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Commit fb6a2a2

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‎Python_Coding_Test1.ipynb‎

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‎Python_Daily_Challenge1.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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"id": "4221f9eb-836e-45e8-86ed-b5399fb7f556",
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"metadata": {
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"id": "4221f9eb-836e-45e8-86ed-b5399fb7f556"
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},
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"source": [
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"# Daily Challenge Questions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e71d7e35-2485-40c2-9e12-18b4d04dd701",
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"metadata": {
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"id": "e71d7e35-2485-40c2-9e12-18b4d04dd701"
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},
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"source": [
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"# Question 1:\n",
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"\n",
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"Given the nested list **data** = [[['a', 'b', 'c'], ['d', 'e', 'f']], [['g', 'h', 'i'], ['j', 'k', 'l']]], using list comprehension, **create a new list containing all vowels from data.**\n",
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"\n",
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"**Output:**\n",
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"\n",
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"For the given data, the output list should be **['a', 'e', 'i'].**"
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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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"id": "72807312-6d82-4b89-89b8-3f5ebf4fc340",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "72807312-6d82-4b89-89b8-3f5ebf4fc340",
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"outputId": "ea13e8bc-eb07-4311-d0ea-59371001142e"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Vowels present in given data are: ['a', 'e', 'i']\n"
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]
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}
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],
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"source": [
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"data = [[['a', 'b', 'c'], ['d', 'e', 'f']], [['g', 'h', 'i'], ['j', 'k', 'l']]]\n",
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"\n",
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"# looping through sub-lists using list comprehension\n",
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"# sub-list in list\n",
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"# sub-sub-list in sub-list\n",
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"# sub-sub-sub-list in sub-sub-list\n",
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"\n",
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"vowel_list = [x for ele_list in data for coll_list in ele_list for x in coll_list if x in ['a', 'e', 'i','o','u']]\n",
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"\n",
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"print(\"Vowels present in given data are: \", vowel_list)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1bf9e7f4-885e-4f16-8e73-1edc83028db6",
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"metadata": {
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"id": "1bf9e7f4-885e-4f16-8e73-1edc83028db6"
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},
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"source": [
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" # Question 2:\n",
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"\n",
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"\n",
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"Given the nested tuple **info** = ((('Alice', 25), ('Bob', 30)), (('Charlie', 22), ('David', 28))), using tuple unpacking and list comprehension, create a new list containing the **ages of all individuals whose names start with 'D'**.\n",
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"\n",
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"**Output:**\n",
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"\n",
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"For the given info, the output list should be [28,26,32,27]."
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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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"id": "1feeba73-778c-49dd-8c9c-580d0dc9b364",
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"metadata": {
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"id": "1feeba73-778c-49dd-8c9c-580d0dc9b364",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "d8b59321-938c-4c8b-9f9c-00cae5f5d464"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[28, 26, 32, 27]\n"
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]
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}
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],
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"source": [
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"info = (\n",
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" (('Alice', 25), ('Bob', 30)),\n",
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" (('Charlie', 22), ('David', 28)),\n",
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" (('Diana', 26), ('Derek', 32)),\n",
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" (('Daniel', 27), ('Emily', 29))\n",
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")\n",
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"\n",
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"# looping through sub-tuples using list comprehension\n",
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"# sub-tuple in tuple\n",
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"# (name,age) in sub-tuple\n",
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"# if name.startswith('D') --> append age into list\n",
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"age_list_whose_name_startswith_letter_D = [age for x in info for (name,age) in x if name.startswith(\"D\")]\n",
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"print(age_list_whose_name_startswith_letter_D)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "07ac759c-f8ac-4779-ba86-0a0a2cea5a6e",
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"metadata": {
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"id": "07ac759c-f8ac-4779-ba86-0a0a2cea5a6e"
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},
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"source": [
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"# Question 3\n",
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"\n",
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"Given the nested dictionary **data** = {'A': {'x': [1, 2, 3]}, 'B': {'y': [4, 5, 6]}}, using dictionary comprehension, create a new dictionary where each key is from the original dictionary and each value is the sum of its associated list values.\n",
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"\n",
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"**Output:**\n",
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"\n",
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"For the given data, the output dictionary should be {'A': 6, 'B': 15}.\n",
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"\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": null,
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"id": "d693d5d8-66b4-4b97-bab8-223ab01f6e24",
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"metadata": {
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"id": "d693d5d8-66b4-4b97-bab8-223ab01f6e24",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "099e7e91-1300-490c-d48e-2208ff5826fa"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'A': 6, 'B': 15}\n"
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]
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}
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],
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"source": [
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"data = {'A': {'x': [1, 2, 3]}, 'B': {'y': [4, 5, 6]}}\n",
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"\n",
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"# looping through sub-values using dictionary comprehension\n",
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"# (key,value)pair in dict.items()\n",
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"# val_list in value.values()\n",
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"# sum(val_list)\n",
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"dict_new = {m: sum(i) for m,n in data.items() for i in n.values()}\n",
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"print(dict_new)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.11.8"
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},
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"colab": {
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"provenance": []
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Python_Daily_Challenge2.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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"id": "4221f9eb-836e-45e8-86ed-b5399fb7f556",
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"metadata": {
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"id": "4221f9eb-836e-45e8-86ed-b5399fb7f556"
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},
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"source": [
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"# Daily Challenge Questions July 16"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e71d7e35-2485-40c2-9e12-18b4d04dd701",
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"metadata": {
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"id": "e71d7e35-2485-40c2-9e12-18b4d04dd701"
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},
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"source": [
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"# Question 1:\n",
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"\n",
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"Given the nested tuple info = ((('Emma', 26), ('Frank', 32)), (('Grace', 29), ('Henry', 27)), (('Ivy', 30), ('Jack', 25)), (('Kate', 28), ('Leo', 31))).\n",
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"\n",
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"Using tuple unpacking and list comprehension, create a new list containing the **ages of all individuals whose names end with 'y'.**"
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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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"id": "72807312-6d82-4b89-89b8-3f5ebf4fc340",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "72807312-6d82-4b89-89b8-3f5ebf4fc340",
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"outputId": "8ec93012-22fb-4846-f644-75ce2b5e531c"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[27, 30]\n"
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]
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}
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],
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"source": [
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"info = ((('Emma', 26), ('Frank', 32)), (('Grace', 29), ('Henry', 27)), (('Ivy', 30), ('Jack', 25)), (('Kate', 28), ('Leo', 31)))\n",
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"\n",
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"age_of_individual_whose_name_ends_with_y = [age for pair in info for (name,age) in pair if name.endswith('y')]\n",
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"\n",
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"print(age_of_individual_whose_name_ends_with_y)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1bf9e7f4-885e-4f16-8e73-1edc83028db6",
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"metadata": {
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"id": "1bf9e7f4-885e-4f16-8e73-1edc83028db6"
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},
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"source": [
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" # Question 2:\n",
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"\n",
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"Given the nested list data = [[['a', 'b', 'c'], ['d', 'e', 'f']], [['g', 'h', 'i'], ['j', 'k', 'l']]].\n",
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"\n",
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"Using list comprehension, create a **new list containing all characters from data that are not vowels ('a', 'e', 'i', 'o', 'u').**"
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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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"id": "1feeba73-778c-49dd-8c9c-580d0dc9b364",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "1feeba73-778c-49dd-8c9c-580d0dc9b364",
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"outputId": "f6b9f36f-fac0-4f3b-cd7f-a5dbddc4f5e3"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"['b', 'c', 'd', 'f', 'g', 'h', 'j', 'k', 'l']\n"
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]
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}
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],
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"source": [
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"data = [[['a', 'b', 'c'], ['d', 'e', 'f']], [['g', 'h', 'i'], ['j', 'k', 'l']]]\n",
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"\n",
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"consonant_in_data = [x for collections in data for items in collections for x in items if x not in ['a', 'e', 'i', 'o', 'u']]\n",
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"\n",
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"print(consonant_in_data)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "07ac759c-f8ac-4779-ba86-0a0a2cea5a6e",
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"metadata": {
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"id": "07ac759c-f8ac-4779-ba86-0a0a2cea5a6e"
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},
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"source": [
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"# Question 3\n",
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"\n",
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"Given the nested dictionary **data** = {'A': {'x': [1, 2, 3]}, 'B': {'y': [4, 5, 6]}}, using dictionary comprehension, create a new dictionary where each key is from the original dictionary and each value is the sum of its associated list values.\n",
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"\n",
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"**Output:**\n",
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"\n",
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"For the given data, the output dictionary should be {'A': 6, 'B': 15}.\n",
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"\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": null,
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"id": "d693d5d8-66b4-4b97-bab8-223ab01f6e24",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "d693d5d8-66b4-4b97-bab8-223ab01f6e24",
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"outputId": "ed0f542b-ec8d-47a9-d903-167f94b2ad1e"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'A': 6, 'B': 15}\n"
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]
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}
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],
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"source": [
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"data = {'A': {'x': [1, 2, 3]}, 'B': {'y': [4, 5, 6]}}\n",
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"\n",
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"new_dict_data = {m : sum(i) for (m,n) in data.items() for i in n.values()}\n",
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"\n",
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"print(new_dict_data)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.11.8"
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},
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"colab": {
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"provenance": []
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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