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jmportillajmportilla
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‎.ipynb_checkpoints/Advanced Dictionaries-checkpoint.ipynb

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{
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"cells": [],
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"metadata": {},
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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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"#Advanced Dictionaries\n",
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"Unlike some of the other Data Structures we've worked with, most of the really useful methods available to us in Dictionaries have already been explored throughout this course. Here we will touch on just a few more for good measure:"
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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": 17,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"d = {'k1':1,'k2':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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"##Dictionary Comprehensions\n",
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"\n",
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"Just like List Comprehensions, Dictionary Data Types also support their own version of comprehension for quick creation. It is not as commonly used as List Comprehensions, but the syntax is:"
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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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"collapsed": false
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},
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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: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}"
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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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"{x:x**2 for x in range(10)}"
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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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"One of the reasons it is not as common is the difficulty in structuring the key names that are not based off the values."
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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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"##Iteration over keys,values, and items\n",
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"Dictionaries can be iterated over using the iter methods available in a dictionary. For example:"
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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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"collapsed": false
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},
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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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"k2\n",
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"k1\n"
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]
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}
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],
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"source": [
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"for k in d.iterkeys():\n",
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" print k"
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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": 15,
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"metadata": {
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"collapsed": false
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},
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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\n",
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"1\n"
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]
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}
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],
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"source": [
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"for v in d.itervalues():\n",
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" print v"
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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": 16,
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"metadata": {
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"collapsed": false
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},
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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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"('k2', 2)\n",
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"('k1', 1)\n"
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]
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}
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],
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"source": [
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"for item in d.iteritems():\n",
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" print item"
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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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"#view items,keys and values\n",
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"You can use the view methods to view items keys and values. For example:"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_items([('k2', 2), ('k1', 1)])"
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]
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},
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"execution_count": 2,
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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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"d.viewitems()"
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_keys(['k2', 'k1'])"
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]
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},
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"execution_count": 5,
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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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"d.viewkeys()"
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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": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_values([2, 1])"
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]
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},
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"execution_count": 18,
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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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"d.viewvalues()"
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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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"**Great! You should now feel very comfortable using the variety of methods available to you in Dictionaries!**"
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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 2",
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"language": "python",
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"name": "python2"
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},
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},
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"file_extension": ".py",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎.ipynb_checkpoints/Advanced Tuples-checkpoint.ipynb

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This file was deleted.

‎.ipynb_checkpoints/blank-checkpoint.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": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"t = (1,2,3)"
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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": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "count() takes exactly one argument (0 given)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-3-3e3482324d98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m: count() takes exactly one argument (0 given)"
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]
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}
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],
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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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},
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"outputs": [],
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}
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],
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"display_name": "Python 2",
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"name": "python2"
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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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