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Diff for: ‎Assignments/EN/Assignment_5.ipynb

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Diff for: ‎Exercises/EN/Basic/04_Conditional_Statements.ipynb

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@@ -1573,7 +1573,7 @@
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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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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
@@ -1587,7 +1587,7 @@
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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": "3.7.1"
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"version": "3.10.9"
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}
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},
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"nbformat": 4,

Diff for: ‎Exercises/EN/Basic/05_Control_Flow.ipynb

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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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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"FizzBuzz\n",
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"1\n",
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"2\n",
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"Fizz\n",
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"4\n",
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"Buzz\n",
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"Fizz\n",
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"7\n",
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"8\n",
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"Fizz\n",
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"Buzz\n",
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"11\n",
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"Fizz\n",
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"13\n",
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"14\n",
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"FizzBuzz\n",
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"16\n",
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"17\n",
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"Fizz\n",
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"19\n",
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"Buzz\n",
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"Fizz\n",
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"22\n",
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"23\n",
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"Fizz\n",
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"Buzz\n",
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"26\n",
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"Fizz\n",
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"28\n",
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"29\n",
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"FizzBuzz\n",
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"31\n",
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"32\n",
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"Fizz\n",
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"34\n",
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"Buzz\n",
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"Fizz\n",
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"37\n",
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"38\n",
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"Fizz\n",
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"Buzz\n",
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"41\n",
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"Fizz\n",
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"43\n",
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"44\n",
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"FizzBuzz\n",
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"46\n",
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"47\n",
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"Fizz\n",
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"49\n",
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"Buzz\n",
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"Fizz\n",
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"52\n",
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"53\n",
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"Fizz\n",
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"Buzz\n",
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"56\n",
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"Fizz\n",
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"58\n",
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"59\n",
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"FizzBuzz\n",
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"61\n",
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"62\n",
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"Fizz\n",
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"64\n",
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"Buzz\n",
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"Fizz\n",
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"67\n",
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"68\n",
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"Fizz\n",
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"Buzz\n",
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"71\n",
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"Fizz\n",
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"73\n",
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"74\n",
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"FizzBuzz\n",
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"76\n",
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"77\n",
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"Fizz\n",
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"79\n",
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"Buzz\n",
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"Fizz\n",
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"82\n",
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"83\n",
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"Fizz\n",
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"Buzz\n",
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"86\n",
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"Fizz\n",
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"88\n",
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"89\n",
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"FizzBuzz\n",
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"91\n",
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"92\n",
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"Fizz\n",
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"94\n",
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"Buzz\n",
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"Fizz\n",
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"97\n",
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"98\n",
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"Fizz\n"
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]
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}
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],
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"source": [
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"for x in range(100):\n",
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" if x%3 == 0:\n",
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" if x%5 == 0:\n",
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" print(\"FizzBuzz\")\n",
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" else:\n",
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" print(\"Fizz\")\n",
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" else:\n",
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" if x%5 == 0:\n",
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" print(\"Buzz\")\n",
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" else:\n",
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" print(x)"
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]
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},
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{
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"cell_type": "markdown",
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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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"source": [
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"for y in list1:\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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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": "3.8.10"
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"version": "3.10.9"
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}
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},
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"nbformat": 4,

Diff for: ‎Exercises/EN/Basic/08_Dictionaries_And_Frequency_Tables.ipynb

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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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"execution_count": 2,
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"metadata": {},
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"outputs": [
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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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"execution_count": 3,
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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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"987\n",
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"622\n"
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]
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}
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],
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"source": [
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"### Start your code below:\n",
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"\n"
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"over_9 = content_ratings['9+']\n",
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"over_17 = content_ratings['17+']\n",
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"print(over_9)\n",
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"print(over_17)"
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]
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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": 14,
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"execution_count": 5,
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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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"{'key_1': 'first_value', 'key_2': 2, 'key_3': 3.14, 'key_4': True, 'key_5': [4, 2, 1], 'key_6': {'inner_key': 6}}\n"
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]
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}
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],
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"source": [
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"### Start your code below:\n",
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"d_1 = {'key_1': 'first_value',\n",
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"'key_2': 2,\n",
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"'key_3': 3.14,\n",
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"'key_4': True,\n",
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"'key_5': [4,2,1],\n",
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"'key_6': {'inner_key' : 6}}\n",
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"\n",
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"\n"
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"\n",
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"print(d_1)\n"
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]
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},
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{
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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": "3.8.10"
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"version": "3.10.9"
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}
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},
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"nbformat": 4,

Diff for: ‎Exercises/EN/Basic/Quiz_Functions_Solutions.ipynb

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@@ -1070,7 +1070,7 @@
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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": "3.8.10"
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"version": "3.10.9"
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}
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},
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"nbformat": 4,

Diff for: ‎Notebooks/Python_Basic/01_Python_Datatypes.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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"cell_type": "raw",
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"metadata": {},
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"source": [
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"# Introduction to Python \n",

Diff for: ‎Notebooks/Python_Basic/07_Functional_Programming.ipynb

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@@ -69,14 +69,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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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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"78.6 ns ± 1.42 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
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"86.6 ns ± 1.04 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n"
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]
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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": 3,
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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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"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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"82.7 ns ± 0.878 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
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"88.9 ns ± 1.17 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n"
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]
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}
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],

Diff for: ‎Notebooks/Python_Tasks/10_NLP_simple text_classification.ipynb

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