224 lines
6.4 KiB
Plaintext
224 lines
6.4 KiB
Plaintext
{
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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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"# 1. Let's mock things!\n",
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"Below you can see `get_wiki_article` function which is a very simple implementation for fetching an article from wikipedia. Your task is to mock it's implementation such that it's going to always return `'Python is cool!'`. However, note that you should be able to check which argument is given to `urlopen` when `get_wiki_article` is called.\n",
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"\n",
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"**Note**: `get_content_of_url` uses [`urrlib`](https://docs.python.org/3/library/urllib.html#module-urllib), which is part of the Standard Library, for creating a HTTP request. Usually it's preferable to use [`requests`](http://docs.python-requests.org/en/master/) library (not part of the Standard Library) for such operations. Actually, `requests` uses `urllib` under the hood so it's good to know what's happening when you start using `requests` - or maybe you have already used it."
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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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"editable": false
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},
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"outputs": [],
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"source": [
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"from urllib.request import urlopen\n",
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"\n",
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"\n",
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"def get_wiki_article(name):\n",
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" url = f\"https://en.wikipedia.org/wiki/{name}\"\n",
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" response = urlopen(url)\n",
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" content = str(response.read())\n",
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" return content"
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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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"# Your implementation here"
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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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"Let's verify it works as expected."
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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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"editable": false
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},
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"outputs": [],
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"source": [
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"article = \"Python_(programming_language)\"\n",
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"res = get_wiki_article(article)\n",
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"assert \"Guido van Rossum\" not in res, \"Guido is still there!\"\n",
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"assert res == \"Python is cool!\"\n",
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"urlopen.assert_called_with(\n",
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" \"https://en.wikipedia.org/wiki/Python_(programming_language)\"\n",
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")\n",
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"\n",
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"print(\"All good!\")"
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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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"# 2. The power of `collections` module"
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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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"## 2.1 Creating a namedtuple\n",
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"Create a namedtuple `Car` which has fields `price`, `mileage`, and `brand`. "
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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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"# Your implemenation here"
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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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"Let's test it."
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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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"editable": false
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},
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"outputs": [],
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"source": [
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"car1 = Car(25000, 2000, \"BMW\")\n",
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"assert car1.price == 25000\n",
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"assert car1.mileage == 2000\n",
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"assert car1.brand == \"BMW\"\n",
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"assert isinstance(car1, tuple)\n",
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"\n",
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"# Note that indexing works also!\n",
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"# This means that if you change a tuple into a namedtuple,\n",
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"# the change will be backwards compatible.\n",
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"assert car1[2] == \"BMW\"\n",
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"\n",
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"print(\"All good!\")"
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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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"The power of namedtuples is their simplicity. If `Car` would have been implemented as a class, the implementation would have been notably longer. However, if you would need to be able to e.g. change the `mileage` or `price` during the lifetime of a `Car` instance, consider using `class` because `tuples` are immutable."
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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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"## 2.2 dict of dicts\n",
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"Implement a `name_mapping` function which takes a collection of names as argument. \n",
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"\n",
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"#### The specification for `name_mapping`\n",
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"* you can assume that all the elements in the names collection are strings\n",
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"* if the provided names collection is empty, returns an empty dict\n",
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"* returns a dictionary of dictionaries\n",
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" * outer dictionary should contain keys `vowel` and `consonant`\n",
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" * `vowel` and `consonant` keys should have dictionaries of names (keys) and their occurences (values) as values\n",
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" * names belong to either `vowel` or `consonant` based on their first letter\n",
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" * vowels are defined by the `VOWELS` constant\n",
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" * if there are only names starting with a vowel, `consonant` key should not be present in the return value (same applies vice versa)\n",
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"* see the tests below for complete examples \n",
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"\n",
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"Tip: `defaultdict` and `Counter` may be helpful here :)"
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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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"editable": false
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},
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"outputs": [],
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"source": [
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"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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"metadata": {},
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"outputs": [],
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"source": [
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"def name_mapping(names):\n",
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" # Your implementation here"
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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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"Let's verify that it works correctly!"
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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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"editable": false
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},
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"outputs": [],
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"source": [
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"names = (\"Alice\", \"John\", \"Lisa\", \"John\", \"Eric\", \"Waldo\", \"annie\", \"Alice\", \"John\")\n",
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"expected = {\n",
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" \"consonant\": {\"John\": 3, \"Waldo\": 1, \"Lisa\": 1},\n",
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" \"vowel\": {\"Alice\": 2, \"annie\": 1, \"Eric\": 1},\n",
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"}\n",
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"assert name_mapping(names) == expected\n",
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"print(\"First ok!\")\n",
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"\n",
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"only_consonants = (\"John\", \"Doe\", \"Doe\")\n",
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"expected2 = {\"consonant\": {\"John\": 1, \"Doe\": 2}}\n",
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"assert name_mapping(only_consonants) == expected2\n",
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"print(\"Second ok!\")\n",
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"\n",
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"assert name_mapping([]) == {}\n",
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"\n",
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"print(\"All ok!\")"
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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": "3.5.4"
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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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}
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