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KeeratKGKeeratKG
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proceeded to hash table chapter and exercises
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+258
-12
lines changed

5 files changed

+258
-12
lines changed

‎data_structures/4_HashTable/4_HashTable.ipynb‎

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@@ -187,6 +187,15 @@
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"<h2>Implement Hash Table</h2>"
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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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"```\n",
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"Hash table is an internal data structure. Dictionary is a python specific implementation of hash table. \n",
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"```"
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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": 31,
@@ -230,7 +239,7 @@
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"source": [
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"class HashTable: \n",
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" def __init__(self):\n",
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" self.MAX = 100\n",
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" self.MAX = 100 # array of size 100 \n",
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" self.arr = [None for i in range(self.MAX)]\n",
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" \n",
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" def get_hash(self, key):\n",

‎data_structures/4_HashTable_2_Collisions/4_hash_table_collision_handling.ipynb‎

Lines changed: 55 additions & 11 deletions
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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 56,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -110,7 +110,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -152,9 +152,53 @@
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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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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"0 ('march 7', 420)\n",
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"2\n",
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"march 7\n"
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]
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}
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],
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"source": [
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"# test \n",
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"# testarr = [[('march 7', 420)],\n",
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"# [('march 8', 67)],\n",
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"# [],\n",
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"# [],\n",
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"# [],\n",
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"# [],\n",
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"# [],\n",
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"# [],\n",
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"# [],\n",
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"# [('march 6', 310), ('march 17', 63457)]]\n",
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"\n",
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"# testh = 0 \n",
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"\n",
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"# for idx, element in enumerate(testarr[testh]):\n",
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"# print(idx, element)\n",
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"# print(len(element))\n",
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"# print(element[0])"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 ('march 6', 310)\n"
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]
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}
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],
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"source": [
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"t = HashTable()\n",
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"t[\"march 6\"] = 310\n",
@@ -165,7 +209,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
@@ -176,7 +220,7 @@
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"310"
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]
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},
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"execution_count": 88,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -187,7 +231,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 89,
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"execution_count": 5,
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"metadata": {
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"scrolled": true
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},
@@ -198,7 +242,7 @@
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"63457"
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]
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},
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"execution_count": 89,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -209,7 +253,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
@@ -227,7 +271,7 @@
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" [('march 6', 310), ('march 17', 63457)]]"
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]
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},
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"execution_count": 90,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -330,7 +374,7 @@
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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": "3.7.3"
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"version": "3.10.5"
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}
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},
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"nbformat": 4,

‎data_structures/4_HashTable_2_Collisions/Solution/mySolution1.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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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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"[[27]\n",
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" [31]\n",
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" [23]\n",
15+
" [34]\n",
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" [37]\n",
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" [38]\n",
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" [29]\n",
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" [30]\n",
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" [35]\n",
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" [30]]\n",
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"31.4\n",
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"38\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np \n",
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"\n",
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"file = \"nyc_weather.csv\"\n",
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"\n",
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"df = pd.read_csv(file)\n",
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"\n",
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"temperatures = np.array(df[['temperature(F)']])\n",
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"\n",
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"# print(temperatures)\n",
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"print(np.mean(temperatures))\n",
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"print(np.amax(temperatures))"
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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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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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},
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"kernelspec": {
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"display_name": "Python 3.10.5 64-bit",
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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": "3.8.13"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎data_structures/4_HashTable_2_Collisions/Solution/mySolution2.ipynb‎

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{
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"cells": [
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"35\n"
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]
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}
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],
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"source": [
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"import pandas as pd \n",
18+
"import numpy as np \n",
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"\n",
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"\n",
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"file = \"nyc_weather.csv\"\n",
22+
"df = pd.read_csv(file)\n",
23+
"datewise_temp = {}\n",
24+
"\n",
25+
"for index in range(len(df)):\n",
26+
" datewise_temp[df.loc[index, 'date']] = df.loc[index, 'temperature(F)']\n",
27+
"\n",
28+
"print(datewise_temp['Jan 9'])"
29+
]
30+
}
31+
],
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"metadata": {
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"interpreter": {
34+
"hash": "931adbb4ff0bfcac64e1aef10e58bfc59614a1acb0dea3e7d1519cd29f479087"
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},
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"kernelspec": {
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"display_name": "Python 3.8.13 ('ds')",
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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": "3.8.13"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎data_structures/4_HashTable_2_Collisions/Solution/mySolution3.ipynb‎

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Original file line numberDiff line numberDiff line change
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{
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"cells": [
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'two': 2, 'roads': 2, 'diverged': 2, 'in': 4, 'a': 3, 'yellow': 1, 'wood': 2, 'and': 9, 'sorry': 1, 'i': 8, 'could': 2, 'not': 1, 'travel': 1, 'both': 2, 'be': 2, 'one': 3, 'traveler': 1, 'long': 1, 'stood': 1, 'looked': 1, 'down': 1, 'as': 5, 'far': 1, 'to': 2, 'where': 1, 'it': 2, 'bent': 1, 'the': 8, 'undergrowth;': 1, 'then': 1, 'took': 2, 'other': 1, 'just': 1, 'fair': 1, 'having': 1, 'perhaps': 1, 'better': 1, 'claim': 1, 'because': 1, 'was': 1, 'grassy': 1, 'wanted': 1, 'wear;': 1, 'though': 1, 'for': 2, 'that': 3, 'passing': 1, 'there': 1, 'had': 2, 'worn': 1, 'them': 1, 'really': 1, 'about': 1, 'same': 1, 'morning': 1, 'equally': 1, 'lay': 1, 'leaves': 1, 'no': 1, 'step': 1, 'trodden': 1, 'black': 1, 'oh': 1, 'kept': 1, 'first': 1, 'another': 1, 'day!': 1, 'yet': 1, 'knowing': 1, 'how': 1, 'way': 2, 'leads': 1, 'on': 1, 'doubted': 1, 'if': 1, 'should': 1, 'ever': 1, 'come': 1, 'back': 1, 'shall': 1, 'telling': 1, 'this': 1, 'with': 1, 'sigh': 1, 'somewhere': 1, 'ages': 2, 'hence:': 1, 'i—': 1, 'less': 1, 'traveled': 1, 'by': 1, 'has': 1, 'made': 1, 'all': 1, 'difference': 1}\n"
13+
]
14+
}
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],
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"source": [
17+
"file = \"poem.txt\"\n",
18+
"words = {}\n",
19+
"\n",
20+
"with open(file, 'r') as f:\n",
21+
" for line in f:\n",
22+
" line = line.replace('\\n', '')\n",
23+
" line = line.replace(',', '')\n",
24+
" line = line.replace('.', '')\n",
25+
" line = line.lower()\n",
26+
" for word in line.split():\n",
27+
" if word not in words: \n",
28+
" words[word] = 1\n",
29+
" else:\n",
30+
" words[word] += 1 \n",
31+
"\n",
32+
"print(words)"
33+
]
34+
}
35+
],
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"metadata": {
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"interpreter": {
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"hash": "931adbb4ff0bfcac64e1aef10e58bfc59614a1acb0dea3e7d1519cd29f479087"
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},
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"kernelspec": {
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"display_name": "Python 3.8.13 ('ds')",
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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": "3.8.13"
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},
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"orig_nbformat": 4
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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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