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change comprehension code into jupyter notebooks
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‎comprehension/.ipynb_checkpoints/list-comprehension-checkpoint.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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"# List Comprehension\n",
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"This directory is here to show the Speed Difference between using \n",
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"List Comprehension vs. calling append() in a For Loop to create a list\n",
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"\n",
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"## Executing\n",
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"Standard append loop:\n",
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"\n",
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"```python\n",
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"l2 = []\n",
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"for i in range(100000000):\n",
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"\tl.append(i)\n",
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"```\n",
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"\n",
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"In a UNIX terminal, run:\n",
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"```bash\n",
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"time python list-append-loop.py\n",
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"\n",
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"real\t0m11.381s\n",
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"user\t0m10.553s\n",
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"sys\t0m0.824s\n",
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"```\n",
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"\n",
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"List comprehension:\n",
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"\n",
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"```python\n",
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"l1 = [x for x in range(100000000)]\n",
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"```\n",
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"\n",
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"In a UNIX terminal, run:\n",
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"```bash\n",
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"time python list-comp.py\n",
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"\n",
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"real\t0m4.228s\n",
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"user\t0m3.428s\n",
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"sys\t0m0.800s\n",
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"```\n",
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"\n",
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"\n",
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"Here we see that the list comprehension was almost 3x as fast\n",
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"as the `list.append()` for loop. This is because everytime the\n",
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"append() method is called, it has to be looked up by the Interpreter,\n",
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"whereas the list comprehension can do it all at once.\n",
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"\n",
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"### Disclaimer\n",
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"Since this is a very simple example where we are just creating a list of\n",
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"numbers with the range() function, we can actually just cast the result\n",
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"of the range() function to a list and get even faster speed than the\n",
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"list comprehension. \n",
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"\n",
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"```python\n",
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"l = list(range(100000000))\n",
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"```\n",
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"\n",
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"In a UNIX terminal, run:\n",
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"```\n",
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"time python cast-list.py\n",
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"\n",
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"real\t0m2.988s\n",
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"user\t0m2.176s\n",
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"sys\t0m0.812s\n",
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"```\n",
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"\n",
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"To show a more practical example where you can't just cast it:\n",
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"\n",
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"More complicated example with append loop:\n",
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"```python\n",
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"l2 = []\n",
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"for i in range(1000000):\n",
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"\tl.append(i**2)\n",
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"```\n",
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"\n",
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"In a UNIX terminal, run:\n",
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"```\n",
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"time python list-append-loop2.py\n",
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"\n",
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"real\t0m29.756s\n",
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"user\t0m28.875s\n",
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"sys\t0m0.880s\n",
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"```\n",
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"\n",
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"Same thing with list comprehension:\n",
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"```python\n",
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"l1 = [x**2 for x in range(1000000)]\n",
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"```\n",
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"\n",
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"In a UNIX terminal, run:\n",
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"\n",
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"```\n",
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"time python list-comp2.py\n",
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"\n",
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"real\t0m21.932s\n",
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"user\t0m21.031s\n",
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"sys\t0m0.900s\n",
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"```\n",
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"\n",
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"In this practical example, we see that the list comprehension finished\n",
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"in about 2/3 of the time it took the for loop append() method, still\n",
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"significantly faster!\n",
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"\n",
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"All in all, it depends on the example, but list comprehensions are often\n",
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"faster than using a loop.\n"
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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",
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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": "3.6.4"
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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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}

‎comprehension/dictionary/.ipynb_checkpoints/dict-comprehension-checkpoint.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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"# Dictionary Comprehension\n",
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"\n",
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"Other than list comprehension, and for comprehensions in general, speed isn't the primary concern.\n",
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"Comprehensions are mostly used for code conciseness and readability. Here is an example of a typical way that you would create a dictionary:"
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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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"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n"
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]
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}
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],
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"source": [
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"d = {}\n",
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"\n",
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"# Map every number from 1-9 to it's perfect square\n",
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"for i in range(1, 10):\n",
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"\td[i] = i**2\n",
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" \n",
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"print(d)"
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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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"But this can be done in just 1 line of code with dict comprehension:"
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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": 6,
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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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"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n"
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]
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}
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],
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"source": [
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"d2 = {i : i**2 for i in range(1, 10)}\n",
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"print(d2)"
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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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"Since this is basically the same concept as list comprehension, as well as set/tuple comprehensions, I don't\n",
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"include any more examples."
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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": "Python 3",
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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": "3.6.4"
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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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}

‎comprehension/dictionary/README.md

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‎comprehension/dictionary/dict-comp.py

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‎comprehension/dictionary/dict-comprehension.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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"# Dictionary Comprehension\n",
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"\n",
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"Other than list comprehension, and for comprehensions in general, speed isn't the primary concern.\n",
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"Comprehensions are mostly used for code conciseness and readability. Here is an example of a typical way that you would create a dictionary:"
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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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"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n"
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]
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}
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],
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"source": [
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"d = {}\n",
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"\n",
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"# Map every number from 1-9 to it's perfect square\n",
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"for i in range(1, 10):\n",
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"\td[i] = i**2\n",
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" \n",
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"print(d)"
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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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"But this can be done in just 1 line of code with dict comprehension:"
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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": 6,
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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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"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n"
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]
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}
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],
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"source": [
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"d2 = {i : i**2 for i in range(1, 10)}\n",
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"print(d2)"
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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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"Since this is basically the same concept as list comprehension, as well as set/tuple comprehensions, I don't\n",
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"include any more examples."
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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": "Python 3",
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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": "3.6.4"
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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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}

‎comprehension/dictionary/dict-loop.py

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