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sinanazemsinanazem
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add sorting-algorithms
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‎sorting-algorithms/sorting_algorithms.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Insertion Sort"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 3, 4, 7]\n"
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]
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}
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],
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"source": [
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"list_ = [4, 3, 2, 7]\n",
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"lst_ = []\n",
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"\n",
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"for i in list_:\n",
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" for j in range(len(lst_)):\n",
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" if i < lst_[j]:\n",
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" lst_.insert(j, i)\n",
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" break\n",
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" else:\n",
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" lst_.append(i)\n",
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"\n",
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"print(lst_)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Merge Sort"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Merge Algorithms"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def merge(list_1, list_2):\n",
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"\n",
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" list_3 = []\n",
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"\n",
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" i = j = 0\n",
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"\n",
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" len1 = len(list_1)\n",
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" len2 = len(list_2)\n",
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"\n",
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" while i < len1 and j < len2:\n",
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"\n",
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" if list_1[i] < list_2[j]:\n",
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" list_3.append(list_1[i])\n",
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" i += 1\n",
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"\n",
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" else:\n",
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" list_3.append(list_2[j])\n",
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" j += 1\n",
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"\n",
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" list_3 += list_1[i:]\n",
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" list_3 += list_2[j:]\n",
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"\n",
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"\n",
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" return list_3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0, 2, 4, 4, 5, 6, 6, 7, 8, 10, 13]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list_1 = [0, 2, 4, 5, 6]\n",
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"list_2 = [4, 6, 7, 8, 10, 13]\n",
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"merge(list_1, list_2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Merge Sort Algorithms"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"def merge_sort(list_):\n",
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" if len(list_) == 1:\n",
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" return list_\n",
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" else:\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Quick Sort"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_list = [3, 4, 5, 6, 9, 1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def quick_sort(_list, low, high):\n",
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"\n",
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" pivot = low\n",
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" i = low\n",
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" j = high\n",
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" while i < j:\n",
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" while _list[i] <= _list[pivot] and i < high:\n",
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" i+= 1\n",
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"\n",
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" while _list[j] > _list[pivot]:\n",
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" j-= 1\n",
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"\n",
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" if i < j:\n",
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" _list[i], _list[j] = _list[j], _list[i]\n",
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"\n",
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" _list[j], _list[pivot] = _list[pivot], _list[j]\n",
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"\n",
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" quick_sort(_list, low, j-1)\n",
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" quick_sort(_list, j+1, high)\n",
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"\n",
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" return _list\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[1, 3, 4, 5, 6, 9]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"quick_sort(sample_list, 0 , len(sample_list)-1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "ml_env",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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