNP17",
"outputId": "3f1885af-8b11-4b33-ed6e-d151b5974f01",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Date': 'Sun, 22 Sep 2024 15:26:54 GMT', 'Content-Type': 'application/json', 'Content-Length': '307', 'Connection': 'keep-alive', 'Server': 'gunicorn/19.9.0', 'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Credentials': 'true'}"
]
},
"metadata": {},
"execution_count": 245
}
],
"source": [
"r.headers"
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:34:36.018195Z",
"start_time": "2021-05-15T15:34:34.687476Z"
},
"id": "vMb7XEH2NP18",
"outputId": "bdcb0f0a-276e-4f2f-f171-d6b08c5933e3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 246
}
],
"source": [
"requests.get('http://volkanyurtseven.com')"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:18:33.490545Z",
"start_time": "2021-04-12T11:18:32.595460Z"
},
"id": "mSrSW1NGNP18",
"outputId": "3d5d55ab-4d91-4742-bdfd-f98dda647142",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"site çalışmıyor\n"
]
}
],
"source": [
"try:\n",
" requests.get('https://volkanyurtseven.com') #https versiyonu yok, hata alcaz\n",
" print(\"site çalışıyor\")\n",
"except:\n",
" print(\"site çalışmıyor\")"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:18:46.903444Z",
"start_time": "2021-04-12T11:18:46.773750Z"
},
"id": "t1BPrEGDNP18",
"outputId": "e1bde44f-db1b-42a1-dfe9-2aa3ad1ce70b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 248
}
],
"source": [
"requests.get('http://volkanyurtseven.com/olmayansayfa') #bu sefer site doğru ama bahsekonu sayfa yoksa 404"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:18:52.376056Z",
"start_time": "2021-04-12T11:18:52.246402Z"
},
"id": "9gXqkxYYNP18",
"outputId": "4cf9fce1-905a-4810-d32f-b5059d3e11e7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"200"
]
},
"metadata": {},
"execution_count": 249
}
],
"source": [
"response = requests.get('http://volkanyurtseven.com')\n",
"response.status_code"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:00.510411Z",
"start_time": "2021-04-12T11:50:00.239067Z"
},
"scrolled": true,
"id": "qVhplpG1NP19",
"outputId": "595db2ba-8acb-4079-d6ee-cc1e8d78d275",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"b'{\\n \"args\": {}, \\n \"headers\": {\\n \"Accept\": \"*/*\", \\n \"Accept-Encoding\": \"gzip, deflate\", \\n \"Host\": \"httpbin.org\", \\n \"User-Agent\": \"python-requests/2.32.3\", \\n \"X-Amzn-Trace-Id\": \"Root=1-66f03742-6923173002866a3c50f6dfc5\"\\n }, \\n \"origin\": \"34.16.168.234\", \\n \"url\": \"https://httpbin.org/get\"\\n}\\n'"
]
},
"metadata": {},
"execution_count": 250
}
],
"source": [
"response = requests.get(httpget3)\n",
"response.content #byte olarak"
]
},
{
"cell_type": "code",
"execution_count": 251,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:02.611257Z",
"start_time": "2021-04-12T11:50:02.604275Z"
},
"id": "CC-Ru5WBNP19",
"outputId": "45bf2b51-72e2-40b7-b3f8-6c69ce234f2e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'{\\n \"args\": {}, \\n \"headers\": {\\n \"Accept\": \"*/*\", \\n \"Accept-Encoding\": \"gzip, deflate\", \\n \"Host\": \"httpbin.org\", \\n \"User-Agent\": \"python-requests/2.32.3\", \\n \"X-Amzn-Trace-Id\": \"Root=1-66f03742-6923173002866a3c50f6dfc5\"\\n }, \\n \"origin\": \"34.16.168.234\", \\n \"url\": \"https://httpbin.org/get\"\\n}\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 251
}
],
"source": [
"response.text #string olarak"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:07.692493Z",
"start_time": "2021-04-12T11:50:07.689491Z"
},
"id": "dQuY_FaMNP19",
"outputId": "69ef30ae-edcb-41db-9ed1-876230c534b1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 252
}
],
"source": [
"response.raw"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aT_D0icCNP19"
},
"source": [
"Böyle çok karışık oldu, bunu json olarak okuyalım. ama tabi önce bunun son versiyonuna tekrar bi get atalım."
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:45.445755Z",
"start_time": "2021-04-12T11:50:45.165506Z"
},
"id": "w_4RI0kZNP1-",
"outputId": "805a936b-4645-4d61-ae0c-ead5c614035a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'{\\n \"args\": {}, \\n \"headers\": {\\n \"Accept\": \"*/*\", \\n \"Accept-Encoding\": \"gzip, deflate\", \\n \"Host\": \"httpbin.org\", \\n \"User-Agent\": \"python-requests/2.32.3\", \\n \"X-Amzn-Trace-Id\": \"Root=1-66f03743-514f57731ffeb7ce34129485\"\\n }, \\n \"origin\": \"34.16.168.234\", \\n \"url\": \"https://httpbin.org/get\"\\n}\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 253
}
],
"source": [
"#hala biraz okunaklı değil gibi\n",
"response = requests.get(httpget3)\n",
"response.text"
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:31.769676Z",
"start_time": "2021-04-12T11:50:31.761693Z"
},
"id": "WQ895eaFNP1-",
"outputId": "0ae60c50-7a8e-4edd-f5c1-0c25a66aea06",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'args': {},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.32.3',\n",
" 'X-Amzn-Trace-Id': 'Root=1-66f03743-514f57731ffeb7ce34129485'},\n",
" 'origin': '34.16.168.234',\n",
" 'url': 'https://httpbin.org/get'}"
]
},
"metadata": {},
"execution_count": 254
}
],
"source": [
"json.loads(response.text)"
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:49:39.489580Z",
"start_time": "2021-04-12T11:49:39.480603Z"
},
"id": "K_c3PGEENP1-",
"outputId": "0bf71ddb-0530-401e-86d2-0d70fa5ead3f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'args': {},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.32.3',\n",
" 'X-Amzn-Trace-Id': 'Root=1-66f03743-514f57731ffeb7ce34129485'},\n",
" 'origin': '34.16.168.234',\n",
" 'url': 'https://httpbin.org/get'}"
]
},
"metadata": {},
"execution_count": 255
}
],
"source": [
"#veya responseun json metopdunu kullanabiliriz\n",
"response.json()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oQpsp8BiNP1-"
},
"source": [
"It should be noted that the success of the call to r.json() does not indicate the success of the response. Some servers may return a JSON object in a failed response (e.g. error details with HTTP 500). Such JSON will be decoded and returned. To check that a request is successful, use r.raise_for_status() or check r.status_code is what you expect."
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:51:01.521114Z",
"start_time": "2021-04-12T11:51:01.517158Z"
},
"id": "Y-yvVvIFNP1-",
"outputId": "2f259fb0-8286-4ec4-aec1-c225ec78b0bf",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"200"
]
},
"metadata": {},
"execution_count": 256
}
],
"source": [
"response.raise_for_status()\n",
"response.status_code"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:51:09.536268Z",
"start_time": "2021-04-12T11:51:09.531280Z"
},
"id": "MRIBCQXlNP1_",
"outputId": "d42a67b4-76b0-44d6-c3b4-e00c1f39f527",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Date': 'Sun, 22 Sep 2024 15:26:59 GMT',\n",
" 'Content-Type': 'application/json',\n",
" 'Content-Length': '307',\n",
" 'Connection': 'keep-alive',\n",
" 'Server': 'gunicorn/19.9.0',\n",
" 'Access-Control-Allow-Origin': '*',\n",
" 'Access-Control-Allow-Credentials': 'true'}"
]
},
"metadata": {},
"execution_count": 257
}
],
"source": [
"dict(response.headers)"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:51:15.329872Z",
"start_time": "2021-04-12T11:51:15.323889Z"
},
"id": "pCFUpP0ONP1_",
"outputId": "90b9edf9-e069-43e3-a532-d0f0f3d8d795",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'application/json'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 258
}
],
"source": [
"response.headers['Content-Type']"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OZXIKvJiNP1_"
},
"source": [
"### QueryString parameters"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:51:35.981224Z",
"start_time": "2021-04-12T11:51:34.739424Z"
},
"id": "A8MuEWfwNP1_",
"outputId": "2b4745f4-53d3-44d2-a5bc-4a45db5a2750",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Repository name: secrules-language-evaluation\n",
"Repository description: Set of Python scripts to perform SecRules language evaluation on a given http request.\n",
"https://api.github.com/search/repositories?q=requests%2Blanguage%3Apython\n"
]
}
],
"source": [
"# Search GitHub's repositories for requests\n",
"response = requests.get(\n",
" 'https://api.github.com/search/repositories',\n",
" params={'q': 'requests+language:python'},\n",
")\n",
"\n",
"# Inspect some attributes of the `requests` repository\n",
"json_response = response.json()\n",
"repository = json_response['items'][0]\n",
"print(f'Repository name: {repository[\"name\"]}') # Python 3.6+\n",
"print(f'Repository description: {repository[\"description\"]}') # Python 3.6+\n",
"print(response.url)"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:51:36.220773Z",
"start_time": "2021-04-12T11:51:35.991157Z"
},
"id": "gji0Xwf1NP1_",
"outputId": "2920a2bd-235f-4cbf-ba34-f42f7a03f573",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"https://www.excelinefendisi.com/httpapiservice/ResponseveRequestTarget.aspx?Anakonu=VBAMakro&Altkonu=Temeller\n"
]
}
],
"source": [
"response = requests.get(\n",
" 'https://www.excelinefendisi.com/httpapiservice/ResponseveRequestTarget.aspx',\n",
" params={'Anakonu':'VBAMakro', 'Altkonu':'Temeller'},\n",
")\n",
"\n",
"print(response.url)"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T11:54:43.262529Z",
"start_time": "2021-04-11T11:54:43.257543Z"
},
"id": "5irgpaj_NP2A",
"outputId": "2c6f96ae-621c-4582-82d8-f89887510002",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 125
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n \\r\\n\\r\\n\\r\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 261
}
],
"source": [
"response.text"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T11:48:29.128562Z",
"start_time": "2021-04-11T11:48:28.375577Z"
},
"id": "wedIz7JuNP2A"
},
"outputs": [],
"source": [
"payload = {'key1': 'value1', 'key2': 'value2'}\n",
"r = requests.get('https://httpbin.org/get', params=payload)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zg-_6jfqNP2A"
},
"source": [
"### Headers"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T11:49:45.766142Z",
"start_time": "2021-04-11T11:49:45.761157Z"
},
"id": "sevAAWy_NP2A",
"outputId": "13cd6f19-cfd0-4a5b-8918-db616b4d6fa4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Date': 'Sun, 22 Sep 2024 15:27:02 GMT', 'Content-Type': 'application/json', 'Content-Length': '378', 'Connection': 'keep-alive', 'Server': 'gunicorn/19.9.0', 'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Credentials': 'true'}"
]
},
"metadata": {},
"execution_count": 263
}
],
"source": [
"r.headers"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:30:34.969712Z",
"start_time": "2021-04-11T15:30:33.026070Z"
},
"id": "Kj9A2F0lNP2A",
"outputId": "a096b30e-00fb-4753-d627-de922d43acef",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Text matches: [{'object_url': 'https://api.github.com/repositories/33210074', 'object_type': 'Repository', 'property': 'description', 'fragment': 'Set of Python scripts to perform SecRules language evaluation on a given http request.', 'matches': [{'text': 'Python', 'indices': [7, 13]}, {'text': 'language', 'indices': [42, 50]}, {'text': 'request', 'indices': [78, 85]}]}]\n"
]
}
],
"source": [
"#custimize header\n",
"response = requests.get(\n",
" 'https://api.github.com/search/repositories',\n",
" params={'q': 'requests+language:python'},\n",
" headers={'Accept': 'application/vnd.github.v3.text-match+json'},\n",
")\n",
"\n",
"# View the new `text-matches` array which provides information\n",
"# about your search term within the results\n",
"json_response = response.json()\n",
"repository = json_response['items'][0]\n",
"print(f'Text matches: {repository[\"text_matches\"]}')"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:32:08.420664Z",
"start_time": "2021-04-11T15:32:04.440680Z"
},
"id": "J8llQRkZNP2B",
"outputId": "6be386b6-0ab9-4389-9880-e13c803bad38",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 265
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 265
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 265
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 265
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 265
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 265
}
],
"source": [
"requests.post('https://httpbin.org/post', data={'key':'value'})\n",
"requests.put('https://httpbin.org/put', data={'key':'value'})\n",
"requests.delete('https://httpbin.org/delete')\n",
"requests.head('https://httpbin.org/get')\n",
"requests.patch('https://httpbin.org/patch', data={'key':'value'})\n",
"requests.options('https://httpbin.org/get')"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:38:34.572083Z",
"start_time": "2021-04-11T15:38:33.892213Z"
},
"id": "d5dkc-WuNP2B",
"outputId": "7ff58a19-b1b7-43b7-b015-38caa766d0d4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'application/json'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 266
}
],
"source": [
"response = requests.head('https://httpbin.org/get')\n",
"response.headers['Content-Type']"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:38:35.242197Z",
"start_time": "2021-04-11T15:38:34.581059Z"
},
"id": "WYFFyWK5NP2B",
"outputId": "c28564c0-9cfa-41be-c4ae-50360092a752",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{}"
]
},
"metadata": {},
"execution_count": 267
}
],
"source": [
"response = requests.delete('https://httpbin.org/delete')\n",
"json_response = response.json()\n",
"json_response['args']"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {
"id": "uBIXUnWINP2B"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "JBq0Jus3NP2B"
},
"source": [
"According to the HTTP specification, POST, PUT, and the less common PATCH requests pass their data through the message body rather than through parameters in the query string."
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:42:05.930360Z",
"start_time": "2021-04-11T15:42:05.214746Z"
},
"id": "TEJ5QWWINP2B",
"outputId": "c5b8451b-8960-4af7-ab43-7156f92fa0e2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 268
}
],
"source": [
"requests.post('https://httpbin.org/post', data={'key':'value'}) #veya data=[('key', 'value')]"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:42:05.930360Z",
"start_time": "2021-04-11T15:42:05.214746Z"
},
"id": "mzxTUWMyNP2C",
"outputId": "061fdd6b-ef46-42ad-ed26-cbf287da833f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'{\"key\": \"value\"}'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 269
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'application/json'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 269
}
],
"source": [
"response = requests.post('https://httpbin.org/post', json={'key':'value'})\n",
"json_response = response.json()\n",
"json_response['data']\n",
"json_response['headers']['Content-Type']"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:43:12.381811Z",
"start_time": "2021-04-11T15:43:10.781580Z"
},
"id": "HkoeyFP8NP2C",
"outputId": "a44efcf5-d338-4e21-a047-706fa48e8471",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'application/json'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 270
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'https://httpbin.org/post'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 270
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"b'{\"key\": \"value\"}'"
]
},
"metadata": {},
"execution_count": 270
}
],
"source": [
"response = requests.post('https://httpbin.org/post', json={'key':'value'})\n",
"response.request.headers['Content-Type']\n",
"response.request.url\n",
"response.request.body"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:44:21.560506Z",
"start_time": "2021-04-11T15:44:21.551487Z"
},
"id": "WkwQy86INP2C",
"outputId": "cb742d6b-a725-47a0-cbc8-5a4780d4d3fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 125
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'https://httpbin.org/post'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 271
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Date': 'Sun, 22 Sep 2024 15:27:06 GMT', 'Content-Type': 'application/json', 'Content-Length': '481', 'Connection': 'keep-alive', 'Server': 'gunicorn/19.9.0', 'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Credentials': 'true'}"
]
},
"metadata": {},
"execution_count": 271
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'https://httpbin.org/post'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 271
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'User-Agent': 'python-requests/2.32.3', 'Accept-Encoding': 'gzip, deflate', 'Accept': '*/*', 'Connection': 'keep-alive', 'Content-Length': '16', 'Content-Type': 'application/json'}"
]
},
"metadata": {},
"execution_count": 271
}
],
"source": [
"response.url\n",
"response.headers\n",
"response.request.url\n",
"response.request.headers"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T15:46:11.366531Z",
"start_time": "2021-04-11T15:46:07.443303Z"
},
"id": "Ar-7Lm2INP2C",
"outputId": "66148973-0f66-4780-9b7c-134a196d23e0",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"··········\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 272
}
],
"source": [
"#authentication\n",
"from getpass import getpass\n",
"requests.get('https://api.github.com/user', auth=('username', getpass()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UEBYM_WtNP2C"
},
"source": [
"### webserive"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TxzHfG0uNP2D"
},
"source": [
"## BeautifulSoup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vx_97dK0NP2D"
},
"source": [
"https://realpython.com/beautiful-soup-web-scraper-python/"
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:36:30.137338Z",
"start_time": "2021-05-15T15:36:28.992174Z"
},
"id": "Si95rGouNP2D"
},
"outputs": [],
"source": [
"URL = 'https://www.monster.com/jobs/search/?q=Software-Developer&where=Australia'\n",
"page = requests.get(URL)"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:36:31.477395Z",
"start_time": "2021-05-15T15:36:30.887143Z"
},
"id": "GJ-K5HruNP2D"
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"\n",
"URL = 'https://www.excelinefendisi.com/Konular/Excel/Giris_PratikKisayollar.aspx'\n",
"page = requests.get(URL)\n",
"\n",
"soup = BeautifulSoup(page.content, 'html.parser')"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:36:33.839511Z",
"start_time": "2021-05-15T15:36:33.831531Z"
},
"scrolled": true,
"id": "K-NWhchbNP2E",
"outputId": "cb6ba55a-a6af-4558-ff6d-9aa2b4f646b1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"| Amaç | \n",
"Kısayol | \n",
"\n",
"| Sayfalar arasında dolaşmak | \n",
"CTRL + PgUp/PgDn | \n",
"
\n",
"\n",
"| Bugünün Tarihini yazmak | \n",
"CTRL + SHIFT +, | \n",
"
\n",
"\n",
"| Tüm açık dosyalarda calculation yapmak | \n",
"F9 | \n",
"
\n",
"\n",
"| Seçili kısmın değerini hesaplayıp göstermek | \n",
"Hücre içindeki formül seçilip F9 | \n",
"
\n",
"\n",
"| Aktif sayfada calculation yapmak | \n",
"SHIFT+F9 | \n",
"
\n",
"\n",
"| Sadece belli range için calculation yapmak | \n",
"VBA ile yapılır. Burdan bakın. | \n",
"
\n",
"\n",
"| Bulunduğun hücrenin CurrentRegion'ını seçme | \n",
"CTRL+ A | \n",
"
\n",
"\n",
"| Bulunduğun hücreden CurrentRegion'ın uç noktlarına gitmek | \n",
"CTRL+ Ok tuşları | \n",
"
\n",
"\n",
"| Bulunduğun hücreden itibaren belli bir yöne doğru seçim yapmak | \n",
"SHIFT+Ok tuşları | \n",
"
\n",
"| Bulunduğun hücreden itibaren CurrentRegion bir ucuna doğru toplu seçim yapmak | \n",
"CTRL+SHIFT+Ok tuşları | \n",
"
\n",
"\n",
"| Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına gitmek | \n",
"CTRL+END | \n",
"
\n",
"\n",
"| Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına kadar seçmek | \n",
"CTRL+SHIFT+END | \n",
"
\n",
"\n",
"| Bulunduğun hücreden A1 hücresine kadar olan alanı(sol yukarı) seçmek | \n",
"CTRL+SHIFT+HOME | \n",
"
\n",
"\n",
"| Bir hücre içinde veri girerken, aynı hücre içinde yeni bir satır açıp oradan devam etmek | \n",
"ALT+ENTER | \n",
"
\n",
"\n",
"| Veri/Formül girişi yaptığınız hücrede alt hücreye geçmeden giriş tamamlamak | \n",
"CTRL+ENTER | \n",
"
\n",
"\n",
"| Ekranda bir sayfa sağa kaymak. | \n",
"ALT+PGE DOWN | \n",
"
\n",
"\n",
"| AutoFilter'ı aktif/pasif hale getirmek | \n",
"CTRL+SHIFT+L | \n",
"
\n",
"\n",
"| Bulunduğunuz hücrenin satır ve sütununa aynı anda freeze uygulamak/kaldırmak | \n",
"Alt+W+FF | \n",
"
\n",
"\n",
"| VBA editörünü açmak | \n",
"Alt+F11 | \n",
"
\n",
"\n",
"| Ribbonu küçültüp/büyütmek | \n",
"CTRL+F1 | \n",
"
\n",
"\n",
"| Üst hücrelerdeki tüm rakamların toplamını almak | \n",
"ALT+= | \n",
"
\n",
"\n",
"| Flash Fill uygulamak | \n",
"CTRL+E | \n",
"
\n",
"\n",
"| Sadece görünen hücreleri seçmek | \n",
"ALT+; | \n",
"
\n",
"
\n",
"\n"
]
}
],
"source": [
"results = soup.find_all(class_='alterantelitable')\n",
"for r in results:\n",
" print(r, end='\\n'*2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "51f4k7lrNP2E"
},
"source": [
"Gereksiz elementlerden ve taglerden kurtulalım"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T16:29:28.266800Z",
"start_time": "2021-04-11T16:29:28.257824Z"
},
"scrolled": true,
"id": "pYqhdq3DNP2E",
"outputId": "226501c1-8de7-4801-cac5-05b138ffbb53",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sayfalar arasında dolaşmak\n",
"CTRL + PgUp/PgDn\n",
"Bugünün Tarihini yazmak\n",
"CTRL + SHIFT +, \n",
"Tüm açık dosyalarda calculation yapmak\n",
"F9\n",
"Seçili kısmın değerini hesaplayıp göstermek\n",
"Hücre içindeki formül seçilip F9\n",
"Aktif sayfada calculation yapmak\n",
"SHIFT+F9\n",
"Sadece belli range için calculation yapmak\n",
"VBA ile yapılır. Burdan bakın.\n",
"Bulunduğun hücrenin CurrentRegion'ını seçme\n",
"CTRL+ A\n",
"Bulunduğun hücreden CurrentRegion'ın uç noktlarına gitmek\n",
"CTRL+ Ok tuşları\n",
"Bulunduğun hücreden itibaren belli bir yöne doğru seçim yapmak\n",
"SHIFT+Ok tuşları\n",
"Bulunduğun hücreden itibaren CurrentRegion bir ucuna doğru toplu seçim yapmak\n",
"CTRL+SHIFT+Ok tuşları\n",
"Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına gitmek\n",
"CTRL+END\n",
"Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına kadar seçmek\n",
"CTRL+SHIFT+END\n",
"Bulunduğun hücreden A1 hücresine kadar olan alanı(sol yukarı) seçmek\n",
"CTRL+SHIFT+HOME\n",
"Bir hücre içinde veri girerken, aynı hücre içinde yeni bir satır açıp oradan devam etmek\n",
"ALT+ENTER\n",
"Veri/Formül girişi yaptığınız hücrede alt hücreye geçmeden giriş tamamlamak \n",
"CTRL+ENTER\n",
" Ekranda bir sayfa sağa kaymak.\n",
"ALT+PGE DOWN\n",
"AutoFilter'ı aktif/pasif hale getirmek\n",
"CTRL+SHIFT+L\n",
"Bulunduğunuz hücrenin satır ve sütununa aynı anda freeze uygulamak/kaldırmak\n",
"Alt+W+FF\n",
"VBA editörünü açmak\n",
"Alt+F11\n",
"Ribbonu küçültüp/büyütmek\n",
"CTRL+F1\n",
"Üst hücrelerdeki tüm rakamların toplamını almak\n",
"ALT+=\n",
"Flash Fill uygulamak\n",
"CTRL+E\n",
"Sadece görünen hücreleri seçmek\n",
"ALT+;\n"
]
}
],
"source": [
"results = soup.find_all(class_='alterantelitable')\n",
"for r in results:\n",
" tds=r.find_all(\"td\")\n",
" for td in tds:\n",
" print(td.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8R_PjPfHNP2E"
},
"source": [
"İşlemle ilgili kısayolu altalta değil de yanyana yazmasını sağlayalım,"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-11T16:43:05.446075Z",
"start_time": "2021-04-11T16:43:05.431291Z"
},
"id": "csDNZGeHNP2F",
"outputId": "de4eefe6-b523-4db6-e90d-d0d269f85f8b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sayfalar arasında dolaşmak : CTRL + PgUp/PgDn\n",
"Bugünün Tarihini yazmak : CTRL + SHIFT +, \n",
"Tüm açık dosyalarda calculation yapmak : F9\n",
"Seçili kısmın değerini hesaplayıp göstermek : Hücre içindeki formül seçilip F9\n",
"Aktif sayfada calculation yapmak : SHIFT+F9\n",
"Sadece belli range için calculation yapmak : VBA ile yapılır. Burdan bakın.\n",
"Bulunduğun hücrenin CurrentRegion'ını seçme : CTRL+ A\n",
"Bulunduğun hücreden CurrentRegion'ın uç noktlarına gitmek : CTRL+ Ok tuşları\n",
"Bulunduğun hücreden itibaren belli bir yöne doğru seçim yapmak : SHIFT+Ok tuşları\n",
"Bulunduğun hücreden itibaren CurrentRegion bir ucuna doğru toplu seçim yapmak : CTRL+SHIFT+Ok tuşları\n",
"Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına gitmek : CTRL+END\n",
"Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına kadar seçmek : CTRL+SHIFT+END\n",
"Bulunduğun hücreden A1 hücresine kadar olan alanı(sol yukarı) seçmek : CTRL+SHIFT+HOME\n",
"Bir hücre içinde veri girerken, aynı hücre içinde yeni bir satır açıp oradan devam etmek : ALT+ENTER\n",
"Veri/Formül girişi yaptığınız hücrede alt hücreye geçmeden giriş tamamlamak : CTRL+ENTER\n",
" Ekranda bir sayfa sağa kaymak. : ALT+PGE DOWN\n",
"AutoFilter'ı aktif/pasif hale getirmek : CTRL+SHIFT+L\n",
"Bulunduğunuz hücrenin satır ve sütununa aynı anda freeze uygulamak/kaldırmak : Alt+W+FF\n",
"VBA editörünü açmak : Alt+F11\n",
"Ribbonu küçültüp/büyütmek : CTRL+F1\n",
"Üst hücrelerdeki tüm rakamların toplamını almak : ALT+=\n",
"Flash Fill uygulamak : CTRL+E\n",
"Sadece görünen hücreleri seçmek : ALT+;\n"
]
}
],
"source": [
"results = soup.find_all(class_='alterantelitable')\n",
"for r in results:\n",
" trs=r.find_all(\"tr\")\n",
" for tr in trs:\n",
" td1=tr.select(\"td\")[0] #tr.find(\"td\") de olurdu ama aşağıdakiyle bütünlük olması adına ikisini de select ile yaptık\n",
" td2=tr.select(\"td\")[1]\n",
" print(td1.text,\":\",td2.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UAMBIdNvNP2G"
},
"source": [
"Bunun bir de MechanicalSoup versiyonu var, onda websitelerindeki formları da otomatik doldurma işlemi yaptırabiliyorsunuz."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MhE55BdvNP2G"
},
"source": [
"## Logging"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bka1Z3k2NP2G"
},
"source": [
"Programınızı test ederken print değil bunu kullanmanız önerilir."
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-12T20:51:08.220308Z",
"start_time": "2021-05-12T20:51:08.214322Z"
},
"scrolled": true,
"id": "5O9wEIgdNP2G",
"outputId": "f041b6b9-13a9-48f0-9ea3-601527e97c13",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:root:This is a warning message\n",
"ERROR:root:This is an error message\n",
"CRITICAL:root:This is a critical message\n"
]
}
],
"source": [
"import logging\n",
"\n",
"logging.debug('This is a debug message')\n",
"logging.info('This is an info message')\n",
"#by default, the logging module logs the messages with a severity level of WARNING or above\n",
"logging.warning('This is a warning message')\n",
"logging.error('This is an error message')\n",
"logging.critical('This is a critical message')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xk3DLOV3NP2G"
},
"source": [
"root= default logger"
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-12T20:55:46.385664Z",
"start_time": "2021-05-12T20:55:46.381676Z"
},
"id": "mg7IqQ4ONP2G",
"outputId": "be829d2c-5910-4b89-c11c-833dc01ecd5a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"DEBUG:root:This will get logged\n"
]
}
],
"source": [
"logging.basicConfig(level=logging.DEBUG,force=True) #son parametre gerekli. Detay için: https://stackoverflow.com/questions/30861524/logging-basicconfig-not-creating-log-file-when-i-run-in-pycharm/42210221\n",
"logging.debug('This will get logged')"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-12T20:56:20.598673Z",
"start_time": "2021-05-12T20:56:20.592689Z"
},
"id": "wfoAAVXPNP2G"
},
"outputs": [],
"source": [
"logging.basicConfig(filename='app.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s',force=True)\n",
"logging.warning('This will get logged to a file')"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-12T21:01:25.865412Z",
"start_time": "2021-05-12T21:01:25.861458Z"
},
"id": "4Pr6c5K-NP2G",
"outputId": "669451d9-de35-47b3-db05-3cc28263ecab",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"root - INFO - 2024-09-22 15:27:21,768 - Admin logged in\n"
]
}
],
"source": [
"logging.basicConfig(format='%(name)s - %(levelname)s - %(asctime)s - %(message)s', level=logging.INFO,force=True)\n",
"logging.info('Admin logged in')"
]
},
{
"cell_type": "code",
"execution_count": 282,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-12T21:01:28.566129Z",
"start_time": "2021-05-12T21:01:28.561176Z"
},
"id": "7ix2oUooNP2H",
"outputId": "680775a0-88d8-48ea-a7d3-2ca323bbf378",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"root - ERROR - 2024-09-22 15:27:21,801 - John raised an error\n"
]
}
],
"source": [
"name = 'John'\n",
"logging.error(f'{name} raised an error')"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-12T21:02:03.896954Z",
"start_time": "2021-05-12T21:02:03.892964Z"
},
"id": "e8kdrYAINP2H",
"outputId": "84511400-71fb-4713-cd41-66df36e571e3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"root - ERROR - 2024-09-22 15:27:21,848 - Exception occurred\n",
"Traceback (most recent call last):\n",
" File \"\", line 5, in \n",
" c = a / b\n",
"ZeroDivisionError: division by zero\n"
]
}
],
"source": [
"a = 5\n",
"b = 0\n",
"\n",
"try:\n",
" c = a / b\n",
"except Exception as e:\n",
" logging.error(\"Exception occurred\", exc_info=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LshsfDgTNP2H"
},
"source": [
"## tqdm"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DJtLjr4tNP2H"
},
"source": [
"Döngüsel işlemlerde progressbar sağlar"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {
"id": "KrYPCMN9NP2H",
"outputId": "1761a60f-4bc2-4b90-a2bb-e356dd9132fd",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [00:01<00:00, 9.61it/s]\n"
]
}
],
"source": [
"from tqdm import tqdm, trange\n",
"from time import sleep\n",
"\n",
"for i in tqdm(range(10)):\n",
" sleep(.1)"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UKdGnJCmNP2I",
"outputId": "4dc2bbd6-2e97-4188-ceb3-27059532243d"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 100/100 [00:01<00:00, 96.83it/s]\n"
]
}
],
"source": [
"# Simple loop\n",
"for i in range(100):\n",
" pass\n",
"\n",
"# Loop with a progress bar\n",
"for i in trange(100):\n",
" time.sleep(0.01)"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"colab": {
"referenced_widgets": [
"751edc7876624c858be3920a656bbe5a",
"a17d5706940d471c8def62d24b380451",
"94759083162b4ba7882beb31f5f5c0e6",
"da19b67ccfca40f0ac6fea0079279b45",
"451f673b411e45c89ba06581e24f3632",
"34289f2781b94924ab52dcc7accc4f9b",
"1178361d7b984b039fd0e5ee6a569adb",
"125bc33900574ed692c44040871f04d0",
"c26eb70f93b248e2b78df67a5b6ccc65",
"13308604b83b4ff881bf2e86f199a654",
"286b1c469c4f48c9b4dca098d506e0e2",
"4b45e53206ac4335ad64c1bc76cf0a54",
"94b5dcb00309447db4caf1ca5f2bca6c",
"f34d953ef0f04222800959bad565983a",
"4a35b3bf482b483bbe7f3979968b2b57",
"f21bf8ef6c2543e5b9bd3a2006f37d99",
"898d07425ae6424d9b373bcb23388625",
"2ce64cb4ef464074a58d8d3985b7a4c3",
"a30f9a14b37949aea2fe0577940e22d3",
"803c924d944242399095d29937f7e046",
"bd81728b56694c7493e07276209917a8",
"e1a40e5aab3f40ebbb01cd40c1994a8b",
"55a59a782aa84a98a6ae9a131707e7e4",
"a2adb592ff3245869a0d6379148a97fa",
"d9fb51af123045bfb01c0ca4ff71aadf",
"f057770d640d4d768887b58b24a05698",
"b2b1f12e421249369d42dee4ee11ff55",
"c017e47d80f04575872ee21fa7dc9271",
"1f31528beea8479db1929519dc36ebe0",
"ff25dde586244614852fb29ffd51dac9",
"2ad95d0a26034b62ad7a0941045a8bd6",
"5dc1487f2b6a4a339d103355b6209569",
"2193d83b7019419c86f9915866b79247",
"a57d90da18644950b9ffc8517fe1df7b",
"da5f50d542c14d8f898c9f76abd9709d",
"8e86a15ad03845ed93fc85c7b82487d4",
"0aa16e4c1d1d4059841e36d8b325971f",
"9fd3dc999c6d4270a9ecfe2d8e8f5e1d",
"6ec9be16d0354a8d87c177f2f68c49da",
"10a3331d6fec4345a45b8f8f9d0a8146",
"f125a24c6cf347a3b296a2906556be7a",
"e618cb21d1d6458ca0374e8618dd4ca8",
"2ddf420ed3dc4d429495f5ba7f922956",
"1516a50c3d4342f693aefe40460d6dd2"
],
"base_uri": "https://localhost:8080/",
"height": 145
},
"id": "wgi0kDEPNP2J",
"outputId": "a1d4a42d-6796-4150-e40e-65b47545c015"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"1st loop: 0%| | 0/3 [00:00Görsel bu sayfadan alınmıştır\"\"\")\n",
" \n",
"def pythonSomeInfo():\n",
" print(\"system packages folder:\",sys.prefix, end=\"\\n\\n\")\n",
" print(\"pip install folder:\",site.getsitepackages(), end=\"\\n\\n\") \n",
" print(\"python version:\", sys.version, end=\"\\n\\n\")\n",
" print(\"executables location:\",sys.executable, end=\"\\n\\n\")\n",
" print(\"pip version:\", os.popen('pip version').read(), end=\"\\n\\n\")\n",
" pathes= sys.path\n",
" print(\"Python pathes\")\n",
" for p in pathes:\n",
" print(p)\n",
"\n",
"\n",
"def showMemoryUsage():\n",
" dict_={}\n",
" global_vars = list(globals().items())\n",
" for var, obj in global_vars:\n",
" if not var.startswith('_'):\n",
" dict_[var]=sys.getsizeof(obj)\n",
" \n",
" final={k: v for k, v in sorted(dict_.items(), key=lambda item: item[1],reverse=True)} \n",
" print(final)\n",
" \n",
"def readfile(path,enc='cp1254'):\n",
" with io.open(path, \"r\", encoding=enc) as f:\n",
" return f.read()\n",
"\n",
"def getFirstItemFromDictionary(dict_):\n",
" return next(iter(dict_)),next(iter(dict_.values()))\n",
" \n",
"\n",
"\n",
"def removeItemsFromList(self,list2,inplace=True): \n",
" \"\"\"\n",
" Extension method for list type. Removes items from list2 from list1.\n",
" First, forbiddenfruit must be installed via https://pypi.org/project/forbiddenfruit/\n",
" \"\"\" \n",
" if inplace:\n",
" for x in set(list2):\n",
" self.remove(x)\n",
" return self\n",
" else:\n",
" temp=self.copy()\n",
" for x in set(list2):\n",
" temp.remove(x)\n",
" return temp\n",
" \n",
"curse(list, \"removeItemsFromList\", removeItemsFromList)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"----\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"execution_count": 294
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"fr\n"
]
}
],
"source": [
"#oku\n",
"#import io #normalde bu satıra gerek yok, open=io.open için bi alias\n",
"dosya = io.open(\"pythonutility.py\", \"r\") #yine bunda da başta io yazmazdık normalde ama os'nin open'ından ayırmak için ekledik\n",
"print(dosya.readline(1))\n",
"print(\"----\")\n",
"print(dosya.read()) #ilk satırı okuduğumuz için ikinci satırdan okumaya devam ediyor\n",
"print(\"----\")\n",
"dosya.seek(0) #başa konumlanalım tekrar\n",
"print(dosya.readline(2)) #baştan ilk 2 karakter"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"id": "NYJPrI_9NP2M",
"outputId": "cda78192-025c-4574-baa5-472ee9fc3b99",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"execution_count": 295
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"1-from __future__ import print_function\n",
"2-import inspect\n",
"3-import os, sys, site\n",
"4-import functools\n",
"5-import time\n",
"6-from forbiddenfruit import curse\n",
"7-\n",
"8-try:\n",
"9- import __builtin__\n",
"10-except ImportError:\n",
"11- import builtins as __builtin__\n",
"12-\n",
"13-# *************************************************************************************************************\n",
"14-#Module level methods\n",
"15-\n",
"16- \n",
"17-def lineno():\n",
"18- previous_frame = inspect.currentframe().f_back.f_back\n",
"19- (filename, line_number, function_name, lines, index) = inspect.getframeinfo(previous_frame)\n",
"20- return (line_number, lines)\n",
"21- #return inspect.currentframe().f_back.f_back.f_lineno, str(inspect.currentframe().f_back)\n",
"22-\n",
"23-def printy(*args, **kwargs):\n",
"24- print(lineno(),\"\\n----------\")\n",
"25- print(*args, **kwargs)\n",
"26- print(\" \",end=\"\\n\")\n",
"27-\n",
"28-\n",
"29-def timeElapse(func):\n",
"30- \"\"\"\n",
"31- usage:\n",
"32- @timeElapse\n",
"33- def somefunc():\n",
"34- ...\n",
"35- ...\n",
"36-\n",
"37- somefunc()\n",
"38- \"\"\"\n",
"39- @functools.wraps(func)\n",
"40- def wrapper(*args,**kwargs):\n",
"41- start=time.time()\n",
"42- value=func(*args,**kwargs)\n",
"43- func()\n",
"44- finito=time.time()\n",
"45- print(\"Time elapsed:{}\".format(finito-start))\n",
"46- return value\n",
"47- return wrapper \n",
"48-\n",
"49-\n",
"50-def multioutput(type=\"all\"):\n",
"51- from IPython.core.interactiveshell import InteractiveShell\n",
"52- InteractiveShell.ast_node_interactivity = type\n",
"53- \n",
"54-def scriptforReload():\n",
"55- print(\"\"\"\n",
"56- %load_ext autoreload\n",
"57- %autoreload 2\"\"\")\n",
"58- \n",
"59-def scriptforTraintest():\n",
"60- print(\"X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=42)\")\n",
"61- \n",
"62-def scriptForCitation():\n",
"63- print(\"\"\" Görsel bu sayfadan alınmıştır \"\"\")\n",
"64- \n",
"65-def pythonSomeInfo():\n",
"66- print(\"system packages folder:\",sys.prefix, end=\"\\n\\n\")\n",
"67- print(\"pip install folder:\",site.getsitepackages(), end=\"\\n\\n\") \n",
"68- print(\"python version:\", sys.version, end=\"\\n\\n\")\n",
"69- print(\"executables location:\",sys.executable, end=\"\\n\\n\")\n",
"70- print(\"pip version:\", os.popen('pip version').read(), end=\"\\n\\n\")\n",
"71- pathes= sys.path\n",
"72- print(\"Python pathes\")\n",
"73- for p in pathes:\n",
"74- print(p)\n",
"75-\n",
"76-\n",
"77-def showMemoryUsage():\n",
"78- dict_={}\n",
"79- global_vars = list(globals().items())\n",
"80- for var, obj in global_vars:\n",
"81- if not var.startswith('_'):\n",
"82- dict_[var]=sys.getsizeof(obj)\n",
"83- \n",
"84- final={k: v for k, v in sorted(dict_.items(), key=lambda item: item[1],reverse=True)} \n",
"85- print(final)\n",
"86- \n",
"87-def readfile(path,enc='cp1254'):\n",
"88- with io.open(path, \"r\", encoding=enc) as f:\n",
"89- return f.read()\n",
"90-\n",
"91-def getFirstItemFromDictionary(dict_):\n",
"92- return next(iter(dict_)),next(iter(dict_.values()))\n",
"93- \n",
"94-\n",
"95-\n",
"96-def removeItemsFromList(self,list2,inplace=True): \n",
"97- \"\"\"\n",
"98- Extension method for list type. Removes items from list2 from list1.\n",
"99- First, forbiddenfruit must be installed via https://pypi.org/project/forbiddenfruit/\n",
"100- \"\"\" \n",
"101- if inplace:\n",
"102- for x in set(list2):\n",
"103- self.remove(x)\n",
"104- return self\n",
"105- else:\n",
"106- temp=self.copy()\n",
"107- for x in set(list2):\n",
"108- temp.remove(x)\n",
"109- return temp\n",
"110- \n",
"111-curse(list, \"removeItemsFromList\", removeItemsFromList)\n",
"112-\n",
"113-\n",
"114-\n",
"115-\n",
"116-\n"
]
}
],
"source": [
"#her satırın başına satır no ekleyelim\n",
"dosya.seek(0)\n",
"i=1\n",
"for satır in dosya.readlines():\n",
" print(\"{}-{}\".format(i,satır),end=\"\")\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {
"id": "Obwq_iNjNP2M"
},
"outputs": [],
"source": [
"#yarat\n",
"yenidosya=io.open(\"test.txt\",\"w\")\n",
"yenidosya.close()"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {
"id": "jWwIjeIKNP2M",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f2b5d520-bd8a-49f1-87e1-deb9155ba69e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"6"
]
},
"metadata": {},
"execution_count": 297
}
],
"source": [
"#varolana yaz, sonuna ekleme\n",
"yenidosya=io.open(\"test.txt\",\"a\")\n",
"yenidosya.write(\"\\nselam\")\n",
"yenidosya.flush() #hemen yazsın. bunu kullanmazsak yaptığımız değişiklikleri hemen görmeyiz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_FPZJr39NP2N"
},
"source": [
"Güvenli dosya işlemleri"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xjGs2hFlNP2N"
},
"source": [
"Dosyalarla işiniz bitince kapatmak önemlidir. Kapandığından emin olmak için with bloğu içinde yazmak gerekir"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {
"scrolled": true,
"id": "TuNhM5TlNP2N",
"outputId": "7fed268e-fadd-4228-dcc9-cc917e176789",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"selam\n"
]
}
],
"source": [
"with io.open(\"test.txt\", \"r\") as dosya:\n",
" print(dosya.read())"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"id": "gefRFUIKNP2N",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6228198b-92de-4558-e601-4e48c71e5477"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"execution_count": 299
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"13"
]
},
"metadata": {},
"execution_count": 299
}
],
"source": [
"#hem okuma hem yazma moduyla açıp başa bilgi ekleme\n",
"with io.open(\"test.txt\", \"r+\") as f:\n",
" content = f.read()\n",
" f.seek(0) #Dosyayı başa sarıyoruz\n",
" f.write(\"volkan\\n\"+content)"
]
},
{
"cell_type": "code",
"source": [
"!rm test.txt"
],
"metadata": {
"id": "6tyKo6fQSnRi"
},
"execution_count": 300,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "OijGzgTxNP2N"
},
"source": [
"**Türkçe karakter**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WJ1vPazkNP2O"
},
"source": [
"\n",
"utf-8 mi cp1254 mü? \n",
"\n",
"https://python-istihza.yazbel.com/karakter_kodlama.html"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T13:53:00.576889Z",
"start_time": "2020-11-22T13:53:00.572903Z"
},
"id": "Yn5SWIheNP2O",
"outputId": "89047128-7dba-4908-c962-37a3d4994ab1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'UTF-8'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 301
}
],
"source": [
"import locale\n",
"locale.getpreferredencoding()"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T13:53:07.023663Z",
"start_time": "2020-11-22T13:53:07.017681Z"
},
"id": "nzTj3YM8NP2O",
"outputId": "4d1b3682-7a90-4162-b804-34518a74cc23",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.metrics import silhouette_samples, silhouette_score\n",
"from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score\n",
"from sklearn.metrics import roc_curve, precision_recall_curve, auc\n",
"from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
"import matplotlib.cm as cm\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.neighbors import NearestNeighbors\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder,binarize\n",
"from sklearn.pipeline import Pipeline \n",
"import os, sys, site\n",
"import itertools \n",
"from numpy.random import uniform\n",
"from random import sample, seed\n",
"from math import isnan\n",
"from multiprocessing import Pool\n",
"from scipy.spatial import distance\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"from sklearn.model_selection import learning_curve\n",
"import networkx as nx\n",
"from sklearn.experimental import enable_halving_search_cv \n",
"from sklearn.model_selection import RandomizedSearchCV,GridSearchCV,HalvingGridSearchCV,HalvingRandomSearchCV\n",
"import warnings\n",
"import statsmodels.api as sm\n",
"import statsmodels.stats.api as sms\n",
"import statsmodels.formula.api as smf\n",
"\n",
"def adjustedr2(R_sq,y,y_pred,x):\n",
" return 1 - (1-R_sq)*(len(y)-1)/(len(y_pred)-x.shape[1]-1)\n",
"\n",
"def calculate_aic_bic(n, mse, num_params):\n",
" \"\"\"\n",
" n=number of instances in y \n",
" \"\"\" \n",
" aic = n *np.log(mse) + 2 * num_params\n",
" bic = n * np.log(mse) + num_params * np.log(n)\n",
" # ssr = fitted.ssr #residual sum of squares\n",
" # AIC = N + N*np.log(2.0*np.pi*ssr/N)+2.0*(p+1)\n",
" # print(AIC)\n",
" # BIC = N + N*np.log(2.0*np.pi*ssr/N) + p*np.log(N)\n",
" # print(BIC)\n",
" return aic, bic \n",
"\n",
" \n",
"def printScores(y_test,y_pred,x=None,*, alg_type='c',f1avg=None):\n",
" \"\"\" \n",
" prints the available performanse scores.\n",
" Args:\n",
" alg_type: c for classfication, r for regressin\n",
" f1avg: if None, taken as binary.\n",
" \"\"\"\n",
" if alg_type=='c':\n",
" acc=accuracy_score(y_test,y_pred)\n",
" print(\"Accuracy:\",acc)\n",
" recall=recall_score(y_test,y_pred)\n",
" print(\"Recall:\",recall)\n",
" precision=precision_score(y_test,y_pred)\n",
" print(\"Precision:\",precision)\n",
" if f1avg is None:\n",
" f1=f1_score(y_test,y_pred)\n",
" else:\n",
" f1=f1_score(y_test,y_pred,average=f1avg)\n",
" print(\"F1:\",f1)\n",
" return acc,recall,precision,f1\n",
" else:\n",
" mse=mean_squared_error(y_test,y_pred) #RMSE için squared=False yapılabilir ama bize mse de lazım\n",
" rmse=round(np.sqrt(mse),2)\n",
" print(\"RMSE:\",rmse)\n",
" mae=round(mean_absolute_error(y_test,y_pred),2)\n",
" print(\"MAE:\",mae) \n",
" r2=round(r2_score(y_test,y_pred),2)\n",
" print(\"r2:\",r2)\n",
" adjr2=round(adjustedr2(r2_score(y_test,y_pred),y_test,y_pred,x),2)\n",
" print(\"Adjusted R2:\",adjr2)\n",
" aic, bic=calculate_aic_bic(len(y_test),mse,len(x))\n",
" print(\"AIC:\",round(aic,2))\n",
" print(\"BIC:\",round(bic,2))\n",
" return (rmse,mae,r2,adjr2,round(aic,2),round(bic,2))\n",
"\n",
"def draw_sihoutte(range_n_clusters,data,isbasic=True,printScores=True,random_state=42):\n",
" \"\"\"\n",
" - isbasic:if True, plots scores as line chart whereas false, plots the sihoutte chart.\n",
" - taken from https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html and modified as needed.\n",
" \"\"\"\n",
" if isbasic==False:\n",
" silhouette_max=0\n",
" for n_clusters in range_n_clusters:\n",
" # Create a subplot with 1 row and 2 columns\n",
" fig, (ax1, ax2) = plt.subplots(1, 2)\n",
" fig.set_size_inches(12,4)\n",
"\n",
" ax1.set_xlim([-1, 1])\n",
" # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
" # plots of individual clusters, to demarcate them clearly.\n",
" ax1.set_ylim([0, len(data) + (n_clusters + 1) * 10])\n",
"\n",
" # Initialize the clusterer with n_clusters value and a random generator\n",
" # seed of 10 for reproducibility.\n",
" clusterer = KMeans(n_clusters=n_clusters, random_state=random_state)\n",
" cluster_labels = clusterer.fit_predict(data)\n",
"\n",
" # The silhouette_score gives the average value for all the samples.\n",
" # This gives a perspective into the density and separation of the formed\n",
" # clusters\n",
" silhouette_avg = silhouette_score(data, cluster_labels)\n",
" if silhouette_avg>silhouette_max:\n",
" silhouette_max,nc=silhouette_avg,n_clusters\n",
" print(\"For n_clusters =\", n_clusters,\n",
" \"The average silhouette_score is :\", silhouette_avg)\n",
"\n",
" # Compute the silhouette scores for each sample\n",
" sample_silhouette_values = silhouette_samples(data, cluster_labels)\n",
"\n",
" y_lower = 10\n",
" for i in range(n_clusters):\n",
" # Aggregate the silhouette scores for samples belonging to\n",
" # cluster i, and sort them\n",
" ith_cluster_silhouette_values = \\\n",
" sample_silhouette_values[cluster_labels == i]\n",
"\n",
" ith_cluster_silhouette_values.sort()\n",
"\n",
" size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
" y_upper = y_lower + size_cluster_i\n",
"\n",
" color = cm.nipy_spectral(float(i) / n_clusters)\n",
" ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
" 0, ith_cluster_silhouette_values,\n",
" facecolor=color, edgecolor=color, alpha=0.7)\n",
"\n",
" # Label the silhouette plots with their cluster numbers at the middle\n",
" ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
"\n",
" # Compute the new y_lower for next plot\n",
" y_lower = y_upper + 10 # 10 for the 0 samples\n",
"\n",
" ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
" ax1.set_xlabel(\"The silhouette coefficient values\")\n",
" ax1.set_ylabel(\"Cluster label\")\n",
"\n",
" # The vertical line for average silhouette score of all the values\n",
" ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
"\n",
" ax1.set_yticks([]) # Clear the yaxis labels / ticks\n",
" ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
"\n",
" # 2nd Plot showing the actual clusters formed\n",
" colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
" ax2.scatter(data[:, 0], data[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n",
" c=colors, edgecolor='k')\n",
"\n",
" # Labeling the clusters\n",
" centers = clusterer.cluster_centers_\n",
" # Draw white circles at cluster centers\n",
" ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
" c=\"white\", alpha=1, s=200, edgecolor='k')\n",
"\n",
" for i, c in enumerate(centers):\n",
" ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
" s=50, edgecolor='k')\n",
"\n",
" ax2.set_title(\"The visualization of the clustered data.\")\n",
" ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
" ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
"\n",
" plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
" \"with n_clusters = %d\" % n_clusters),\n",
" fontsize=14, fontweight='bold') \n",
" plt.show()\n",
" print(f\"Best score is {silhouette_max} for {nc}\")\n",
" else:\n",
" ss = []\n",
" for n in range_n_clusters:\n",
" kmeans = KMeans(n_clusters=n, random_state=random_state)\n",
" kmeans.fit_transform(data)\n",
" labels = kmeans.labels_\n",
" score = silhouette_score(data, labels)\n",
" ss.append(score)\n",
" if printScores==True:\n",
" print(n,score)\n",
" plt.plot(range_n_clusters,ss)\n",
" plt.xticks(range_n_clusters) #so it shows all the ticks\n",
"\n",
"def drawEpsilonDecider(data,n):\n",
" \"\"\"\n",
" for DBSCAN\n",
" n: # of neighbours(in the nearest neighbour calculation, the point itself will appear as the first nearest neighbour. so, this should be\n",
" given as min_samples+1.\n",
" data:numpy array\n",
" \"\"\"\n",
" neigh = NearestNeighbors(n_neighbors=n)\n",
" nbrs = neigh.fit(data)\n",
" distances, indices = nbrs.kneighbors(data)\n",
" distances = np.sort(distances, axis=0)\n",
" distances = distances[:,1]\n",
" plt.ylabel(\"eps\")\n",
" plt.xlabel(\"number of data points\")\n",
" plt.plot(distances)\n",
" \n",
"def draw_elbow(ks,data):\n",
" wcss = []\n",
" for i in ks:\n",
" kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0) \n",
" kmeans.fit(data)\n",
" wcss.append(kmeans.inertia_)\n",
" plt.plot(ks, wcss)\n",
" plt.title('Elbow Method')\n",
" plt.xlabel('# of clusters')\n",
" plt.ylabel('WCSS')\n",
" plt.xticks(ks)\n",
" plt.show()\n",
" \n",
"def biplot(score,coeff,y,variance,labels=None):\n",
" \"\"\"\n",
" PCA biplot.\n",
" Found at https://stackoverflow.com/questions/39216897/plot-pca-loadings-and-loading-in-biplot-in-sklearn-like-rs-autoplot\n",
" \"\"\"\n",
" xs = score[:,0]\n",
" ys = score[:,1]\n",
" n = coeff.shape[0]\n",
" scalex = 1.0/(xs.max() - xs.min())\n",
" scaley = 1.0/(ys.max() - ys.min())\n",
" plt.scatter(xs * scalex,ys * scaley, c = y)\n",
" for i in range(n):\n",
" plt.arrow(0, 0, coeff[i,0], coeff[i,1],color = 'r',alpha = 0.5)\n",
" if labels is None:\n",
" plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, \"Var\"+str(i+1), color = 'g', ha = 'center', va = 'center')\n",
" else:\n",
" plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color = 'g', ha = 'center', va = 'center')\n",
" plt.xlim(-1,1)\n",
" plt.ylim(-1,1)\n",
" plt.xlabel(\"PC{},Variance:{}\".format(1,variance[0]))\n",
" plt.ylabel(\"PC{},Variance:{}\".format(2,variance[1]))\n",
" plt.grid()\n",
"\n",
" \n",
" \n",
"\n",
"def get_feature_names_from_columntransformer(ct):\n",
" \"\"\"\n",
" returns feature names in a dataframe passet to a column transformer. Useful if you have lost the names due to conversion to numpy.\n",
" if it doesn't work, try out the one at https://johaupt.github.io/blog/columnTransformer_feature_names.html or at https://lifesaver.codes/answer/cannot-get-feature-names-after-columntransformer-12525\n",
" \"\"\"\n",
" final_features=[]\n",
" try:\n",
" \n",
" for trs in ct.transformers_:\n",
" trName=trs[0]\n",
" trClass=trs[1]\n",
" features=trs[2]\n",
" if isinstance(trClass,Pipeline): \n",
" n,tr=zip(*trClass.steps)\n",
" for t in tr: #t is a transformator object, tr is the list of all transoformators in the pipeline \n",
" if isinstance(t,OneHotEncoder):\n",
" for f in t.get_feature_names_out(features):\n",
" final_features.append(\"OHE_\"+f) \n",
" break\n",
" else: #if not found onehotencoder, add the features directly\n",
" for f in features:\n",
" final_features.append(f) \n",
" elif isinstance(trClass,OneHotEncoder): #?type(trClass)==OneHotEncoder:\n",
" for f in trClass.get_feature_names_out(features):\n",
" final_features.append(\"OHE_\"+f) \n",
" else:\n",
" #remainders\n",
" if trName==\"remainder\":\n",
" for i in features:\n",
" final_features.append(ct.feature_names_in_[i])\n",
" #all the others\n",
" else:\n",
" for f in features:\n",
" final_features.append(f) \n",
" except AttributeError:\n",
" print(\"Your sklearn version may be old and you may need to upgrade it via 'python -m pip install scikit-learn -U'\")\n",
"\n",
" return final_features \n",
"\n",
"def featureImportanceEncoded(feature_importance_array,feature_names,figsize=(8,6)):\n",
" \"\"\"\n",
" plots the feature importance plot.\n",
" feature_importance_array:feature_importance_ attribute\n",
" \"\"\"\n",
" plt.figure(figsize=figsize)\n",
" dfimp=pd.DataFrame(feature_importance_array.reshape(-1,1).T,columns=feature_names).T\n",
" dfimp.index.name=\"Encoded\"\n",
" dfimp.rename(columns={0: \"Importance\"},inplace=True)\n",
" dfimp.reset_index(inplace=True)\n",
" dfimp[\"Feature\"]=dfimp[\"Encoded\"].apply(lambda x:x[4:].split('_')[0] if \"OHE\" in x else x)\n",
" dfimp.groupby(by='Feature')[\"Importance\"].sum().sort_values().plot(kind='barh');\n",
" \n",
" \n",
"def compareEstimatorsInGridSearch(gs,tableorplot='plot',figsize=(10,5),est=\"param_clf\"):\n",
" \"\"\"\n",
" Gives a comparison table/plot of the estimators in a gridsearch.\n",
" \"\"\"\n",
" cvres = gs.cv_results_\n",
" cv_results = pd.DataFrame(cvres)\n",
" cv_results[est]=cv_results[est].apply(lambda x:str(x).split('(')[0])\n",
" cols={\"mean_test_score\":\"MAX of mean_test_score\",\"mean_fit_time\":\"MIN of mean_fit_time\"}\n",
" summary=cv_results.groupby(by=est).agg({\"mean_test_score\":\"max\", \"mean_fit_time\":\"min\"}).rename(columns=cols)\n",
" summary.sort_values(by='MAX of mean_test_score', ascending=False,inplace=True)\n",
" \n",
" \n",
" if tableorplot=='table':\n",
" return summary\n",
" else:\n",
" _, ax1 = plt.subplots(figsize=figsize)\n",
" color = 'tab:red'\n",
" ax1.xaxis.set_ticks(range(len(summary)))\n",
" ax1.set_xticklabels(summary.index, rotation=45,ha='right')\n",
" \n",
" ax1.set_ylabel('MAX of mean_test_score', color=color)\n",
" ax1.bar(summary.index, summary['MAX of mean_test_score'], color=color)\n",
" ax1.tick_params(axis='y', labelcolor=color)\n",
" ax1.set_ylim(0,summary[\"MAX of mean_test_score\"].max()*1.1)\n",
"\n",
" ax2 = ax1.twinx() \n",
" color = 'tab:blue'\n",
" ax2.set_ylabel('MIN of mean_fit_time', color=color) \n",
" ax2.plot(summary.index, summary['MIN of mean_fit_time'], color=color)\n",
" ax2.tick_params(axis='y', labelcolor=color) \n",
" ax2.set_ylim(0,summary[\"MIN of mean_fit_time\"].max()*1.1)\n",
"\n",
" plt.show() \n",
"\n",
"def plot_confusion_matrix(cm, classes,\n",
" normalize=False,\n",
" title='Confusion matrix',\n",
" cmap=plt.cm.Blues):\n",
" \"\"\"\n",
" Depreceated. use 'sklearn.metrics.ConfusionMatrixDisplay(cm).plot();'\n",
" \"\"\"\n",
" warnings.warn(\"use 'sklearn.metrics.ConfusionMatrixDisplay(cm).plot();'\") \n",
" \n",
"def CheckForClusteringTendencyWithHopkins(X,random_state=42): \n",
" \"\"\"\n",
" taken from https://matevzkunaver.wordpress.com/2017/06/20/hopkins-test-for-cluster-tendency/\n",
" X:numpy array or dataframe\n",
" the closer to 1, the higher probability of clustering tendency \n",
" X must be scaled priorly.\n",
" \"\"\"\n",
" \n",
" d = X.shape[1]\n",
" #d = len(vars) # columns\n",
" n = len(X) # rows\n",
" m = int(0.1 * n) # heuristic from article [1]\n",
" if type(X)==np.ndarray:\n",
" nbrs = NearestNeighbors(n_neighbors=1).fit(X)\n",
" else:\n",
" nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
" seed(random_state) \n",
" rand_X = sample(range(0, n, 1), m)\n",
" \n",
" ujd = []\n",
" wjd = []\n",
" for j in range(0, m):\n",
" #-------------------bi ara random state yap----------\n",
" u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
" ujd.append(u_dist[0][1])\n",
" if type(X)==np.ndarray:\n",
" w_dist, _ = nbrs.kneighbors(X[rand_X[j]].reshape(1, -1), 2, return_distance=True)\n",
" else:\n",
" w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
" wjd.append(w_dist[0][1])\n",
" \n",
" H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
" if isnan(H):\n",
" print(ujd, wjd)\n",
" H = 0\n",
" \n",
" return H \n",
"\n",
"def getNumberofCatsAndNumsFromDatasets(path,size=10_000_000):\n",
" \"\"\"\n",
" returns the number of features by their main type(i.e categorical or numeric or datetime)\n",
" args:\n",
" path:path of the files residing in.\n",
" size:size of the file(default is ~10MB). if chosen larger, it will take longer to return.\n",
" \"\"\"\n",
" os.chdir(path)\n",
" files=os.listdir()\n",
" liste=[]\n",
" for d in files: \n",
" try:\n",
" if os.path.isfile(d) and os.path.getsize(d)` for the various\n",
" cross-validators that can be used here.\n",
"\n",
" n_jobs : int or None, default=None\n",
" Number of jobs to run in parallel.\n",
" ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
" ``-1`` means using all processors. See :term:`Glossary `\n",
" for more details.\n",
"\n",
" train_sizes : array-like of shape (n_ticks,)\n",
" Relative or absolute numbers of training examples that will be used to\n",
" generate the learning curve. If the ``dtype`` is float, it is regarded\n",
" as a fraction of the maximum size of the training set (that is\n",
" determined by the selected validation method), i.e. it has to be within\n",
" (0, 1]. Otherwise it is interpreted as absolute sizes of the training\n",
" sets. Note that for classification the number of samples usually have\n",
" to be big enough to contain at least one sample from each class.\n",
" (default: np.linspace(0.1, 1.0, 5))\n",
" \"\"\"\n",
" if axes is None:\n",
" _, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
"\n",
" axes[0].set_title(title)\n",
" if ylim is not None:\n",
" axes[0].set_ylim(*ylim)\n",
" axes[0].set_xlabel(\"Training examples\")\n",
" axes[0].set_ylabel(\"Score\")\n",
"\n",
" train_sizes, train_scores, test_scores, fit_times, _ = learning_curve(\n",
" estimator,\n",
" X,\n",
" y,\n",
" cv=cv,\n",
" n_jobs=n_jobs,\n",
" train_sizes=train_sizes,\n",
" return_times=True,\n",
" random_state=random_state\n",
" )\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" fit_times_mean = np.mean(fit_times, axis=1)\n",
" fit_times_std = np.std(fit_times, axis=1)\n",
"\n",
" # Plot learning curve\n",
" axes[0].grid()\n",
" axes[0].fill_between(\n",
" train_sizes,\n",
" train_scores_mean - train_scores_std,\n",
" train_scores_mean + train_scores_std,\n",
" alpha=0.1,\n",
" color=\"r\",\n",
" )\n",
" axes[0].fill_between(\n",
" train_sizes,\n",
" test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std,\n",
" alpha=0.1,\n",
" color=\"g\",\n",
" )\n",
" axes[0].plot(\n",
" train_sizes, train_scores_mean, \"o-\", color=\"r\", label=\"Training score\"\n",
" )\n",
" axes[0].plot(\n",
" train_sizes, test_scores_mean, \"o-\", color=\"g\", label=\"Cross-validation score\"\n",
" )\n",
" axes[0].legend(loc=\"best\")\n",
"\n",
" # Plot n_samples vs fit_times\n",
" axes[1].grid()\n",
" axes[1].plot(train_sizes, fit_times_mean, \"o-\")\n",
" axes[1].fill_between(\n",
" train_sizes,\n",
" fit_times_mean - fit_times_std,\n",
" fit_times_mean + fit_times_std,\n",
" alpha=0.1,\n",
" )\n",
" axes[1].set_xlabel(\"Training examples\")\n",
" axes[1].set_ylabel(\"fit_times\")\n",
" axes[1].set_title(\"Scalability of the model\")\n",
"\n",
" # Plot fit_time vs score\n",
" fit_time_argsort = fit_times_mean.argsort()\n",
" fit_time_sorted = fit_times_mean[fit_time_argsort]\n",
" test_scores_mean_sorted = test_scores_mean[fit_time_argsort]\n",
" test_scores_std_sorted = test_scores_std[fit_time_argsort]\n",
" axes[2].grid()\n",
" axes[2].plot(fit_time_sorted, test_scores_mean_sorted, \"o-\")\n",
" axes[2].fill_between(\n",
" fit_time_sorted,\n",
" test_scores_mean_sorted - test_scores_std_sorted,\n",
" test_scores_mean_sorted + test_scores_std_sorted,\n",
" alpha=0.1,\n",
" )\n",
" axes[2].set_xlabel(\"fit_times\")\n",
" axes[2].set_ylabel(\"Score\")\n",
" axes[2].set_title(\"Performance of the model\")\n",
"\n",
" plt.show()\n",
"\n",
"\n",
"def drawNeuralNetwork(layers,figsize=(10,8)):\n",
" \"\"\"\n",
" Draws a represantion of the neural network using networkx.\n",
" layers:list of the # of layers including input and output.\n",
" \"\"\" \n",
" plt.figure(figsize=figsize)\n",
" pos={} \n",
" for e,l in enumerate(layers):\n",
" for i in range(l):\n",
" pos[str(l)+\"_\"+str(i)]=((e+1)*50,i*5+50)\n",
"\n",
"\n",
" X=nx.Graph()\n",
" nx.draw_networkx_nodes(X,pos,nodelist=pos.keys(),node_color='r')\n",
" X.add_nodes_from(pos.keys())\n",
"\n",
" edgelist=[] #list of tuple\n",
" for e,l in enumerate(layers):\n",
" for i in range(l):\n",
" try:\n",
" for k in range(layers[e+1]):\n",
" try:\n",
" edgelist.append((str(l)+\"_\"+str(i),str(layers[e+1])+\"_\"+str(k)))\n",
" except:\n",
" pass\n",
" except:\n",
" pass\n",
"\n",
"\n",
" X.add_edges_from(edgelist)\n",
" for n, p in pos.items():\n",
" X.nodes[n]['pos'] = p \n",
"\n",
" nx.draw(X, pos); \n",
"\n",
"def draw_network_graph(ws):\n",
" \"\"\"\n",
" Draws a network graph of a neural network with dynamic weights.\n",
"\n",
" Args:\n",
" ws: A list of weight matrices.\n",
" \"\"\"\n",
" # Create a directed graph\n",
" G = nx.DiGraph()\n",
"\n",
" # Add nodes\n",
" layer_count = len(ws) + 1 # Include input layer\n",
" node_count = [ws[0].shape[0]] + [w.shape[1] for w in ws]\n",
"\n",
" for layer in range(layer_count):\n",
" if layer == 0:\n",
" for i in range(node_count[layer]):\n",
" G.add_node(f\"Input {i+1}\", layer=layer)\n",
" elif layer == layer_count - 1:\n",
" for i in range(node_count[layer]):\n",
" G.add_node(f\"Output {i+1}\", layer=layer)\n",
" else:\n",
" for i in range(node_count[layer]):\n",
" G.add_node(f\"Hidden {layer}_{i+1}\", layer=layer)\n",
"\n",
" # Add edges with weights\n",
" for layer in range(layer_count - 1):\n",
" for i in range(node_count[layer]):\n",
" for j in range(node_count[layer + 1]):\n",
" if layer == 0:\n",
" G.add_edge(f\"Input {i+1}\", f\"Hidden {layer + 1}_{j+1}\", weight=ws[layer][i, j])\n",
" elif layer == layer_count - 2:\n",
" G.add_edge(f\"Hidden {layer}_{i+1}\", f\"Output {j+1}\", weight=ws[layer][i, j])\n",
" else:\n",
" G.add_edge(f\"Hidden {layer}_{i+1}\", f\"Hidden {layer + 1}_{j+1}\", weight=ws[layer][i, j])\n",
"\n",
" # Draw the graph\n",
" pos = {}\n",
" pos.update({node: (0, i) for i, node in enumerate(\n",
" [f\"Input {i+1}\" for i in range(node_count[0])]\n",
" )})\n",
" pos.update({node: (layer, i) for layer in range(1, layer_count -1) for i, node in enumerate(\n",
" [f\"Hidden {layer}_{i+1}\" for i in range(node_count[layer])]\n",
" )})\n",
" pos.update({node: (layer_count - 1, i) for i, node in enumerate(\n",
" [f\"Output {i+1}\" for i in range(node_count[-1])]\n",
" )})\n",
"\n",
" nx.draw(G, pos, with_labels=True, node_size=1000, node_color='lightblue', font_size=10)\n",
"\n",
" # Add edge labels with weights\n",
" edge_labels = {(u, v): f\"{d['weight']:.2f}\" for u, v, d in G.edges(data=True)}\n",
" nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)\n",
"\n",
" plt.show()\n",
"\n",
"def plotROC(y_test,X_test,estimator,pos_label=1,figsize=(6,6)):\n",
" cm = confusion_matrix(y_test, estimator.predict(X_test)) \n",
" fpr, tpr, _ = roc_curve(y_test, estimator.predict_proba(X_test)[:,1],pos_label=pos_label)\n",
" roc_auc = auc(fpr, tpr) #or roc_auc_score(y_test, y_scores)\n",
" plt.figure(figsize=figsize)\n",
" plt.plot(fpr, tpr, label='(ROC-AUC = %0.2f)' % roc_auc)\n",
" plt.plot([0, 1], [0, 1], 'k--')\n",
" tn, fp, fn, tp = [i for i in cm.ravel()]\n",
" plt.plot(fp/(fp+tn), tp/(tp+fn), 'ro', markersize=8, label='Decision Point(Optimal threshold)')\n",
" plt.xlim([0.0, 1.0])\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlabel('False Positive Rate(1-sepecifity)')\n",
" plt.ylabel('True Positive Rate(Recall/Sensitivity)')\n",
" plt.title('ROC Curve (TPR vs FPR at each probability threshold)')\n",
" plt.legend(loc=\"lower right\")\n",
" plt.show();\n",
"\n",
"def plot_precision_recall_curve(y_test_encoded,X_test,estimator,threshs=np.linspace(0.0, 0.98, 40),figsize=(16,6)):\n",
" \"\"\"\n",
" y_test should be labelencoded.\n",
" \"\"\"\n",
" pred_prob = estimator.predict_proba(X_test) \n",
" precision, recall, thresholds = precision_recall_curve(y_test_encoded, pred_prob[:,1])\n",
" pr_auc = auc(recall, precision) \n",
"\n",
" Xt = [] ; Yp = [] ; Yr = [] \n",
" for thresh in threshs:\n",
" with warnings.catch_warnings():\n",
" warnings.filterwarnings(\"error\")\n",
" try:\n",
" y_pred = binarize(pred_prob, threshold=thresh)[:,1]\n",
" Xt.append(thresh)\n",
" Yp.append(precision_score(y_test_encoded, y_pred)) #,zero_division=1\n",
" Yr.append(recall_score(y_test_encoded, y_pred))\n",
" except Warning as e:\n",
" print(f\"{thresh:.2f}, error , probably division by zero\")\n",
"\n",
" \n",
" plt.figure(figsize=figsize)\n",
" plt.subplot(121)\n",
" plt.plot(Xt, Yp, \"--\", label='Precision', color='red')\n",
" plt.plot(Xt, Yr, \"--\", label='Recall', color='blue')\n",
" plt.title(\"Precision vs Recall based on decision threshold\")\n",
" plt.xlabel('Decision threshold') ; plt.ylabel('Precision - Recall')\n",
" plt.legend()\n",
" plt.subplot(122)\n",
" plt.step(Yr, Yp, color='black', label='LR (PR-AUC = %0.2f)' % pr_auc)\n",
" # calculate the no skill line as the proportion of the positive class (0.145)\n",
" no_skill = len(y_test_encoded[y_test_encoded==1]) / len(y_test_encoded)\n",
" # plot the no skill precision-recall curve\n",
" plt.plot([0, 1], [no_skill, no_skill], linestyle='--', color='green', label='No Skill')\n",
" # plot the perfect PR curve\n",
" plt.plot([0, 1],[1, 1], color='blue', label='Perfect')\n",
" plt.plot([1, 1],[1, len(y_test_encoded[y_test_encoded==1]) / len(y_test_encoded)], color='blue')\n",
" plt.title('PR Curve')\n",
" plt.xlabel('Recall: TP / (TP+FN)') ; plt.ylabel('Precison: TP / (TP+FP)')\n",
" plt.legend(loc=\"upper right\")\n",
" plt.show();\n",
"\n",
"\n",
"def find_best_cutoff_for_classification(estimator, y_test_le, X_test, costlist,threshs=np.linspace(0., 0.98, 20)): \n",
" \"\"\"\n",
" y_test should be labelencoded as y_test_le\n",
" costlist=cost list for TN, TP, FN, FP\n",
" \"\"\"\n",
" y_pred_prob = estimator.predict_proba(X_test)\n",
" y_pred = estimator.predict(X_test)\n",
" Xp = [] ; Yp = [] # initialization\n",
"\n",
" print(\"Cutoff\\t Cost/Instance\\t Accuracy\\t FN\\t FP\\t TP\\t TN\\t Recall\\t Precision F1-score\")\n",
" for cutoff in threshs:\n",
" with warnings.catch_warnings():\n",
" warnings.filterwarnings(\"error\")\n",
" try:\n",
" y_pred = binarize(y_pred_prob, threshold=cutoff)[:,1]\n",
" cm = confusion_matrix(y_test_le, y_pred)\n",
" TP = cm[1,1]\n",
" TN = cm[0,0]\n",
" FP = cm[0,1]\n",
" FN = cm[1,0]\n",
" cost = costlist[0]*TN + costlist[1]*TP + costlist[2]*FN + costlist[3]*FP\n",
" cost_per_instance = cost/len(y_test_le)\n",
" Xp.append(cutoff)\n",
" Yp.append(cost_per_instance)\n",
" acc=accuracy_score(y_test_le, y_pred)\n",
" rec = cm[1,1]/(cm[1,1]+cm[1,0])\n",
" pre = cm[1,1]/(cm[1,1]+cm[0,1])\n",
" f1 = 2*pre*rec/(pre+rec)\n",
" print(f\"{cutoff:.2f}\\t {cost_per_instance:.2f}\\t\\t {acc:.3f}\\t\\t {FN}\\t {FP}\\t {TP}\\t {TN}\\t {rec:.3f}\\t {pre:.3f}\\t {f1:.3f}\")\n",
" except Warning as e:\n",
" print(f\"{cutoff:.2f}\\t {cost_per_instance:.2f}\\t\\t {acc:.3f}\\t\\t {FN}\\t {FP}\\t {TP}\\t {TN}\\t error might have happened from here anywhere\")\n",
"\n",
" plt.figure(figsize=(10,6))\n",
" plt.plot(Xp, Yp)\n",
" plt.xlabel('Threshold value for probability')\n",
" plt.ylabel('Cost per instance')\n",
" plt.axhline(y=min(Yp), xmin=0., xmax=1., linewidth=1, color = 'r')\n",
" plt.show();\n",
"\n",
"\n",
"def plot_gain_and_lift(estimator,X_test,y_test,pos_label=\"Yes\",figsize=(16,6)):\n",
" \"\"\" \n",
" y_test as numpy array\n",
" prints the gain and lift values and plots the charts. \n",
" \"\"\"\n",
" prob_df=pd.DataFrame({\"Prob\":estimator.predict_proba(X_test)[:,1]})\n",
" prob_df[\"label\"]=np.where(y_test==pos_label,1,0)\n",
" prob_df = prob_df.sort_values(by=\"Prob\",ascending=False)\n",
" prob_df['Decile'] = pd.qcut(prob_df['Prob'], 10, labels=list(range(1,11))[::-1])\n",
"\n",
" #Calculate the actual churn in each decile\n",
" res = pd.crosstab(prob_df['Decile'], prob_df['label'])[1].reset_index().rename(columns = {1: 'Number of Responses'})\n",
" lg = prob_df['Decile'].value_counts(sort = False).reset_index().rename(columns = {'Decile': 'Number of Cases', 'index': 'Decile'})\n",
" lg = pd.merge(lg, res, on = 'Decile').sort_values(by = 'Decile', ascending = False).reset_index(drop = True)\n",
" #Calculate the cumulative\n",
" lg['Cumulative Responses'] = lg['Number of Responses'].cumsum()\n",
" #Calculate the percentage of positive in each decile compared to the total nu\n",
" lg['% of Events'] = np.round(((lg['Number of Responses']/lg['Number of Responses'].sum())*100),2)\n",
" #Calculate the Gain in each decile\n",
" lg['Gain'] = lg['% of Events'].cumsum()\n",
" lg['Decile'] = lg['Decile'].astype('int')\n",
" lg['lift'] = np.round((lg['Gain']/(lg['Decile']*10)),2)\n",
" display(lg)\n",
"\n",
" plt.figure(figsize=figsize)\n",
" plt.subplot(121)\n",
" plt.plot(lg[\"Decile\"],lg[\"lift\"],label=\"Model\")\n",
" plt.plot(lg[\"Decile\"],[1 for i in range(10)],label=\"Random\")\n",
" plt.title(\"Lift Chart\")\n",
" plt.legend()\n",
" plt.xlabel(\"Decile\")\n",
" plt.ylabel(\"Lift\") \n",
" \n",
" plt.subplot(122)\n",
" plt.plot(lg[\"Decile\"],lg[\"Gain\"],label=\"Model\")\n",
" plt.plot(lg[\"Decile\"],[10*(i+1) for i in range(10)],label=\"Random\")\n",
" plt.title(\"Gain Chart\")\n",
" plt.legend()\n",
" plt.xlabel(\"Decile\")\n",
" plt.ylabel(\"Gain\")\n",
" plt.xlim(0,11)\n",
" plt.ylim(0,110)\n",
" plt.show(); \n",
"\n",
"def plot_gain_and_lift_orj(estimator,X_test,y_test,pos_label=\"Yes\"):\n",
" \"\"\" \n",
" y_test as numpy array\n",
" prints the gain and lift values and plots the charts. \n",
" \"\"\"\n",
" prob_df=pd.DataFrame({\"Prob\":estimator.predict_proba(X_test)[:,1]})\n",
" prob_df[\"label\"]=np.where(y_test==pos_label,1,0)\n",
" prob_df = prob_df.sort_values(by=\"Prob\",ascending=False)\n",
" prob_df['Decile'] = pd.qcut(prob_df['Prob'], 10, labels=list(range(1,11))[::-1])\n",
"\n",
" #Calculate the actual churn in each decile\n",
" res = pd.crosstab(prob_df['Decile'], prob_df['label'])[1].reset_index().rename(columns = {1: 'Number of Responses'})\n",
" lg = prob_df['Decile'].value_counts(sort = False).reset_index().rename(columns = {'Decile': 'Number of Cases', 'index': 'Decile'})\n",
" lg = pd.merge(lg, res, on = 'Decile').sort_values(by = 'Decile', ascending = False).reset_index(drop = True)\n",
" #Calculate the cumulative\n",
" lg['Cumulative Responses'] = lg['Number of Responses'].cumsum()\n",
" #Calculate the percentage of positive in each decile compared to the total nu\n",
" lg['% of Events'] = np.round(((lg['Number of Responses']/lg['Number of Responses'].sum())*100),2)\n",
" #Calculate the Gain in each decile\n",
" lg['Gain'] = lg['% of Events'].cumsum()\n",
" lg['Decile'] = lg['Decile'].astype('int')\n",
" lg['lift'] = np.round((lg['Gain']/(lg['Decile']*10)),2)\n",
" display(lg)\n",
" \n",
" plt.plot(lg[\"Decile\"],lg[\"lift\"],label=\"Model\")\n",
" plt.plot(lg[\"Decile\"],[1 for i in range(10)],label=\"Random\")\n",
" plt.title(\"Lift Chart\")\n",
" plt.legend()\n",
" plt.xlabel(\"Decile\")\n",
" plt.ylabel(\"Lift\")\n",
" plt.show();\n",
" \n",
" plt.plot(lg[\"Decile\"],lg[\"Gain\"],label=\"Model\")\n",
" plt.plot(lg[\"Decile\"],[10*(i+1) for i in range(10)],label=\"Random\")\n",
" plt.title(\"Gain Chart\")\n",
" plt.legend()\n",
" plt.xlabel(\"Decile\")\n",
" plt.ylabel(\"Gain\")\n",
" plt.xlim(0,11)\n",
" plt.ylim(0,110)\n",
" plt.show(); \n",
"\n",
"def linear_model_feature_importance(estimator,preprocessor,feature_selector=None,clfreg_name=\"clf\"):\n",
" \"\"\"\n",
" plots the feature importance, namely coefficients for linear models.\n",
" args:\n",
" estimator:either pipeline or gridsearch/randomizedsearch object\n",
" preprocessor:variable name of the preprocessor, which is a columtransformer\n",
" feature_selector:if there is a feature selector step, its name.\n",
" clfreg_name:name of the linear model, usually clf for a classifier, reg for a regressor \n",
" \"\"\"\n",
" \n",
" if feature_selector is not None:\n",
" if isinstance(estimator,GridSearchCV) or isinstance(estimator,RandomizedSearchCV)\\\n",
" or isinstance(estimator,HalvingGridSearchCV) or isinstance(estimator,HalvingRandomSearchCV):\n",
" est=estimator.best_estimator_\n",
" elif isinstance(estimator,Pipeline):\n",
" est=estimator\n",
" else:\n",
" print(\"Either pipeline or gridsearch/randomsearch should be passes for estimator\")\n",
" return\n",
" \n",
" selecteds=est[feature_selector].get_support()\n",
" final_features=[x for e,x in enumerate(get_feature_names_from_columntransformer(preprocessor)) if e in np.argwhere(selecteds==True).ravel()]\n",
" else:\n",
" final_features=get_feature_names_from_columntransformer(preprocessor)\n",
"\n",
" importance=est[clfreg_name].coef_[0]\n",
" plt.bar(final_features, importance)\n",
" plt.xticks(rotation= 45,horizontalalignment=\"right\"); \n",
"\n",
"\n",
"\n",
"def gridsearch_to_df(searcher,topN=5):\n",
" \"\"\"\n",
" searcher: any of grid/randomized searcher objects or their halving versions\n",
" \"\"\"\n",
" cvresultdf = pd.DataFrame(searcher.cv_results_)\n",
" cvresultdf = cvresultdf.sort_values(\"mean_test_score\", ascending=False)\n",
" cols=[x for x in searcher.cv_results_.keys() if \"param_\" in x]+[\"mean_test_score\",\"std_test_score\"]\n",
" return cvresultdf[cols].head(topN) \n",
"\n",
"\n",
"def getAnotherEstimatorFromGridSearch(gs_object,estimator):\n",
" cvres = gs_object.cv_results_\n",
" cv_results = pd.DataFrame(cvres)\n",
" cv_results[\"param_clf\"]=cv_results[\"param_clf\"].apply(lambda x:str(x).split('(')[0])\n",
"\n",
" dtc=cv_results[cv_results[\"param_clf\"]==estimator]\n",
" return dtc.getRowOnAggregation_(\"mean_test_score\",\"max\")[\"params\"].values \n",
"\n",
"def cooksdistance(X,y,figsize=(8,6),ylim=0.5): \n",
" model = sm.OLS(y,X)\n",
" fitted = model.fit()\n",
" # Cook's distance\n",
" pr=X.shape[1]\n",
" CD = 4.0/(X.shape[0]-pr-1)\n",
" influence = fitted.get_influence()\n",
" #c is the distance and p is p-value\n",
" (c, p) = influence.cooks_distance\n",
" plt.figure(figsize=figsize)\n",
" plt.stem(np.arange(len(c)), c, markerfmt=\",\")\n",
" plt.axhline(y=CD, color='r')\n",
" plt.ylabel('Cook\\'s D')\n",
" plt.xlabel('Observation Number')\n",
" plt.ylim(0,ylim)\n",
" plt.show();\n",
"\n"
]
}
],
"source": [
"with io.open(\"/content/drive/MyDrive/Programming/PythonRocks/mypyext/ml.py\", \"r\", encoding='cp1254') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "suEJ8XztNP2O"
},
"source": [
"## Yazma"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"id": "055w2MM7NP2O",
"outputId": "8d6326fd-2706-42f4-a5e6-14a43b11e344",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"8"
]
},
"metadata": {},
"execution_count": 303
}
],
"source": [
"yenidosya=io.open(\"writetest.txt\",\"w\") #x\n",
"yenidosya.write(\"merhaba\\n\")\n",
"lines=[\"satır1\\n\",\"satır2\\n\"]\n",
"yenidosya.writelines(lines)\n",
"yenidosya.close()"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"id": "GUbenUslNP2O",
"outputId": "801d967b-4cbb-4f9e-dfd6-cf42afa67d18",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"6"
]
},
"metadata": {},
"execution_count": 304
}
],
"source": [
"#varolana yaz, sonuna ekleme\n",
"yenidosya=io.open(\"writetest.txt\",\"a\")\n",
"yenidosya.write(\"\\nselam\")\n",
"yenidosya.flush() #hemen yazsın. bunu kullanmazsak yaptığımız değişiklikleri hemen görmeyiz\n",
"yenidosya.close()"
]
},
{
"cell_type": "code",
"source": [
"!rm writetest.txt"
],
"metadata": {
"id": "6e04xrowTOeF"
},
"execution_count": 305,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lofzAC10NP2Q"
},
"source": [
"# Veritabanı işlemleri"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"id": "EgfOzPiPNP2Q"
},
"outputs": [],
"source": [
"import sqlite3 as sql #python içinde otomatikman gelir"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7vGExp6mNP2Q"
},
"source": [
"sqlite sayfasından chinook databaseini indirin"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"id": "6pgtkIIlNP2R"
},
"outputs": [],
"source": [
"vt = sql.connect('/content/drive/MyDrive/Programming/PythonRocks/dataset/chinook.db')"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {
"id": "51kSF6MbNP2R"
},
"outputs": [],
"source": [
"cur=vt.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {
"scrolled": true,
"id": "QYENBSRZNP2R",
"outputId": "0bbdea17-d1f4-4637-976b-dd02da428eeb",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 309
}
],
"source": [
"cur.execute(\"select * from albums\")"
]
},
{
"cell_type": "code",
"execution_count": 310,
"metadata": {
"id": "wIlYv6fENP2S",
"outputId": "0abaa3e1-62da-4a27-f961-abd80f39e181",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[(1, 'For Those About To Rock We Salute You', 1),\n",
" (2, 'Balls to the Wall', 2),\n",
" (3, 'Restless and Wild', 2)]"
]
},
"metadata": {},
"execution_count": 310
}
],
"source": [
"cur.fetchmany(3)"
]
},
{
"cell_type": "code",
"execution_count": 311,
"metadata": {
"id": "2vkd8fvdNP2S"
},
"outputs": [],
"source": [
"veriler = cur.fetchall()"
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {
"scrolled": true,
"id": "KpHtTp8GNP2S",
"outputId": "fb30f204-9963-41be-b053-82ed1429d974",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[(4, 'Let There Be Rock', 1),\n",
" (5, 'Big Ones', 3),\n",
" (6, 'Jagged Little Pill', 4),\n",
" (7, 'Facelift', 5),\n",
" (8, 'Warner 25 Anos', 6)]"
]
},
"metadata": {},
"execution_count": 312
}
],
"source": [
"veriler[:5] #ilk 3ünü çektiğimiz için 4ten devam ediyor"
]
},
{
"cell_type": "code",
"execution_count": 313,
"metadata": {
"id": "bbmc2UcjNP2S"
},
"outputs": [],
"source": [
"vt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XF2lNJ1jNP2S"
},
"source": [
"NOT:sqlite3 çok basit bir veritabanı olup, oracle veya sql server gibi güçlü veritabanlarını sorgulamak için sqlalchemy veya pyodbc gibi modülleri kullanırız ve buradan aldığmız datayı pandas ile işleyebiliriz. Bunun için benim github repomdaki Python Veri Analizi notebookuna bakmanızı tavsiye ederim. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7yl4D60NNP2S"
},
"source": [
"http://sqlitebrowser.org/. sitesi de incelenebilir"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aWdQ3O35NP2S"
},
"source": [
"# Classlar"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dsnWXgT5NP2T"
},
"source": [
"Python nesne yönelimli(oo) bir dildir ve tüm oo dillerde olduğu gibi sınıflar yaratılabilir. Örnek bir sınıf yaratımı aşağıdaki gibi olup detaylar için googlelamanızı rica ederim."
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {
"id": "ISjW6xL4NP2T",
"outputId": "3a6022fe-62c6-464f-e478-3f7c80b24c16",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"yeni araç hazır\n",
"yeni araç hazır\n",
"yeni araç hazır\n",
"<__main__.Araba object at 0x7d45a4bfff70>\n",
"çalışıyor\n",
"durdu\n",
"Mekanik\n",
"Mekanik\n"
]
}
],
"source": [
"class Araba:\n",
" aractipi=\"Mekanik\" #class seviyesinde, tüm Arabalar tarafından paylaşılan bir değer\n",
" def __init__(self,model,marka,km):\n",
" self.model=model\n",
" self.marka=marka\n",
" self.km=km\n",
" print(\"yeni araç hazır\")\n",
" def run(self):\n",
" print(\"çalışıyor\")\n",
" def stop(self):\n",
" print(\"durdu\")\n",
"\n",
"bmw0=Araba(2011,\"bmw\",0)\n",
"bmw1=Araba(2014,\"bmw\",0)\n",
"audi=Araba(2011,\"audi\",0)\n",
"print(bmw0)\n",
"bmw0.run()\n",
"bmw0.stop()\n",
"print(bmw0.aractipi)\n",
"print(audi.aractipi)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jy6MGCozNP2T"
},
"source": [
"## Paralelleştirme"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4vVeILX-NP2T"
},
"source": [
"Özellikle analitik model kurma sırasında çok işimize yarayan bir olgudur. Bunun için ayrı bir notebook'um olacak. Önden araştırmak isteyenler şu kavramları araştırabilir. Multi-threading, multiprocessing, conccurency ve parallelism"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fabSEm7ANP2T"
},
"source": [
"# Verimlilik ve Diğer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ocn1iv8JNP2U"
},
"source": [
"## debugging"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {
"scrolled": true,
"id": "CDamCKWINP2U",
"outputId": "ce10d6b7-bded-43af-80a1-84bda466c6ca",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n",
"PYDEV DEBUGGER WARNING:\n",
"sys.settrace() should not be used when the debugger is being used.\n",
"This may cause the debugger to stop working correctly.\n",
"If this is needed, please check: \n",
"http://pydev.blogspot.com/2007/06/why-cant-pydev-debugger-work-with.html\n",
"to see how to restore the debug tracing back correctly.\n",
"Call Location:\n",
" File \"/usr/lib/python3.10/bdb.py\", line 336, in set_trace\n",
" sys.settrace(self.trace_dispatch)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"--Call--\n",
"> \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/displayhook.py\u001b[0m(252)\u001b[0;36m__call__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 250 \u001b[0;31m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 251 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m--> 252 \u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 253 \u001b[0;31m \"\"\"Printing with history cache management.\n",
"\u001b[0m\u001b[0;32m 254 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> c\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n",
"PYDEV DEBUGGER WARNING:\n",
"sys.settrace() should not be used when the debugger is being used.\n",
"This may cause the debugger to stop working correctly.\n",
"If this is needed, please check: \n",
"http://pydev.blogspot.com/2007/06/why-cant-pydev-debugger-work-with.html\n",
"to see how to restore the debug tracing back correctly.\n",
"Call Location:\n",
" File \"/usr/lib/python3.10/bdb.py\", line 347, in set_continue\n",
" sys.settrace(None)\n",
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"asda\n"
]
}
],
"source": [
"import pdb\n",
"print(4)\n",
"pdb.set_trace() #c devam, n:next gibi seçenekler var\n",
"print(\"asda\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "won9XpBlNP2U"
},
"source": [
"## memory yönetimi"
]
},
{
"cell_type": "code",
"execution_count": 317,
"metadata": {
"scrolled": true,
"id": "AdmijMKPNP2V",
"outputId": "e654c22f-8181-4b11-b6fa-64b2e449a166",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"64\n",
"88\n",
"104\n"
]
}
],
"source": [
"import sys\n",
"import array\n",
"t=(1,2,3)\n",
"l=[1,2,\"3\"]\n",
"a=array.array(\"l\",[1,2,3])\n",
"print(sys.getsizeof(t)) #immutabel olduğu için daha az\n",
"print(sys.getsizeof(l)) #mutable olduğu için tupldan daha çok, içine farklı tipler alabileceğim için arraydan daha çok\n",
"print(sys.getsizeof(a)) #eleman tipi belli olduğu için listten daha az"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C0XtcQ6ONP2V"
},
"source": [
"# Cheatsheet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UE-hIG00NP2V"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1YeCwWllNP2V"
},
"source": [
"# Kendinizi test edin"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uj1XC9KjNP2V"
},
"source": [
"Aşağıdaki adreslerden birinden challange sorularını görebilirsiniz.\n",
"* https://mybinder.org/v2/gh/VolkiTheDreamer/PythonRocks/master (Interaktiftir, download etmenize gerek yok)\n",
"* üstteki açılmazsa: https://nbviewer.org/github/VolkiTheDreamer/PythonRocks/blob/master/Python%20Challenges.ipynb\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bI3tbqMJNP2V"
},
"source": [
"Bunun dışında şu sitelerde de pratik yapma imkanı bulabilirsiniz.\n",
"\n",
"- https://www.w3resource.com/python-exercises/\n",
"- https://www.practicepython.org/\n",
"- https://www.w3schools.com/python/python_exercises.asp\n",
"- https://pynative.com/python-exercises-with-solutions/"
]
}
],
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"hide_input": false,
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "708px",
"left": "300px",
"top": "148px",
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"delete_cmd_prefix": "del ",
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"r": {
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"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
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"window_display": false
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